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70
.obsidian/workspace.json vendored
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@ -83,12 +83,12 @@
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@ -257,51 +257,51 @@
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Gruppen/Engineering 1.md
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@ -8,10 +8,10 @@ tags:
# Mitglieder # Mitglieder
| Name | Punkte | Durchschnitt | Jupyter Kennung | Mail | | Name | Punkte | Durchschnitt | Jupyter Kennung | Mail |
| -------------- | ------ | ------------ | -------------------------------------------------------------------------- | ------------------------------------------------------------------------- | | -------------- | ------ | ------------ | -------------------------------- | ------------------------------------------------------------------------- |
| Janna Heiny | | | | [j.heiny@tu-braunschweig.de](mailto:j.heiny@tu-braunschweig.de) | | Janna Heiny | | | 3140c4b62381a2203803f8b237118244 | [j.heiny@tu-braunschweig.de](mailto:j.heiny@tu-braunschweig.de) |
| Milena Krieger | | | | [m.krieger@tu-braunschweig.de](mailto:m.krieger@tu-braunschweig.de) | | Milena Krieger | | | 8be9a4cc0b240a18171892b873dc2cb8 | [m.krieger@tu-braunschweig.de](mailto:m.krieger@tu-braunschweig.de) |
| Xiaowei Wang | | | <span style="color:rgb(255, 0, 0)">39dc5bd7686c3280247aacee82c9818e</span> | [xiaowei.wang@tu-braunschweig.de](mailto:xiaowei.wang@tu-braunschweig.de) | | Xiaowei Wang | | | 39dc5bd7686c3280247aacee82c9818e | [xiaowei.wang@tu-braunschweig.de](mailto:xiaowei.wang@tu-braunschweig.de) |
| | | | | | | | | | | |
| | | | | | | | | | | |

2
Gruppen/MeWi 2.md
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@ -12,7 +12,7 @@ tags:
| Izabel Mike | 29.5 | | 8c710a24debf6159659d1e58dd975ce2 | [i.mike@tu-braunschweig.de](mailto:i.mike@tu-braunschweig.de) | | Izabel Mike | 29.5 | | 8c710a24debf6159659d1e58dd975ce2 | [i.mike@tu-braunschweig.de](mailto:i.mike@tu-braunschweig.de) |
| Lara Troschke | 20.5 | | 7b441c67713f2a49811625905612f19b | [l.troschke@tu-braunschweig.de](mailto:l.troschke@tu-braunschweig.de) | | Lara Troschke | 20.5 | | 7b441c67713f2a49811625905612f19b | [l.troschke@tu-braunschweig.de](mailto:l.troschke@tu-braunschweig.de) |
| Inga-Brit Turschner | 25.5 | | 72f0b5fd2cdf4dd808ca9a3add584c75 | [i.turschner@tu-braunschweig.de](mailto:i.turschner@tu-braunschweig.de) | | Inga-Brit Turschner | 25.5 | | 72f0b5fd2cdf4dd808ca9a3add584c75 | [i.turschner@tu-braunschweig.de](mailto:i.turschner@tu-braunschweig.de) |
| | | | | | | Yannik Haupt | | | f4f597c57d8a31960750e0647f917ed3 | |
| | | | | | | | | | | |
# Notizen # Notizen

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Gruppen/MeWi 4.md
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@ -8,12 +8,12 @@ tags:
# Mitglieder # Mitglieder
| Name | Punkte | Durchschnitt | Jupyter Kennung | Mail | | Name | Punkte | Durchschnitt | Jupyter Kennung | Mail |
| ------------------ | ------ | ------------ | --------------------------------------------------------------------- | ----------------------------------------------------------------- | | -------------- | ------ | ------------ | -------------------------------- | ----------------------------------------------------------------- |
| Nova Eib | 31 | | b313c08a73772a8237e0593ec5c3ee27 | [n.eib@tu-braunschweig.de](mailto:n.eib@tu-braunschweig.de) | | Nova Eib | 31 | | b313c08a73772a8237e0593ec5c3ee27 | [n.eib@tu-braunschweig.de](mailto:n.eib@tu-braunschweig.de) |
| Julia Renner | | | | [j.renner@tu-braunschweig.de](mailto:j.renner@tu-braunschweig.de) | | Julia Renner | | | 9efda636813423536dfd581ebeae4edc | [j.renner@tu-braunschweig.de](mailto:j.renner@tu-braunschweig.de) |
| Isabel Rudolf | | | <span style="color:rgb(255, 0, 0)">4306ac2b1bf2fe7189d53aad469</span> | [i.rudolf@tu-braunschweig.de](mailto:i.rudolf@tu-braunschweig.de) | | Isabel Rudolf | | | 4306ac2b1bf2fe7189d53aad46999f31 | [i.rudolf@tu-braunschweig.de](mailto:i.rudolf@tu-braunschweig.de) |
| Katharina Walz | 31 | | 6349002488dfe4343537174fb9381f95 | [k.walz@tu-braunschweig.de](mailto:k.walz@tu-braunschweig.de) | | Katharina Walz | 31 | | 6349002488dfe4343537174fb9381f95 | [k.walz@tu-braunschweig.de](mailto:k.walz@tu-braunschweig.de) |
| Unsichtbare Person | | | | | | Cam Thu Do | | | dcccfe28b7e78cc77c118532574b1075 | |
# Notizen # Notizen

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Gruppen/MeWi 5.md
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@ -8,12 +8,12 @@ tags:
# Mitglieder # Mitglieder
| Name | Punkte | Durchschnitt | Jupyter Kennung | Mail | | Name | Punkte | Durchschnitt | Jupyter Kennung | Mail |
| -------------- | ------ | ------------ | -------------------------------------------------------------------------- | --------------------------------------------------------------------- | | -------------- | ------ | ------------ | -------------------------------- | --------------------------------------------------------------------- |
| Vikoria Litza | | | | [v.litza@tu-braunschweig.de](mailto:v.litza@tu-braunschweig.de) | | Vikoria Litza | | | 055a44301e7b7281e0ee98815f99c4dd | [v.litza@tu-braunschweig.de](mailto:v.litza@tu-braunschweig.de) |
| Lea Noglik | | | <span style="color:rgb(255, 0, 0)">f24ccc1cefe390cd1036419b89f31d4f</span> | [l.noglik@tu-braunschweig.de](mailto:l.noglik@tu-braunschweig.de) | | Lea Noglik | | | f24ccc1cefe390cd1036419b89f31d4f | [l.noglik@tu-braunschweig.de](mailto:l.noglik@tu-braunschweig.de) |
| Donika Nuhiu | | | | [d.nuhiu@tu-braunschweig.de](mailto:d.nuhiu@tu-braunschweig.de) | | Donika Nuhiu | | | bb62dfd14ba80f21678bee50e4f69131 | [d.nuhiu@tu-braunschweig.de](mailto:d.nuhiu@tu-braunschweig.de) |
| Alea Unger | 30 | | f8c2ba8abf5b7d89a240902634a5c53a | [a.unger@tu-braunschweig.de](mailto:a.unger@tu-braunschweig.de) | | Alea Unger | 30 | | f8c2ba8abf5b7d89a240902634a5c53a | [a.unger@tu-braunschweig.de](mailto:a.unger@tu-braunschweig.de) |
| Marie Wallbaum | | | <span style="color:rgb(255, 0, 0)">eec48a6d211105d6f87267fbd428ab69</span> | [m.wallbaum@tu-braunschweig.de](mailto:m.wallbaum@tu-braunschweig.de) | | Marie Wallbaum | | | eec48a6d211105d6f87267fbd428ab69 | [m.wallbaum@tu-braunschweig.de](mailto:m.wallbaum@tu-braunschweig.de) |
# Notizen # Notizen

8
Gruppen/MeWi 6.md
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@ -8,11 +8,11 @@ tags:
# Mitglieder # Mitglieder
| Name | Punkte | Durchschnitt | Jupyter Kennung | Mail | | Name | Punkte | Durchschnitt | Jupyter Kennung | Mail |
| --------------- | ------ | ------------ | -------------------------------------------------------------------------- | ----------------------------------------------------------------------- | | --------------- | ------ | ------------ | -------------------------------- | ----------------------------------------------------------------------- |
| Nele Grundke | | | <span style="color:rgb(255, 0, 0)">f61621cbe911f21ddd781c21e4528b07</span> | [n.grundke@tu-braunschweig.de](mailto:n.grundke@tu-braunschweig.de) | | Nele Grundke | | | f61621cbe911f21ddd781c21e4528b07 | [n.grundke@tu-braunschweig.de](mailto:n.grundke@tu-braunschweig.de) |
| Julia Limbach | | | | [j.limbach@tu-braunschweig.de](mailto:j.limbach@tu-braunschweig.de) | | Julia Limbach | | | | [j.limbach@tu-braunschweig.de](mailto:j.limbach@tu-braunschweig.de) |
| Melina Sablotny | | | <span style="color:rgb(255, 0, 0)">4111400b4ae2c863a1c4b73a21f87093</span> | [m.sablotny@tu-braunschweig.de](mailto:m.sablotny@tu-braunschweig.de) | | Melina Sablotny | | | 4111400b4ae2c863a1c4b73a21f87093 | [m.sablotny@tu-braunschweig.de](mailto:m.sablotny@tu-braunschweig.de) |
| Lucy Thiele | | | <span style="color:rgb(255, 0, 0)">4c0ddab5bed6ff025cee04f8d73301a3</span> | [lucy.thiele@tu-braunschweig.de](mailto:lucy.thiele@tu-braunschweig.de) | | Lucy Thiele | | | 4c0ddab5bed6ff025cee04f8d73301a3 | [lucy.thiele@tu-braunschweig.de](mailto:lucy.thiele@tu-braunschweig.de) |
| | | | | | | | | | | |
# Notizen # Notizen

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Gruppen/MeWi 7 (DiKum).md
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@ -8,12 +8,12 @@ tags:
# Mitglieder # Mitglieder
| Name | Punkte | Durchschnitt | Jupyter Kennung | Mail | | Name | Punkte | Durchschnitt | Jupyter Kennung | Mail |
| ------------------- | ------ | ------------ | -------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | | ------------------- | ------ | ------------ | -------------------------------- | --------------------------------------------------------------------------------- |
| Abdalaziz Abunjaila | 30.5 | | 79b388885f89954decaefc9e19aa8871 | [a.abunjaila@tu-braunschweig.de](mailto:a.abunjaila@tu-braunschweig.de) | | Abdalaziz Abunjaila | 30.5 | | 79b388885f89954decaefc9e19aa8871 | [a.abunjaila@tu-braunschweig.de](mailto:a.abunjaila@tu-braunschweig.de) |
| Marleen Adolphi | | | | [m.adolphi@tu-braunschweig.de](mailto:m.adolphi@tu-braunschweig.de) | | Marleen Adolphi | | | bb549f9016ee05a07ce271c10482879d | [m.adolphi@tu-braunschweig.de](mailto:m.adolphi@tu-braunschweig.de) |
| Alea Schleier | | | | [a.schleier@tu-braunschweig.de](mailto:a.schleier@tu-braunschweig.de) | | Alea Schleier | | | beb3bcd7515400b58f6fab7567193cbf | [a.schleier@tu-braunschweig.de](mailto:a.schleier@tu-braunschweig.de) |
| Marie Seeger | | | <span style="color:rgb(255, 0, 0)">f7017b11a2904a74302c9f4f217779fb</span> | [marie.seeger@tu-braunschweig.de](mailto:marie.seeger@tu-braunschweig.de) | | Marie Seeger | | | f7017b11a2904a74302c9f4f217779fb | [marie.seeger@tu-braunschweig.de](mailto:marie.seeger@tu-braunschweig.de) |
| Lilly-Lu Warnken | | | | [lilly-lu.warnken@tu-braunschweig.de](mailto:lilly-lu.warnken@tu-braunschweig.de) | | Lilly-Lu Warnken | | | 5fe894b59ff39da82ac4361dcb2d35b8 | [lilly-lu.warnken@tu-braunschweig.de](mailto:lilly-lu.warnken@tu-braunschweig.de) |
# Notizen # Notizen

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Material/V3.ipynb
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@ -8,6 +8,14 @@
"# 3. Vorlesung" "# 3. Vorlesung"
] ]
}, },
{
"cell_type": "markdown",
"id": "a21df6bb-f501-474a-9e1a-7dd2a90cd92d",
"metadata": {},
"source": [
"### Einfache Zählschleife"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
@ -25,10 +33,11 @@
} }
], ],
"source": [ "source": [
"count = 1\n", "# Als While Loop\n",
"while count < 4:\n", "count = 1 # Zählvariable\n",
"while count < 4: # Bedingung\n",
" print(count)\n", " print(count)\n",
" count += 1 " " count += 1 # Hochzählen"
] ]
}, },
{ {
@ -48,6 +57,7 @@
} }
], ],
"source": [ "source": [
"# Als For Loop\n",
"for count in [1, 2, 3]:\n", "for count in [1, 2, 3]:\n",
" print(count)" " print(count)"
] ]
@ -57,6 +67,8 @@
"id": "daaa7cbe-0cb7-45c9-89a8-241561908db2", "id": "daaa7cbe-0cb7-45c9-89a8-241561908db2",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Beispiel einer Zählschleife in C:\n",
"\n",
"```C\n", "```C\n",
"for (int i = 0; i < 4, i++) {}\n", "for (int i = 0; i < 4, i++) {}\n",
"```" "```"
@ -64,7 +76,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 2,
"id": "3e461857-f366-46f8-ad51-9800348b4521", "id": "3e461857-f366-46f8-ad51-9800348b4521",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -73,18 +85,110 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"1\n", "1\n",
"2\n",
"3\n" "3\n"
] ]
} }
], ],
"source": [ "source": [
"for count in range(1,4,2):\n", "# Zählschleife mittels range Funktion\n",
"for count in range(1,4):\n",
" print(count)" " print(count)"
] ]
}, },
{
"cell_type": "markdown",
"id": "b572967d-7488-4be7-b8b7-8b0237eddc86",
"metadata": {},
"source": [
"`range` kann bis zu 3 Parameter nehmen.\n",
"\n",
"- 1 Parameter `range(4)` -> Zählt in 1er Schritten bis exklusive der eingegebenen Zahl *0,1,2,3*\n",
"\n",
"Der folgend genutzte Stern `*` sagt Python er soll den `iterator` entpacken."
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 4,
"id": "30d52051-cee6-4bcd-a622-c70bdd0cae1e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1 2 3\n"
]
}
],
"source": [
"print(*range(4))"
]
},
{
"cell_type": "markdown",
"id": "8e2dbb80-5bfd-43ee-83b6-8ef299c70391",
"metadata": {},
"source": [
"- 2 Parameter `range(1,4)` -> Zählt in 1er Schritten von dem ersten Parameter bis exklusiv zum zweiten Parameter *1,2,3*"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fe434e93-729b-466c-a530-125c668f2329",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 2 3\n"
]
}
],
"source": [
"print(*range(1,4))"
]
},
{
"cell_type": "markdown",
"id": "7d5d28a6-b873-4a2b-8e45-b02e75982c10",
"metadata": {},
"source": [
"- 3 Parameter `range(1,11,2)` -> Zählt in `2`er Schritten von dem ersten Parameter bis exklusiv zum zweiten Parameter "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "03e36a0d-9d0f-4dcd-8e02-3d234da9fb52",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 3 5 7 9\n"
]
}
],
"source": [
"print(*range(1,11,2))"
]
},
{
"cell_type": "markdown",
"id": "698e2a24-d96e-4f39-b76a-bfa2b6d20297",
"metadata": {},
"source": [
"`For-Loops` itertieren über Iteratoren. Listen sind z.b. Iteratoren."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4f4d9b6c-c262-45a0-ab7a-ac8d3f13d110", "id": "4f4d9b6c-c262-45a0-ab7a-ac8d3f13d110",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -94,7 +198,7 @@
"[0, 1, 2, 3, 4]" "[0, 1, 2, 3, 4]"
] ]
}, },
"execution_count": 8, "execution_count": 7,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -106,7 +210,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 8,
"id": "fbcb9b7d-2850-41fe-82a5-09ad75191329", "id": "fbcb9b7d-2850-41fe-82a5-09ad75191329",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -129,7 +233,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 9,
"id": "c1cb9b0a-170c-4b45-b329-e28b0f8ee818", "id": "c1cb9b0a-170c-4b45-b329-e28b0f8ee818",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -139,18 +243,18 @@
"5" "5"
] ]
}, },
"execution_count": 10, "execution_count": 9,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"len(l)" "len(l) # Anzahl 'Länge' der Liste l"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 10,
"id": "f595c1f5-4945-4ee4-89e7-cde25d2a7e41", "id": "f595c1f5-4945-4ee4-89e7-cde25d2a7e41",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -167,13 +271,14 @@
} }
], ],
"source": [ "source": [
"# range zählt bis 'exklusive' seines Eingabeparameters um folgendes verhalten zu emulieren\n",
"for i in range(len(l)):\n", "for i in range(len(l)):\n",
" print(i)" " print(i)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 11,
"id": "6902e5e5-0a49-4bce-a03a-f4c4d812ffa7", "id": "6902e5e5-0a49-4bce-a03a-f4c4d812ffa7",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -190,13 +295,14 @@
} }
], ],
"source": [ "source": [
"# Iteration über die Indexe der Liste \n",
"for i in range(len(l)):\n", "for i in range(len(l)):\n",
" print(l[i])" " print(l[i]) # Zugriff über Index auf die Elemente der Liste"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 12,
"id": "4e2f0c81-894d-424d-848f-3e7cc36bd70b", "id": "4e2f0c81-894d-424d-848f-3e7cc36bd70b",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -214,13 +320,22 @@
} }
], ],
"source": [ "source": [
"# _ wird verwendet für Loops die einfach etwas immer und immer wiederholen sollen\n",
"for _ in range(6):\n", "for _ in range(6):\n",
" print(\"Hello\")" " print(\"Hello\")"
] ]
}, },
{
"cell_type": "markdown",
"id": "c555a1d3-dc65-43e1-b19a-070653a34645",
"metadata": {},
"source": [
"Folgende Dict beispiele Eklären sich dementsprechend selber"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 13,
"id": "e1fbf047-ed8c-4a27-9729-6b05ed55140a", "id": "e1fbf047-ed8c-4a27-9729-6b05ed55140a",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -230,7 +345,7 @@
"{'a': 5, 'b': 8, 'c': 10}" "{'a': 5, 'b': 8, 'c': 10}"
] ]
}, },
"execution_count": 16, "execution_count": 13,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -242,7 +357,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 14,
"id": "faf3bea9-a308-4317-8a5d-ba4281a86671", "id": "faf3bea9-a308-4317-8a5d-ba4281a86671",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -252,7 +367,7 @@
"dict_values([5, 8, 10])" "dict_values([5, 8, 10])"
] ]
}, },
"execution_count": 17, "execution_count": 14,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -263,7 +378,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 15,
"id": "52262d79-76d2-4bf4-8f06-8ed55dcff7cc", "id": "52262d79-76d2-4bf4-8f06-8ed55dcff7cc",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -284,7 +399,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 16,
"id": "280eb1d9-bfe8-4715-a54a-4b40ef542618", "id": "280eb1d9-bfe8-4715-a54a-4b40ef542618",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -305,7 +420,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 17,
"id": "7a0fde62-9fa8-4089-b257-d2a2263b2b0d", "id": "7a0fde62-9fa8-4089-b257-d2a2263b2b0d",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -320,13 +435,14 @@
} }
], ],
"source": [ "source": [
"# Items gibt eine Liste mit tupeln zurück, jedes tuple wird in seine Elemente zerlegt und den Variablen k & v zugewiesen\n",
"for k, v in d.items():\n", "for k, v in d.items():\n",
" print(f\"Key: {k} mit Wert: {v}\")" " print(f\"Key: {k} mit Wert: {v}\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 18,
"id": "dc988e8a-135d-483f-9ae0-d20cc861c558", "id": "dc988e8a-135d-483f-9ae0-d20cc861c558",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -336,12 +452,13 @@
"[0, 1, 4, 9, 16, 25]" "[0, 1, 4, 9, 16, 25]"
] ]
}, },
"execution_count": 22, "execution_count": 18,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"# Liste füllen\n",
"squared = []\n", "squared = []\n",
"for i in range(6):\n", "for i in range(6):\n",
" squared.append(i*i)\n", " squared.append(i*i)\n",
@ -350,7 +467,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 19,
"id": "94f148fb-a1f3-4bd9-82b0-baa3ad0b9d35", "id": "94f148fb-a1f3-4bd9-82b0-baa3ad0b9d35",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -360,19 +477,20 @@
"[0, 1, 4, 9, 16, 25]" "[0, 1, 4, 9, 16, 25]"
] ]
}, },
"execution_count": 23, "execution_count": 19,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"# List Comprehension \n",
"sq = [n**2 for n in range(6)]\n", "sq = [n**2 for n in range(6)]\n",
"sq" "sq"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 20,
"id": "3e6d5db7-3cc1-4b21-9ad0-4d3402b4765b", "id": "3e6d5db7-3cc1-4b21-9ad0-4d3402b4765b",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -382,12 +500,13 @@
"{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}" "{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}"
] ]
}, },
"execution_count": 25, "execution_count": 20,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"# Dict füllen\n",
"di = {}\n", "di = {}\n",
"for n in range(6):\n", "for n in range(6):\n",
" di[n] = n**2\n", " di[n] = n**2\n",
@ -396,7 +515,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 21,
"id": "6bd693d3-8e27-48c2-9fe4-8ecafb98b181", "id": "6bd693d3-8e27-48c2-9fe4-8ecafb98b181",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -406,16 +525,25 @@
"{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}" "{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}"
] ]
}, },
"execution_count": 26, "execution_count": 21,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"# Dictionary Comprehension\n",
"dic = {n: n**2 for n in range(6)}\n", "dic = {n: n**2 for n in range(6)}\n",
"dic" "dic"
] ]
}, },
{
"cell_type": "markdown",
"id": "bf09cbc2-c2c5-4f59-8ad7-c7c5e9e50f63",
"metadata": {},
"source": [
"## System Interaction"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 27,
@ -511,9 +639,17 @@
"input(\"Gebe bitte eine Zahl ein:\")" "input(\"Gebe bitte eine Zahl ein:\")"
] ]
}, },
{
"cell_type": "markdown",
"id": "60afb4f7-e8c5-431e-a592-b9b719f9b68c",
"metadata": {},
"source": [
"## File Handling"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 24,
"id": "d47d956b-f131-4c4c-acad-4adc5ff1508e", "id": "d47d956b-f131-4c4c-acad-4adc5ff1508e",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -523,19 +659,19 @@
"<_io.TextIOWrapper name='test.txt' mode='r' encoding='UTF-8'>" "<_io.TextIOWrapper name='test.txt' mode='r' encoding='UTF-8'>"
] ]
}, },
"execution_count": 34, "execution_count": 24,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"f = open('test.txt')\n", "f = open('test.txt') # Öffne File und gebe den Handler an f, Standard im Lesemodus\n",
"f" "f"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 25,
"id": "4d38875a-18f9-4ad6-991b-fc61ea1dd08a", "id": "4d38875a-18f9-4ad6-991b-fc61ea1dd08a",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -545,18 +681,18 @@
"['Super Secret Message\\n', 'Hallo Welt\\n', 'Geiler Kurs\\n', 'Freitag 15h yeah']" "['Super Secret Message\\n', 'Hallo Welt\\n', 'Geiler Kurs\\n', 'Freitag 15h yeah']"
] ]
}, },
"execution_count": 35, "execution_count": 25,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"f.readlines()" "f.readlines() # Lese den Inhalt aus f"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 39, "execution_count": 28,
"id": "1c0610b1-b6c2-430f-94c0-e50def936b16", "id": "1c0610b1-b6c2-430f-94c0-e50def936b16",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -566,19 +702,19 @@
"<_io.TextIOWrapper name='data.txt' mode='w' encoding='UTF-8'>" "<_io.TextIOWrapper name='data.txt' mode='w' encoding='UTF-8'>"
] ]
}, },
"execution_count": 39, "execution_count": 28,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"data = open('data.txt', 'w')\n", "data = open('data.txt', 'w') # Öffne eine beschreibare File\n",
"data" "data"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 40, "execution_count": 29,
"id": "1b74ffb0-487a-4ec7-9ed1-3e51b5c76450", "id": "1b74ffb0-487a-4ec7-9ed1-3e51b5c76450",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -588,29 +724,30 @@
"18" "18"
] ]
}, },
"execution_count": 40, "execution_count": 29,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"data.write(\"Ich will nachhause\")" "data.write(\"Ich will nachhause\") # Schreibe in die File "
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 41, "execution_count": 31,
"id": "f831efc1-b548-4a49-bbed-62c8018ecdfe", "id": "f831efc1-b548-4a49-bbed-62c8018ecdfe",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# Schliese die Files\n",
"f.close()\n", "f.close()\n",
"data.close()" "data.close()"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 42, "execution_count": 32,
"id": "4580acb8-cc79-440c-a463-140547883ded", "id": "4580acb8-cc79-440c-a463-140547883ded",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -623,6 +760,7 @@
} }
], ],
"source": [ "source": [
"# Standard File handling\n",
"f = open('test.txt')\n", "f = open('test.txt')\n",
"print(f.readlines())\n", "print(f.readlines())\n",
"f.close()" "f.close()"
@ -630,7 +768,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 44, "execution_count": 33,
"id": "50f35e0c-5138-478c-abe9-dae163c467a4", "id": "50f35e0c-5138-478c-abe9-dae163c467a4",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -641,18 +779,18 @@
"traceback": [ "traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[44], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadlines\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "Cell \u001b[0;32mIn[33], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadlines\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# File ist geschlossen also ist lesen nicht möglich\u001b[39;00m\n",
"\u001b[0;31mValueError\u001b[0m: I/O operation on closed file." "\u001b[0;31mValueError\u001b[0m: I/O operation on closed file."
] ]
} }
], ],
"source": [ "source": [
"f.readlines()" "f.readlines() # File ist geschlossen also ist lesen nicht möglich"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 45, "execution_count": 35,
"id": "999e5179-4d96-4b8b-bec6-3f8b0a857291", "id": "999e5179-4d96-4b8b-bec6-3f8b0a857291",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -670,15 +808,26 @@
"traceback": [ "traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[45], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest.txt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(f\u001b[38;5;241m.\u001b[39mreadlines())\n\u001b[0;32m----> 3\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadlines\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "Cell \u001b[0;32mIn[35], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(f\u001b[38;5;241m.\u001b[39mreadlines())\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# File ist bereits geschlossen \u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadlines\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Wirft Fehler\u001b[39;00m\n",
"\u001b[0;31mValueError\u001b[0m: I/O operation on closed file." "\u001b[0;31mValueError\u001b[0m: I/O operation on closed file."
] ]
} }
], ],
"source": [ "source": [
"# Contexte nehmen einem die Arbeit ab\n",
"with open('test.txt', 'r') as f:\n", "with open('test.txt', 'r') as f:\n",
" print(f.readlines())\n", " print(f.readlines())\n",
"f.readlines()" "\n",
"# File ist bereits geschlossen \n",
"f.readlines() # Wirft Fehler"
]
},
{
"cell_type": "markdown",
"id": "d63dce40-9d51-4ab6-92e6-65fedb982dd8",
"metadata": {},
"source": [
"# Importing"
] ]
}, },
{ {
@ -777,7 +926,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 51, "execution_count": 37,
"id": "c4c97328-95dd-4e6b-bc9c-857ee5d04e25", "id": "c4c97328-95dd-4e6b-bc9c-857ee5d04e25",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -785,6 +934,27 @@
"from math import sqrt" "from math import sqrt"
] ]
}, },
{
"cell_type": "code",
"execution_count": 38,
"id": "1f29d236-0368-4fd4-97c1-a33a6adc7bf3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<function math.sqrt(x, /)>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sqrt"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 52, "execution_count": 52,
@ -808,17 +978,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 36,
"id": "b83a73fe-8f6e-4b22-97bc-c9016206a6bd", "id": "b83a73fe-8f6e-4b22-97bc-c9016206a6bd",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from math import *" "from math import * # Böse nicht mache führt nur zu unerklärbaren Fehlern"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 53, "execution_count": 40,
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@ -828,7 +998,7 @@
}, },
{ {
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"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -838,7 +1008,7 @@
"np.float64(94.86832980505137)" "np.float64(94.86832980505137)"
] ]
}, },
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} }
@ -849,11 +1019,24 @@
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"source": [] {
"data": {
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@ -872,7 +1055,7 @@
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"nbformat": 4, "nbformat": 4,

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Age,Sex,Scale Python Exp,Course,Has Voice Assistent Contact,Voice Assistent,Scale Study Satisfaction,Uses Smartphone,Which Smartphone,Has Computer,Which OS,Scale Programming Exp
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26,Weiblich,3,Medienwissenschaften,Ja,Amazon Alexa,2,Ja,Xiaomi,Ja,Windows 10,3
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26,Weiblich,4,Medienwissenschaften,Ja,Apple Siri,4,Ja,Samsung,Ja,Windows 10,2
24,Weiblich,4,Psychologie,Nein,,4,Ja,Apple,Ja,Windows 11,3
23,Männlich,3,Medienwissenschaften,Ja,Amazon Alexa,4,Ja,Samsung,Ja,Windows 10,3
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22,Weiblich,4,Medienwissenschaften,Nein,,3,Ja,Samsung,Ja,Windows 10,2
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30,Männlich,4,Medienwissenschaften,Ja,Amazon Alexa,3,Ja,Samsung,Ja,Windows 11,2
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1 Age Sex Scale Python Exp Course Has Voice Assistent Contact Voice Assistent Scale Study Satisfaction Uses Smartphone Which Smartphone Has Computer Which OS Scale Programming Exp
2 22 Männlich 4 Medienwissenschaften Ja Apple Siri 4 Ja Apple Ja Mac OS 2
3 26 Weiblich 3 Medienwissenschaften Ja Amazon Alexa 2 Ja Xiaomi Ja Windows 10 3
4 21 Männlich 3 Medienwissenschaften Ja Google Now 4 Ja Sonstige Ja Windows 10 3
5 26 Weiblich 4 Medienwissenschaften Ja Apple Siri 4 Ja Samsung Ja Windows 10 2
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7 23 Männlich 3 Medienwissenschaften Ja Amazon Alexa 4 Ja Samsung Ja Windows 10 3
8 21 Männlich 3 Medienwissenschaften Ja Amazon Alexa 4 Ja Samsung Ja Windows 10 2
9 22 Weiblich 4 Medienwissenschaften Nein 3 Ja Samsung Ja Windows 10 2
10 19 Weiblich 4 Medienwissenschaften Ja Apple Siri 3 Ja Apple Ja Windows 11 2
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12 20 Weiblich 4 Medienwissenschaften Ja Apple Siri 4 Ja Apple Ja Mac OS 2
13 21 Weiblich 4 Medienwissenschaften Nein Apple Siri 4 Ja Apple Ja Mac OS 2
14 21 Weiblich 4 Medienwissenschaften Ja Amazon Alexa 3 Ja Samsung Ja Windows 11 4
15 20 Männlich 4 Medienwissenschaften Nein 3 Ja Samsung Ja Windows 10 3
16 22 Weiblich 4 Medienwissenschaften Ja Apple Siri 2 Ja Apple Ja Windows 11 2
17 22 Weiblich 4 Medienwissenschaften Ja Amazon Alexa 3 Ja Apple Ja Mac OS 1
18 21 Weiblich 4 Medienwissenschaften Nein 3 Ja Apple Ja Mac OS 4
19 19 Männlich 3 Medienwissenschaften Ja Apple Siri 4 Ja Apple Ja Windows 10 2
20 30 Weiblich 3 Medienwissenschaften Ja Apple Siri 3 Ja Apple Ja Mac OS 2
21 27 Weiblich 4 Medienwissenschaften Ja Apple Siri 3 Ja Apple Ja Windows 11 2
22 22 Weiblich 5 Medienwissenschaften Ja Amazon Alexa 5 Ja Xiaomi Ja Linux 1
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24 30 Männlich 4 Medienwissenschaften Ja Amazon Alexa 3 Ja Samsung Ja Windows 11 2
25 23 Weiblich 5 Medienwissenschaften Ja Apple Siri 2 Ja Apple Ja Mac OS 1
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Python
Python
Python
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Python
Python
Python
Python
Python
Python
Python
Python
Python
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Python
Python
Python
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Python
Python
Python
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Python
Python
Python
Python
Python
Python
Python
Python
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Python
Python
Python
Python
Python
Python
Python
Python
Python
Python
Python
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Python
Python
Python
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Python

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{
"cells": [
{
"cell_type": "markdown",
"id": "965d3b35-ff65-4a31-93ac-0389e578772a",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"# How to Jupyter\n",
"\n",
"Jupyter Notebook is an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text.\n",
"\n",
"It's widely used for interactive computing, data analysis, scientific research, education, and data visualization.\n",
"\n",
"Some key features and components of Jupyter Notebook includes:\n",
"\n",
"---\n",
"\n",
"**At the bottom left is a control pad with which you can navigate through the slides.**\n",
"\n",
"(How to create slides will be explained later.)"
]
},
{
"cell_type": "markdown",
"id": "263e7df3-97cc-4568-9d50-59d073d4a7d9",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"1. **Interactive Environment**\n",
"2. **Support for Multiple Programming Languages**\n",
"3. **Rich Text Support**\n",
"4. **Data Visualization**\n",
"5. **Equation Rendering**\n",
"6. **Easy Sharing**\n",
"7. **Notebook Extensions**\n",
"8. **Data Analysis and Exploration**\n",
"9. **Education and Learning**"
]
},
{
"cell_type": "markdown",
"id": "733cbe91-df68-44d1-b546-8c229fa0dc90",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "skip"
},
"tags": []
},
"source": [
"1. **Interactive Environment**: Jupyter Notebook provides an interactive environment where you can write and execute code in chunks called cells. This allows you to see the immediate results of your code as you work on it.\n",
"\n",
"2. **Support for Multiple Programming Languages**: While Jupyter was originally designed for Python, it supports various programming languages such as Julia, R, and more through language-specific kernels. Each kernel enables you to execute code written in a specific language."
]
},
{
"cell_type": "markdown",
"id": "01244173-08be-4c4c-9675-27f09620e34b",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "skip"
},
"tags": []
},
"source": [
"3. **Rich Text Support**: You can combine code cells with text cells to create a narrative that explains the code, its purpose, and the analysis being performed. This makes it a powerful tool for creating data-driven documents and reports.\n",
"\n",
"4. **Data Visualization**: Jupyter Notebook supports the integration of various data visualization libraries such as Matplotlib, Seaborn, Plotly, and more. This allows you to create charts, graphs, and other visualizations to better understand your data."
]
},
{
"cell_type": "markdown",
"id": "2d52825d-b8fa-456b-98f4-574585e3b3bb",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "skip"
},
"tags": []
},
"source": [
"5. **Equation Rendering**: It supports rendering mathematical equations using LaTeX notation, which is useful for scientific and mathematical documentation.\n",
"\n",
"6. **Easy Sharing**: Jupyter Notebooks can be easily shared with colleagues, collaborators, or the public. Notebooks can be exported to various formats such as HTML, PDF, and slideshows. There are also platforms like GitHub and JupyterHub that allow for collaborative editing and sharing."
]
},
{
"cell_type": "markdown",
"id": "2c1679b9-b0db-4bf4-9bbc-9d33ca7ffeb0",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "skip"
},
"tags": []
},
"source": [
"7. **Notebook Extensions**: Jupyter Notebook has a wide range of extensions that can be added to enhance functionality. These extensions can provide additional features like code linting, spell checking, and more.\n",
"\n",
"8. **Data Analysis and Exploration**: Jupyter Notebook is widely used for data analysis and exploration tasks. Analysts and researchers can import data, clean it, perform statistical analysis, and visualize the results all within the same document.\n",
"\n",
"9. **Education and Learning**: Jupyter Notebook is used in educational settings to teach programming, data science, and various scientific concepts. Its interactive nature helps learners experiment and grasp concepts more effectively."
]
},
{
"cell_type": "markdown",
"id": "10d93371-42d5-40df-8a30-2ff49dde94d5",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "skip"
},
"tags": []
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "6bce7ad3-61cd-4087-b35e-bddcb8c9d3c8",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"## Install Python\n",
"\n",
"In this module we will learn the programming language Python. To do this, we need to install it on our system in order to be able to use Jupyter Notebook in advance.\n",
"\n",
"The [Python.org](https://www.python.org/) website contains a download link for each operating system. Under [www.python.org/downloads/](https://www.python.org/downloads/) you can download the latest Python version.\n",
"\n",
"After following the installation wizard (this depends heavily on which operating system you are using, so we do not show this here), you have successfully installed Python.\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "eec4a5bc-9c06-4680-9aae-d388f0a2bec1",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"## Opening a Terminal\n",
"\n",
"**Windows**: \n",
"\n",
"1. `Press Start` (⊞ - Windows Symbol) -> type and search for `cmd`\n",
"2. Or Press the Windows Symbol ⊞ + the R key `(Windows + R)` -> type `cmd` in the window that appeard -> press `Enter`\n",
"\n",
"**Mac**:\n",
"\n",
"Open launchpad and Search for `Terminal`\n",
"\n",
"**Linux**:\n",
"\n",
"It depends on your Environment. But `Ctrl + Alt + T` should do the Trick on every System."
]
},
{
"cell_type": "markdown",
"id": "50a7f663-3a14-4de7-bf03-edaf571681d8",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"## Upgrading Pip and Installing Jupyter\n",
"\n",
"---\n",
"\n",
"### Pip - Python Package Index\n",
"\n",
"pip is the de-facto and recommended package management programme for Python packages from the Python Package Index (PyPI). At the beginning, the project was called \"pyinstall\".\n",
"\n",
"It's website is found under [pypi.org](https://pypi.org/). Every Package you need, can and should be derived from PyPI.\n",
"\n",
"---\n",
"\n",
"After opening the terminal, pip should first be updated to the latest version. To do this, enter the command:\n",
"\n",
"`python3 -m pip install -U pip`"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "99fcae70-17c9-447d-afdb-1e2cd41c3457",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pip in /home/phil/Desktop/einfuhrung-in-die-programmierung/env/lib/python3.11/site-packages (23.2.1)\n"
]
}
],
"source": [
"!python3 -m pip install -U pip"
]
},
{
"cell_type": "markdown",
"id": "99db8dd0-cd07-4a13-a9e8-073188d7a492",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "skip"
},
"tags": []
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "3f2a5398-4480-4db6-a4dd-7bba2bdf90a3",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"### Installing Jupyter\n",
"\n",
"Now we can install some software we only need two packages depending on your need.\n",
"\n",
"`virtualenv` is a tool to create isolated Python environments. You can read more about it in the [Virtualenv documentation](https://virtualenv.pypa.io/en/stable/)."
]
},
{
"cell_type": "markdown",
"id": "3a6b4743-7d3a-4e87-8e6a-108d526df1ca",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"1. We need to install the package `virtualenv` (venv)\n",
"\n",
"`python3 -m pip install virtualenv`"
]
},
{
"cell_type": "markdown",
"id": "9eaf8cf1-91e8-47cb-85a6-7687b641ecac",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"2. Now we can create an virtual environment in any folder we want. (A good practice is a venv for every project)\n",
"\n",
"`python3 -m venv env`\n",
"\n",
"the name `env` is the folder which has all of our environment information, it can be named everything, but for convinence `env` should be used."
]
},
{
"cell_type": "markdown",
"id": "810aa9c6-4a5d-4f85-8168-31497850f88b",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"3. After installing `virtualenv` we need to activate it\n",
"\n",
"**Windows**: `.\\env\\Scripts\\activate`\n",
"\n",
"**Linux / Mac**: `source env/bin/activate`\n",
"\n",
"your command prompt will be modified to reflect the change."
]
},
{
"cell_type": "markdown",
"id": "d9f09c52-1bea-41e7-a6f8-f19d73553687",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"4. Now you can install jupyter and other dependencies without tinkering with your system\n",
"\n",
"`pip install jupyterlab`\n",
"\n",
"-> We can use pip direct because we specified the python version with venv implicitly."
]
},
{
"cell_type": "markdown",
"id": "92bbebdf-2dd0-471e-a98d-74ebdf3e1926",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "skip"
},
"tags": []
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "c889baa0-4202-4811-89b9-8b8bb578f05a",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"## Starting Jupyter for the first time\n",
"\n",
"the last thing we need to tab out of the command line is to start Jupyter\n",
"\n",
"therefor type `jupyter lab` and Enter. Do not close the command line it will stop jupyter!\n",
"\n",
"After a little waiting time jupyter prompts you with messages one of them should look like:\n",
"\n",
"`http(s)://<server:port>/<lab-location>/lab`\n",
"\n",
"copy the url and open it in your Webbrowser of Choice. Now we can Proceed.\n",
"\n",
"**Note**: This step needs to be done everytime you want to start Jupyter!\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "594997a1-37ed-42a4-835d-5a4ab92eea70",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"# Alternatives "
]
},
{
"cell_type": "markdown",
"id": "1a432129-1fcb-4ee8-a2eb-ebca101dd501",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"## (Mini)conda \n",
"\n",
"conda from Ananconda Inc. is an open source package and environment manager for many languages that encludes everything we needs.\n",
"\n",
"For most purposes miniconda should do the trick. You can download it here [conda docs](https://docs.conda.io/en/latest/miniconda.html)\n",
"\n",
"After installing you can change every `python3 -m pip` and `pip` command with `conda`."
]
},
{
"cell_type": "markdown",
"id": "31415a9f-6949-434a-b714-c1c36dddf99a",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"## Jupyter Lab Desktop \n",
"\n",
"As there description on [GitHub](https://github.com/jupyterlab/jupyterlab-desktop) stated \n",
"\n",
"**JupyterLab Desktop is the cross-platform desktop application for JupyterLab. It is the quickest and easiest way to get started with Jupyter notebooks on your personal computer, with the flexibility for advanced use cases.**\n",
"\n",
"its nothing else than a selfcontained webbrowser bundeld with Jupyter Lab. You can download a binary for your operating System under there [GitHub Releases](https://github.com/jupyterlab/jupyterlab-desktop/releases) page.\n"
]
},
{
"cell_type": "markdown",
"id": "a9d2fead-7d7e-427c-bf35-0f5d379f9ddc",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"## Note\n",
"\n",
"Every Process has its Advantages and Disadvantages and depends on your needs and workflow.\n",
"\n",
"The easiest way isn't always the best.\n",
"\n",
"If you have no command line experience try to get used to it and don't use Jupyter Lab Desktop."
]
},
{
"cell_type": "markdown",
"id": "68563ada-3ac1-421b-ab82-5be195a75f23",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "skip"
},
"tags": []
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "e66d9571-a154-4e4b-b612-9ba9151cd6cd",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "skip"
},
"tags": []
},
"source": [
"# Jupyter Tricks\n",
"\n",
"## Package handeling\n",
"\n",
"Our course provides a requirements.txt file, take a look.\n",
"\n",
"This file can be created using `pip freeze > requirements.txt` and contains all of your environment data.\n",
"\n",
"To use the file we need to install the content. Thankfully pythons pip provides an easy way to this\n",
"\n",
"just start a new jupyter notebook and type in the first cell `!pip install -r requirements.txt` and run the cell.\n",
"\n",
"## Markdown \n",
"\n",
"Making notes in Jupyter is just as powerful. It uses a technology named Markdown which is just another way to write [HTML](https://en.wikipedia.org/wiki/HTML) (The Backbone of the Internet)\n",
"\n",
"Some good cheatSheets to learn this simple typewriter can be found under:\n",
"\n",
"1. [Markdown Guide](https://www.markdownguide.org/cheat-sheet/)\n",
"2. [Adam P Markdown Wiki](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)\n",
"3. [Markdown Table Generator](https://www.tablesgenerator.com/markdown_tables)\n",
"\n",
"## Slides and Presentations\n",
"\n",
"We have a whole Lesson for that but here is a quick example to look in [mljar.com](https://mljar.com/blog/jupyter-notebook-presentation/)\n",
"\n",
"## Debugging\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0f136f2-5975-489d-819b-b6e6296286a6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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33
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@ -1,33 +0,0 @@
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2
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фпЕ╕8и╣
Лjгн├░ёЦK║вi╒Р$& =╨л╨(N▐g│≈╔СTв

93
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@ -1,93 +0,0 @@
from dash import Dash, html, dcc, dash_table
import dash_bootstrap_components as dbc
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
# Read Data
df = pd.read_excel('evaluation.xlsx', sheet_name='results')
# Add graph data
course = ["{} <br> n={}".format(c,s) for c, s in zip(df['Course'], df['Submissions'])]
avg = df['Avg. Score']
max = df['Max Score']
# Relative data
max_rel = df['Max Score'].sum()
avg_rel = np.array([c/s*100 for c, s in zip(df['Avg. Score'], df['Max Score'])])
avg_rel_sum = df['Avg. Score'].sum() / max_rel * 100
# Make traces for graph
avg_trace = go.Bar(x=course, y=avg, xaxis='x1', yaxis='y1',
marker=dict(color='#0099ff'),
name='Average Score<br>over all Students')
max_trace = go.Bar(x=course, y=max, xaxis='x1', yaxis='y1',
marker=dict(color='#404040'),
name='Possible Max Score<br>Per Notebook')
avg_rel_trace = go.Scatter(x=course, y=avg_rel, xaxis='x1', yaxis='y2',
marker=dict(color='#e00030'),
name='Average Score (in %)<br>Per Notebook')
perc_trace = go.Scatter(x=course, y=avg_rel, xaxis='x1', yaxis='y2',
mode="text+lines",
marker=dict(color='#e00030'),
name='Average Score (in %)<br>Per Notebook',
text=[str(n) for n in avg_rel], texttemplate='%{text:.0f}% ',
textposition=[
'top center', 'top right', 'middle left',
'middle left', 'middle left', 'top left',
'top left', 'bottom left'
],
textfont={'size': 12})
def bar():
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.update_layout(template="plotly_dark")
# Y-Axis Title
fig.update_yaxes(title_text="<b>Relative Score (in %)</b>", secondary_y=True)
fig.update_yaxes(title_text="<b>Score</b>", secondary_y=False)
# Add Plots
fig.add_trace(avg_trace)
fig.add_trace(max_trace)
fig.add_trace(perc_trace)
#fig.add_trace(avg_rel_trace)
return fig
# Create Dash
app = Dash(__name__, external_stylesheets=[dbc.themes.CYBORG, dbc.icons.FONT_AWESOME])
header = dbc.Row([
html.H1('Einführung in die Programmierung für Nicht-Informatiker*innen', className="text-primary text-center fs-3"),
html.H2('WiSe 23/24 Results', className="text-secondary text-center fs-4")
])
app.layout = dbc.Container([
header,
dbc.Row([dcc.Graph(figure=bar())]),
html.Br(),
dbc.Row([
dbc.Alert([
html.I(className="bi bi-info-circle-fill me-2"),
html.Big("{:.0f}% of all tasks were solved correctly.".format(avg_rel_sum), className="text-center")
],
color='info', className="d-flex align-items-center")
])
])
if __name__ == '__main__':
#app.run(debug=True)
#from plotly.offline.offline import _plot_html
import plotly
plotly.offline.plot(
bar(),
show_link=False,
filename = 'eval.html'
)

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@ -1,162 +0,0 @@
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jupyterlab-widgets @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_widgets_1680020489668/work
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kiwisolver @ file:///Users/runner/miniforge3/conda-bld/kiwisolver_1666805801984/work
latex2mathml==3.76.0
lesscpy==0.15.1
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MarkupSafe @ file:///Users/runner/miniforge3/conda-bld/markupsafe_1674135896840/work
matplotlib @ file:///Users/runner/miniforge3/conda-bld/matplotlib-suite_1678135672482/work
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mistune @ file:///home/conda/feedstock_root/build_artifacts/mistune_1675771498296/work
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webcolors==1.13
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y-py @ file:///Users/runner/miniforge3/conda-bld/y-py_1677231418476/work
ypy-websocket @ file:///home/conda/feedstock_root/build_artifacts/ypy-websocket_1670333059911/work
ziafont==0.6
ziamath==0.8.1
zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1677313463193/work
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77
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@ -1,77 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "3069696b-bbe6-4fd6-ba13-2affa071d865",
"metadata": {},
"source": [
"# Was ist das eigentliche Lernziel? \n",
"\n",
"- Arbeiten mit Daten\n",
"- Daten interpretieren \n",
" - Visuell (Karten, Graphen, Tabellen)\n",
" - Mittels Stochastischer Analyse (Mittelwert, Median)\n",
" - Zufallszahlen (Zum generieren von Testdaten)?\n",
"- Googlen lernen\n",
"- Dokumentationen Lesen\n",
"\n",
"# TO DO\n",
"\n",
"- [] Slideshow erklären\n",
"- [] Mehr Bilder\n",
"- [] Python einführung?\n",
"- [] Folium als eigene Lerneinheit -> Projekt schwieriger machen?\n",
"- [] SciPy Funktionen und/oder arbeiten mit Statistic Daten?"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "313bf238-67bc-4cb4-8331-6e183427bf70",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip freeze > requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3fa17fc4-29a7-4e97-9836-50e85693cec1",
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"source": []
}
],
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"display_name": "Python 3 (ipykernel)",
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},
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\section{Project Proposal - Geomapping}
\begin{figure}[h]
\includegraphics[width=0.9\textwidth]{fig/geomapping.png}
\centering
\end{figure}
Geomapping involves using geographic data to create maps that illustrate how features or phenomena are related spatially. There are a number of reasons why this skill is important. One advantage of geomapping is that it can help people better understand and analyze spatial patterns. For example, geomapping can be used to show how the distribution of certain resources, such as water or vegetation, varies across a region. This can help people make more informed decisions about how to manage those resources.
As part of the exercise, students will learn how to create simple maps by working with data sets. For example, if you want to show how the distribution of universities in Lower Saxony looks like, this can be shown with conventional methods of data analysis, but it hides certain data that only become visible through the spatial representation. In the exercise, the universities of Lower Saxony are plotted and the students are instructed to write a short text about how they interpret data or what they see. Thereby it becomes obvious that most of the universities are located in Hannover or that there are preferred locations for universities that are concentrated in the eastern part of Lower Saxony. All in all, the ability of geomapping should help to look at data(-sets) differently. \\
\textbf{What will You Learn from This Python Project?}
\begin{itemize}
\item Create maps in Python
\item Visualization of coordinates with different kinds of marker (dot, circle, rectangle)
\item Customization of markers (color, symbol)
\item Add popup information to markers
\end{itemize}

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Material/wise_24_25/lernmaterial/Folium & Pandas/requirements.txt
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alembic==1.8.1
anyio==3.6.2
appnope==0.1.3
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
asttokens==2.1.0
attrs==22.1.0
Babel==2.11.0
backcall==0.2.0
beautifulsoup4==4.11.1
bleach==5.0.1
branca==0.6.0
certifi==2022.9.24
cffi==1.15.1
charset-normalizer==2.1.1
debugpy==1.6.3
decorator==5.1.1
defusedxml==0.7.1
entrypoints==0.4
executing==1.2.0
fastjsonschema==2.16.2
folium==0.13.0
greenlet==2.0.1
idna==3.4
ipykernel==6.17.1
ipython==8.6.0
ipython-genutils==0.2.0
ipywidgets==8.0.2
jedi==0.18.1
Jinja2==3.1.2
json5==0.9.10
jsonschema==4.17.0
jupyter-server==1.23.2
jupyter_client==7.4.6
jupyter_core==5.0.0
jupyterlab==3.5.0
jupyterlab-pygments==0.2.2
jupyterlab-widgets==3.0.3
jupyterlab_server==2.16.3
Mako==1.2.4
MarkupSafe==2.1.1
matplotlib-inline==0.1.6
mistune==2.0.4
nbclassic==0.3.7
nbclient==0.6.3
nbconvert==7.2.5
nbformat==5.7.0
nbgrader==0.8.1
nest-asyncio==1.5.6
notebook==6.4.12
notebook_shim==0.2.2
numpy==1.23.4
packaging==21.3
pandocfilters==1.5.0
parso==0.8.3
pexpect==4.8.0
pickleshare==0.7.5
platformdirs==2.5.4
prometheus-client==0.15.0
prompt-toolkit==3.0.32
psutil==5.9.4
ptyprocess==0.7.0
pure-eval==0.2.2
pycparser==2.21
Pygments==2.13.0
pyparsing==3.0.9
pyrsistent==0.19.2
python-dateutil==2.8.2
pytz==2022.6
pyzmq==24.0.1
rapidfuzz==2.13.2
requests==2.28.1
Send2Trash==1.8.0
six==1.16.0
sniffio==1.3.0
soupsieve==2.3.2.post1
SQLAlchemy==1.4.44
stack-data==0.6.1
terminado==0.17.0
tinycss2==1.2.1
tomli==2.0.1
tornado==6.2
traitlets==5.1.1
urllib3==1.26.12
wcwidth==0.2.5
webencodings==0.5.1
websocket-client==1.4.2
widgetsnbextension==4.0.3

41
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@ -1,41 +0,0 @@
University name,Type of university,Sponsorship,Right of promotion,Founding year,Number of students,Address,lat,lon,plz,pic
Hochschule für Bildende Künste Braunschweig,Artistic university,public,yes,1963,976,Johannes-Selenka-Platz 1,52.2577384,10.5023145,38118 Braunschweig,https://www.hbk-bs.de/fileadmin/_processed_/5/1/csm_HBK_Logo_9f3f898a2b.png
Technische Universität Carolo-Wilhelmina zu Braunschweig,University,public,yes,1745,17709,Universitätspl. 2,52.27355,10.530097,38106 Braunschweig,https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Siegel_TU_Braunschweig_transparent.svg/1200px-Siegel_TU_Braunschweig_transparent.svg.png
Hochschule 21,University of Applied Sciences,privat,no,2005,1084,Harburger Str. 6,53.47765,9.70465,21614 Buxtehude,https://upload.wikimedia.org/wikipedia/commons/thumb/b/bd/Hochschule_21_logo.svg/2560px-Hochschule_21_logo.svg.png
Technische Universität Clausthal,University,public,yes,1775,3446,Adolph-Roemer-Straße 2A,51.80484,10.33411,38678 Clausthal-Zellerfeld,https://www.presse.tu-clausthal.de/fileadmin/TU_Clausthal/images/CorporateDesign/Logo/Logo_TUC_en_RGB_gross.gif
Hochschule Emden/Leer,University of Applied Sciences,public,no,2009,4481,Constantiapl. 4,53.36816,7.18141,26723 Emden,https://sta-hisweb.hs-emden-leer.de/QIS/images//logo_el.jpg
PFH – Private Hochschule Göttingen,University of Applied Sciences,privat,no,1995,4226,Weender Landstraße 3-7,51.53891,9.93322,37073 Göttingen,https://goettingen-campus.de/fileadmin/_processed_/d/7/csm_logopfh_20f8eee765.jpg
Georg-August-Universität Göttingen,University,public,yes,1737,28614,Wilhelmsplatz 1,51.53407,9.93785,37073 Göttingen,https://upload.wikimedia.org/wikipedia/commons/c/c0/Logo_Uni_G%C3%B6ttingen_2022.png
Fachhochschule für die Wirtschaft Hannover,University of Applied Sciences,privat,no,1996,641,Freundallee 15,52.3662,9.77247,30173 Hannover,https://upload.wikimedia.org/wikipedia/commons/5/5c/Fachhochschule_f%C3%BCr_die_Wirtschaft_logo.svg
Hochschule Hannover,University of Applied Sciences,public,no,1971,9209,Ricklinger Stadtweg 120,52.35419,9.72238,30459 Hannover,https://upload.wikimedia.org/wikipedia/commons/thumb/0/0e/HsH_Logo.svg/1200px-HsH_Logo.svg.png
"Hochschule für Musik, Theater und Medien Hannover",Artistic university,public,yes,1897,1409,Neues Haus 1,52.37738,9.75392,30175 Hannover,https://upload.wikimedia.org/wikipedia/commons/thumb/7/78/HMTM-Logo-2010.svg/1200px-HMTM-Logo-2010.svg.png
Leibniz-Fachhochschule,University of Applied Sciences,privat,no,1920,589,Expo Plaza 11,52.32115,9.81868,30539 Hannover,https://www.visit-hannover.com/var/storage/images/_aliases/image_full/media/01-data-neu/bilder/redaktion-hannover.de/portale/initiative-wissenschaft/leibniz-fh/leibniz-fachhochschule-logo/8135360-1-ger-DE/Leibniz-Fachhochschule-Logo.jpg
Medizinische Hochschule Hannover (MHH),University,public,yes,1963,3778,Carl-Neuberg-Straße 1,52.38405,9.80603,30625 Hannover,https://upload.wikimedia.org/wikipedia/commons/thumb/3/3d/Medizinische_Hochschule_Hannover_logo.svg/2560px-Medizinische_Hochschule_Hannover_logo.svg.png
Stiftung Tierärztliche Hochschule Hannover,University,public,yes,1778,2381,Bünteweg 2,52.35468,9.79773,30559 Hannover,https://upload.wikimedia.org/wikipedia/de/thumb/5/59/Tier%C3%A4rztliche_Hochschule_Hannover_logo.svg/1200px-Tier%C3%A4rztliche_Hochschule_Hannover_logo.svg.png
Gottfried Wilhelm Leibniz Universität Hannover,University,public,yes,1831,28935,Welfengarten 1,52.38225,9.71777,30167 Hannover,https://www.uni-hannover.de/fileadmin/_processed_/1/5/csm_luh-logo-3x2_8dea6c08fc.jpg
Fachhochschule für Interkulturelle Theologie Hermannsburg,University of Applied Sciences,privat,no,2012,91,Missionsstraße 3-5,52.708843,10.14071,29320 Südheide,https://cdn.max-e5.info/damfiles/logo/fh_hermannsburg/fh_hermannsburg/Kopfgrafik/Logo-FIT--weiss.jpg-b5f510cb468ab8840e0f2e62b703208e.jpg
Universität Hildesheim,University,public,yes,1978,8378,Universitätspl. 1,52.13401,9.97469,31141 Hildesheim,https://www.uni-hildesheim.de/media/_processed_/d/8/csm_Bildkombo_Logo_Uni_Hildesheim-1850_8fd99cc21e.jpg
HAWK Hochschule für angewandte Wissenschaft und Kunst Hildesheim,University of Applied Sciences,public,no,1971,6495,Hohnsen 4,52.14246,9.95798,31134 Hildesheim,https://upload.wikimedia.org/wikipedia/commons/0/02/HAWK-Logo.jpg
HAWK Hochschule für angewandte Wissenschaft und Kunst Holzminden,University of Applied Sciences,public,no,1971,6495,Haarmannpl. 3,51.82726,9.45069,37603 Holzminden,https://upload.wikimedia.org/wikipedia/commons/0/02/HAWK-Logo.jpg
HAWK Hochschule für angewandte Wissenschaft und Kunst Göttingen,University of Applied Sciences,public,no,1971,6495,Von-Ossietzky-Straße 99,51.52175,9.96967,37085 Göttingen,https://upload.wikimedia.org/wikipedia/commons/0/02/HAWK-Logo.jpg
Leuphana Universität Lüneburg,University,public,yes,1946,6497,Universitätsallee 1,53.228531,10.40171,21335 Lüneburg,https://upload.wikimedia.org/wikipedia/commons/thumb/9/93/Leuphana_Universit%C3%A4t_L%C3%BCneburg_Logo_2020.svg/2560px-Leuphana_Universit%C3%A4t_L%C3%BCneburg_Logo_2020.svg.png
Norddeutsche Hochschule für Rechtspflege – Niedersachsen,University of Administration,public,no,2007,6409,Godehardspl. 6,52.14484,9.94923,31134 Hildesheim,https://static.studycheck.de/media/images/institute_logos/small/hr-nord.jpg
Kommunale Hochschule für Verwaltung in Niedersachsen,University of Administration,public,no,2007,1570,Wielandstraße 8,52.3705,9.72239,30169 Hannover,https://www.nsi-hsvn.de/fileadmin/user_upload/02_Studium/big-hsvn_logo.png
"Carl von Ossietzky Universität Oldenburg
",University,public,yes,1973,15635,Uhlhornsweg 49-55,53.14734,8.17902,26129 Oldenburg,https://upload.wikimedia.org/wikipedia/commons/thumb/2/22/Carl_von_Ossietzky_Universit%C3%A4t_Oldenburg_logo.svg/1200px-Carl_von_Ossietzky_Universit%C3%A4t_Oldenburg_logo.svg.png
Hochschule Osnabrück,University of Applied Sciences,public,no,1971,13620,Albrechtstraße 30,52.28268,8.02501,49076 Osnabrück,https://login.hs-osnabrueck.de/nidp/hsos/images/hsos-logo.png
Universität Osnabrück,University,public,yes,1973,13640,Neuer Graben 29,52.27137,8.04454,49074 Osnabrück,https://www.eh-tabor.de/sites/default/files/styles/width980px/public/logo-universitaet-osnabrueck.png?itok=DmZEq9ka
"Hochschule Braunschweig/Wolfenbüttel, Ostfalia Hochschule für angewandte Wissenschaften",University of Applied Sciences,public,no,1971,11577,Salzdahlumer Str. 46/48,52.17683,10.54865,38302 Wolfenbüttel,https://www.ostfalia.de/export/system/modules/de.ostfalia.module.template/resources/images/logo/Ostfalia_German.png_230952558.png
"Hochschule Wolfsburg, Ostfalia Hochschule für angewandte Wissenschaften",University of Applied Sciences,public,no,1971,11577,Robert-Koch-Platz 8A,52.42595,10.78711,38440 Wolfsburg,https://www.ostfalia.de/export/system/modules/de.ostfalia.module.template/resources/images/logo/Ostfalia_German.png_230952558.png
"Hochschule Suderburg, Ostfalia Hochschule für angewandte Wissenschaften",University of Applied Sciences,public,no,1971,11577,Herbert-Meyer-Straße 7,52.89761,10.44659,29556 Suderburg,https://www.ostfalia.de/export/system/modules/de.ostfalia.module.template/resources/images/logo/Ostfalia_German.png_230952558.png
"Hochschule Salzgitter, Ostfalia Hochschule für angewandte Wissenschaften",University of Applied Sciences,public,no,1971,11577,Karl-Scharfenberg-Straße 55/57,52.08724,10.38055,38229 Salzgitter,https://www.ostfalia.de/export/system/modules/de.ostfalia.module.template/resources/images/logo/Ostfalia_German.png_230952558.png
"Hochschule für Künste im Sozialen, Ottersberg",University of Applied Sciences,privat,no,1967,342,Große Str. 107,53.10668,9.1631,28870 Ottersberg,https://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/Logo_HKS_Ottersberg.svg/1200px-Logo_HKS_Ottersberg.svg.png
Private Hochschule für Wirtschaft und Technik Vechta,University of Applied Sciences,privat,no,1998,558,Rombergstraße 40,52.72125,8.27891,49377 Vechta,https://www.phwt.de/wp-content/uploads/2020/09/phwt-logo-free.png
Private Hochschule für Wirtschaft und Technik Diepholz,University of Applied Sciences,privat,no,1998,558,Schlesier Str. 13A,52.61171,8.36334,49356 Diepholz,https://www.phwt.de/wp-content/uploads/2020/09/phwt-logo-free.png
Universität Vechta,University,public,yes,1995,4.551,Driverstraße 22,52.72117,8.2938,49377 Vechta,https://upload.wikimedia.org/wikipedia/commons/0/08/Logo_Uni_Vechta-neu.png
Hochschule Weserbergland,University of Applied Sciences,privat,no,2010,485,Am Stockhof 2,52.09875,9.35542,31785 Hameln,https://upload.wikimedia.org/wikipedia/commons/thumb/0/04/Hochschule_Weserbergland_logo.svg/1200px-Hochschule_Weserbergland_logo.svg.png
Jade Hochschule – Wilhelmshaven,University of Applied Sciences,public,no,2009,6789,Friedrich-Paffrath-Straße 101,53.54787,8.08804,26389 Wilhelmshaven,https://www.jade-hs.de/fileadmin/layout2016/assets/jadehs-logo.png
Jade Hochschule – Oldenburg,University of Applied Sciences,public,no,2009,6789,Ofener Str. 16/19,53.14179,8.20213,26121 Oldenburg,https://www.jade-hs.de/fileadmin/layout2016/assets/jadehs-logo.png
Jade Hochschule – Elsfleth,University of Applied Sciences,public,no,2009,6789,Weserstraße 52,53.24244,8.46651,26931 Elsfleth,https://www.jade-hs.de/fileadmin/layout2016/assets/jadehs-logo.png
Steuerakademie Niedersachsen Rinteln,University of Administration,public,no,2006,500,Wilhelm-Busch-Weg 29,52.20696,9.09112,31737 Rinteln,https://www.steuerakademie.niedersachsen.de/assets/image/232/85611
Steuerakademie Niedersachsen Bad Eilsen,University of Administration,public,no,2006,500,Bahnhofstraße 5,52.23981,9.10423,31707 Bad Eilsen,https://www.steuerakademie.niedersachsen.de/assets/image/232/85611
1 University name Type of university Sponsorship Right of promotion Founding year Number of students Address lat lon plz pic
2 Hochschule für Bildende Künste Braunschweig Artistic university public yes 1963 976 Johannes-Selenka-Platz 1 52.2577384 10.5023145 38118 Braunschweig https://www.hbk-bs.de/fileadmin/_processed_/5/1/csm_HBK_Logo_9f3f898a2b.png
3 Technische Universität Carolo-Wilhelmina zu Braunschweig University public yes 1745 17709 Universitätspl. 2 52.27355 10.530097 38106 Braunschweig https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Siegel_TU_Braunschweig_transparent.svg/1200px-Siegel_TU_Braunschweig_transparent.svg.png
4 Hochschule 21 University of Applied Sciences privat no 2005 1084 Harburger Str. 6 53.47765 9.70465 21614 Buxtehude https://upload.wikimedia.org/wikipedia/commons/thumb/b/bd/Hochschule_21_logo.svg/2560px-Hochschule_21_logo.svg.png
5 Technische Universität Clausthal University public yes 1775 3446 Adolph-Roemer-Straße 2A 51.80484 10.33411 38678 Clausthal-Zellerfeld https://www.presse.tu-clausthal.de/fileadmin/TU_Clausthal/images/CorporateDesign/Logo/Logo_TUC_en_RGB_gross.gif
6 Hochschule Emden/Leer University of Applied Sciences public no 2009 4481 Constantiapl. 4 53.36816 7.18141 26723 Emden https://sta-hisweb.hs-emden-leer.de/QIS/images//logo_el.jpg
7 PFH – Private Hochschule Göttingen University of Applied Sciences privat no 1995 4226 Weender Landstraße 3-7 51.53891 9.93322 37073 Göttingen https://goettingen-campus.de/fileadmin/_processed_/d/7/csm_logopfh_20f8eee765.jpg
8 Georg-August-Universität Göttingen University public yes 1737 28614 Wilhelmsplatz 1 51.53407 9.93785 37073 Göttingen https://upload.wikimedia.org/wikipedia/commons/c/c0/Logo_Uni_G%C3%B6ttingen_2022.png
9 Fachhochschule für die Wirtschaft Hannover University of Applied Sciences privat no 1996 641 Freundallee 15 52.3662 9.77247 30173 Hannover https://upload.wikimedia.org/wikipedia/commons/5/5c/Fachhochschule_f%C3%BCr_die_Wirtschaft_logo.svg
10 Hochschule Hannover University of Applied Sciences public no 1971 9209 Ricklinger Stadtweg 120 52.35419 9.72238 30459 Hannover https://upload.wikimedia.org/wikipedia/commons/thumb/0/0e/HsH_Logo.svg/1200px-HsH_Logo.svg.png
11 Hochschule für Musik, Theater und Medien Hannover Artistic university public yes 1897 1409 Neues Haus 1 52.37738 9.75392 30175 Hannover https://upload.wikimedia.org/wikipedia/commons/thumb/7/78/HMTM-Logo-2010.svg/1200px-HMTM-Logo-2010.svg.png
12 Leibniz-Fachhochschule University of Applied Sciences privat no 1920 589 Expo Plaza 11 52.32115 9.81868 30539 Hannover https://www.visit-hannover.com/var/storage/images/_aliases/image_full/media/01-data-neu/bilder/redaktion-hannover.de/portale/initiative-wissenschaft/leibniz-fh/leibniz-fachhochschule-logo/8135360-1-ger-DE/Leibniz-Fachhochschule-Logo.jpg
13 Medizinische Hochschule Hannover (MHH) University public yes 1963 3778 Carl-Neuberg-Straße 1 52.38405 9.80603 30625 Hannover https://upload.wikimedia.org/wikipedia/commons/thumb/3/3d/Medizinische_Hochschule_Hannover_logo.svg/2560px-Medizinische_Hochschule_Hannover_logo.svg.png
14 Stiftung Tierärztliche Hochschule Hannover University public yes 1778 2381 Bünteweg 2 52.35468 9.79773 30559 Hannover https://upload.wikimedia.org/wikipedia/de/thumb/5/59/Tier%C3%A4rztliche_Hochschule_Hannover_logo.svg/1200px-Tier%C3%A4rztliche_Hochschule_Hannover_logo.svg.png
15 Gottfried Wilhelm Leibniz Universität Hannover University public yes 1831 28935 Welfengarten 1 52.38225 9.71777 30167 Hannover https://www.uni-hannover.de/fileadmin/_processed_/1/5/csm_luh-logo-3x2_8dea6c08fc.jpg
16 Fachhochschule für Interkulturelle Theologie Hermannsburg University of Applied Sciences privat no 2012 91 Missionsstraße 3-5 52.708843 10.14071 29320 Südheide https://cdn.max-e5.info/damfiles/logo/fh_hermannsburg/fh_hermannsburg/Kopfgrafik/Logo-FIT--weiss.jpg-b5f510cb468ab8840e0f2e62b703208e.jpg
17 Universität Hildesheim University public yes 1978 8378 Universitätspl. 1 52.13401 9.97469 31141 Hildesheim https://www.uni-hildesheim.de/media/_processed_/d/8/csm_Bildkombo_Logo_Uni_Hildesheim-1850_8fd99cc21e.jpg
18 HAWK Hochschule für angewandte Wissenschaft und Kunst Hildesheim University of Applied Sciences public no 1971 6495 Hohnsen 4 52.14246 9.95798 31134 Hildesheim https://upload.wikimedia.org/wikipedia/commons/0/02/HAWK-Logo.jpg
19 HAWK Hochschule für angewandte Wissenschaft und Kunst Holzminden University of Applied Sciences public no 1971 6495 Haarmannpl. 3 51.82726 9.45069 37603 Holzminden https://upload.wikimedia.org/wikipedia/commons/0/02/HAWK-Logo.jpg
20 HAWK Hochschule für angewandte Wissenschaft und Kunst Göttingen University of Applied Sciences public no 1971 6495 Von-Ossietzky-Straße 99 51.52175 9.96967 37085 Göttingen https://upload.wikimedia.org/wikipedia/commons/0/02/HAWK-Logo.jpg
21 Leuphana Universität Lüneburg University public yes 1946 6497 Universitätsallee 1 53.228531 10.40171 21335 Lüneburg https://upload.wikimedia.org/wikipedia/commons/thumb/9/93/Leuphana_Universit%C3%A4t_L%C3%BCneburg_Logo_2020.svg/2560px-Leuphana_Universit%C3%A4t_L%C3%BCneburg_Logo_2020.svg.png
22 Norddeutsche Hochschule für Rechtspflege – Niedersachsen University of Administration public no 2007 6409 Godehardspl. 6 52.14484 9.94923 31134 Hildesheim https://static.studycheck.de/media/images/institute_logos/small/hr-nord.jpg
23 Kommunale Hochschule für Verwaltung in Niedersachsen University of Administration public no 2007 1570 Wielandstraße 8 52.3705 9.72239 30169 Hannover https://www.nsi-hsvn.de/fileadmin/user_upload/02_Studium/big-hsvn_logo.png
24 Carl von Ossietzky Universität Oldenburg University public yes 1973 15635 Uhlhornsweg 49-55 53.14734 8.17902 26129 Oldenburg https://upload.wikimedia.org/wikipedia/commons/thumb/2/22/Carl_von_Ossietzky_Universit%C3%A4t_Oldenburg_logo.svg/1200px-Carl_von_Ossietzky_Universit%C3%A4t_Oldenburg_logo.svg.png
25 Hochschule Osnabrück University of Applied Sciences public no 1971 13620 Albrechtstraße 30 52.28268 8.02501 49076 Osnabrück https://login.hs-osnabrueck.de/nidp/hsos/images/hsos-logo.png
26 Universität Osnabrück University public yes 1973 13640 Neuer Graben 29 52.27137 8.04454 49074 Osnabrück https://www.eh-tabor.de/sites/default/files/styles/width980px/public/logo-universitaet-osnabrueck.png?itok=DmZEq9ka
27 Hochschule Braunschweig/Wolfenbüttel, Ostfalia Hochschule für angewandte Wissenschaften University of Applied Sciences public no 1971 11577 Salzdahlumer Str. 46/48 52.17683 10.54865 38302 Wolfenbüttel https://www.ostfalia.de/export/system/modules/de.ostfalia.module.template/resources/images/logo/Ostfalia_German.png_230952558.png
28 Hochschule Wolfsburg, Ostfalia Hochschule für angewandte Wissenschaften University of Applied Sciences public no 1971 11577 Robert-Koch-Platz 8A 52.42595 10.78711 38440 Wolfsburg https://www.ostfalia.de/export/system/modules/de.ostfalia.module.template/resources/images/logo/Ostfalia_German.png_230952558.png
29 Hochschule Suderburg, Ostfalia Hochschule für angewandte Wissenschaften University of Applied Sciences public no 1971 11577 Herbert-Meyer-Straße 7 52.89761 10.44659 29556 Suderburg https://www.ostfalia.de/export/system/modules/de.ostfalia.module.template/resources/images/logo/Ostfalia_German.png_230952558.png
30 Hochschule Salzgitter, Ostfalia Hochschule für angewandte Wissenschaften University of Applied Sciences public no 1971 11577 Karl-Scharfenberg-Straße 55/57 52.08724 10.38055 38229 Salzgitter https://www.ostfalia.de/export/system/modules/de.ostfalia.module.template/resources/images/logo/Ostfalia_German.png_230952558.png
31 Hochschule für Künste im Sozialen, Ottersberg University of Applied Sciences privat no 1967 342 Große Str. 107 53.10668 9.1631 28870 Ottersberg https://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/Logo_HKS_Ottersberg.svg/1200px-Logo_HKS_Ottersberg.svg.png
32 Private Hochschule für Wirtschaft und Technik Vechta University of Applied Sciences privat no 1998 558 Rombergstraße 40 52.72125 8.27891 49377 Vechta https://www.phwt.de/wp-content/uploads/2020/09/phwt-logo-free.png
33 Private Hochschule für Wirtschaft und Technik Diepholz University of Applied Sciences privat no 1998 558 Schlesier Str. 13A 52.61171 8.36334 49356 Diepholz https://www.phwt.de/wp-content/uploads/2020/09/phwt-logo-free.png
34 Universität Vechta University public yes 1995 4.551 Driverstraße 22 52.72117 8.2938 49377 Vechta https://upload.wikimedia.org/wikipedia/commons/0/08/Logo_Uni_Vechta-neu.png
35 Hochschule Weserbergland University of Applied Sciences privat no 2010 485 Am Stockhof 2 52.09875 9.35542 31785 Hameln https://upload.wikimedia.org/wikipedia/commons/thumb/0/04/Hochschule_Weserbergland_logo.svg/1200px-Hochschule_Weserbergland_logo.svg.png
36 Jade Hochschule – Wilhelmshaven University of Applied Sciences public no 2009 6789 Friedrich-Paffrath-Straße 101 53.54787 8.08804 26389 Wilhelmshaven https://www.jade-hs.de/fileadmin/layout2016/assets/jadehs-logo.png
37 Jade Hochschule – Oldenburg University of Applied Sciences public no 2009 6789 Ofener Str. 16/19 53.14179 8.20213 26121 Oldenburg https://www.jade-hs.de/fileadmin/layout2016/assets/jadehs-logo.png
38 Jade Hochschule – Elsfleth University of Applied Sciences public no 2009 6789 Weserstraße 52 53.24244 8.46651 26931 Elsfleth https://www.jade-hs.de/fileadmin/layout2016/assets/jadehs-logo.png
39 Steuerakademie Niedersachsen Rinteln University of Administration public no 2006 500 Wilhelm-Busch-Weg 29 52.20696 9.09112 31737 Rinteln https://www.steuerakademie.niedersachsen.de/assets/image/232/85611
40 Steuerakademie Niedersachsen Bad Eilsen University of Administration public no 2006 500 Bahnhofstraße 5 52.23981 9.10423 31707 Bad Eilsen https://www.steuerakademie.niedersachsen.de/assets/image/232/85611

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Survived,Pclass,Name,Sex,Age,Siblings/Spouses Aboard,Parents/Children Aboard,Fare
0,3,Mr. Owen Harris Braund,male,22,1,0,7.25
1,1,Mrs. John Bradley (Florence Briggs Thayer) Cumings,female,38,1,0,71.2833
1,3,Miss. Laina Heikkinen,female,26,0,0,7.925
1,1,Mrs. Jacques Heath (Lily May Peel) Futrelle,female,35,1,0,53.1
0,3,Mr. William Henry Allen,male,35,0,0,8.05
0,3,Mr. James Moran,male,27,0,0,8.4583
0,1,Mr. Timothy J McCarthy,male,54,0,0,51.8625
0,3,Master. Gosta Leonard Palsson,male,2,3,1,21.075
1,3,Mrs. Oscar W (Elisabeth Vilhelmina Berg) Johnson,female,27,0,2,11.1333
1,2,Mrs. Nicholas (Adele Achem) Nasser,female,14,1,0,30.0708
1,3,Miss. Marguerite Rut Sandstrom,female,4,1,1,16.7
1,1,Miss. Elizabeth Bonnell,female,58,0,0,26.55
0,3,Mr. William Henry Saundercock,male,20,0,0,8.05
0,3,Mr. Anders Johan Andersson,male,39,1,5,31.275
0,3,Miss. Hulda Amanda Adolfina Vestrom,female,14,0,0,7.8542
1,2,Mrs. (Mary D Kingcome) Hewlett,female,55,0,0,16
0,3,Master. Eugene Rice,male,2,4,1,29.125
1,2,Mr. Charles Eugene Williams,male,23,0,0,13
0,3,Mrs. Julius (Emelia Maria Vandemoortele) Vander Planke,female,31,1,0,18
1,3,Mrs. Fatima Masselmani,female,22,0,0,7.225
0,2,Mr. Joseph J Fynney,male,35,0,0,26
1,2,Mr. Lawrence Beesley,male,34,0,0,13
1,3,Miss. Anna McGowan,female,15,0,0,8.0292
1,1,Mr. William Thompson Sloper,male,28,0,0,35.5
0,3,Miss. Torborg Danira Palsson,female,8,3,1,21.075
1,3,Mrs. Carl Oscar (Selma Augusta Emilia Johansson) Asplund,female,38,1,5,31.3875
0,3,Mr. Farred Chehab Emir,male,26,0,0,7.225
0,1,Mr. Charles Alexander Fortune,male,19,3,2,263
1,3,Miss. Ellen O'Dwyer,female,24,0,0,7.8792
0,3,Mr. Lalio Todoroff,male,23,0,0,7.8958
0,1,Don. Manuel E Uruchurtu,male,40,0,0,27.7208
1,1,Mrs. William Augustus (Marie Eugenie) Spencer,female,48,1,0,146.5208
1,3,Miss. Mary Agatha Glynn,female,18,0,0,7.75
0,2,Mr. Edward H Wheadon,male,66,0,0,10.5
0,1,Mr. Edgar Joseph Meyer,male,28,1,0,82.1708
0,1,Mr. Alexander Oskar Holverson,male,42,1,0,52
1,3,Mr. Hanna Mamee,male,18,0,0,7.2292
0,3,Mr. Ernest Charles Cann,male,21,0,0,8.05
0,3,Miss. Augusta Maria Vander Planke,female,18,2,0,18
1,3,Miss. Jamila Nicola-Yarred,female,14,1,0,11.2417
0,3,Mrs. Johan (Johanna Persdotter Larsson) Ahlin,female,40,1,0,9.475
0,2,Mrs. William John Robert (Dorothy Ann Wonnacott) Turpin,female,27,1,0,21
1,2,Miss. Simonne Marie Anne Andree Laroche,female,3,1,2,41.5792
1,3,Miss. Margaret Delia Devaney,female,19,0,0,7.8792
0,3,Mr. William John Rogers,male,30,0,0,8.05
0,3,Mr. Denis Lennon,male,20,1,0,15.5
1,3,Miss. Bridget O'Driscoll,female,27,0,0,7.75
0,3,Mr. Youssef Samaan,male,16,2,0,21.6792
0,3,Mrs. Josef (Josefine Franchi) Arnold-Franchi,female,18,1,0,17.8
0,3,Master. Juha Niilo Panula,male,7,4,1,39.6875
0,3,Mr. Richard Cater Nosworthy,male,21,0,0,7.8
1,1,Mrs. Henry Sleeper (Myna Haxtun) Harper,female,49,1,0,76.7292
1,2,Mrs. Lizzie (Elizabeth Anne Wilkinson) Faunthorpe,female,29,1,0,26
0,1,Mr. Engelhart Cornelius Ostby,male,65,0,1,61.9792
1,1,Mr. Hugh Woolner,male,46,0,0,35.5
1,2,Miss. Emily Rugg,female,21,0,0,10.5
0,3,Mr. Mansouer Novel,male,28.5,0,0,7.2292
1,2,Miss. Constance Mirium West,female,5,1,2,27.75
0,3,Master. William Frederick Goodwin,male,11,5,2,46.9
0,3,Mr. Orsen Sirayanian,male,22,0,0,7.2292
1,1,Miss. Amelie Icard,female,38,0,0,80
0,1,Mr. Henry Birkhardt Harris,male,45,1,0,83.475
0,3,Master. Harald Skoog,male,4,3,2,27.9
0,1,Mr. Albert A Stewart,male,64,0,0,27.7208
1,3,Master. Gerios Moubarek,male,7,1,1,15.2458
1,2,Mrs. (Elizabeth Ramell) Nye,female,29,0,0,10.5
0,3,Mr. Ernest James Crease,male,19,0,0,8.1583
1,3,Miss. Erna Alexandra Andersson,female,17,4,2,7.925
0,3,Mr. Vincenz Kink,male,26,2,0,8.6625
0,2,Mr. Stephen Curnow Jenkin,male,32,0,0,10.5
0,3,Miss. Lillian Amy Goodwin,female,16,5,2,46.9
0,2,Mr. Ambrose Jr Hood,male,21,0,0,73.5
0,3,Mr. Apostolos Chronopoulos,male,26,1,0,14.4542
1,3,Mr. Lee Bing,male,32,0,0,56.4958
0,3,Mr. Sigurd Hansen Moen,male,25,0,0,7.65
0,3,Mr. Ivan Staneff,male,23,0,0,7.8958
0,3,Mr. Rahamin Haim Moutal,male,28,0,0,8.05
1,2,Master. Alden Gates Caldwell,male,0.83,0,2,29
1,3,Miss. Elizabeth Dowdell,female,30,0,0,12.475
0,3,Mr. Achille Waelens,male,22,0,0,9
1,3,Mr. Jan Baptist Sheerlinck,male,29,0,0,9.5
1,3,Miss. Brigdet Delia McDermott,female,31,0,0,7.7875
0,1,Mr. Francisco M Carrau,male,28,0,0,47.1
1,2,Miss. Bertha Ilett,female,17,0,0,10.5
1,3,Mrs. Karl Alfred (Maria Mathilda Gustafsson) Backstrom,female,33,3,0,15.85
0,3,Mr. William Neal Ford,male,16,1,3,34.375
0,3,Mr. Selman Francis Slocovski,male,20,0,0,8.05
1,1,Miss. Mabel Helen Fortune,female,23,3,2,263
0,3,Mr. Francesco Celotti,male,24,0,0,8.05
0,3,Mr. Emil Christmann,male,29,0,0,8.05
0,3,Mr. Paul Edvin Andreasson,male,20,0,0,7.8542
0,1,Mr. Herbert Fuller Chaffee,male,46,1,0,61.175
0,3,Mr. Bertram Frank Dean,male,26,1,2,20.575
0,3,Mr. Daniel Coxon,male,59,0,0,7.25
0,3,Mr. Charles Joseph Shorney,male,22,0,0,8.05
0,1,Mr. George B Goldschmidt,male,71,0,0,34.6542
1,1,Mr. William Bertram Greenfield,male,23,0,1,63.3583
1,2,Mrs. John T (Ada Julia Bone) Doling,female,34,0,1,23
0,2,Mr. Sinai Kantor,male,34,1,0,26
0,3,Miss. Matilda Petranec,female,28,0,0,7.8958
0,3,Mr. Pastcho Petroff,male,29,0,0,7.8958
0,1,Mr. Richard Frasar White,male,21,0,1,77.2875
0,3,Mr. Gustaf Joel Johansson,male,33,0,0,8.6542
0,3,Mr. Anders Vilhelm Gustafsson,male,37,2,0,7.925
0,3,Mr. Stoytcho Mionoff,male,28,0,0,7.8958
1,3,Miss. Anna Kristine Salkjelsvik,female,21,0,0,7.65
1,3,Mr. Albert Johan Moss,male,29,0,0,7.775
0,3,Mr. Tido Rekic,male,38,0,0,7.8958
1,3,Miss. Bertha Moran,female,28,1,0,24.15
0,1,Mr. Walter Chamberlain Porter,male,47,0,0,52
0,3,Miss. Hileni Zabour,female,14.5,1,0,14.4542
0,3,Mr. David John Barton,male,22,0,0,8.05
0,3,Miss. Katriina Jussila,female,20,1,0,9.825
0,3,Miss. Malake Attalah,female,17,0,0,14.4583
0,3,Mr. Edvard Pekoniemi,male,21,0,0,7.925
0,3,Mr. Patrick Connors,male,70.5,0,0,7.75
0,2,Mr. William John Robert Turpin,male,29,1,0,21
0,1,Mr. Quigg Edmond Baxter,male,24,0,1,247.5208
0,3,Miss. Ellis Anna Maria Andersson,female,2,4,2,31.275
0,2,Mr. Stanley George Hickman,male,21,2,0,73.5
0,3,Mr. Leonard Charles Moore,male,19,0,0,8.05
0,2,Mr. Nicholas Nasser,male,32.5,1,0,30.0708
1,2,Miss. Susan Webber,female,32.5,0,0,13
0,1,Mr. Percival Wayland White,male,54,0,1,77.2875
1,3,Master. Elias Nicola-Yarred,male,12,1,0,11.2417
0,3,Mr. Martin McMahon,male,19,0,0,7.75
1,3,Mr. Fridtjof Arne Madsen,male,24,0,0,7.1417
1,3,Miss. Anna Peter,female,2,1,1,22.3583
0,3,Mr. Johan Ekstrom,male,45,0,0,6.975
0,3,Mr. Jozef Drazenoic,male,33,0,0,7.8958
0,3,Mr. Domingos Fernandeo Coelho,male,20,0,0,7.05
0,3,Mrs. Alexander A (Grace Charity Laury) Robins,female,47,1,0,14.5
1,2,Mrs. Leopold (Mathilde Francoise Pede) Weisz,female,29,1,0,26
0,2,Mr. Samuel James Hayden Sobey,male,25,0,0,13
0,2,Mr. Emile Richard,male,23,0,0,15.0458
1,1,Miss. Helen Monypeny Newsom,female,19,0,2,26.2833
0,1,Mr. Jacques Heath Futrelle,male,37,1,0,53.1
0,3,Mr. Olaf Elon Osen,male,16,0,0,9.2167
0,1,Mr. Victor Giglio,male,24,0,0,79.2
0,3,Mrs. Joseph (Sultana) Boulos,female,40,0,2,15.2458
1,3,Miss. Anna Sofia Nysten,female,22,0,0,7.75
1,3,Mrs. Pekka Pietari (Elin Matilda Dolck) Hakkarainen,female,24,1,0,15.85
0,3,Mr. Jeremiah Burke,male,19,0,0,6.75
0,2,Mr. Edgardo Samuel Andrew,male,18,0,0,11.5
0,2,Mr. Joseph Charles Nicholls,male,19,1,1,36.75
1,3,Mr. August Edvard Andersson,male,27,0,0,7.7958
0,3,Miss. Robina Maggie Ford,female,9,2,2,34.375
0,2,Mr. Michel Navratil,male,36.5,0,2,26
0,2,Rev. Thomas Roussel Davids Byles,male,42,0,0,13
0,2,Rev. Robert James Bateman,male,51,0,0,12.525
1,1,Mrs. Thomas (Edith Wearne) Pears,female,22,1,0,66.6
0,3,Mr. Alfonzo Meo,male,55.5,0,0,8.05
0,3,Mr. Austin Blyler van Billiard,male,40.5,0,2,14.5
0,3,Mr. Ole Martin Olsen,male,27,0,0,7.3125
0,1,Mr. Charles Duane Williams,male,51,0,1,61.3792
1,3,Miss. Katherine Gilnagh,female,16,0,0,7.7333
0,3,Mr. Harry Corn,male,30,0,0,8.05
0,3,Mr. Mile Smiljanic,male,37,0,0,8.6625
0,3,Master. Thomas Henry Sage,male,5,8,2,69.55
0,3,Mr. John Hatfield Cribb,male,44,0,1,16.1
1,2,Mrs. James (Elizabeth Inglis Milne) Watt,female,40,0,0,15.75
0,3,Mr. John Viktor Bengtsson,male,26,0,0,7.775
0,3,Mr. Jovo Calic,male,17,0,0,8.6625
0,3,Master. Eino Viljami Panula,male,1,4,1,39.6875
1,3,Master. Frank John William Goldsmith,male,9,0,2,20.525
1,1,Mrs. (Edith Martha Bowerman) Chibnall,female,48,0,1,55
0,3,Mrs. William (Anna Bernhardina Karlsson) Skoog,female,45,1,4,27.9
0,1,Mr. John D Baumann,male,60,0,0,25.925
0,3,Mr. Lee Ling,male,28,0,0,56.4958
0,1,Mr. Wyckoff Van der hoef,male,61,0,0,33.5
0,3,Master. Arthur Rice,male,4,4,1,29.125
1,3,Miss. Eleanor Ileen Johnson,female,1,1,1,11.1333
0,3,Mr. Antti Wilhelm Sivola,male,21,0,0,7.925
0,1,Mr. James Clinch Smith,male,56,0,0,30.6958
0,3,Mr. Klas Albin Klasen,male,18,1,1,7.8542
0,3,Master. Henry Forbes Lefebre,male,5,3,1,25.4667
0,1,Miss. Ann Elizabeth Isham,female,50,0,0,28.7125
0,2,Mr. Reginald Hale,male,30,0,0,13
0,3,Mr. Lionel Leonard,male,36,0,0,0
0,3,Miss. Constance Gladys Sage,female,8,8,2,69.55
0,2,Mr. Rene Pernot,male,39,0,0,15.05
0,3,Master. Clarence Gustaf Hugo Asplund,male,9,4,2,31.3875
1,2,Master. Richard F Becker,male,1,2,1,39
1,3,Miss. Luise Gretchen Kink-Heilmann,female,4,0,2,22.025
0,1,Mr. Hugh Roscoe Rood,male,39,0,0,50
1,3,Mrs. Thomas (Johanna Godfrey) O'Brien,female,26,1,0,15.5
1,1,Mr. Charles Hallace Romaine,male,45,0,0,26.55
0,3,Mr. John Bourke,male,40,1,1,15.5
0,3,Mr. Stjepan Turcin,male,36,0,0,7.8958
1,2,Mrs. (Rosa) Pinsky,female,32,0,0,13
0,2,Mr. William Carbines,male,19,0,0,13
1,3,Miss. Carla Christine Nielsine Andersen-Jensen,female,19,1,0,7.8542
1,2,Master. Michel M Navratil,male,3,1,1,26
1,1,Mrs. James Joseph (Margaret Tobin) Brown,female,44,0,0,27.7208
1,1,Miss. Elise Lurette,female,58,0,0,146.5208
0,3,Mr. Robert Mernagh,male,28,0,0,7.75
0,3,Mr. Karl Siegwart Andreas Olsen,male,42,0,1,8.4042
1,3,Miss. Margaret Madigan,female,21,0,0,7.75
0,2,Miss. Henriette Yrois,female,24,0,0,13
0,3,Mr. Nestor Cyriel Vande Walle,male,28,0,0,9.5
0,3,Mr. Frederick Sage,male,17,8,2,69.55
0,3,Mr. Jakob Alfred Johanson,male,34,0,0,6.4958
0,3,Mr. Gerious Youseff,male,45.5,0,0,7.225
1,3,Mr. Gurshon Cohen,male,18,0,0,8.05
0,3,Miss. Telma Matilda Strom,female,2,0,1,10.4625
0,3,Mr. Karl Alfred Backstrom,male,32,1,0,15.85
1,3,Mr. Nassef Cassem Albimona,male,26,0,0,18.7875
1,3,Miss. Helen Carr,female,16,0,0,7.75
1,1,Mr. Henry Blank,male,40,0,0,31
0,3,Mr. Ahmed Ali,male,24,0,0,7.05
1,2,Miss. Clear Annie Cameron,female,35,0,0,21
0,3,Mr. John Henry Perkin,male,22,0,0,7.25
0,2,Mr. Hans Kristensen Givard,male,30,0,0,13
0,3,Mr. Philip Kiernan,male,22,1,0,7.75
1,1,Miss. Madeleine Newell,female,31,1,0,113.275
1,3,Miss. Eliina Honkanen,female,27,0,0,7.925
0,2,Mr. Sidney Samuel Jacobsohn,male,42,1,0,27
1,1,Miss. Albina Bazzani,female,32,0,0,76.2917
0,2,Mr. Walter Harris,male,30,0,0,10.5
1,3,Mr. Victor Francis Sunderland,male,16,0,0,8.05
0,2,Mr. James H Bracken,male,27,0,0,13
0,3,Mr. George Henry Green,male,51,0,0,8.05
0,3,Mr. Christo Nenkoff,male,22,0,0,7.8958
1,1,Mr. Frederick Maxfield Hoyt,male,38,1,0,90
0,3,Mr. Karl Ivar Sven Berglund,male,22,0,0,9.35
1,2,Mr. William John Mellors,male,19,0,0,10.5
0,3,Mr. John Hall Lovell,male,20.5,0,0,7.25
0,2,Mr. Arne Jonas Fahlstrom,male,18,0,0,13
0,3,Miss. Mathilde Lefebre,female,12,3,1,25.4667
1,1,Mrs. Henry Birkhardt (Irene Wallach) Harris,female,35,1,0,83.475
0,3,Mr. Bengt Edvin Larsson,male,29,0,0,7.775
0,2,Mr. Ernst Adolf Sjostedt,male,59,0,0,13.5
1,3,Miss. Lillian Gertrud Asplund,female,5,4,2,31.3875
0,2,Mr. Robert William Norman Leyson,male,24,0,0,10.5
0,3,Miss. Alice Phoebe Harknett,female,21,0,0,7.55
0,2,Mr. Stephen Hold,male,44,1,0,26
1,2,Miss. Marjorie Collyer,female,8,0,2,26.25
0,2,Mr. Frederick William Pengelly,male,19,0,0,10.5
0,2,Mr. George Henry Hunt,male,33,0,0,12.275
0,3,Miss. Thamine Zabour,female,19,1,0,14.4542
1,3,Miss. Katherine Murphy,female,18,1,0,15.5
0,2,Mr. Reginald Charles Coleridge,male,29,0,0,10.5
0,3,Mr. Matti Alexanteri Maenpaa,male,22,0,0,7.125
0,3,Mr. Sleiman Attalah,male,30,0,0,7.225
0,1,Dr. William Edward Minahan,male,44,2,0,90
0,3,Miss. Agda Thorilda Viktoria Lindahl,female,25,0,0,7.775
1,2,Mrs. William (Anna) Hamalainen,female,24,0,2,14.5
1,1,Mr. Richard Leonard Beckwith,male,37,1,1,52.5542
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0,3,Mrs. Wilhelm (Elna Matilda Persson) Strom,female,29,1,1,10.4625
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1,1,Mrs. Gertrude Maybelle Thorne,female,38,0,0,79.2
1,1,Miss. Gladys Cherry,female,30,0,0,86.5
1,1,Miss. Anna Ward,female,35,0,0,512.3292
1,2,Mrs. (Lutie Davis) Parrish,female,50,0,1,26
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0,3,Mr. Ernesti Arvid Panula,male,16,4,1,39.6875
1,3,Mr. Ernst Ulrik Persson,male,25,1,0,7.775
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1,1,Miss. Amelia Bissette,female,35,0,0,135.6333
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1,3,Mr. William Henry Tornquist,male,25,0,0,0
1,2,Mrs. (Elizabeth Anne Maidment) Mellinger,female,41,0,1,19.5
0,1,Mr. Charles H Natsch,male,37,0,1,29.7
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1,1,Miss. Kornelia Theodosia Andrews,female,63,1,0,77.9583
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0,3,Master. Eric Rice,male,7,4,1,29.125
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0,3,Mr. Nils Johan Goransson Olsson,male,28,0,0,7.8542
0,3,Mr. Alfons de Pelsmaeker,male,16,0,0,9.5
1,3,Mr. Edward Arthur Dorking,male,19,0,0,8.05
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0,3,Mr. Ivan Stankovic,male,33,0,0,8.6625
1,3,Mr. Theodore de Mulder,male,30,0,0,9.5
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0,3,Mr. Ivan Mineff,male,24,0,0,7.8958
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1,3,Miss. Anna Katherine Kelly,female,20,0,0,7.75
1,3,Mr. Bernard McCoy,male,24,2,0,23.25
0,3,Mr. William Cahoone Jr Johnson,male,19,0,0,0
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1,1,Miss. Laura Mabel Francatelli,female,30,0,0,56.9292
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1,1,Miss. Emily Borie Ryerson,female,18,2,2,262.375
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1,3,Miss. Helmina Josefina Nilsson,female,26,0,0,7.8542
1,2,Mrs. Sinai (Miriam Sternin) Kantor,female,24,1,0,26
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0,3,Mr. Yoto Danoff,male,27,0,0,7.8958
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0,2,Mr. Charles Frederick Waddington Sedgwick,male,25,0,0,13
0,2,Mr. Stanley Hubert Fox,male,36,0,0,13
1,2,Miss. Amelia Brown,female,24,0,0,13
1,2,Miss. Marion Elsie Smith,female,40,0,0,13
1,3,Mrs. Thomas Henry (Mary E Finck) Davison,female,34,1,0,16.1
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1,3,Miss. Mary McGovern,female,22,0,0,7.8792
1,3,Miss. Helen Mary Mockler,female,23,0,0,7.8792
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0,3,Mr. Adola Asim,male,35,0,0,7.05
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0,3,Mr. William Thomas Beavan,male,19,0,0,8.05
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1,3,Miss. Aurora Adelia Landergren,female,22,0,0,7.25
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0,3,Mr. Tannous Betros,male,20,0,0,4.0125
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0,3,Mr. Matthew Sadlier,male,19,0,0,7.7292
1,2,Miss. Bertha Lehmann,female,17,0,0,12
1,1,Mr. William Ernest Carter,male,36,1,2,120
1,3,Mr. Carl Olof Jansson,male,21,0,0,7.7958
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1,2,Mrs. William H (Jessie L) Trout,female,28,0,0,12.65
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0,3,Miss. Mari Aina Jussila,female,21,1,0,9.825
0,3,Mr. Pekka Pietari Hakkarainen,male,28,1,0,15.85
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0,2,Mr. Shadrach Gale,male,34,1,0,21
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0,3,Mr. Todor Sdycoff,male,42,0,0,7.8958
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1,3,Mr. Johan Julian Sundman,male,44,0,0,7.925
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1,2,Miss. Lyyli Karoliina Silven,female,18,0,2,13
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1,3,Mr. Berk (Berk Trembisky) Pickard,male,32,0,0,8.05
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0,1,Mr. Benjamin Laventall Foreman,male,30,0,0,27.75
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1,3,Mr. Ivan Jalsevac,male,29,0,0,7.8958
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1,2,Miss. Ellen Toomey,female,50,0,0,10.5
0,3,Mr. Maurice O'Connor,male,17,0,0,7.75
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0,3,Mr. William Morley,male,34,0,0,8.05
0,1,Mr. Arthur H Gee,male,47,0,0,38.5
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0,3,Mr. Simon Maisner,male,34,0,0,8.05
0,3,Mr. Manuel Estanslas Goncalves,male,38,0,0,7.05
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0,3,Miss. Ida Sofia Strandberg,female,22,0,0,9.8375
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0,2,Mr. Anthony Wood Frost,male,37,0,0,0
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1,3,Mr. Eugene Patrick Daly,male,29,0,0,7.75
0,3,Mr. James Webber,male,66,0,0,8.05
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1,1,Miss. Harriet R Crosby,female,36,0,2,71
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0,3,Miss. Sigrid Elisabeth Andersson,female,11,4,2,31.275
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0,1,Mr. Walter Donald Douglas,male,50,1,0,106.425
0,1,Mr. Arthur Ernest Nicholson,male,64,0,0,26
1,2,Mrs. Edward (Ethel Clarke) Beane,female,19,1,0,26
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0,3,Mr. Thomas Rowan Morrow,male,30,0,0,7.75
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0,3,Mr. Tannous Doharr,male,28,0,0,7.2292
1,3,Mr. Carl Jonsson,male,32,0,0,7.8542
1,2,Mr. George Harris,male,62,0,0,10.5
1,1,Mrs. Edward Dale (Charlotte Lamson) Appleton,female,53,2,0,51.4792
1,1,Mr. John Irwin Flynn,male,36,0,0,26.3875
1,3,Miss. Mary Kelly,female,22,0,0,7.75
0,3,Mr. Alfred George John Rush,male,16,0,0,8.05
0,3,Mr. George Patchett,male,19,0,0,14.5
1,2,Miss. Ethel Garside,female,34,0,0,13
1,1,Mrs. William Baird (Alice Munger) Silvey,female,39,1,0,55.9
0,3,Mrs. Joseph (Maria Elias) Caram,female,18,1,0,14.4583
1,3,Mr. Eiriik Jussila,male,32,0,0,7.925
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1,1,Mrs. John Borland (Marian Longstreth Morris) Thayer,female,39,1,1,110.8833
0,2,Mr. William James Downton,male,54,0,0,26
0,1,Mr. John Hugo Ross,male,36,0,0,40.125
0,3,Mr. Uscher Paulner,male,16,0,0,8.7125
1,1,Miss. Ruth Taussig,female,18,0,2,79.65
0,2,Mr. John Denzil Jarvis,male,47,0,0,15
1,1,Mr. Maxmillian Frolicher-Stehli,male,60,1,1,79.2
0,3,Mr. Eliezer Gilinski,male,22,0,0,8.05
0,3,Mr. Joseph Murdlin,male,22,0,0,8.05
0,3,Mr. Matti Rintamaki,male,35,0,0,7.125
1,1,Mrs. Walter Bertram (Martha Eustis) Stephenson,female,52,1,0,78.2667
0,3,Mr. William James Elsbury,male,47,0,0,7.25
0,3,Miss. Mary Bourke,female,40,0,2,7.75
0,2,Mr. John Henry Chapman,male,37,1,0,26
0,3,Mr. Jean Baptiste Van Impe,male,36,1,1,24.15
1,2,Miss. Jessie Wills Leitch,female,31,0,0,33
0,3,Mr. Alfred Johnson,male,49,0,0,0
0,3,Mr. Hanna Boulos,male,18,0,0,7.225
1,1,Sir. Cosmo Edmund Duff Gordon,male,49,1,0,56.9292
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0,3,Mr. Petco Slabenoff,male,42,0,0,7.8958
0,1,Mr. Charles H Harrington,male,37,0,0,42.4
0,3,Mr. Ernst William Torber,male,44,0,0,8.05
1,1,Mr. Harry Homer,male,35,0,0,26.55
0,3,Mr. Edvard Bengtsson Lindell,male,36,1,0,15.55
0,3,Mr. Milan Karaic,male,30,0,0,7.8958
1,1,Mr. Robert Williams Daniel,male,27,0,0,30.5
1,2,Mrs. Joseph (Juliette Marie Louise Lafargue) Laroche,female,22,1,2,41.5792
1,1,Miss. Elizabeth W Shutes,female,40,0,0,153.4625
0,3,Mrs. Anders Johan (Alfrida Konstantia Brogren) Andersson,female,39,1,5,31.275
0,3,Mr. Jose Neto Jardin,male,21,0,0,7.05
1,3,Miss. Margaret Jane Murphy,female,18,1,0,15.5
0,3,Mr. John Horgan,male,22,0,0,7.75
0,3,Mr. William Alfred Brocklebank,male,35,0,0,8.05
1,2,Miss. Alice Herman,female,24,1,2,65
0,3,Mr. Ernst Gilbert Danbom,male,34,1,1,14.4
0,3,Mrs. William Arthur (Cordelia K Stanlick) Lobb,female,26,1,0,16.1
1,2,Miss. Marion Louise Becker,female,4,2,1,39
0,2,Mr. Lawrence Gavey,male,26,0,0,10.5
0,3,Mr. Antoni Yasbeck,male,27,1,0,14.4542
1,1,Mr. Edwin Nelson Jr Kimball,male,42,1,0,52.5542
1,3,Mr. Sahid Nakid,male,20,1,1,15.7417
0,3,Mr. Henry Damsgaard Hansen,male,21,0,0,7.8542
0,3,Mr. David John Bowen,male,21,0,0,16.1
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0,2,Rev. Charles Leonard Kirkland,male,57,0,0,12.35
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0,3,Mr. Guentcho Bostandyeff,male,26,0,0,7.8958
0,3,Mr. Patrick D O'Connell,male,18,0,0,7.7333
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0,3,Mr. Johan Svensson Lundahl,male,51,0,0,7.0542
1,1,Dr. Max Stahelin-Maeglin,male,32,0,0,30.5
0,1,Mr. William Henry Marsh Parr,male,30,0,0,0
0,3,Miss. Mabel Skoog,female,9,3,2,27.9
1,2,Miss. Mary Davis,female,28,0,0,13
0,3,Mr. Antti Gustaf Leinonen,male,32,0,0,7.925
0,2,Mr. Harvey Collyer,male,31,1,1,26.25
0,3,Mrs. Juha (Maria Emilia Ojala) Panula,female,41,0,5,39.6875
0,3,Mr. Percival Thorneycroft,male,37,1,0,16.1
0,3,Mr. Hans Peder Jensen,male,20,0,0,7.8542
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1,3,Mr. Choong Foo,male,32,0,0,56.4958
1,3,Miss. Eugenie Baclini,female,0.75,2,1,19.2583
1,1,Mr. Henry Sleeper Harper,male,48,1,0,76.7292
0,3,Mr. Liudevit Cor,male,19,0,0,7.8958
1,1,Col. Oberst Alfons Simonius-Blumer,male,56,0,0,35.5
0,3,Mr. Edward Willey,male,21,0,0,7.55
1,3,Miss. Amy Zillah Elsie Stanley,female,23,0,0,7.55
0,3,Mr. Mito Mitkoff,male,23,0,0,7.8958
1,2,Miss. Elsie Doling,female,18,0,1,23
0,3,Mr. Johannes Halvorsen Kalvik,male,21,0,0,8.4333
1,3,Miss. Hanora O'Leary,female,16,0,0,7.8292
0,3,Miss. Hanora Hegarty,female,18,0,0,6.75
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0,3,Mr. Alexander Radeff,male,27,0,0,7.8958
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0,2,Mr. George Floyd Eitemiller,male,23,0,0,13
0,1,Mr. Arthur Webster Newell,male,58,0,2,113.275
1,1,Dr. Henry William Frauenthal,male,50,2,0,133.65
0,3,Mr. Mohamed Badt,male,40,0,0,7.225
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0,2,Mr. Reginald Fenton Butler,male,25,0,0,13
0,3,Mr. Knud Paust Rommetvedt,male,49,0,0,7.775
0,3,Mr. Jacob Cook,male,43,0,0,8.05
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1,2,Mrs. Thomas William Solomon (Elizabeth Catherine Ford) Brown,female,40,1,1,39
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0,2,Mr. Henry Michael Mitchell,male,70,0,0,10.5
1,2,Mr. Charles Wilhelms,male,31,0,0,13
0,2,Mr. Ennis Hastings Watson,male,19,0,0,0
0,3,Mr. Gustaf Hjalmar Edvardsson,male,18,0,0,7.775
0,3,Mr. Frederick Charles Sawyer,male,24.5,0,0,8.05
1,3,Miss. Anna Sofia Turja,female,18,0,0,9.8417
0,3,Mrs. Frederick (Augusta Tyler) Goodwin,female,43,1,6,46.9
1,1,Mr. Thomas Drake Martinez Cardeza,male,36,0,1,512.3292
0,3,Miss. Katie Peters,female,28,0,0,8.1375
1,1,Mr. Hammad Hassab,male,27,0,0,76.7292
0,3,Mr. Thor Anderson Olsvigen,male,20,0,0,9.225
0,3,Mr. Charles Edward Goodwin,male,14,5,2,46.9
0,2,Mr. Thomas William Solomon Brown,male,60,1,1,39
0,2,Mr. Joseph Philippe Lemercier Laroche,male,25,1,2,41.5792
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0,3,Mr. Eberhard Thelander Fischer,male,18,0,0,7.7958
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1,1,Mr. Albert Adrian Dick,male,31,1,0,57
1,3,Miss. Manca Karun,female,4,0,1,13.4167
1,3,Mr. Ali Lam,male,37,0,0,56.4958
0,3,Mr. Khalil Saad,male,25,0,0,7.225
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0,2,Mr. Charles Henry Chapman,male,52,0,0,13.5
0,3,Mr. James Kelly,male,44,0,0,8.05
1,3,Miss. Katherine Mullens,female,19,0,0,7.7333
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0,3,Mr. Adolf Mathias Nicolai Olsen Humblen,male,42,0,0,7.65
1,1,Mrs. John Jacob (Madeleine Talmadge Force) Astor,female,18,1,0,227.525
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0,3,Mr. Martin Gallagher,male,25,0,0,7.7417
0,3,Mr. Henrik Juul Hansen,male,26,1,0,7.8542
0,2,Mr. Henry Samuel Morley,male,39,0,0,26
1,2,Mrs. Florence Kelly,female,45,0,0,13.5
1,1,Mr. Edward Pennington Calderhead,male,42,0,0,26.2875
1,1,Miss. Alice Cleaver,female,22,0,0,151.55
1,3,Master. Halim Gonios Moubarek,male,4,1,1,15.2458
1,1,Mlle. Berthe Antonine Mayne,female,24,0,0,49.5042
0,1,Mr. Herman Klaber,male,41,0,0,26.55
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0,3,Mr. August Viktor Larsson,male,29,0,0,9.4833
0,2,Mr. Samuel Greenberg,male,52,0,0,13
0,3,Mr. Peter Andreas Lauritz Andersen Soholt,male,19,0,0,7.65
1,1,Miss. Caroline Louise Endres,female,38,0,0,227.525
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0,3,Mr. Malkolm Joackim Johnson,male,33,0,0,7.775
1,2,Miss. Annie Jessie Harper,female,6,0,1,33
0,3,Mr. Svend Lauritz Jensen,male,17,1,0,7.0542
0,2,Mr. William Henry Gillespie,male,34,0,0,13
0,2,Mr. Henry Price Hodges,male,50,0,0,13
1,1,Mr. Norman Campbell Chambers,male,27,1,0,53.1
0,3,Mr. Luka Oreskovic,male,20,0,0,8.6625
1,2,Mrs. Peter Henry (Lillian Jefferys) Renouf,female,30,3,0,21
1,3,Miss. Margareth Mannion,female,28,0,0,7.7375
0,2,Mr. Kurt Arnold Gottfrid Bryhl,male,25,1,0,26
0,3,Miss. Pieta Sofia Ilmakangas,female,25,1,0,7.925
1,1,Miss. Elisabeth Walton Allen,female,29,0,0,211.3375
0,3,Mr. Houssein G N Hassan,male,11,0,0,18.7875
0,2,Mr. Robert J Knight,male,41,0,0,0
0,2,Mr. William John Berriman,male,23,0,0,13
0,2,Mr. Moses Aaron Troupiansky,male,23,0,0,13
0,3,Mr. Leslie Williams,male,28.5,0,0,16.1
0,3,Mrs. Edward (Margaret Ann Watson) Ford,female,48,1,3,34.375
1,1,Mr. Gustave J Lesurer,male,35,0,0,512.3292
0,3,Mr. Kanio Ivanoff,male,20,0,0,7.8958
0,3,Mr. Minko Nankoff,male,32,0,0,7.8958
1,1,Mr. Walter James Hawksford,male,45,0,0,30
0,1,Mr. Tyrell William Cavendish,male,36,1,0,78.85
1,1,Miss. Susan Parker Ryerson,female,21,2,2,262.375
0,3,Mr. Neal McNamee,male,24,1,0,16.1
1,3,Mr. Juho Stranden,male,31,0,0,7.925
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0,3,Mr. Rossmore Edward Abbott,male,16,1,1,20.25
1,2,Miss. Anna Sinkkonen,female,30,0,0,13
0,1,Mr. Daniel Warner Marvin,male,19,1,0,53.1
0,3,Mr. Michael Connaghton,male,31,0,0,7.75
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1,3,Master. Meier Moor,male,6,0,1,12.475
0,3,Mr. Johannes Joseph Vande Velde,male,33,0,0,9.5
0,3,Mr. Lalio Jonkoff,male,23,0,0,7.8958
1,2,Mrs. Samuel (Jane Laver) Herman,female,48,1,2,65
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0,2,Mr. Percy Andrew Bailey,male,18,0,0,11.5
0,3,Mr. Thomas Leonard Theobald,male,34,0,0,8.05
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0,3,Mr. John Garfirth,male,23,0,0,14.5
0,3,Mr. Iisakki Antino Aijo Nirva,male,41,0,0,7.125
1,3,Mr. Hanna Assi Barah,male,20,0,0,7.2292
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0,3,Mr. Hans Linus Eklund,male,16,0,0,7.775
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0,1,Dr. Arthur Jackson Brewe,male,46,0,0,39.6
0,3,Miss. Mary Mangan,female,30.5,0,0,7.75
0,3,Mr. Daniel J Moran,male,28,1,0,24.15
0,3,Mr. Daniel Danielsen Gronnestad,male,32,0,0,8.3625
0,3,Mr. Rene Aime Lievens,male,24,0,0,9.5
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0,2,Mrs. (Mary) Mack,female,57,0,0,10.5
0,3,Mr. Dibo Elias,male,29,0,0,7.225
1,2,Mrs. Elizabeth (Eliza Needs) Hocking,female,54,1,3,23
0,3,Mr. Pehr Fabian Oliver Malkolm Myhrman,male,18,0,0,7.75
0,3,Mr. Roger Tobin,male,20,0,0,7.75
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0,3,Mr. Thomas J Kilgannon,male,22,0,0,7.7375
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1,3,Miss. Banoura Ayoub,female,13,0,0,7.2292
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0,3,Mr. Abraham (David Lishin) Harmer,male,25,0,0,7.25
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0,3,Master. George Hugh Rice,male,8,4,1,29.125
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0,3,Miss. Stella Anna Sage,female,21,8,2,69.55
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1,3,Mr. Oskar Arvid Hedman,male,27,0,0,6.975
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0,3,Miss. Ellen Natalia Pettersson,female,18,0,0,7.775
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0,3,Mr. Owen George Allum,male,18,0,0,8.3
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1,1,Mr. Pierre Marechal,male,29,0,0,29.7
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1,3,Mrs. Sam (Leah Rosen) Aks,female,18,0,1,9.35
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1,1,Mr. Peter Denis Daly,male,51,0,0,26.55
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1,2,Mrs. (Karolina) Bystrom,female,42,0,0,13
1,2,Miss. Asuncion Duran y More,female,27,1,0,13.8583
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0,2,Mr. Frederick James Banfield,male,28,0,0,10.5
0,3,Mr. Henry Jr Sutehall,male,25,0,0,7.05
0,3,Mrs. William (Margaret Norton) Rice,female,39,0,5,29.125
0,2,Rev. Juozas Montvila,male,27,0,0,13
1,1,Miss. Margaret Edith Graham,female,19,0,0,30
0,3,Miss. Catherine Helen Johnston,female,7,1,2,23.45
1,1,Mr. Karl Howell Behr,male,26,0,0,30
0,3,Mr. Patrick Dooley,male,32,0,0,7.75
1 Survived Pclass Name Sex Age Siblings/Spouses Aboard Parents/Children Aboard Fare
2 0 3 Mr. Owen Harris Braund male 22 1 0 7.25
3 1 1 Mrs. John Bradley (Florence Briggs Thayer) Cumings female 38 1 0 71.2833
4 1 3 Miss. Laina Heikkinen female 26 0 0 7.925
5 1 1 Mrs. Jacques Heath (Lily May Peel) Futrelle female 35 1 0 53.1
6 0 3 Mr. William Henry Allen male 35 0 0 8.05
7 0 3 Mr. James Moran male 27 0 0 8.4583
8 0 1 Mr. Timothy J McCarthy male 54 0 0 51.8625
9 0 3 Master. Gosta Leonard Palsson male 2 3 1 21.075
10 1 3 Mrs. Oscar W (Elisabeth Vilhelmina Berg) Johnson female 27 0 2 11.1333
11 1 2 Mrs. Nicholas (Adele Achem) Nasser female 14 1 0 30.0708
12 1 3 Miss. Marguerite Rut Sandstrom female 4 1 1 16.7
13 1 1 Miss. Elizabeth Bonnell female 58 0 0 26.55
14 0 3 Mr. William Henry Saundercock male 20 0 0 8.05
15 0 3 Mr. Anders Johan Andersson male 39 1 5 31.275
16 0 3 Miss. Hulda Amanda Adolfina Vestrom female 14 0 0 7.8542
17 1 2 Mrs. (Mary D Kingcome) Hewlett female 55 0 0 16
18 0 3 Master. Eugene Rice male 2 4 1 29.125
19 1 2 Mr. Charles Eugene Williams male 23 0 0 13
20 0 3 Mrs. Julius (Emelia Maria Vandemoortele) Vander Planke female 31 1 0 18
21 1 3 Mrs. Fatima Masselmani female 22 0 0 7.225
22 0 2 Mr. Joseph J Fynney male 35 0 0 26
23 1 2 Mr. Lawrence Beesley male 34 0 0 13
24 1 3 Miss. Anna McGowan female 15 0 0 8.0292
25 1 1 Mr. William Thompson Sloper male 28 0 0 35.5
26 0 3 Miss. Torborg Danira Palsson female 8 3 1 21.075
27 1 3 Mrs. Carl Oscar (Selma Augusta Emilia Johansson) Asplund female 38 1 5 31.3875
28 0 3 Mr. Farred Chehab Emir male 26 0 0 7.225
29 0 1 Mr. Charles Alexander Fortune male 19 3 2 263
30 1 3 Miss. Ellen O'Dwyer female 24 0 0 7.8792
31 0 3 Mr. Lalio Todoroff male 23 0 0 7.8958
32 0 1 Don. Manuel E Uruchurtu male 40 0 0 27.7208
33 1 1 Mrs. William Augustus (Marie Eugenie) Spencer female 48 1 0 146.5208
34 1 3 Miss. Mary Agatha Glynn female 18 0 0 7.75
35 0 2 Mr. Edward H Wheadon male 66 0 0 10.5
36 0 1 Mr. Edgar Joseph Meyer male 28 1 0 82.1708
37 0 1 Mr. Alexander Oskar Holverson male 42 1 0 52
38 1 3 Mr. Hanna Mamee male 18 0 0 7.2292
39 0 3 Mr. Ernest Charles Cann male 21 0 0 8.05
40 0 3 Miss. Augusta Maria Vander Planke female 18 2 0 18
41 1 3 Miss. Jamila Nicola-Yarred female 14 1 0 11.2417
42 0 3 Mrs. Johan (Johanna Persdotter Larsson) Ahlin female 40 1 0 9.475
43 0 2 Mrs. William John Robert (Dorothy Ann Wonnacott) Turpin female 27 1 0 21
44 1 2 Miss. Simonne Marie Anne Andree Laroche female 3 1 2 41.5792
45 1 3 Miss. Margaret Delia Devaney female 19 0 0 7.8792
46 0 3 Mr. William John Rogers male 30 0 0 8.05
47 0 3 Mr. Denis Lennon male 20 1 0 15.5
48 1 3 Miss. Bridget O'Driscoll female 27 0 0 7.75
49 0 3 Mr. Youssef Samaan male 16 2 0 21.6792
50 0 3 Mrs. Josef (Josefine Franchi) Arnold-Franchi female 18 1 0 17.8
51 0 3 Master. Juha Niilo Panula male 7 4 1 39.6875
52 0 3 Mr. Richard Cater Nosworthy male 21 0 0 7.8
53 1 1 Mrs. Henry Sleeper (Myna Haxtun) Harper female 49 1 0 76.7292
54 1 2 Mrs. Lizzie (Elizabeth Anne Wilkinson) Faunthorpe female 29 1 0 26
55 0 1 Mr. Engelhart Cornelius Ostby male 65 0 1 61.9792
56 1 1 Mr. Hugh Woolner male 46 0 0 35.5
57 1 2 Miss. Emily Rugg female 21 0 0 10.5
58 0 3 Mr. Mansouer Novel male 28.5 0 0 7.2292
59 1 2 Miss. Constance Mirium West female 5 1 2 27.75
60 0 3 Master. William Frederick Goodwin male 11 5 2 46.9
61 0 3 Mr. Orsen Sirayanian male 22 0 0 7.2292
62 1 1 Miss. Amelie Icard female 38 0 0 80
63 0 1 Mr. Henry Birkhardt Harris male 45 1 0 83.475
64 0 3 Master. Harald Skoog male 4 3 2 27.9
65 0 1 Mr. Albert A Stewart male 64 0 0 27.7208
66 1 3 Master. Gerios Moubarek male 7 1 1 15.2458
67 1 2 Mrs. (Elizabeth Ramell) Nye female 29 0 0 10.5
68 0 3 Mr. Ernest James Crease male 19 0 0 8.1583
69 1 3 Miss. Erna Alexandra Andersson female 17 4 2 7.925
70 0 3 Mr. Vincenz Kink male 26 2 0 8.6625
71 0 2 Mr. Stephen Curnow Jenkin male 32 0 0 10.5
72 0 3 Miss. Lillian Amy Goodwin female 16 5 2 46.9
73 0 2 Mr. Ambrose Jr Hood male 21 0 0 73.5
74 0 3 Mr. Apostolos Chronopoulos male 26 1 0 14.4542
75 1 3 Mr. Lee Bing male 32 0 0 56.4958
76 0 3 Mr. Sigurd Hansen Moen male 25 0 0 7.65
77 0 3 Mr. Ivan Staneff male 23 0 0 7.8958
78 0 3 Mr. Rahamin Haim Moutal male 28 0 0 8.05
79 1 2 Master. Alden Gates Caldwell male 0.83 0 2 29
80 1 3 Miss. Elizabeth Dowdell female 30 0 0 12.475
81 0 3 Mr. Achille Waelens male 22 0 0 9
82 1 3 Mr. Jan Baptist Sheerlinck male 29 0 0 9.5
83 1 3 Miss. Brigdet Delia McDermott female 31 0 0 7.7875
84 0 1 Mr. Francisco M Carrau male 28 0 0 47.1
85 1 2 Miss. Bertha Ilett female 17 0 0 10.5
86 1 3 Mrs. Karl Alfred (Maria Mathilda Gustafsson) Backstrom female 33 3 0 15.85
87 0 3 Mr. William Neal Ford male 16 1 3 34.375
88 0 3 Mr. Selman Francis Slocovski male 20 0 0 8.05
89 1 1 Miss. Mabel Helen Fortune female 23 3 2 263
90 0 3 Mr. Francesco Celotti male 24 0 0 8.05
91 0 3 Mr. Emil Christmann male 29 0 0 8.05
92 0 3 Mr. Paul Edvin Andreasson male 20 0 0 7.8542
93 0 1 Mr. Herbert Fuller Chaffee male 46 1 0 61.175
94 0 3 Mr. Bertram Frank Dean male 26 1 2 20.575
95 0 3 Mr. Daniel Coxon male 59 0 0 7.25
96 0 3 Mr. Charles Joseph Shorney male 22 0 0 8.05
97 0 1 Mr. George B Goldschmidt male 71 0 0 34.6542
98 1 1 Mr. William Bertram Greenfield male 23 0 1 63.3583
99 1 2 Mrs. John T (Ada Julia Bone) Doling female 34 0 1 23
100 0 2 Mr. Sinai Kantor male 34 1 0 26
101 0 3 Miss. Matilda Petranec female 28 0 0 7.8958
102 0 3 Mr. Pastcho Petroff male 29 0 0 7.8958
103 0 1 Mr. Richard Frasar White male 21 0 1 77.2875
104 0 3 Mr. Gustaf Joel Johansson male 33 0 0 8.6542
105 0 3 Mr. Anders Vilhelm Gustafsson male 37 2 0 7.925
106 0 3 Mr. Stoytcho Mionoff male 28 0 0 7.8958
107 1 3 Miss. Anna Kristine Salkjelsvik female 21 0 0 7.65
108 1 3 Mr. Albert Johan Moss male 29 0 0 7.775
109 0 3 Mr. Tido Rekic male 38 0 0 7.8958
110 1 3 Miss. Bertha Moran female 28 1 0 24.15
111 0 1 Mr. Walter Chamberlain Porter male 47 0 0 52
112 0 3 Miss. Hileni Zabour female 14.5 1 0 14.4542
113 0 3 Mr. David John Barton male 22 0 0 8.05
114 0 3 Miss. Katriina Jussila female 20 1 0 9.825
115 0 3 Miss. Malake Attalah female 17 0 0 14.4583
116 0 3 Mr. Edvard Pekoniemi male 21 0 0 7.925
117 0 3 Mr. Patrick Connors male 70.5 0 0 7.75
118 0 2 Mr. William John Robert Turpin male 29 1 0 21
119 0 1 Mr. Quigg Edmond Baxter male 24 0 1 247.5208
120 0 3 Miss. Ellis Anna Maria Andersson female 2 4 2 31.275
121 0 2 Mr. Stanley George Hickman male 21 2 0 73.5
122 0 3 Mr. Leonard Charles Moore male 19 0 0 8.05
123 0 2 Mr. Nicholas Nasser male 32.5 1 0 30.0708
124 1 2 Miss. Susan Webber female 32.5 0 0 13
125 0 1 Mr. Percival Wayland White male 54 0 1 77.2875
126 1 3 Master. Elias Nicola-Yarred male 12 1 0 11.2417
127 0 3 Mr. Martin McMahon male 19 0 0 7.75
128 1 3 Mr. Fridtjof Arne Madsen male 24 0 0 7.1417
129 1 3 Miss. Anna Peter female 2 1 1 22.3583
130 0 3 Mr. Johan Ekstrom male 45 0 0 6.975
131 0 3 Mr. Jozef Drazenoic male 33 0 0 7.8958
132 0 3 Mr. Domingos Fernandeo Coelho male 20 0 0 7.05
133 0 3 Mrs. Alexander A (Grace Charity Laury) Robins female 47 1 0 14.5
134 1 2 Mrs. Leopold (Mathilde Francoise Pede) Weisz female 29 1 0 26
135 0 2 Mr. Samuel James Hayden Sobey male 25 0 0 13
136 0 2 Mr. Emile Richard male 23 0 0 15.0458
137 1 1 Miss. Helen Monypeny Newsom female 19 0 2 26.2833
138 0 1 Mr. Jacques Heath Futrelle male 37 1 0 53.1
139 0 3 Mr. Olaf Elon Osen male 16 0 0 9.2167
140 0 1 Mr. Victor Giglio male 24 0 0 79.2
141 0 3 Mrs. Joseph (Sultana) Boulos female 40 0 2 15.2458
142 1 3 Miss. Anna Sofia Nysten female 22 0 0 7.75
143 1 3 Mrs. Pekka Pietari (Elin Matilda Dolck) Hakkarainen female 24 1 0 15.85
144 0 3 Mr. Jeremiah Burke male 19 0 0 6.75
145 0 2 Mr. Edgardo Samuel Andrew male 18 0 0 11.5
146 0 2 Mr. Joseph Charles Nicholls male 19 1 1 36.75
147 1 3 Mr. August Edvard Andersson male 27 0 0 7.7958
148 0 3 Miss. Robina Maggie Ford female 9 2 2 34.375
149 0 2 Mr. Michel Navratil male 36.5 0 2 26
150 0 2 Rev. Thomas Roussel Davids Byles male 42 0 0 13
151 0 2 Rev. Robert James Bateman male 51 0 0 12.525
152 1 1 Mrs. Thomas (Edith Wearne) Pears female 22 1 0 66.6
153 0 3 Mr. Alfonzo Meo male 55.5 0 0 8.05
154 0 3 Mr. Austin Blyler van Billiard male 40.5 0 2 14.5
155 0 3 Mr. Ole Martin Olsen male 27 0 0 7.3125
156 0 1 Mr. Charles Duane Williams male 51 0 1 61.3792
157 1 3 Miss. Katherine Gilnagh female 16 0 0 7.7333
158 0 3 Mr. Harry Corn male 30 0 0 8.05
159 0 3 Mr. Mile Smiljanic male 37 0 0 8.6625
160 0 3 Master. Thomas Henry Sage male 5 8 2 69.55
161 0 3 Mr. John Hatfield Cribb male 44 0 1 16.1
162 1 2 Mrs. James (Elizabeth Inglis Milne) Watt female 40 0 0 15.75
163 0 3 Mr. John Viktor Bengtsson male 26 0 0 7.775
164 0 3 Mr. Jovo Calic male 17 0 0 8.6625
165 0 3 Master. Eino Viljami Panula male 1 4 1 39.6875
166 1 3 Master. Frank John William Goldsmith male 9 0 2 20.525
167 1 1 Mrs. (Edith Martha Bowerman) Chibnall female 48 0 1 55
168 0 3 Mrs. William (Anna Bernhardina Karlsson) Skoog female 45 1 4 27.9
169 0 1 Mr. John D Baumann male 60 0 0 25.925
170 0 3 Mr. Lee Ling male 28 0 0 56.4958
171 0 1 Mr. Wyckoff Van der hoef male 61 0 0 33.5
172 0 3 Master. Arthur Rice male 4 4 1 29.125
173 1 3 Miss. Eleanor Ileen Johnson female 1 1 1 11.1333
174 0 3 Mr. Antti Wilhelm Sivola male 21 0 0 7.925
175 0 1 Mr. James Clinch Smith male 56 0 0 30.6958
176 0 3 Mr. Klas Albin Klasen male 18 1 1 7.8542
177 0 3 Master. Henry Forbes Lefebre male 5 3 1 25.4667
178 0 1 Miss. Ann Elizabeth Isham female 50 0 0 28.7125
179 0 2 Mr. Reginald Hale male 30 0 0 13
180 0 3 Mr. Lionel Leonard male 36 0 0 0
181 0 3 Miss. Constance Gladys Sage female 8 8 2 69.55
182 0 2 Mr. Rene Pernot male 39 0 0 15.05
183 0 3 Master. Clarence Gustaf Hugo Asplund male 9 4 2 31.3875
184 1 2 Master. Richard F Becker male 1 2 1 39
185 1 3 Miss. Luise Gretchen Kink-Heilmann female 4 0 2 22.025
186 0 1 Mr. Hugh Roscoe Rood male 39 0 0 50
187 1 3 Mrs. Thomas (Johanna Godfrey) O'Brien female 26 1 0 15.5
188 1 1 Mr. Charles Hallace Romaine male 45 0 0 26.55
189 0 3 Mr. John Bourke male 40 1 1 15.5
190 0 3 Mr. Stjepan Turcin male 36 0 0 7.8958
191 1 2 Mrs. (Rosa) Pinsky female 32 0 0 13
192 0 2 Mr. William Carbines male 19 0 0 13
193 1 3 Miss. Carla Christine Nielsine Andersen-Jensen female 19 1 0 7.8542
194 1 2 Master. Michel M Navratil male 3 1 1 26
195 1 1 Mrs. James Joseph (Margaret Tobin) Brown female 44 0 0 27.7208
196 1 1 Miss. Elise Lurette female 58 0 0 146.5208
197 0 3 Mr. Robert Mernagh male 28 0 0 7.75
198 0 3 Mr. Karl Siegwart Andreas Olsen male 42 0 1 8.4042
199 1 3 Miss. Margaret Madigan female 21 0 0 7.75
200 0 2 Miss. Henriette Yrois female 24 0 0 13
201 0 3 Mr. Nestor Cyriel Vande Walle male 28 0 0 9.5
202 0 3 Mr. Frederick Sage male 17 8 2 69.55
203 0 3 Mr. Jakob Alfred Johanson male 34 0 0 6.4958
204 0 3 Mr. Gerious Youseff male 45.5 0 0 7.225
205 1 3 Mr. Gurshon Cohen male 18 0 0 8.05
206 0 3 Miss. Telma Matilda Strom female 2 0 1 10.4625
207 0 3 Mr. Karl Alfred Backstrom male 32 1 0 15.85
208 1 3 Mr. Nassef Cassem Albimona male 26 0 0 18.7875
209 1 3 Miss. Helen Carr female 16 0 0 7.75
210 1 1 Mr. Henry Blank male 40 0 0 31
211 0 3 Mr. Ahmed Ali male 24 0 0 7.05
212 1 2 Miss. Clear Annie Cameron female 35 0 0 21
213 0 3 Mr. John Henry Perkin male 22 0 0 7.25
214 0 2 Mr. Hans Kristensen Givard male 30 0 0 13
215 0 3 Mr. Philip Kiernan male 22 1 0 7.75
216 1 1 Miss. Madeleine Newell female 31 1 0 113.275
217 1 3 Miss. Eliina Honkanen female 27 0 0 7.925
218 0 2 Mr. Sidney Samuel Jacobsohn male 42 1 0 27
219 1 1 Miss. Albina Bazzani female 32 0 0 76.2917
220 0 2 Mr. Walter Harris male 30 0 0 10.5
221 1 3 Mr. Victor Francis Sunderland male 16 0 0 8.05
222 0 2 Mr. James H Bracken male 27 0 0 13
223 0 3 Mr. George Henry Green male 51 0 0 8.05
224 0 3 Mr. Christo Nenkoff male 22 0 0 7.8958
225 1 1 Mr. Frederick Maxfield Hoyt male 38 1 0 90
226 0 3 Mr. Karl Ivar Sven Berglund male 22 0 0 9.35
227 1 2 Mr. William John Mellors male 19 0 0 10.5
228 0 3 Mr. John Hall Lovell male 20.5 0 0 7.25
229 0 2 Mr. Arne Jonas Fahlstrom male 18 0 0 13
230 0 3 Miss. Mathilde Lefebre female 12 3 1 25.4667
231 1 1 Mrs. Henry Birkhardt (Irene Wallach) Harris female 35 1 0 83.475
232 0 3 Mr. Bengt Edvin Larsson male 29 0 0 7.775
233 0 2 Mr. Ernst Adolf Sjostedt male 59 0 0 13.5
234 1 3 Miss. Lillian Gertrud Asplund female 5 4 2 31.3875
235 0 2 Mr. Robert William Norman Leyson male 24 0 0 10.5
236 0 3 Miss. Alice Phoebe Harknett female 21 0 0 7.55
237 0 2 Mr. Stephen Hold male 44 1 0 26
238 1 2 Miss. Marjorie Collyer female 8 0 2 26.25
239 0 2 Mr. Frederick William Pengelly male 19 0 0 10.5
240 0 2 Mr. George Henry Hunt male 33 0 0 12.275
241 0 3 Miss. Thamine Zabour female 19 1 0 14.4542
242 1 3 Miss. Katherine Murphy female 18 1 0 15.5
243 0 2 Mr. Reginald Charles Coleridge male 29 0 0 10.5
244 0 3 Mr. Matti Alexanteri Maenpaa male 22 0 0 7.125
245 0 3 Mr. Sleiman Attalah male 30 0 0 7.225
246 0 1 Dr. William Edward Minahan male 44 2 0 90
247 0 3 Miss. Agda Thorilda Viktoria Lindahl female 25 0 0 7.775
248 1 2 Mrs. William (Anna) Hamalainen female 24 0 2 14.5
249 1 1 Mr. Richard Leonard Beckwith male 37 1 1 52.5542
250 0 2 Rev. Ernest Courtenay Carter male 54 1 0 26
251 0 3 Mr. James George Reed male 18 0 0 7.25
252 0 3 Mrs. Wilhelm (Elna Matilda Persson) Strom female 29 1 1 10.4625
253 0 1 Mr. William Thomas Stead male 62 0 0 26.55
254 0 3 Mr. William Arthur Lobb male 30 1 0 16.1
255 0 3 Mrs. Viktor (Helena Wilhelmina) Rosblom female 41 0 2 20.2125
256 1 3 Mrs. Darwis (Hanne Youssef Razi) Touma female 29 0 2 15.2458
257 1 1 Mrs. Gertrude Maybelle Thorne female 38 0 0 79.2
258 1 1 Miss. Gladys Cherry female 30 0 0 86.5
259 1 1 Miss. Anna Ward female 35 0 0 512.3292
260 1 2 Mrs. (Lutie Davis) Parrish female 50 0 1 26
261 1 3 Master. Edvin Rojj Felix Asplund male 3 4 2 31.3875
262 0 1 Mr. Emil Taussig male 52 1 1 79.65
263 0 1 Mr. William Harrison male 40 0 0 0
264 0 3 Miss. Delia Henry female 21 0 0 7.75
265 0 2 Mr. David Reeves male 36 0 0 10.5
266 0 3 Mr. Ernesti Arvid Panula male 16 4 1 39.6875
267 1 3 Mr. Ernst Ulrik Persson male 25 1 0 7.775
268 1 1 Mrs. William Thompson (Edith Junkins) Graham female 58 0 1 153.4625
269 1 1 Miss. Amelia Bissette female 35 0 0 135.6333
270 0 1 Mr. Alexander Cairns male 28 0 0 31
271 1 3 Mr. William Henry Tornquist male 25 0 0 0
272 1 2 Mrs. (Elizabeth Anne Maidment) Mellinger female 41 0 1 19.5
273 0 1 Mr. Charles H Natsch male 37 0 1 29.7
274 1 3 Miss. Hanora Healy female 33 0 0 7.75
275 1 1 Miss. Kornelia Theodosia Andrews female 63 1 0 77.9583
276 0 3 Miss. Augusta Charlotta Lindblom female 45 0 0 7.75
277 0 2 Mr. Francis Parkes male 21 0 0 0
278 0 3 Master. Eric Rice male 7 4 1 29.125
279 1 3 Mrs. Stanton (Rosa Hunt) Abbott female 35 1 1 20.25
280 0 3 Mr. Frank Duane male 65 0 0 7.75
281 0 3 Mr. Nils Johan Goransson Olsson male 28 0 0 7.8542
282 0 3 Mr. Alfons de Pelsmaeker male 16 0 0 9.5
283 1 3 Mr. Edward Arthur Dorking male 19 0 0 8.05
284 0 1 Mr. Richard William Smith male 57 0 0 26
285 0 3 Mr. Ivan Stankovic male 33 0 0 8.6625
286 1 3 Mr. Theodore de Mulder male 30 0 0 9.5
287 0 3 Mr. Penko Naidenoff male 22 0 0 7.8958
288 1 2 Mr. Masabumi Hosono male 42 0 0 13
289 1 3 Miss. Kate Connolly female 22 0 0 7.75
290 1 1 Miss. Ellen Barber female 26 0 0 78.85
291 1 1 Mrs. Dickinson H (Helen Walton) Bishop female 19 1 0 91.0792
292 0 2 Mr. Rene Jacques Levy male 36 0 0 12.875
293 0 3 Miss. Aloisia Haas female 24 0 0 8.85
294 0 3 Mr. Ivan Mineff male 24 0 0 7.8958
295 0 1 Mr. Ervin G Lewy male 30 0 0 27.7208
296 0 3 Mr. Mansour Hanna male 23.5 0 0 7.2292
297 0 1 Miss. Helen Loraine Allison female 2 1 2 151.55
298 1 1 Mr. Adolphe Saalfeld male 47 0 0 30.5
299 1 1 Mrs. James (Helene DeLaudeniere Chaput) Baxter female 50 0 1 247.5208
300 1 3 Miss. Anna Katherine Kelly female 20 0 0 7.75
301 1 3 Mr. Bernard McCoy male 24 2 0 23.25
302 0 3 Mr. William Cahoone Jr Johnson male 19 0 0 0
303 1 2 Miss. Nora A Keane female 46 0 0 12.35
304 0 3 Mr. Howard Hugh Williams male 28 0 0 8.05
305 1 1 Master. Hudson Trevor Allison male 0.92 1 2 151.55
306 1 1 Miss. Margaret Fleming female 42 0 0 110.8833
307 1 1 Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo) Penasco y Castellana female 17 1 0 108.9
308 0 2 Mr. Samuel Abelson male 30 1 0 24
309 1 1 Miss. Laura Mabel Francatelli female 30 0 0 56.9292
310 1 1 Miss. Margaret Bechstein Hays female 24 0 0 83.1583
311 1 1 Miss. Emily Borie Ryerson female 18 2 2 262.375
312 0 2 Mrs. William (Anna Sylfven) Lahtinen female 26 1 1 26
313 0 3 Mr. Ignjac Hendekovic male 28 0 0 7.8958
314 0 2 Mr. Benjamin Hart male 43 1 1 26.25
315 1 3 Miss. Helmina Josefina Nilsson female 26 0 0 7.8542
316 1 2 Mrs. Sinai (Miriam Sternin) Kantor female 24 1 0 26
317 0 2 Dr. Ernest Moraweck male 54 0 0 14
318 1 1 Miss. Mary Natalie Wick female 31 0 2 164.8667
319 1 1 Mrs. Frederic Oakley (Margaretta Corning Stone) Spedden female 40 1 1 134.5
320 0 3 Mr. Samuel Dennis male 22 0 0 7.25
321 0 3 Mr. Yoto Danoff male 27 0 0 7.8958
322 1 2 Miss. Hilda Mary Slayter female 30 0 0 12.35
323 1 2 Mrs. Albert Francis (Sylvia Mae Harbaugh) Caldwell female 22 1 1 29
324 0 3 Mr. George John Jr Sage male 20 8 2 69.55
325 1 1 Miss. Marie Grice Young female 36 0 0 135.6333
326 0 3 Mr. Johan Hansen Nysveen male 61 0 0 6.2375
327 1 2 Mrs. (Ada E Hall) Ball female 36 0 0 13
328 1 3 Mrs. Frank John (Emily Alice Brown) Goldsmith female 31 1 1 20.525
329 1 1 Miss. Jean Gertrude Hippach female 16 0 1 57.9792
330 1 3 Miss. Agnes McCoy female 28 2 0 23.25
331 0 1 Mr. Austen Partner male 45.5 0 0 28.5
332 0 1 Mr. George Edward Graham male 38 0 1 153.4625
333 0 3 Mr. Leo Edmondus Vander Planke male 16 2 0 18
334 1 1 Mrs. Henry William (Clara Heinsheimer) Frauenthal female 42 1 0 133.65
335 0 3 Mr. Mitto Denkoff male 30 0 0 7.8958
336 0 1 Mr. Thomas Clinton Pears male 29 1 0 66.6
337 1 1 Miss. Elizabeth Margaret Burns female 41 0 0 134.5
338 1 3 Mr. Karl Edwart Dahl male 45 0 0 8.05
339 0 1 Mr. Stephen Weart Blackwell male 45 0 0 35.5
340 1 2 Master. Edmond Roger Navratil male 2 1 1 26
341 1 1 Miss. Alice Elizabeth Fortune female 24 3 2 263
342 0 2 Mr. Erik Gustaf Collander male 28 0 0 13
343 0 2 Mr. Charles Frederick Waddington Sedgwick male 25 0 0 13
344 0 2 Mr. Stanley Hubert Fox male 36 0 0 13
345 1 2 Miss. Amelia Brown female 24 0 0 13
346 1 2 Miss. Marion Elsie Smith female 40 0 0 13
347 1 3 Mrs. Thomas Henry (Mary E Finck) Davison female 34 1 0 16.1
348 1 3 Master. William Loch Coutts male 3 1 1 15.9
349 0 3 Mr. Jovan Dimic male 42 0 0 8.6625
350 0 3 Mr. Nils Martin Odahl male 23 0 0 9.225
351 0 1 Mr. Fletcher Fellows Williams-Lambert male 43 0 0 35
352 0 3 Mr. Tannous Elias male 15 1 1 7.2292
353 0 3 Mr. Josef Arnold-Franchi male 25 1 0 17.8
354 0 3 Mr. Wazli Yousif male 23 0 0 7.225
355 0 3 Mr. Leo Peter Vanden Steen male 28 0 0 9.5
356 1 1 Miss. Elsie Edith Bowerman female 22 0 1 55
357 0 2 Miss. Annie Clemmer Funk female 38 0 0 13
358 1 3 Miss. Mary McGovern female 22 0 0 7.8792
359 1 3 Miss. Helen Mary Mockler female 23 0 0 7.8792
360 0 3 Mr. Wilhelm Skoog male 40 1 4 27.9
361 0 2 Mr. Sebastiano del Carlo male 29 1 0 27.7208
362 0 3 Mrs. (Catherine David) Barbara female 45 0 1 14.4542
363 0 3 Mr. Adola Asim male 35 0 0 7.05
364 0 3 Mr. Thomas O'Brien male 27 1 0 15.5
365 0 3 Mr. Mauritz Nils Martin Adahl male 30 0 0 7.25
366 1 1 Mrs. Frank Manley (Anna Sophia Atkinson) Warren female 60 1 0 75.25
367 1 3 Mrs. (Mantoura Boulos) Moussa female 35 0 0 7.2292
368 1 3 Miss. Annie Jermyn female 22 0 0 7.75
369 1 1 Mme. Leontine Pauline Aubart female 24 0 0 69.3
370 1 1 Mr. George Achilles Harder male 25 1 0 55.4417
371 0 3 Mr. Jakob Alfred Wiklund male 18 1 0 6.4958
372 0 3 Mr. William Thomas Beavan male 19 0 0 8.05
373 0 1 Mr. Sante Ringhini male 22 0 0 135.6333
374 0 3 Miss. Stina Viola Palsson female 3 3 1 21.075
375 1 1 Mrs. Edgar Joseph (Leila Saks) Meyer female 25 1 0 82.1708
376 1 3 Miss. Aurora Adelia Landergren female 22 0 0 7.25
377 0 1 Mr. Harry Elkins Widener male 27 0 2 211.5
378 0 3 Mr. Tannous Betros male 20 0 0 4.0125
379 0 3 Mr. Karl Gideon Gustafsson male 19 0 0 7.775
380 1 1 Miss. Rosalie Bidois female 42 0 0 227.525
381 1 3 Miss. Maria Nakid female 1 0 2 15.7417
382 0 3 Mr. Juho Tikkanen male 32 0 0 7.925
383 1 1 Mrs. Alexander Oskar (Mary Aline Towner) Holverson female 35 1 0 52
384 0 3 Mr. Vasil Plotcharsky male 27 0 0 7.8958
385 0 2 Mr. Charles Henry Davies male 18 0 0 73.5
386 0 3 Master. Sidney Leonard Goodwin male 1 5 2 46.9
387 1 2 Miss. Kate Buss female 36 0 0 13
388 0 3 Mr. Matthew Sadlier male 19 0 0 7.7292
389 1 2 Miss. Bertha Lehmann female 17 0 0 12
390 1 1 Mr. William Ernest Carter male 36 1 2 120
391 1 3 Mr. Carl Olof Jansson male 21 0 0 7.7958
392 0 3 Mr. Johan Birger Gustafsson male 28 2 0 7.925
393 1 1 Miss. Marjorie Newell female 23 1 0 113.275
394 1 3 Mrs. Hjalmar (Agnes Charlotta Bengtsson) Sandstrom female 24 0 2 16.7
395 0 3 Mr. Erik Johansson male 22 0 0 7.7958
396 0 3 Miss. Elina Olsson female 31 0 0 7.8542
397 0 2 Mr. Peter David McKane male 46 0 0 26
398 0 2 Dr. Alfred Pain male 23 0 0 10.5
399 1 2 Mrs. William H (Jessie L) Trout female 28 0 0 12.65
400 1 3 Mr. Juha Niskanen male 39 0 0 7.925
401 0 3 Mr. John Adams male 26 0 0 8.05
402 0 3 Miss. Mari Aina Jussila female 21 1 0 9.825
403 0 3 Mr. Pekka Pietari Hakkarainen male 28 1 0 15.85
404 0 3 Miss. Marija Oreskovic female 20 0 0 8.6625
405 0 2 Mr. Shadrach Gale male 34 1 0 21
406 0 3 Mr. Carl/Charles Peter Widegren male 51 0 0 7.75
407 1 2 Master. William Rowe Richards male 3 1 1 18.75
408 0 3 Mr. Hans Martin Monsen Birkeland male 21 0 0 7.775
409 0 3 Miss. Ida Lefebre female 3 3 1 25.4667
410 0 3 Mr. Todor Sdycoff male 42 0 0 7.8958
411 0 3 Mr. Henry Hart male 27 0 0 6.8583
412 1 1 Miss. Daisy E Minahan female 33 1 0 90
413 0 2 Mr. Alfred Fleming Cunningham male 22 0 0 0
414 1 3 Mr. Johan Julian Sundman male 44 0 0 7.925
415 0 3 Mrs. Thomas (Annie Louise Rowley) Meek female 32 0 0 8.05
416 1 2 Mrs. James Vivian (Lulu Thorne Christian) Drew female 34 1 1 32.5
417 1 2 Miss. Lyyli Karoliina Silven female 18 0 2 13
418 0 2 Mr. William John Matthews male 30 0 0 13
419 0 3 Miss. Catharina Van Impe female 10 0 2 24.15
420 0 3 Mr. David Charters male 21 0 0 7.7333
421 0 3 Mr. Leo Zimmerman male 29 0 0 7.875
422 0 3 Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren) Danbom female 28 1 1 14.4
423 0 3 Mr. Viktor Richard Rosblom male 18 1 1 20.2125
424 0 3 Mr. Phillippe Wiseman male 54 0 0 7.25
425 1 2 Mrs. Charles V (Ada Maria Winfield) Clarke female 28 1 0 26
426 1 2 Miss. Kate Florence Phillips female 19 0 0 26
427 0 3 Mr. James Flynn male 28 0 0 7.75
428 1 3 Mr. Berk (Berk Trembisky) Pickard male 32 0 0 8.05
429 1 1 Mr. Mauritz Hakan Bjornstrom-Steffansson male 28 0 0 26.55
430 1 3 Mrs. Percival (Florence Kate White) Thorneycroft female 33 1 0 16.1
431 1 2 Mrs. Charles Alexander (Alice Adelaide Slow) Louch female 42 1 0 26
432 0 3 Mr. Nikolai Erland Kallio male 17 0 0 7.125
433 0 1 Mr. William Baird Silvey male 50 1 0 55.9
434 1 1 Miss. Lucile Polk Carter female 14 1 2 120
435 0 3 Miss. Doolina Margaret Ford female 21 2 2 34.375
436 1 2 Mrs. Sidney (Emily Hocking) Richards female 24 2 3 18.75
437 0 1 Mr. Mark Fortune male 64 1 4 263
438 0 2 Mr. Johan Henrik Johannesson Kvillner male 31 0 0 10.5
439 1 2 Mrs. Benjamin (Esther Ada Bloomfield) Hart female 45 1 1 26.25
440 0 3 Mr. Leon Hampe male 20 0 0 9.5
441 0 3 Mr. Johan Emil Petterson male 25 1 0 7.775
442 1 2 Ms. Encarnacion Reynaldo female 28 0 0 13
443 1 3 Mr. Bernt Johannesen-Bratthammer male 29 0 0 8.1125
444 1 1 Master. Washington Dodge male 4 0 2 81.8583
445 1 2 Miss. Madeleine Violet Mellinger female 13 0 1 19.5
446 1 1 Mr. Frederic Kimber Seward male 34 0 0 26.55
447 1 3 Miss. Marie Catherine Baclini female 5 2 1 19.2583
448 1 1 Major. Arthur Godfrey Peuchen male 52 0 0 30.5
449 0 2 Mr. Edwy Arthur West male 36 1 2 27.75
450 0 3 Mr. Ingvald Olai Olsen Hagland male 28 1 0 19.9667
451 0 1 Mr. Benjamin Laventall Foreman male 30 0 0 27.75
452 1 1 Mr. Samuel L Goldenberg male 49 1 0 89.1042
453 0 3 Mr. Joseph Peduzzi male 24 0 0 8.05
454 1 3 Mr. Ivan Jalsevac male 29 0 0 7.8958
455 0 1 Mr. Francis Davis Millet male 65 0 0 26.55
456 1 1 Mrs. Frederick R (Marion) Kenyon female 41 1 0 51.8625
457 1 2 Miss. Ellen Toomey female 50 0 0 10.5
458 0 3 Mr. Maurice O'Connor male 17 0 0 7.75
459 1 1 Mr. Harry Anderson male 48 0 0 26.55
460 0 3 Mr. William Morley male 34 0 0 8.05
461 0 1 Mr. Arthur H Gee male 47 0 0 38.5
462 0 2 Mr. Jacob Christian Milling male 48 0 0 13
463 0 3 Mr. Simon Maisner male 34 0 0 8.05
464 0 3 Mr. Manuel Estanslas Goncalves male 38 0 0 7.05
465 0 2 Mr. William Campbell male 21 0 0 0
466 0 1 Mr. John Montgomery Smart male 56 0 0 26.55
467 0 3 Mr. James Scanlan male 22 0 0 7.725
468 1 3 Miss. Helene Barbara Baclini female 0.75 2 1 19.2583
469 0 3 Mr. Arthur Keefe male 39 0 0 7.25
470 0 3 Mr. Luka Cacic male 38 0 0 8.6625
471 1 2 Mrs. Edwy Arthur (Ada Mary Worth) West female 33 1 2 27.75
472 1 2 Mrs. Amin S (Marie Marthe Thuillard) Jerwan female 23 0 0 13.7917
473 0 3 Miss. Ida Sofia Strandberg female 22 0 0 9.8375
474 0 1 Mr. George Quincy Clifford male 40 0 0 52
475 0 2 Mr. Peter Henry Renouf male 34 1 0 21
476 0 3 Mr. Lewis Richard Braund male 29 1 0 7.0458
477 0 3 Mr. Nils August Karlsson male 22 0 0 7.5208
478 1 3 Miss. Hildur E Hirvonen female 2 0 1 12.2875
479 0 3 Master. Harold Victor Goodwin male 9 5 2 46.9
480 0 2 Mr. Anthony Wood Frost male 37 0 0 0
481 0 3 Mr. Richard Henry Rouse male 50 0 0 8.05
482 1 3 Mrs. (Hedwig) Turkula female 63 0 0 9.5875
483 1 1 Mr. Dickinson H Bishop male 25 1 0 91.0792
484 0 3 Miss. Jeannie Lefebre female 8 3 1 25.4667
485 1 1 Mrs. Frederick Maxfield (Jane Anne Forby) Hoyt female 35 1 0 90
486 0 1 Mr. Edward Austin Kent male 58 0 0 29.7
487 0 3 Mr. Francis William Somerton male 30 0 0 8.05
488 1 3 Master. Eden Leslie Coutts male 9 1 1 15.9
489 0 3 Mr. Konrad Mathias Reiersen Hagland male 19 1 0 19.9667
490 0 3 Mr. Einar Windelov male 21 0 0 7.25
491 0 1 Mr. Harry Markland Molson male 55 0 0 30.5
492 0 1 Mr. Ramon Artagaveytia male 71 0 0 49.5042
493 0 3 Mr. Edward Roland Stanley male 21 0 0 8.05
494 0 3 Mr. Gerious Yousseff male 26 0 0 14.4583
495 1 1 Miss. Elizabeth Mussey Eustis female 54 1 0 78.2667
496 0 3 Mr. Frederick William Shellard male 55 0 0 15.1
497 0 1 Mrs. Hudson J C (Bessie Waldo Daniels) Allison female 25 1 2 151.55
498 0 3 Mr. Olof Svensson male 24 0 0 7.7958
499 0 3 Mr. Petar Calic male 17 0 0 8.6625
500 0 3 Miss. Mary Canavan female 21 0 0 7.75
501 0 3 Miss. Bridget Mary O'Sullivan female 21 0 0 7.6292
502 0 3 Miss. Kristina Sofia Laitinen female 37 0 0 9.5875
503 1 1 Miss. Roberta Maioni female 16 0 0 86.5
504 0 1 Mr. Victor de Satode Penasco y Castellana male 18 1 0 108.9
505 1 2 Mrs. Frederick Charles (Jane Richards) Quick female 33 0 2 26
506 1 1 Mr. George Bradley male 37 0 0 26.55
507 0 3 Mr. Henry Margido Olsen male 28 0 0 22.525
508 1 3 Mr. Fang Lang male 26 0 0 56.4958
509 1 3 Mr. Eugene Patrick Daly male 29 0 0 7.75
510 0 3 Mr. James Webber male 66 0 0 8.05
511 1 1 Mr. James Robert McGough male 36 0 0 26.2875
512 1 1 Mrs. Martin (Elizabeth L. Barrett) Rothschild female 54 1 0 59.4
513 0 3 Mr. Satio Coleff male 24 0 0 7.4958
514 0 1 Mr. William Anderson Walker male 47 0 0 34.0208
515 1 2 Mrs. (Amelia Milley) Lemore female 34 0 0 10.5
516 0 3 Mr. Patrick Ryan male 30 0 0 24.15
517 1 2 Mrs. William A (Florence Agnes Hughes) Angle female 36 1 0 26
518 0 3 Mr. Stefo Pavlovic male 32 0 0 7.8958
519 1 1 Miss. Anne Perreault female 30 0 0 93.5
520 0 3 Mr. Janko Vovk male 22 0 0 7.8958
521 0 3 Mr. Sarkis Lahoud male 35 0 0 7.225
522 1 1 Mrs. Louis Albert (Ida Sophia Fischer) Hippach female 44 0 1 57.9792
523 0 3 Mr. Fared Kassem male 18 0 0 7.2292
524 0 3 Mr. James Farrell male 40.5 0 0 7.75
525 1 2 Miss. Lucy Ridsdale female 50 0 0 10.5
526 0 1 Mr. John Farthing male 49 0 0 221.7792
527 0 3 Mr. Johan Werner Salonen male 39 0 0 7.925
528 0 2 Mr. Richard George Hocking male 23 2 1 11.5
529 1 2 Miss. Phyllis May Quick female 2 1 1 26
530 0 3 Mr. Nakli Toufik male 17 0 0 7.2292
531 0 3 Mr. Joseph Jr Elias male 17 1 1 7.2292
532 1 3 Mrs. Catherine (Catherine Rizk) Peter female 24 0 2 22.3583
533 0 3 Miss. Marija Cacic female 30 0 0 8.6625
534 1 2 Miss. Eva Miriam Hart female 7 0 2 26.25
535 0 1 Major. Archibald Willingham Butt male 45 0 0 26.55
536 1 1 Miss. Bertha LeRoy female 30 0 0 106.425
537 0 3 Mr. Samuel Beard Risien male 69 0 0 14.5
538 1 1 Miss. Hedwig Margaritha Frolicher female 22 0 2 49.5
539 1 1 Miss. Harriet R Crosby female 36 0 2 71
540 0 3 Miss. Ingeborg Constanzia Andersson female 9 4 2 31.275
541 0 3 Miss. Sigrid Elisabeth Andersson female 11 4 2 31.275
542 1 2 Mr. Edward Beane male 32 1 0 26
543 0 1 Mr. Walter Donald Douglas male 50 1 0 106.425
544 0 1 Mr. Arthur Ernest Nicholson male 64 0 0 26
545 1 2 Mrs. Edward (Ethel Clarke) Beane female 19 1 0 26
546 1 2 Mr. Julian Padro y Manent male 27 0 0 13.8625
547 0 3 Mr. Frank John Goldsmith male 33 1 1 20.525
548 1 2 Master. John Morgan Jr Davies male 8 1 1 36.75
549 1 1 Mr. John Borland Jr Thayer male 17 0 2 110.8833
550 0 2 Mr. Percival James R Sharp male 27 0 0 26
551 0 3 Mr. Timothy O'Brien male 21 0 0 7.8292
552 1 3 Mr. Fahim Leeni male 22 0 0 7.225
553 1 3 Miss. Velin Ohman female 22 0 0 7.775
554 0 1 Mr. George Wright male 62 0 0 26.55
555 1 1 Lady. (Lucille Christiana Sutherland)Duff Gordon female 48 1 0 39.6
556 0 1 Mr. Victor Robbins male 45 0 0 227.525
557 1 1 Mrs. Emil (Tillie Mandelbaum) Taussig female 39 1 1 79.65
558 1 3 Mrs. Guillaume Joseph (Emma) de Messemaeker female 36 1 0 17.4
559 0 3 Mr. Thomas Rowan Morrow male 30 0 0 7.75
560 0 3 Mr. Husein Sivic male 40 0 0 7.8958
561 0 2 Mr. Robert Douglas Norman male 28 0 0 13.5
562 0 3 Mr. John Simmons male 40 0 0 8.05
563 0 3 Miss. (Marion Ogden) Meanwell female 62 0 0 8.05
564 0 3 Mr. Alfred J Davies male 24 2 0 24.15
565 0 3 Mr. Ilia Stoytcheff male 19 0 0 7.8958
566 0 3 Mrs. Nils (Alma Cornelia Berglund) Palsson female 29 0 4 21.075
567 0 3 Mr. Tannous Doharr male 28 0 0 7.2292
568 1 3 Mr. Carl Jonsson male 32 0 0 7.8542
569 1 2 Mr. George Harris male 62 0 0 10.5
570 1 1 Mrs. Edward Dale (Charlotte Lamson) Appleton female 53 2 0 51.4792
571 1 1 Mr. John Irwin Flynn male 36 0 0 26.3875
572 1 3 Miss. Mary Kelly female 22 0 0 7.75
573 0 3 Mr. Alfred George John Rush male 16 0 0 8.05
574 0 3 Mr. George Patchett male 19 0 0 14.5
575 1 2 Miss. Ethel Garside female 34 0 0 13
576 1 1 Mrs. William Baird (Alice Munger) Silvey female 39 1 0 55.9
577 0 3 Mrs. Joseph (Maria Elias) Caram female 18 1 0 14.4583
578 1 3 Mr. Eiriik Jussila male 32 0 0 7.925
579 1 2 Miss. Julie Rachel Christy female 25 1 1 30
580 1 1 Mrs. John Borland (Marian Longstreth Morris) Thayer female 39 1 1 110.8833
581 0 2 Mr. William James Downton male 54 0 0 26
582 0 1 Mr. John Hugo Ross male 36 0 0 40.125
583 0 3 Mr. Uscher Paulner male 16 0 0 8.7125
584 1 1 Miss. Ruth Taussig female 18 0 2 79.65
585 0 2 Mr. John Denzil Jarvis male 47 0 0 15
586 1 1 Mr. Maxmillian Frolicher-Stehli male 60 1 1 79.2
587 0 3 Mr. Eliezer Gilinski male 22 0 0 8.05
588 0 3 Mr. Joseph Murdlin male 22 0 0 8.05
589 0 3 Mr. Matti Rintamaki male 35 0 0 7.125
590 1 1 Mrs. Walter Bertram (Martha Eustis) Stephenson female 52 1 0 78.2667
591 0 3 Mr. William James Elsbury male 47 0 0 7.25
592 0 3 Miss. Mary Bourke female 40 0 2 7.75
593 0 2 Mr. John Henry Chapman male 37 1 0 26
594 0 3 Mr. Jean Baptiste Van Impe male 36 1 1 24.15
595 1 2 Miss. Jessie Wills Leitch female 31 0 0 33
596 0 3 Mr. Alfred Johnson male 49 0 0 0
597 0 3 Mr. Hanna Boulos male 18 0 0 7.225
598 1 1 Sir. Cosmo Edmund Duff Gordon male 49 1 0 56.9292
599 1 2 Mrs. Sidney Samuel (Amy Frances Christy) Jacobsohn female 24 2 1 27
600 0 3 Mr. Petco Slabenoff male 42 0 0 7.8958
601 0 1 Mr. Charles H Harrington male 37 0 0 42.4
602 0 3 Mr. Ernst William Torber male 44 0 0 8.05
603 1 1 Mr. Harry Homer male 35 0 0 26.55
604 0 3 Mr. Edvard Bengtsson Lindell male 36 1 0 15.55
605 0 3 Mr. Milan Karaic male 30 0 0 7.8958
606 1 1 Mr. Robert Williams Daniel male 27 0 0 30.5
607 1 2 Mrs. Joseph (Juliette Marie Louise Lafargue) Laroche female 22 1 2 41.5792
608 1 1 Miss. Elizabeth W Shutes female 40 0 0 153.4625
609 0 3 Mrs. Anders Johan (Alfrida Konstantia Brogren) Andersson female 39 1 5 31.275
610 0 3 Mr. Jose Neto Jardin male 21 0 0 7.05
611 1 3 Miss. Margaret Jane Murphy female 18 1 0 15.5
612 0 3 Mr. John Horgan male 22 0 0 7.75
613 0 3 Mr. William Alfred Brocklebank male 35 0 0 8.05
614 1 2 Miss. Alice Herman female 24 1 2 65
615 0 3 Mr. Ernst Gilbert Danbom male 34 1 1 14.4
616 0 3 Mrs. William Arthur (Cordelia K Stanlick) Lobb female 26 1 0 16.1
617 1 2 Miss. Marion Louise Becker female 4 2 1 39
618 0 2 Mr. Lawrence Gavey male 26 0 0 10.5
619 0 3 Mr. Antoni Yasbeck male 27 1 0 14.4542
620 1 1 Mr. Edwin Nelson Jr Kimball male 42 1 0 52.5542
621 1 3 Mr. Sahid Nakid male 20 1 1 15.7417
622 0 3 Mr. Henry Damsgaard Hansen male 21 0 0 7.8542
623 0 3 Mr. David John Bowen male 21 0 0 16.1
624 0 1 Mr. Frederick Sutton male 61 0 0 32.3208
625 0 2 Rev. Charles Leonard Kirkland male 57 0 0 12.35
626 1 1 Miss. Gretchen Fiske Longley female 21 0 0 77.9583
627 0 3 Mr. Guentcho Bostandyeff male 26 0 0 7.8958
628 0 3 Mr. Patrick D O'Connell male 18 0 0 7.7333
629 1 1 Mr. Algernon Henry Wilson Barkworth male 80 0 0 30
630 0 3 Mr. Johan Svensson Lundahl male 51 0 0 7.0542
631 1 1 Dr. Max Stahelin-Maeglin male 32 0 0 30.5
632 0 1 Mr. William Henry Marsh Parr male 30 0 0 0
633 0 3 Miss. Mabel Skoog female 9 3 2 27.9
634 1 2 Miss. Mary Davis female 28 0 0 13
635 0 3 Mr. Antti Gustaf Leinonen male 32 0 0 7.925
636 0 2 Mr. Harvey Collyer male 31 1 1 26.25
637 0 3 Mrs. Juha (Maria Emilia Ojala) Panula female 41 0 5 39.6875
638 0 3 Mr. Percival Thorneycroft male 37 1 0 16.1
639 0 3 Mr. Hans Peder Jensen male 20 0 0 7.8542
640 1 1 Mlle. Emma Sagesser female 24 0 0 69.3
641 0 3 Miss. Margit Elizabeth Skoog female 2 3 2 27.9
642 1 3 Mr. Choong Foo male 32 0 0 56.4958
643 1 3 Miss. Eugenie Baclini female 0.75 2 1 19.2583
644 1 1 Mr. Henry Sleeper Harper male 48 1 0 76.7292
645 0 3 Mr. Liudevit Cor male 19 0 0 7.8958
646 1 1 Col. Oberst Alfons Simonius-Blumer male 56 0 0 35.5
647 0 3 Mr. Edward Willey male 21 0 0 7.55
648 1 3 Miss. Amy Zillah Elsie Stanley female 23 0 0 7.55
649 0 3 Mr. Mito Mitkoff male 23 0 0 7.8958
650 1 2 Miss. Elsie Doling female 18 0 1 23
651 0 3 Mr. Johannes Halvorsen Kalvik male 21 0 0 8.4333
652 1 3 Miss. Hanora O'Leary female 16 0 0 7.8292
653 0 3 Miss. Hanora Hegarty female 18 0 0 6.75
654 0 2 Mr. Leonard Mark Hickman male 24 2 0 73.5
655 0 3 Mr. Alexander Radeff male 27 0 0 7.8958
656 0 3 Mrs. John (Catherine) Bourke female 32 1 1 15.5
657 0 2 Mr. George Floyd Eitemiller male 23 0 0 13
658 0 1 Mr. Arthur Webster Newell male 58 0 2 113.275
659 1 1 Dr. Henry William Frauenthal male 50 2 0 133.65
660 0 3 Mr. Mohamed Badt male 40 0 0 7.225
661 0 1 Mr. Edward Pomeroy Colley male 47 0 0 25.5875
662 0 3 Mr. Peju Coleff male 36 0 0 7.4958
663 1 3 Mr. Eino William Lindqvist male 20 1 0 7.925
664 0 2 Mr. Lewis Hickman male 32 2 0 73.5
665 0 2 Mr. Reginald Fenton Butler male 25 0 0 13
666 0 3 Mr. Knud Paust Rommetvedt male 49 0 0 7.775
667 0 3 Mr. Jacob Cook male 43 0 0 8.05
668 1 1 Mrs. Elmer Zebley (Juliet Cummins Wright) Taylor female 48 1 0 52
669 1 2 Mrs. Thomas William Solomon (Elizabeth Catherine Ford) Brown female 40 1 1 39
670 0 1 Mr. Thornton Davidson male 31 1 0 52
671 0 2 Mr. Henry Michael Mitchell male 70 0 0 10.5
672 1 2 Mr. Charles Wilhelms male 31 0 0 13
673 0 2 Mr. Ennis Hastings Watson male 19 0 0 0
674 0 3 Mr. Gustaf Hjalmar Edvardsson male 18 0 0 7.775
675 0 3 Mr. Frederick Charles Sawyer male 24.5 0 0 8.05
676 1 3 Miss. Anna Sofia Turja female 18 0 0 9.8417
677 0 3 Mrs. Frederick (Augusta Tyler) Goodwin female 43 1 6 46.9
678 1 1 Mr. Thomas Drake Martinez Cardeza male 36 0 1 512.3292
679 0 3 Miss. Katie Peters female 28 0 0 8.1375
680 1 1 Mr. Hammad Hassab male 27 0 0 76.7292
681 0 3 Mr. Thor Anderson Olsvigen male 20 0 0 9.225
682 0 3 Mr. Charles Edward Goodwin male 14 5 2 46.9
683 0 2 Mr. Thomas William Solomon Brown male 60 1 1 39
684 0 2 Mr. Joseph Philippe Lemercier Laroche male 25 1 2 41.5792
685 0 3 Mr. Jaako Arnold Panula male 14 4 1 39.6875
686 0 3 Mr. Branko Dakic male 19 0 0 10.1708
687 0 3 Mr. Eberhard Thelander Fischer male 18 0 0 7.7958
688 1 1 Miss. Georgette Alexandra Madill female 15 0 1 211.3375
689 1 1 Mr. Albert Adrian Dick male 31 1 0 57
690 1 3 Miss. Manca Karun female 4 0 1 13.4167
691 1 3 Mr. Ali Lam male 37 0 0 56.4958
692 0 3 Mr. Khalil Saad male 25 0 0 7.225
693 0 1 Col. John Weir male 60 0 0 26.55
694 0 2 Mr. Charles Henry Chapman male 52 0 0 13.5
695 0 3 Mr. James Kelly male 44 0 0 8.05
696 1 3 Miss. Katherine Mullens female 19 0 0 7.7333
697 0 1 Mr. John Borland Thayer male 49 1 1 110.8833
698 0 3 Mr. Adolf Mathias Nicolai Olsen Humblen male 42 0 0 7.65
699 1 1 Mrs. John Jacob (Madeleine Talmadge Force) Astor female 18 1 0 227.525
700 1 1 Mr. Spencer Victor Silverthorne male 35 0 0 26.2875
701 0 3 Miss. Saiide Barbara female 18 0 1 14.4542
702 0 3 Mr. Martin Gallagher male 25 0 0 7.7417
703 0 3 Mr. Henrik Juul Hansen male 26 1 0 7.8542
704 0 2 Mr. Henry Samuel Morley male 39 0 0 26
705 1 2 Mrs. Florence Kelly female 45 0 0 13.5
706 1 1 Mr. Edward Pennington Calderhead male 42 0 0 26.2875
707 1 1 Miss. Alice Cleaver female 22 0 0 151.55
708 1 3 Master. Halim Gonios Moubarek male 4 1 1 15.2458
709 1 1 Mlle. Berthe Antonine Mayne female 24 0 0 49.5042
710 0 1 Mr. Herman Klaber male 41 0 0 26.55
711 1 1 Mr. Elmer Zebley Taylor male 48 1 0 52
712 0 3 Mr. August Viktor Larsson male 29 0 0 9.4833
713 0 2 Mr. Samuel Greenberg male 52 0 0 13
714 0 3 Mr. Peter Andreas Lauritz Andersen Soholt male 19 0 0 7.65
715 1 1 Miss. Caroline Louise Endres female 38 0 0 227.525
716 1 2 Miss. Edwina Celia Troutt female 27 0 0 10.5
717 0 3 Mr. Malkolm Joackim Johnson male 33 0 0 7.775
718 1 2 Miss. Annie Jessie Harper female 6 0 1 33
719 0 3 Mr. Svend Lauritz Jensen male 17 1 0 7.0542
720 0 2 Mr. William Henry Gillespie male 34 0 0 13
721 0 2 Mr. Henry Price Hodges male 50 0 0 13
722 1 1 Mr. Norman Campbell Chambers male 27 1 0 53.1
723 0 3 Mr. Luka Oreskovic male 20 0 0 8.6625
724 1 2 Mrs. Peter Henry (Lillian Jefferys) Renouf female 30 3 0 21
725 1 3 Miss. Margareth Mannion female 28 0 0 7.7375
726 0 2 Mr. Kurt Arnold Gottfrid Bryhl male 25 1 0 26
727 0 3 Miss. Pieta Sofia Ilmakangas female 25 1 0 7.925
728 1 1 Miss. Elisabeth Walton Allen female 29 0 0 211.3375
729 0 3 Mr. Houssein G N Hassan male 11 0 0 18.7875
730 0 2 Mr. Robert J Knight male 41 0 0 0
731 0 2 Mr. William John Berriman male 23 0 0 13
732 0 2 Mr. Moses Aaron Troupiansky male 23 0 0 13
733 0 3 Mr. Leslie Williams male 28.5 0 0 16.1
734 0 3 Mrs. Edward (Margaret Ann Watson) Ford female 48 1 3 34.375
735 1 1 Mr. Gustave J Lesurer male 35 0 0 512.3292
736 0 3 Mr. Kanio Ivanoff male 20 0 0 7.8958
737 0 3 Mr. Minko Nankoff male 32 0 0 7.8958
738 1 1 Mr. Walter James Hawksford male 45 0 0 30
739 0 1 Mr. Tyrell William Cavendish male 36 1 0 78.85
740 1 1 Miss. Susan Parker Ryerson female 21 2 2 262.375
741 0 3 Mr. Neal McNamee male 24 1 0 16.1
742 1 3 Mr. Juho Stranden male 31 0 0 7.925
743 0 1 Capt. Edward Gifford Crosby male 70 1 1 71
744 0 3 Mr. Rossmore Edward Abbott male 16 1 1 20.25
745 1 2 Miss. Anna Sinkkonen female 30 0 0 13
746 0 1 Mr. Daniel Warner Marvin male 19 1 0 53.1
747 0 3 Mr. Michael Connaghton male 31 0 0 7.75
748 1 2 Miss. Joan Wells female 4 1 1 23
749 1 3 Master. Meier Moor male 6 0 1 12.475
750 0 3 Mr. Johannes Joseph Vande Velde male 33 0 0 9.5
751 0 3 Mr. Lalio Jonkoff male 23 0 0 7.8958
752 1 2 Mrs. Samuel (Jane Laver) Herman female 48 1 2 65
753 1 2 Master. Viljo Hamalainen male 0.67 1 1 14.5
754 0 3 Mr. August Sigfrid Carlsson male 28 0 0 7.7958
755 0 2 Mr. Percy Andrew Bailey male 18 0 0 11.5
756 0 3 Mr. Thomas Leonard Theobald male 34 0 0 8.05
757 1 1 the Countess. of (Lucy Noel Martha Dyer-Edwards) Rothes female 33 0 0 86.5
758 0 3 Mr. John Garfirth male 23 0 0 14.5
759 0 3 Mr. Iisakki Antino Aijo Nirva male 41 0 0 7.125
760 1 3 Mr. Hanna Assi Barah male 20 0 0 7.2292
761 1 1 Mrs. William Ernest (Lucile Polk) Carter female 36 1 2 120
762 0 3 Mr. Hans Linus Eklund male 16 0 0 7.775
763 1 1 Mrs. John C (Anna Andrews) Hogeboom female 51 1 0 77.9583
764 0 1 Dr. Arthur Jackson Brewe male 46 0 0 39.6
765 0 3 Miss. Mary Mangan female 30.5 0 0 7.75
766 0 3 Mr. Daniel J Moran male 28 1 0 24.15
767 0 3 Mr. Daniel Danielsen Gronnestad male 32 0 0 8.3625
768 0 3 Mr. Rene Aime Lievens male 24 0 0 9.5
769 0 3 Mr. Niels Peder Jensen male 48 0 0 7.8542
770 0 2 Mrs. (Mary) Mack female 57 0 0 10.5
771 0 3 Mr. Dibo Elias male 29 0 0 7.225
772 1 2 Mrs. Elizabeth (Eliza Needs) Hocking female 54 1 3 23
773 0 3 Mr. Pehr Fabian Oliver Malkolm Myhrman male 18 0 0 7.75
774 0 3 Mr. Roger Tobin male 20 0 0 7.75
775 1 3 Miss. Virginia Ethel Emanuel female 5 0 0 12.475
776 0 3 Mr. Thomas J Kilgannon male 22 0 0 7.7375
777 1 1 Mrs. Edward Scott (Elisabeth Walton McMillan) Robert female 43 0 1 211.3375
778 1 3 Miss. Banoura Ayoub female 13 0 0 7.2292
779 1 1 Mrs. Albert Adrian (Vera Gillespie) Dick female 17 1 0 57
780 0 1 Mr. Milton Clyde Long male 29 0 0 30
781 0 3 Mr. Andrew G Johnston male 35 1 2 23.45
782 0 3 Mr. William Ali male 25 0 0 7.05
783 0 3 Mr. Abraham (David Lishin) Harmer male 25 0 0 7.25
784 1 3 Miss. Anna Sofia Sjoblom female 18 0 0 7.4958
785 0 3 Master. George Hugh Rice male 8 4 1 29.125
786 1 3 Master. Bertram Vere Dean male 1 1 2 20.575
787 0 1 Mr. Benjamin Guggenheim male 46 0 0 79.2
788 0 3 Mr. Andrew Keane male 20 0 0 7.75
789 0 2 Mr. Alfred Gaskell male 16 0 0 26
790 0 3 Miss. Stella Anna Sage female 21 8 2 69.55
791 0 1 Mr. William Fisher Hoyt male 43 0 0 30.6958
792 0 3 Mr. Ristiu Dantcheff male 25 0 0 7.8958
793 0 2 Mr. Richard Otter male 39 0 0 13
794 1 1 Dr. Alice (Farnham) Leader female 49 0 0 25.9292
795 1 3 Mrs. Mara Osman female 31 0 0 8.6833
796 0 3 Mr. Yousseff Ibrahim Shawah male 30 0 0 7.2292
797 0 3 Mrs. Jean Baptiste (Rosalie Paula Govaert) Van Impe female 30 1 1 24.15
798 0 2 Mr. Martin Ponesell male 34 0 0 13
799 1 2 Mrs. Harvey (Charlotte Annie Tate) Collyer female 31 1 1 26.25
800 1 1 Master. William Thornton II Carter male 11 1 2 120
801 1 3 Master. Assad Alexander Thomas male 0.42 0 1 8.5167
802 1 3 Mr. Oskar Arvid Hedman male 27 0 0 6.975
803 0 3 Mr. Karl Johan Johansson male 31 0 0 7.775
804 0 1 Mr. Thomas Jr Andrews male 39 0 0 0
805 0 3 Miss. Ellen Natalia Pettersson female 18 0 0 7.775
806 0 2 Mr. August Meyer male 39 0 0 13
807 1 1 Mrs. Norman Campbell (Bertha Griggs) Chambers female 33 1 0 53.1
808 0 3 Mr. William Alexander male 26 0 0 7.8875
809 0 3 Mr. James Lester male 39 0 0 24.15
810 0 2 Mr. Richard James Slemen male 35 0 0 10.5
811 0 3 Miss. Ebba Iris Alfrida Andersson female 6 4 2 31.275
812 0 3 Mr. Ernest Portage Tomlin male 30.5 0 0 8.05
813 0 1 Mr. Richard Fry male 39 0 0 0
814 0 3 Miss. Wendla Maria Heininen female 23 0 0 7.925
815 0 2 Mr. Albert Mallet male 31 1 1 37.0042
816 0 3 Mr. John Fredrik Alexander Holm male 43 0 0 6.45
817 0 3 Master. Karl Thorsten Skoog male 10 3 2 27.9
818 1 1 Mrs. Charles Melville (Clara Jennings Gregg) Hays female 52 1 1 93.5
819 1 3 Mr. Nikola Lulic male 27 0 0 8.6625
820 0 1 Jonkheer. John George Reuchlin male 38 0 0 0
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823 0 3 Mr. John Flynn male 36 0 0 6.95
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871 0 3 Mr. Victor Vander Cruyssen male 47 0 0 9
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17
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@ -175,6 +175,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"id": "e7b78221-3568-45c5-964f-422b2668f4e5", "id": "e7b78221-3568-45c5-964f-422b2668f4e5",
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@ -212,7 +213,8 @@
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"outputs": [ "outputs": [
{ {
@ -640,7 +642,8 @@
"schema_version": 3, "schema_version": 3,
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"task": false "task": false
} },
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}, },
"outputs": [ "outputs": [
{ {
@ -1087,6 +1090,7 @@
"cell_type": "markdown", "cell_type": "markdown",
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@ -1239,6 +1243,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"id": "d5275062-b7f5-4193-9b5a-70f4e861c819", "id": "d5275062-b7f5-4193-9b5a-70f4e861c819",
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@ -1450,7 +1455,8 @@
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{ {
@ -1603,7 +1609,8 @@
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{ {
@ -1714,7 +1721,7 @@
"name": "python", "name": "python",
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"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
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40
Material/wise_24_25/lernmaterial/numpy & matplotlib/numpy.ipynb → Material/wise_24_25/lernmaterial/numpy.ipynb
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@ -1,5 +1,13 @@
{ {
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{
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@ -896,7 +905,7 @@
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@ -912,29 +921,10 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"[91.67441575549084,\n", "20"
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" 1.7567502091441867,\n",
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" 29.830322658786066,\n",
" 32.993271323881935,\n",
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@ -944,7 +934,7 @@
"from numpy.random import SeedSequence, Generator, PCG64\n", "from numpy.random import SeedSequence, Generator, PCG64\n",
"sg = SeedSequence(42)\n", "sg = SeedSequence(42)\n",
"pcgs = [Generator(PCG64(s)).random()*100 for s in sg.spawn(20)]\n", "pcgs = [Generator(PCG64(s)).random()*100 for s in sg.spawn(20)]\n",
"pcgs" "len(pcgs)"
] ]
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{ {
@ -1394,7 +1384,7 @@
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@ -1,923 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
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"source": [
"# Graphical representations, Matplotlib, Contour Plots\n",
"\n",
"A Python plotting library called Matplotlib creates publication-quality graphics in a range of physical formats and in cross-platform interactive settings.\n",
"Four graphical user interface toolkits, the Python and IPython shells, the Jupyter notebook, web application servers, and Python scripts can all make use of Matplotlib. The open source documentation can be found under: https://matplotlib.org/stable/index.html#\n",
"\n",
"__Creating Plots__\n",
"\n",
"Figure\n",
"\n",
"|Operator |\tDescription |\n",
"|---------|-------------|\n",
"|fig = plt.figures() | a container that contains all plot elements |\n",
"\n",
"Axes\n",
"\n",
"|Operator |\tDescription |\n",
"|---------|-------------|\n",
"|fig.add_axes() <br> a = fig.add_subplot(222) | Initializes subplot <br> A subplot is an axes on a grid system row-col-num. |\n",
"|fig, b = plt.subplots(nrows=3, nclos=2) | Adds subplot |\n",
"|ax = plt.subplots(2, 2) |\tCreates subplot |\n",
"\n",
"__Plotting__\n",
"\n",
"1D Data\n",
"\n",
"|Operator |\tDescription|\n",
"|---------|------------|\n",
"|lines = plt.plot(x,y) | Plot data connected by lines|\n",
"|plt.scatter(x,y) | Creates a scatterplot, unconnected data points|\n",
"|plt.bar(xvalue, data , width, color...) | simple vertical bar chart|\n",
"|plt.barh(yvalue, data, width, color...) | simple horizontal bar|\n",
"|plt.hist(x, y) | Plots a histogram|\n",
"|plt.boxplot(x,y) | Box and Whisker plot|\n",
"|plt.violinplot(x, y) |\tCreates violin plot|\n",
"|ax.fill(x, y, color='lightblue') <br> ax.fill_between(x,y,color='lightblue') | Fill area under/between plots|\n",
"\n",
"2D Data\n",
"\n",
"|Operator |\tDescription|\n",
"|---------|------------|\n",
"|fig,ax = plt.subplots() <br> im = ax.imshow(img,cmap,vmin...) | Colormap or RGB arrays | \n",
"\n",
"Saving plots\n",
"\n",
"|Operator |\tDescription|\n",
"|---------|------------|\n",
"|plt.savefig('fig.png') | Saves plot/figure to image |\n",
"\n",
"__Customization__\n",
"\n",
"Color\n",
"\n",
"|Operator | Description|\n",
"|---------|------------|\n",
"|plt.plot(x,y,color='lightblue') <br> plt.plot(x,y,alpha = 0.4) | Colors plot to light bluw color |\n",
"|plt.colorbar(mappable,orientation='horizontal') | mappable:the image,contourset to which colorbar applies. |\n",
"\n",
"Markers\n",
"\n",
"|Operator | Description|\n",
"|---------|------------|\n",
"|plt.plot(x,y,marker='*') | adds * for every data point |\n",
"|plt.plot(x,y,marker='.') | adds . for every data point |\n",
"\n",
"Lines\n",
"\n",
"|Operator | Description|\n",
"|---------|------------|\n",
"|plt.plot(x, y, linewidth=2) | Sets line width|\n",
"|plt.plot(x, y, ls='solid') | Sets linestyle, ls can be ommitted, see 2 below|\n",
"|plt.plot(x, y, ls='--') | Sets linestyle, ls can be ommitted, see below|\n",
"|plt.plot(x,y,'--', x\\*\\*2, y\\*\\*2, '-.') | Lines are '--' and '-.' |\n",
"|plt.setp(lines,color='red',linewidth=2) | Sets properties of plot lines|\n",
"\n",
"Text\n",
"\n",
"|Operator | Description|\n",
"|---------|------------|\n",
"|plt.text(1,1,'Text',style='italic') | Places text at coordinates (1,1)|\n",
"|ax.annotate('point A',xy=(10,10)) | Annotate the point with coordinates xy|\n",
"|pt.title(r'\\$delta\\_i=20\\$',fontsize=10)| Math text|\n",
"\n",
"Limits\n",
"\n",
"|Operators | Description|\n",
"|----------|------------|\n",
"|plt.xlim(0, 7) | Sets x-axis to display 0 - 7|\n",
"|other = array.copy() | Creates deep copy of array|\n",
"|plt.ylim(-0.5, 9) | Sets y-axis to display -0.5 - 9|\n",
"|ax.set(xlim=[0, 7], ylim=[-0.5, 9]) <br> ax.set_xlim(0, 7) | Sets limits|\n",
"|plt.margins(x=1.0, y=1.0) | Set margins: add padding to a plot, values 0 - 1|\n",
"|plt.axis('equal') | Set the aspect ratio of the plot to 1|\n",
"\n",
"Legends/Labels\n",
"\n",
"|Operator | Description|\n",
"|---------|------------|\n",
"|plt.title('Title') | Sets title of plot|\n",
"|plt.xlabel('x-axis') | Sets label next to x-axis|\n",
"|plt.ylabel('y-axis') | Sets label next to y-axis|\n",
"|ax.set(title='axis', ylabel='Y-Axis', xlabel='X-Axis') | Set title and axis labels|\n",
"|ax.legend(loc='best') | No overlapping plot elements|\n",
"\n",
"Ticks\n",
"\n",
"|Operator | Description|\n",
"|---------|------------|\n",
"|plt.xticks(x, labels, rotation='vertical') | Set ticks|\n",
"|ax.xaxis.set(ticks=range(1,5), ticklabels=[3,100,-12,\"foo\"]) | Set x-ticks|\n",
"|ax.tick_params(axis='y', direction='inout', length=10) | Make y-ticks longer and go in and out|"
]
},
{
"cell_type": "markdown",
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"Load the necessary packages and define the arrays __x__, __y__ and __z__ by running the cell below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import numpy as np\n",
"x = np.arange(0,100)\n",
"y = x*2\n",
"z = x**2"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
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"source": [
"\n",
"## Exercise 1\n",
"\n",
"Follow along with these steps:\n",
"\n",
"- Create a figure object called __fig__ using plt.figure()\n",
"- Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis __ax__.\n",
"- Plot (x,y) on that axes and set the labels and titles to match the plot below:\n",
"\n",
"![Plot XY](exercise1.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
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"outputs": [],
"source": [
"\n",
"fig = plt.figure()\n",
"ax = fig.add_axes([0,0,1,1])\n",
"ax.plot(x,y)\n",
"ax.set_xlabel('x')\n",
"ax.set_ylabel('y')\n",
"ax.set_title('plot xy')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-f487f69f6d693b81",
"locked": true,
"points": 0,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [],
"source": [
"### BEGIN HIDDEN TEST\n",
"assert isinstance(fig,plt.Figure), f\"{fig} is not an instance of plt.Figure\"\n",
"assert isinstance(ax,matplotlib.axes._axes.Axes), f\"{ax} is not an axis \"\n",
"assert ax.get_title().lower() == 'plot xy', f\"{ax.get_title()} is not the same as plot xy\"\n",
"assert ax.get_xlabel().lower() == 'x'\n",
"assert ax.get_ylabel().lower() == 'y'\n",
"### END HIDDEN TEST"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-009335e6db4359f4",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
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"tags": []
},
"source": [
"\n",
"## Exercise 2\n",
"\n",
"Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.5,0.5,0.3,0.3] respectively."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"fig = plt.figure()\n",
"\n",
"ax1 = fig.add_axes([0,0,1,1])\n",
"ax2 = fig.add_axes([0.5,0.5,0.3,0.3])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"### BEGIN HIDDEN TEST\n",
"assert isinstance(ax1,matplotlib.axes._axes.Axes), f\"{ax1} is not an axis \"\n",
"assert isinstance(ax2,matplotlib.axes._axes.Axes), f\"{ax2} is not an axis \"\n",
"### END HIDDEN TEST"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-ad87f38d4c194c66",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
"Now plot (x,y) on axes __ax1__ and on axes __ax2__ plot (x,z) as shown in \n",
"\n",
"![Alt text](exercise2.png). \n",
"\n",
"And call your figure object to show it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"ax1.plot(x,y)\n",
"ax1.set_title('plot xy')\n",
"ax1.set_xlabel('x')\n",
"ax1.set_ylabel('y')\n",
"\n",
"ax2.plot(x,z)\n",
"ax2.set_title('plot xz')\n",
"ax2.set_xlabel('x')\n",
"ax2.set_ylabel('z')\n",
"\n",
"fig # Show figure object\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-52e7d1a8a36277f7",
"locked": true,
"points": 0,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [],
"source": [
"### BEGIN HIDDEN TEST\n",
"assert ax1.get_title().lower() == 'plot xy', f\"{ax.get_title()} is not the same as plot xy\"\n",
"assert ax1.get_xlabel().lower() == 'x'\n",
"assert ax1.get_ylabel().lower() == 'y'\n",
"\n",
"assert ax2.get_title().lower() == 'plot xz', f\"{ax.get_title()} is not the same as plot xz\"\n",
"assert ax2.get_xlabel().lower() == 'x'\n",
"assert ax2.get_ylabel().lower() == 'z'\n",
"### END HIDDEN TEST"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-383cddeb2332477d",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
"\n",
"## Exercise 3\n",
"\n",
"Create the plot below by adding two axes to a figure object at [0,0,1,1] and name it __Plot A__ and at [0.2,0.5,0.4,0.4] and name it as __Plot B__.\n",
"![Exercise 3](exercise3.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"fig = plt.figure()\n",
"\n",
"ax = fig.add_axes([0,0,1,1])\n",
"ax.set_title('Plot A')\n",
"\n",
"ax2 = fig.add_axes([0.2,0.5,0.4,0.4])\n",
"ax2.set_title('Plot B')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-f1b47006770bcca0",
"locked": true,
"points": 0,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [],
"source": [
"### BEGIN HIDDEN TEST\n",
"assert isinstance(ax,matplotlib.axes._axes.Axes), f\"{ax} is not an axis \"\n",
"assert isinstance(ax2,matplotlib.axes._axes.Axes), f\"{ax2} is not an axis \"\n",
"assert ax.get_title().lower() == 'plot a', f\"{ax.get_title()} is not the same as plot A\"\n",
"assert ax2.get_title().lower() == 'plot b', f\"{ax2.get_title()} is not the same as plot B\"\n",
"### END HIDDEN TEST"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-91b7c64bab74fa07",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
"Now use x and z arrays to create a zoomed version as shown in the plot below. Notice the x limits and y limits on the inserted plot:![Exercise 3.b](exercise3_b.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"ax.plot(x,z)\n",
"ax.set_xlabel('X')\n",
"ax.set_ylabel('Z')\n",
"ax.set_title('full')\n",
"\n",
"ax2.plot(x,z)\n",
"ax2.set_xlabel('X')\n",
"ax2.set_ylabel('Z')\n",
"ax2.set_title('zoom')\n",
"ax2.set_xlim(20,40)\n",
"ax2.set_ylim(0,2000)\n",
"\n",
"fig\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-725ebaa6bc9455b8",
"locked": true,
"points": 0,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [],
"source": [
"### BEGIN HIDDEN TEST\n",
"assert isinstance(ax,matplotlib.axes._axes.Axes), f\"{ax1} is not an axis \"\n",
"assert isinstance(ax2,matplotlib.axes._axes.Axes), f\"{ax2} is not an axis \"\n",
"assert ax.get_title().lower() == 'full', f\"{ax.get_title()} is not the same as full\"\n",
"assert ax2.get_title().lower() == 'zoom', f\"{ax.get_title()} is not the same as zoom\"\n",
"### END HIDDEN TEST"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-29c04b7329cfa60e",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
"## Exercise 4\n",
"\n",
"Use plt.subplots(nrows=1, ncols=2) to create empty plot below. Name the first subplot as __xy__ and second subplot as __xz__.\n",
"\n",
"![Exercise 4_a](exercise4_a.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"fig, axes = plt.subplots(nrows=1, ncols=2)\n",
"axes[0].set_title('xy')\n",
"axes[1].set_title('xz')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-046fc90eb41678c4",
"locked": true,
"points": 0,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [],
"source": [
"### BEGIN HIDDEN TEST\n",
"assert isinstance(axes,np.ndarray)\n",
"assert isinstance(axes[0],matplotlib.axes._axes.Axes) and isinstance(axes[1],matplotlib.axes._axes.Axes)\n",
"assert axes[0].get_title().lower() == 'xy', f\"{axes[0].get_title()} is not the same as xy\"\n",
"assert axes[1].get_title().lower() == 'xz', f\"{axes[1].get_title()} is not the same as xz\"\n",
"### END HIDDEN TEST"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-3e6118aac1523838",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
" Now plot (x,y) and (x,z) on the respective axes. Set the linewidth to 3pt, marker to '__*__' and color of __xy__ plot to red and for the __xz__ plot, set color to blue and linestyle to dashed. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"axes[0].plot(x,y,color=\"blue\", lw=3, marker='*')\n",
"axes[0].set_xlabel('x')\n",
"axes[0].set_ylabel('y')\n",
"\n",
"axes[1].plot(x,z,color=\"red\", lw=3, ls='--')\n",
"axes[1].set_xlabel('x')\n",
"axes[1].set_ylabel('z')\n",
"\n",
"fig\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-8690f8c5e6e59a90",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
"Plot the same figure with a size of (10,2) by adding the __figsize__ argument in plt.subplots()."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(10,2))\n",
"\n",
"axes[0].plot(x,y,color=\"blue\", lw=3, marker='*')\n",
"axes[0].set_xlabel('x')\n",
"axes[0].set_ylabel('y')\n",
"\n",
"axes[1].plot(x,z,color=\"red\", lw=3, ls='--')\n",
"axes[1].set_xlabel('x')\n",
"axes[1].set_ylabel('z')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-becf64c4bbc48d1f",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
"## Bar plots\n",
"\n",
"Bar plot enables us to visualize the distribution of categorical data variables. They represent distribution of discrete values. Thus, it represents the comparison of categorical values. The x axis represents the discrete values while the y axis represents the numeric values of comparison and vice versa.The open source documentation can be found under https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-d509dfb9ce8c8417",
"locked": true,
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},
"tags": []
},
"source": [
"## Exercise 5\n",
"\n",
"Initialize the variables with the following data and plot a bar graph to vizualize the popularity of various Programming languages.\n",
"\n",
"|Programming Languages|Java|Python|PHP|JavaScript|C\\#|C++|\n",
"|--|--|--|--|--|--|--|\n",
"|Popularity|22.2|17.6|8.8|8|7.7|6.7|\n",
"\n",
"- The plot should have grids on (both major and minor). \n",
"- Name the title of the figure as __Popularity of Programming Language Worldwide__.\n",
"- Label both x and y axes with respective labels.\n",
"\n",
"The final plot should look like this:\n",
"\n",
"![Exercise 5a](exercise5_a.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"x = ['Java', 'Python', 'PHP', 'JavaScript', 'C#', 'C++']\n",
"p = [22.2, 17.6, 8.8, 8, 7.7, 6.7]\n",
"x_pos = [i for i,val in enumerate(x)]\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_axes([0,0,1,1])\n",
"\n",
"ax.set_title('Popularity of Programming Language Worldwide')\n",
"ax.set_xlabel('Languages')\n",
"ax.set_ylabel('Popularity')\n",
"\n",
"ax.minorticks_on()\n",
"ax.grid(which='minor',linestyle=':')\n",
"ax.grid(which='major',linestyle='-')\n",
"\n",
"bar = ax.bar(x_pos,p)\n",
"ax.set_xticks(x_pos,x)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-5e9ee62323d8fb2d",
"locked": true,
"schema_version": 3,
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"task": false
},
"tags": []
},
"source": [
"Change the color of the bars to ['red', 'black', 'green', 'blue', 'yellow', 'cyan'] :\n",
"\n",
"![Exercise 5c](exercise5_c.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"colors = ['red', 'black', 'green', 'blue', 'yellow', 'cyan']\n",
"for i,color in enumerate(colors):\n",
" bar[i].set_color(color)\n",
"fig\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-345c3a5c2f31b1c4",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
"Plot the same data as horizontal bar graph in a new figure as shown below:\n",
"\n",
"![Exercise 5b](exercise5_b.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"fig = plt.figure()\n",
"ax = fig.add_axes([0,0,1,1])\n",
"ax.set_title('Popularity of Programming Language Worldwide')\n",
"ax.set_xlabel('Popularity')\n",
"ax.set_ylabel('Languages')\n",
"\n",
"ax.minorticks_on()\n",
"ax.grid(which='minor',linestyle=':')\n",
"ax.grid(which='major',linestyle='-')\n",
"\n",
"ax.barh(x_pos,p)\n",
"ax.set_yticks(x_pos,x)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-4356eca51fa89384",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
"## Pie Charts\n",
"\n",
"A pie chart is a circular statistical graphic, which is divided into slices to illustrate numerical proportions. In a pie chart, the arc length of each slice is proportional to the quantity it represents. Pie charts are a popular way to represent the results of polls. The open source documentation can be found under https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pie.html#matplotlib.pyplot.pie"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-6c1fcc8a4ad86fac",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
"## Exercise 6\n",
"\n",
"Initialize the following data and plot a pie chart to visualize the prefered sports among people:\n",
"|Sports|Cricket|Football|Hockey|F1|\n",
"|--|--|--|--|--|\n",
"|People|15|30|45|10|\n",
"\n",
"The sports title should be the labels of the pie chart as shown below:\n",
"\n",
"![Exercise 6a](exercise6_a.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"s = ['Cricket', 'Football', 'Hockey', 'F1']\n",
"p = [15, 30, 45, 10]\n",
"\n",
"fig,ax = plt.subplots()\n",
"ax.pie(p,labels=s)\n",
"# ax.axis('equal')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-d16322e4cb78e72c",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
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"tags": []
},
"source": [
"# Contour plots\n",
"\n",
"Contour plots also called level plots are a tool for doing multivariate analysis and visualizing 3-D plots in 2-D space. If we consider X and Y as our variables we want to plot then the response Z will be plotted as slices on the X-Y plane due to which contours are sometimes referred as Z-slices or iso-response.\n",
"\n",
"Contour plots are widely used to visualize density, altitudes or heights of the mountain as well as in the meteorological department. Due to such wide usage matplotlib.pyplot provides a method contour to make it easy for us to draw contour plots."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-75ecdb619bbc13d3",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"source": [
"## Exercise 7\n",
"\n",
"Make a contour plot of the equation $Z=X^2+Y^2$ by first creating a mesh grid between $x$ and $y$. The plot should look as follows:\n",
"\n",
"![Exercise 7a](exercise7_a.png)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"x = np.arange(0,100)\n",
"y = x*2\n",
"\n",
"\n",
"[X, Y] = np.meshgrid(x, y)\n",
"fig, ax = plt.subplots(1, 1)\n",
" \n",
"Z = X**2 + Y**2 \n",
"ax.contour(X, Y, Z)\n",
"\n",
"ax.set_title('Contour Plot')\n",
"ax.set_xlabel('X')\n",
"ax.set_ylabel('Y')\n"
]
}
],
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{
"cells": [
{
"cell_type": "markdown",
"id": "c850ea25-9bde-4feb-a1d0-056c5870d59e",
"metadata": {},
"source": [
"# Regular Expressions (Regex)\n",
"\n",
"Wir schreiben das Jahr 1950 der Mathematiker __Stephen Cole Kleene__ erfand das Konzept der _Regulären Sprache_. Ein Konzept der theoretischen Informatik zum Beschreiben von syntaktischen Ausdrücken. Damit einhergehend lassen sich durch spezifische ausdrücke, den _Regular Expressions_, verschiedene Formen des _pattern matching_ durchführen. Eine der mit abstand wichtigensten Anwendungsfälle für _regual expressions_ ist das Kompilieren von Quellcode in Maschinensprache. Dabei werden ausdrücke wie _while_, _for_, _if_ etc. formalisiert und können einfacher in Übersetzt (Kompiliert) werden. \n",
"\n",
"Ein weiterer Nutzen von _regual expressions_ ist das _just-in-time compiling_ von dem auch Python als interpretierte Sprache gebrauch macht. Dabei wird der Quellcode zur Laufzeit für die Maschine übersetzt (meist nicht direkt der Quellcode, sondern eine zwischenstufe die als _Bytecode_ bezeichnet wird). Es wäre sonst nicht möglich so einfach Jupyter Notebooks zu verwenden.\n",
"\n",
"\n",
"Ein paar Fakten zu _regular expressions_:\n",
"\n",
"- _Regex_ findet sich in vielen Dialekten wieder. (vgl. [Regular Expression Engine Comparison](https://gist.github.com/CMCDragonkai/6c933f4a7d713ef712145c5eb94a1816))\n",
"- Die Programmiersprache _Perl_ entstand aus einer Bibliothek von Henry Spencer zum nutzen von _Regex_ \n",
"- Eine frei Nutzbare Seite (Achtung mit Werbung) zum testen und prüfen von Regulären Ausdrücken in verschiedenen Dialekten ist [Regex101](https://regex101.com/)\n",
"- Jedes Unix(-ähnliche) System (Linux, MacOS, BSD, etc.) hat das Programm _grep (**G**lobal/**R**egular **E**xpression/**P**rint)_ zum analysieren von Datenströmen/Textdateien vorinstalliert.\n",
"\n",
"\n",
"<p><a href=\"https://commons.wikimedia.org/wiki/File:Kleene.jpg#/media/File:Kleene.jpg\"><img src=\"https://upload.wikimedia.org/wikipedia/commons/1/1c/Kleene.jpg\" alt=\"Kleene.jpg\" width=\"10%\"></a><br>By Konrad Jacobs, Erlangen, Copyright is MFO - Mathematisches Forschungsinstitut Oberwolfach,&lt;a rel=\"nofollow\" class=\"external free\" href=\"https://opc.mfo.de/detail?photo_id=2122\"&gt;https://opc.mfo.de/detail?photo_id=2122&lt;/a&gt;, <a href=\"https://creativecommons.org/licenses/by-sa/2.0/de/deed.en\" title=\"Creative Commons Attribution-Share Alike 2.0 de\">CC BY-SA 2.0 de</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=12342617\">Link</a></p>"
]
},
{
"cell_type": "markdown",
"id": "b689ee80",
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},
"source": [
"## Introduction\n",
"\n",
"You can find _a lot_ of material on regular expressions (regex) online.\n",
"Therefore, we will not repeat the background but focus on some practical exercises in this notebook. Some tutorials/useful links can be found below.\n",
"\n",
"The way that we need and use regular expressions is to describe patterns of characters to match in a given string.\n",
"\n",
"You can think of them as a string of characters, which describe a certain pattern, e.g., \"four numbers followed by a word of at least 5 characters\". \n",
"This can then be used to test given strings/texts and match the pattern specified in the regex.\n",
"This is done using the [Python Standard Library `re`](https://docs.python.org/3/library/re.html).\n",
"\n",
"\n",
"**Material on Regular Expressions:**\n",
"\n",
"- [RegEx Howto in Python](https://docs.python.org/3/howto/regex.html)\n",
"- [RegEx Tutorial](https://www.regular-expressions.info/tutorial.html)\n",
"- [Interactive RegEx Tutorial](https://regexone.com/)\n",
"- [WikiBook on RegEx](https://en.wikibooks.org/wiki/Regular_Expressions)\n",
"- [RegExr: Testing & Visualizing RegEx](https://regexr.com/)\n",
"- [Debuggex: Visualization of individual regex as finite state machine](https://www.debuggex.com/)\n",
"\n",
"**Testing with Regular Expressions:**\n",
"- [Regex101](https://regex101.com/)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8a5d3654",
"metadata": {
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"outputs": [],
"source": [
"import re"
]
},
{
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"id": "b6ccac77",
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},
"source": [
"## Example 1\n",
"The regular expression `Hello [A-Z][a-z]+` specifies a pattern that begins with the literal string `Hello ` and is followed by a capital letter (specified by `[A-Z]`) and at least one small letter. (`[a-z]` describes the lowercase letters and `+` specifies that there is at least one of them)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7e25056b",
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"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing the string: 'Hello World'\n",
"Found pattern at characters: 0 to 11\n",
"---------------------------------------------\n",
"Testing the string: 'Hello You!'\n",
"Found pattern at characters: 0 to 9\n",
"---------------------------------------------\n",
"Testing the string: 'This does not match the pattern...'\n",
"Pattern not found in string.\n",
"---------------------------------------------\n",
"Testing the string: 'We can also have the Hello World pattern somewhere within the string.'\n",
"Found pattern at characters: 21 to 32\n",
"---------------------------------------------\n",
"Testing the string: 'Hello world does not match'\n",
"Pattern not found in string.\n",
"---------------------------------------------\n",
"Testing the string: 'Hello W does not match either'\n",
"Pattern not found in string.\n",
"---------------------------------------------\n"
]
}
],
"source": [
"example_re = r'Hello [A-Z][a-z]+'\n",
"test_strings = ['Hello World',\n",
" 'Hello You!',\n",
" 'This does not match the pattern...',\n",
" 'We can also have the Hello World pattern somewhere within the string.',\n",
" 'Hello world does not match',\n",
" 'Hello W does not match either']\n",
"\n",
"\n",
"for test_word in test_strings:\n",
" print(f\"Testing the string: '{test_word}'\")\n",
" match_object = re.search(example_re, test_word)\n",
" if match_object:\n",
" print(f\"Found pattern at characters: {match_object.span()[0]:d} to {match_object.span()[1]:d}\")\n",
" else:\n",
" print(\"Pattern not found in string.\")\n",
" print(\"-\"*45)"
]
},
{
"cell_type": "markdown",
"id": "5ec979b2",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-aca8488169bc0df9",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"_Note:_ Since regex often use special characters like backslash `\\`, it is helpful to define them in Python as raw strings, i.e., using a preceding `r` (see `example_re` above)."
]
},
{
"cell_type": "markdown",
"id": "820c31ae",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-4d3281e8922cd534",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Task 1\n",
"\n",
"Write a regular expression `r1` which matches the following words:\n",
"- hello\n",
"- yellow\n",
"- jello"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e7e426b0",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-c48986402655ab08",
"locked": false,
"schema_version": 3,
"solution": true,
"task": false
},
"tags": []
},
"outputs": [],
"source": [
"### BEGIN SOLUTION ###\n",
"r1 = r'.*ello.*'\n",
"### END SOLUTION"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "223fa54c",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-0a761cfdabd44f1b",
"locked": true,
"points": 1,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<re.Match object; span=(0, 5), match='hello'>\n",
"<re.Match object; span=(0, 6), match='yellow'>\n",
"<re.Match object; span=(0, 5), match='jello'>\n"
]
}
],
"source": [
"# Test Cell\n",
"\n",
"test_words = ['hello', 'yellow', 'jello']\n",
"for _word in test_words:\n",
" match = re.match(r1, _word)\n",
" print(match)\n",
" if match is None: assert False\n",
" assert match[0] == _word"
]
},
{
"cell_type": "markdown",
"id": "c3086449",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-bea454dd22c7499a",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Example 2\n",
"\n",
"In the first example, we have use the `[A-Z]` and `[a-z]` patterns to specify capital and lowercase letters, respectively.\n",
"There are a lot more of such predefined patterns, e.g., `[0-9]` or `\\d` for matching a (single-digit) number.\n",
"\n",
"A list of these special characters can be found in the [`re` documentation](https://docs.python.org/3/library/re.html#regular-expression-syntax).\n",
"\n",
"\n",
"The following regex can be used to match a word with at least 3 letters (both capital and lowercase are accepted), followed by a two-digit number, a comma, and a four-digit number where the first number is either a one or a two."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5a02b00a",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-1a01734fc48cc488",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing the string: 'November 21, 2022'\n",
"Found pattern at characters: 0 to 17\n",
"---------------------------------------------\n",
"Testing the string: 'Jan 01, 1970'\n",
"Found pattern at characters: 0 to 12\n",
"---------------------------------------------\n",
"Testing the string: 'JuNE 45, 4521'\n",
"Pattern not found in string.\n",
"---------------------------------------------\n",
"Testing the string: 'Abc 1, 2020'\n",
"Pattern not found in string.\n",
"---------------------------------------------\n",
"Testing the string: 'July 02, 90'\n",
"Pattern not found in string.\n",
"---------------------------------------------\n"
]
}
],
"source": [
"example_re2 = r'[A-Za-z]{3,} \\d{2}, [12]\\d{3}'\n",
"\n",
"test_strings = ['November 21, 2022',\n",
" 'Jan 01, 1970',\n",
" 'JuNE 45, 4521',\n",
" 'Abc 1, 2020',\n",
" 'July 02, 90']\n",
"\n",
"\n",
"for test_word in test_strings:\n",
" print(f\"Testing the string: '{test_word}'\")\n",
" match_object = re.search(example_re2, test_word)\n",
" if match_object:\n",
" print(f\"Found pattern at characters: {match_object.span()[0]:d} to {match_object.span()[1]:d}\")\n",
" else:\n",
" print(\"Pattern not found in string.\")\n",
" print(\"-\"*45)"
]
},
{
"cell_type": "markdown",
"id": "b565244d",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-0abe35e63e18f0d9",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Task 2\n",
"\n",
"Write a regular expression `r2` that only matches dates in the ISO format `YYYY-MM-DD`.\n",
"It should _only_ match a string, if the whole string is a date. If the date is only part of the string, it should *not* match it.\n",
"\n",
"_Hint:_ You can use `(a[0-9]|b[01])` to specify the pattern that matches either an `a` followed by a single digit **or** a `b` followed by either `0` or `1`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1e2bb2bd",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-c264d2e9cac73db0",
"locked": false,
"schema_version": 3,
"solution": true,
"task": false
},
"tags": []
},
"outputs": [],
"source": [
"### BEGIN SOLUTION\n",
"r2 = r'^(\\d{4})-(0[1-9]|1[012])-(0[1-9]|[12][0-9]|3[01])$'\n",
"### END SOLUTION"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5bbd62f5",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-c80282e7adcccb6a",
"locked": true,
"points": 1,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<re.Match object; span=(0, 10), match='1970-01-01'>\n",
"<re.Match object; span=(0, 10), match='1999-12-31'>\n",
"<re.Match object; span=(0, 10), match='2000-02-28'>\n",
"<re.Match object; span=(0, 10), match='2022-12-09'>\n",
"<re.Match object; span=(0, 10), match='4250-09-10'>\n"
]
}
],
"source": [
"# Test Cell\n",
"\n",
"# The following strings should be matched\n",
"dates = [\"1970-01-01\", \"1999-12-31\", \"2000-02-28\", \"2022-12-09\", \"4250-09-10\"]\n",
"for _date in dates:\n",
" match = re.match(r2, _date)\n",
" print(match)\n",
" if match is None: assert False\n",
" assert match[0] == _date"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0d8e4b98",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-e46e8f78178eb2b7",
"locked": true,
"points": 1,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"None\n",
"None\n",
"None\n",
"None\n",
"None\n",
"None\n",
"None\n"
]
}
],
"source": [
"# Test Cell\n",
"\n",
"# The following strings should not be matched\n",
"no_dates = [\"1970-01-32\", \"abcd-12-31\", \"2000/02/28\", \"2022-14-20\", \"2002.12.02\", \"1234-2-1\", \"77-09-02\"]\n",
"for _date in no_dates:\n",
" match = re.match(r2, _date)\n",
" print(match)\n",
" if match is not None: assert False"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b72e49ac",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-48f63facb72e517a",
"locked": true,
"points": 1,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"None\n",
"None\n"
]
}
],
"source": [
"# Test Cell\n",
"\n",
"# The following strings should not be matched\n",
"no_match = [\"This text contains the date 1999-12-31 but it should not be matched.\",\n",
" \"2020-02-20 is a date in the beginning of the string\"]\n",
"for _text in no_match:\n",
" match = re.match(r2, _text)\n",
" print(match)\n",
" if match is not None: assert False"
]
},
{
"cell_type": "markdown",
"id": "ce239065",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-31d99fd79761847d",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Example 3\n",
"\n",
"You can save parts of the found pattern in a group to have access to it later.\n",
"\n",
"In the following example, we modify the regex from [Example 2](#Example-2) to capture the individual parts into groups."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "89ba4f51",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-7d320972e47ae922",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('November', '21', '2022')\n"
]
}
],
"source": [
"example_re3 = r'([A-Za-z]{3,}) (\\d{2}), ([12]\\d{3})'\n",
"\n",
"test_string = 'November 21, 2022'\n",
"match = re.search(example_re3, test_string)\n",
"print(match.groups())"
]
},
{
"cell_type": "markdown",
"id": "393ff9c6",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-68cbff25c972809f",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Task 3\n",
"\n",
"Write a regular expression `r3` which matches text between `<li>...</li>` tags and adds the found text to a group. This should be the only capturing group!\n",
"\n",
"_Hint:_ You might want to check how to define non-capturing groups and non-greedy matching."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c93ee04d",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-420f01248c7eddeb",
"locked": false,
"schema_version": 3,
"solution": true,
"task": false
},
"tags": []
},
"outputs": [],
"source": [
"### BEGIN SOLUTION\n",
"r3 = r'<li>((?:.|\\n)*?)</li>'\n",
"### END SOLUTION"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "37681e3d",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-488cd60d5bed2019",
"locked": true,
"points": 2,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Item 1', '\\nItem 2', '\\n Item 3\\n ']\n"
]
}
],
"source": [
"# Test Cell\n",
"\n",
"test_html = \"\"\"\n",
"<html>\n",
" <head>\n",
" <title>Test HTML</title>\n",
" </head>\n",
" <body>\n",
" <h1>Heading 1</h1>\n",
" <ol>\n",
" <li>Item 1</li>\n",
" <li>\n",
"Item 2</li>\n",
" <li>\n",
" Item 3\n",
" </li>\n",
" </ol>\n",
" </body>\n",
"</html>\n",
"\"\"\"\n",
"\n",
"matches = re.findall(r3, test_html)\n",
"print(matches)\n",
"assert len(matches) == 3\n",
"assert matches == ['Item 1', '\\nItem 2', '\\n Item 3\\n ']"
]
},
{
"cell_type": "markdown",
"id": "4370f245",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-53152b78922af0b1",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Task 4\n",
"\n",
"Write a regular expression `r4` to find all words in a string that are acronmyms, i.e., written in all capital letters, and all words that have a capital letter in them which is not at the first position.\n",
"\n",
"Next, write a function `shield_acronyms` that uses this regular expression and adds curly brackets `{...}` around the found words and returns a new string.\n",
"\n",
"_Hint:_ You can use the [`re.sub` function](https://docs.python.org/3/library/re.html#re.sub) for this task."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "ed6b99f1",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-545bc5786ee8e947",
"locked": false,
"schema_version": 3,
"solution": true,
"task": false
},
"tags": []
},
"outputs": [],
"source": [
"# Define r4 here\n",
"### BEGIN SOLUTION\n",
"r4 = r'([0-9A-Z]+\\b|[a-zA-Z]+[A-Z0-9]+[a-zA-Z\\b]*)'\n",
"### END SOLUTION"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "504cd6d3",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-900922b2243d5a55",
"locked": true,
"points": 1,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['MIMO']\n",
"['M2M']\n",
"['IN', 'mmWave']\n",
"['5G', 'SHIELded']\n",
"[]\n"
]
}
],
"source": [
"# Test Cell\n",
"\n",
"test_words = [(\"MIMO\", [\"MIMO\"]),\n",
" (\"M2M\", [\"M2M\"]),\n",
" (r\"Acro IN mmWave Title\", [\"IN\", \"mmWave\"]),\n",
" (r\"5G should be SHIELded\", [\"5G\", \"SHIELded\"]),\n",
" (r\"Regular title with Names\", []),\n",
" ]\n",
"for text, matches in test_words:\n",
" result = re.findall(r4, text)\n",
" print(result)\n",
" assert result == matches"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "f955d228",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-2c36d0ef19bac550",
"locked": false,
"schema_version": 3,
"solution": true,
"task": false
},
"tags": []
},
"outputs": [],
"source": [
"def shield_acronyms(text: str) -> str:\n",
" ### BEGIN SOLUTION\n",
" r4 = r4 = r'([0-9A-Z]+\\b|[a-zA-Z]+[A-Z0-9]+[a-zA-Z\\b]*)'\n",
" new_text = re.sub(r4, r'{\\g<0>}', text)\n",
" return new_text\n",
" ### END SOLUTION"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "3b71b683",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "cell-550110e95fccc717",
"locked": true,
"points": 2,
"schema_version": 3,
"solution": false,
"task": false
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{MIMO}\n",
"{M2M}\n",
"Acro {IN} {mmWave} Title\n",
"{5G} should be {SHIELded}\n",
"Regular title with Names\n"
]
}
],
"source": [
"# Test Cell\n",
"\n",
"test_words = [(\"MIMO\", r\"{MIMO}\"),\n",
" (\"M2M\", r\"{M2M}\"),\n",
" (r\"Acro IN mmWave Title\", r\"Acro {IN} {mmWave} Title\"),\n",
" (r\"5G should be SHIELded\", r\"{5G} should be {SHIELded}\"),\n",
" (r\"Regular title with Names\", r'Regular title with Names'),\n",
" ]\n",
"for text, expected in test_words:\n",
" result = shield_acronyms(text)\n",
" print(result)\n",
" assert result == expected"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41222440-923d-44a4-8dc7-d7a6309d4e0a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Create Assignment",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

171
Material/wise_24_25/lernmaterial/regex/Web Parsing.ipynb
View File

@ -1,171 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "8f7ee9ed",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-fd19a00f47ad1a34",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"- [Beautiful Soup Documentation](https://beautiful-soup-4.readthedocs.io/en/latest/)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ebaad76f",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-9138585fc343d8a7",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup"
]
},
{
"cell_type": "markdown",
"id": "1336423a",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-235041934d89cb33",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"## Example of Parsing a Website"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8bf54e3b",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-c6761d82e17018f0",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [],
"source": [
"with open(\"example.html\") as html_file:\n",
" soup = BeautifulSoup(html_file)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "14566e25",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-93b2d5726c5469a8",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<title>Test HTML</title>\n",
"Test HTML\n",
"------------------------------\n",
"Print all list elements on the website:\n",
"Item 1\n",
"\n",
"Item 2\n",
"\n",
" Item 3\n",
" \n"
]
}
],
"source": [
"print(soup.title)\n",
"print(soup.title.get_text())\n",
"\n",
"\n",
"print(\"-\"*30)\n",
"print(\"Print all list elements on the website:\")\n",
"\n",
"li = soup.find_all(\"li\")\n",
"for element in li:\n",
" print(element.get_text()) # you can use .strip() to get rid of trailing whitespace"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d64b13b5",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "cell-3a99db5db1577717",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [],
"source": [
"import requests"
]
},
{
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Material/wise_24_25/lernmaterial/regex/example.html
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<html>
<head>
<title>Test HTML</title>
</head>
<body>
<h1>Heading 1</h1>
<ol class="mylist">
<li>Item 1</li>
<li>
Item 2</li>
<li>
Item 3
</li>
</ol>
</body>
</html>

26
Material/wise_24_25/lernmaterial/survey.csv
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Age,Sex,Scale Python Exp,Course,Has Voice Assistent Contact,Voice Assistent,Scale Study Satisfaction,Uses Smartphone,Which Smartphone,Has Computer,Which OS,Scale Programming Exp
22,Männlich,4,Medienwissenschaften,Ja,Apple Siri,4,Ja,Apple,Ja,Mac OS,2
26,Weiblich,3,Medienwissenschaften,Ja,Amazon Alexa,2,Ja,Xiaomi,Ja,Windows 10,3
21,Männlich,3,Medienwissenschaften,Ja,Google Now,4,Ja,Sonstige,Ja,Windows 10,3
26,Weiblich,4,Medienwissenschaften,Ja,Apple Siri,4,Ja,Samsung,Ja,Windows 10,2
24,Weiblich,4,Psychologie,Nein,,4,Ja,Apple,Ja,Windows 11,3
23,Männlich,3,Medienwissenschaften,Ja,Amazon Alexa,4,Ja,Samsung,Ja,Windows 10,3
21,Männlich,3,Medienwissenschaften,Ja,Amazon Alexa,4,Ja,Samsung,Ja,Windows 10,2
22,Weiblich,4,Medienwissenschaften,Nein,,3,Ja,Samsung,Ja,Windows 10,2
19,Weiblich,4,Medienwissenschaften,Ja,Apple Siri,3,Ja,Apple,Ja,Windows 11,2
21,Weiblich,4,Medienwissenschaften,Ja,Google Now,3,Ja,Samsung,Ja,Windows 10,2
20,Weiblich,4,Medienwissenschaften,Ja,Apple Siri,4,Ja,Apple,Ja,Mac OS,2
21,Weiblich,4,Medienwissenschaften,Nein,Apple Siri,4,Ja,Apple,Ja,Mac OS,2
21,Weiblich,4,Medienwissenschaften,Ja,Amazon Alexa,3,Ja,Samsung,Ja,Windows 11,4
20,Männlich,4,Medienwissenschaften,Nein,,3,Ja,Samsung,Ja,Windows 10,3
22,Weiblich,4,Medienwissenschaften,Ja,Apple Siri,2,Ja,Apple,Ja,Windows 11,2
22,Weiblich,4,Medienwissenschaften,Ja,Amazon Alexa,3,Ja,Apple,Ja,Mac OS,1
21,Weiblich,4,Medienwissenschaften,Nein,,3,Ja,Apple,Ja,Mac OS,4
19,Männlich,3,Medienwissenschaften,Ja,Apple Siri,4,Ja,Apple,Ja,Windows 10,2
30,Weiblich,3,Medienwissenschaften,Ja,Apple Siri,3,Ja,Apple,Ja,Mac OS,2
27,Weiblich,4,Medienwissenschaften,Ja,Apple Siri,3,Ja,Apple,Ja,Windows 11,2
22,Weiblich,5,Medienwissenschaften,Ja,Amazon Alexa,5,Ja,Xiaomi,Ja,Linux,1
21,Männlich,5,Medienwissenschaften,Ja,Apple Siri,4,Ja,Apple,Ja,Windows 10,2
30,Männlich,4,Medienwissenschaften,Ja,Amazon Alexa,3,Ja,Samsung,Ja,Windows 11,2
23,Weiblich,5,Medienwissenschaften,Ja,Apple Siri,2,Ja,Apple,Ja,Mac OS,1
22,Weiblich,3,Medienwissenschaften,Ja,Apple Siri,4,Ja,Apple,Ja,Mac OS,3
1 Age Sex Scale Python Exp Course Has Voice Assistent Contact Voice Assistent Scale Study Satisfaction Uses Smartphone Which Smartphone Has Computer Which OS Scale Programming Exp
2 22 Männlich 4 Medienwissenschaften Ja Apple Siri 4 Ja Apple Ja Mac OS 2
3 26 Weiblich 3 Medienwissenschaften Ja Amazon Alexa 2 Ja Xiaomi Ja Windows 10 3
4 21 Männlich 3 Medienwissenschaften Ja Google Now 4 Ja Sonstige Ja Windows 10 3
5 26 Weiblich 4 Medienwissenschaften Ja Apple Siri 4 Ja Samsung Ja Windows 10 2
6 24 Weiblich 4 Psychologie Nein 4 Ja Apple Ja Windows 11 3
7 23 Männlich 3 Medienwissenschaften Ja Amazon Alexa 4 Ja Samsung Ja Windows 10 3
8 21 Männlich 3 Medienwissenschaften Ja Amazon Alexa 4 Ja Samsung Ja Windows 10 2
9 22 Weiblich 4 Medienwissenschaften Nein 3 Ja Samsung Ja Windows 10 2
10 19 Weiblich 4 Medienwissenschaften Ja Apple Siri 3 Ja Apple Ja Windows 11 2
11 21 Weiblich 4 Medienwissenschaften Ja Google Now 3 Ja Samsung Ja Windows 10 2
12 20 Weiblich 4 Medienwissenschaften Ja Apple Siri 4 Ja Apple Ja Mac OS 2
13 21 Weiblich 4 Medienwissenschaften Nein Apple Siri 4 Ja Apple Ja Mac OS 2
14 21 Weiblich 4 Medienwissenschaften Ja Amazon Alexa 3 Ja Samsung Ja Windows 11 4
15 20 Männlich 4 Medienwissenschaften Nein 3 Ja Samsung Ja Windows 10 3
16 22 Weiblich 4 Medienwissenschaften Ja Apple Siri 2 Ja Apple Ja Windows 11 2
17 22 Weiblich 4 Medienwissenschaften Ja Amazon Alexa 3 Ja Apple Ja Mac OS 1
18 21 Weiblich 4 Medienwissenschaften Nein 3 Ja Apple Ja Mac OS 4
19 19 Männlich 3 Medienwissenschaften Ja Apple Siri 4 Ja Apple Ja Windows 10 2
20 30 Weiblich 3 Medienwissenschaften Ja Apple Siri 3 Ja Apple Ja Mac OS 2
21 27 Weiblich 4 Medienwissenschaften Ja Apple Siri 3 Ja Apple Ja Windows 11 2
22 22 Weiblich 5 Medienwissenschaften Ja Amazon Alexa 5 Ja Xiaomi Ja Linux 1
23 21 Männlich 5 Medienwissenschaften Ja Apple Siri 4 Ja Apple Ja Windows 10 2
24 30 Männlich 4 Medienwissenschaften Ja Amazon Alexa 3 Ja Samsung Ja Windows 11 2
25 23 Weiblich 5 Medienwissenschaften Ja Apple Siri 2 Ja Apple Ja Mac OS 1
26 22 Weiblich 3 Medienwissenschaften Ja Apple Siri 4 Ja Apple Ja Mac OS 3

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