diff --git a/Titanic Machine Learning.ipynb b/Titanic Machine Learning.ipynb new file mode 100644 index 0000000..7c3e822 --- /dev/null +++ b/Titanic Machine Learning.ipynb @@ -0,0 +1,1745 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:ad89783cd6dfdb5446290e1631b14a61dadd5e9f42e31fa589e39d3806032155" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sb\n", + "import random" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "train = pd.read_csv(\"train.csv\")\n", + "train.info()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "Int64Index: 891 entries, 0 to 890\n", + "Data columns (total 12 columns):\n", + "PassengerId 891 non-null int64\n", + "Survived 891 non-null int64\n", + "Pclass 891 non-null int64\n", + "Name 891 non-null object\n", + "Sex 891 non-null object\n", + "Age 714 non-null float64\n", + "SibSp 891 non-null int64\n", + "Parch 891 non-null int64\n", + "Ticket 891 non-null object\n", + "Fare 891 non-null float64\n", + "Cabin 204 non-null object\n", + "Embarked 889 non-null object\n", + "dtypes: float64(2), int64(5), object(5)\n", + "memory usage: 90.5+ KB\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Pivot Table" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pd.pivot_table(train, index=[\"Sex\"], values=[\"Survived\"]).plot(kind=\"barh\")\n", + "plt.axvline(x=0.5, linewidth=2, color='r')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test_age = pd.read_csv(\"test.csv\")\n", + "test_age[\"Survived\"] = 0\n", + "test_age.loc[test_age[\"Sex\"] == \"female\", \"Survived\"] = 1\n", + "test_age = test_age[[\"PassengerId\", \"Survived\"]]\n", + "test_age.to_csv(\"gendermodel.csv\", index=False)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test_age.info()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "Int64Index: 418 entries, 0 to 417\n", + "Data columns (total 2 columns):\n", + "PassengerId 418 non-null int64\n", + "Survived 418 non-null int64\n", + "dtypes: int64(2)\n", + "memory usage: 9.8 KB\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# train[\"AgeRange\"] = train[\"Age\"].map(lambda x: \"adult\" if x >= 18 else \"child\")\n", + "# pd.pivot_table(train, index=[\"Sex\", \"AgeRange\"], values=[\"Survived\"]).plot(kind=\"barh\")\n", + "# plt.axvline(x=0.5, linewidth=2, color='r')\n", + "\n", + "#add a column with child or adult\n", + "train[\"AgeRange\"] = train[\"Age\"].map(lambda x: \"adult\" if x >= 18 else \"child\")\n", + "pd.pivot_table(train, index=[\"Sex\", \"AgeRange\"], values=[\"Survived\"]).plot(kind=\"barh\")\n", + "plt.axvline(x=0.5, linewidth=2, color=\"r\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def ticket_price(fare):\n", + " if fare < 10:\n", + " return \"< $10\"\n", + " elif fare < 20:\n", + " return \"$10-20\"\n", + " elif fare < 30:\n", + " return \"$20-30\"\n", + " else:\n", + " return \"> $30\"\n", + " \n", + "train[\"TicketPrice\"] = train[\"Fare\"].map(ticket_price)\n", + "\n", + "pd.pivot_table(train, index=[\"Sex\", \"Pclass\", \"TicketPrice\"], values=[\"Survived\"]) \\\n", + " .plot(kind=\"barh\")\n", + "plt.axvline(x=0.5, linewidth=2, color='r')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 9, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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uNbMpwADgTDObLum5eGxHC426B9iUZZ21vVnWPftfoC8hmTrOMpRSDqa9iN01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\niAkWAqMlXVukvn2BU+K1b5J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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 15, + "text": [ + " Gender Pclass Age AgeFill\n", + "5 1 3 NaN 25.0\n", + "17 1 2 NaN 30.0\n", + "19 0 3 NaN 21.5\n", + "26 1 3 NaN 25.0\n", + "28 0 3 NaN 21.5" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "train['AgeIsNull'] = pd.isnull(train.Age).astype(int)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 16 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def calc_median_ages(df):\n", + " median_ages = np.zeros((2,3))\n", + "\n", + " for i in range(0, 2):\n", + " for j in range(0, 3):\n", + " median_ages[i,j] = df[(df['Gender'] == i) & \\\n", + " (df['Pclass'] == j+1)]['Age'].dropna().median()\n", + " \n", + " return median_ages\n", + "\n", + "\n", + "def guess_ages(df, median_ages=None):\n", + " if median_ages is None:\n", + " median_ages = calc_median_ages(df)\n", + " \n", + " for i in range(0, 2):\n", + " for j in range(0, 3):\n", + " df.loc[(df.Age.isnull()) & (df.Gender == i) & (df.Pclass == j+1),\\\n", + " 'Age'] = median_ages[i,j]\n", + " \n", + " df['GuessedAge'] = pd.isnull(df.Age).astype(int)\n", + " return df\n", + "\n", + "def clean(df, median_ages=None):\n", + " df['Gender'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n", + " df = guess_ages(df, median_ages)\n", + " df = df.drop(['Ticket', 'Cabin', 'Sex'], axis=1)\n", + " \n", + " return df" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 17 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 17 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "train = pd.read_csv(\"train.csv\")\n", + "train = clean(train)\n", + "train.info()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "Int64Index: 891 entries, 0 to 890\n", + "Data columns (total 11 columns):\n", + "PassengerId 891 non-null int64\n", + "Survived 891 non-null int64\n", + "Pclass 891 non-null int64\n", + "Name 891 non-null object\n", + "Age 891 non-null float64\n", + "SibSp 891 non-null int64\n", + "Parch 891 non-null int64\n", + "Fare 891 non-null float64\n", + "Embarked 889 non-null object\n", + "Gender 891 non-null int64\n", + "GuessedAge 891 non-null int64\n", + "dtypes: float64(2), int64(7), object(2)\n", + "memory usage: 83.5+ KB\n" + ] + } + ], + "prompt_number": 18 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "train.dtypes[train.dtypes.map(lambda x: x=='object')]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 19, + "text": [ + "Name object\n", + "Embarked object\n", + "dtype: object" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def clean(df):\n", + " df['EmbarcationNum'] = df['Embarked'].map( {'Q': 0, 'C': 1, \"S\": 2} ).dropna().astype(int)\n", + "\n", + " \n", + " return train\n", + "\n", + "clean(train)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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PassengerIdSurvivedPclassNameAgeSibSpParchFareEmbarkedGenderGuessedAgeEmbarcationNum
0 1 0 3 Braund, Mr. Owen Harris 22.0 1 0 7.2500 S 1 0 2
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... 38.0 1 0 71.2833 C 0 0 1
2 3 1 3 Heikkinen, Miss. Laina 26.0 0 0 7.9250 S 0 0 2
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0 1 0 53.1000 S 0 0 2
4 5 0 3 Allen, Mr. William Henry 35.0 0 0 8.0500 S 1 0 2
5 6 0 3 Moran, Mr. James 25.0 0 0 8.4583 Q 1 0 0
6 7 0 1 McCarthy, Mr. Timothy J 54.0 0 0 51.8625 S 1 0 2
7 8 0 3 Palsson, Master. Gosta Leonard 2.0 3 1 21.0750 S 1 0 2
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 27.0 0 2 11.1333 S 0 0 2
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) 14.0 1 0 30.0708 C 0 0 1
10 11 1 3 Sandstrom, Miss. Marguerite Rut 4.0 1 1 16.7000 S 0 0 2
11 12 1 1 Bonnell, Miss. Elizabeth 58.0 0 0 26.5500 S 0 0 2
12 13 0 3 Saundercock, Mr. William Henry 20.0 0 0 8.0500 S 1 0 2
13 14 0 3 Andersson, Mr. Anders Johan 39.0 1 5 31.2750 S 1 0 2
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina 14.0 0 0 7.8542 S 0 0 2
15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) 55.0 0 0 16.0000 S 0 0 2
16 17 0 3 Rice, Master. Eugene 2.0 4 1 29.1250 Q 1 0 0
17 18 1 2 Williams, Mr. Charles Eugene 30.0 0 0 13.0000 S 1 0 2
18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... 31.0 1 0 18.0000 S 0 0 2
19 20 1 3 Masselmani, Mrs. Fatima 21.5 0 0 7.2250 C 0 0 1
20 21 0 2 Fynney, Mr. Joseph J 35.0 0 0 26.0000 S 1 0 2
21 22 1 2 Beesley, Mr. Lawrence 34.0 0 0 13.0000 S 1 0 2
22 23 1 3 McGowan, Miss. Anna \"Annie\" 15.0 0 0 8.0292 Q 0 0 0
23 24 1 1 Sloper, Mr. William Thompson 28.0 0 0 35.5000 S 1 0 2
24 25 0 3 Palsson, Miss. Torborg Danira 8.0 3 1 21.0750 S 0 0 2
25 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... 38.0 1 5 31.3875 S 0 0 2
26 27 0 3 Emir, Mr. Farred Chehab 25.0 0 0 7.2250 C 1 0 1
27 28 0 1 Fortune, Mr. Charles Alexander 19.0 3 2 263.0000 S 1 0 2
28 29 1 3 O'Dwyer, Miss. Ellen \"Nellie\" 21.5 0 0 7.8792 Q 0 0 0
29 30 0 3 Todoroff, Mr. Lalio 25.0 0 0 7.8958 S 1 0 2
.......................................
861 862 0 2 Giles, Mr. Frederick Edward 21.0 1 0 11.5000 S 1 0 2
862 863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Ba... 48.0 0 0 25.9292 S 0 0 2
863 864 0 3 Sage, Miss. Dorothy Edith \"Dolly\" 21.5 8 2 69.5500 S 0 0 2
864 865 0 2 Gill, Mr. John William 24.0 0 0 13.0000 S 1 0 2
865 866 1 2 Bystrom, Mrs. (Karolina) 42.0 0 0 13.0000 S 0 0 2
866 867 1 2 Duran y More, Miss. Asuncion 27.0 1 0 13.8583 C 0 0 1
867 868 0 1 Roebling, Mr. Washington Augustus II 31.0 0 0 50.4958 S 1 0 2
868 869 0 3 van Melkebeke, Mr. Philemon 25.0 0 0 9.5000 S 1 0 2
869 870 1 3 Johnson, Master. Harold Theodor 4.0 1 1 11.1333 S 1 0 2
870 871 0 3 Balkic, Mr. Cerin 26.0 0 0 7.8958 S 1 0 2
871 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 47.0 1 1 52.5542 S 0 0 2
872 873 0 1 Carlsson, Mr. Frans Olof 33.0 0 0 5.0000 S 1 0 2
873 874 0 3 Vander Cruyssen, Mr. Victor 47.0 0 0 9.0000 S 1 0 2
874 875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) 28.0 1 0 24.0000 C 0 0 1
875 876 1 3 Najib, Miss. Adele Kiamie \"Jane\" 15.0 0 0 7.2250 C 0 0 1
876 877 0 3 Gustafsson, Mr. Alfred Ossian 20.0 0 0 9.8458 S 1 0 2
877 878 0 3 Petroff, Mr. Nedelio 19.0 0 0 7.8958 S 1 0 2
878 879 0 3 Laleff, Mr. Kristo 25.0 0 0 7.8958 S 1 0 2
879 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) 56.0 0 1 83.1583 C 0 0 1
880 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) 25.0 0 1 26.0000 S 0 0 2
881 882 0 3 Markun, Mr. Johann 33.0 0 0 7.8958 S 1 0 2
882 883 0 3 Dahlberg, Miss. Gerda Ulrika 22.0 0 0 10.5167 S 0 0 2
883 884 0 2 Banfield, Mr. Frederick James 28.0 0 0 10.5000 S 1 0 2
884 885 0 3 Sutehall, Mr. Henry Jr 25.0 0 0 7.0500 S 1 0 2
885 886 0 3 Rice, Mrs. William (Margaret Norton) 39.0 0 5 29.1250 Q 0 0 0
886 887 0 2 Montvila, Rev. Juozas 27.0 0 0 13.0000 S 1 0 2
887 888 1 1 Graham, Miss. Margaret Edith 19.0 0 0 30.0000 S 0 0 2
888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" 21.5 1 2 23.4500 S 0 0 2
889 890 1 1 Behr, Mr. Karl Howell 26.0 0 0 30.0000 C 1 0 1
890 891 0 3 Dooley, Mr. Patrick 32.0 0 0 7.7500 Q 1 0 0
\n", + "

891 rows \u00d7 12 columns

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Laina 26.0 0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0 1 0 \n", + "4 Allen, Mr. William Henry 35.0 0 0 \n", + "5 Moran, Mr. James 25.0 0 0 \n", + "6 McCarthy, Mr. Timothy J 54.0 0 0 \n", + "7 Palsson, Master. Gosta Leonard 2.0 3 1 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 27.0 0 2 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) 14.0 1 0 \n", + "10 Sandstrom, Miss. Marguerite Rut 4.0 1 1 \n", + "11 Bonnell, Miss. Elizabeth 58.0 0 0 \n", + "12 Saundercock, Mr. William Henry 20.0 0 0 \n", + "13 Andersson, Mr. Anders Johan 39.0 1 5 \n", + "14 Vestrom, Miss. Hulda Amanda Adolfina 14.0 0 0 \n", + "15 Hewlett, Mrs. (Mary D Kingcome) 55.0 0 0 \n", + "16 Rice, Master. Eugene 2.0 4 1 \n", + "17 Williams, Mr. Charles Eugene 30.0 0 0 \n", + "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... 31.0 1 0 \n", + "19 Masselmani, Mrs. Fatima 21.5 0 0 \n", + "20 Fynney, Mr. Joseph J 35.0 0 0 \n", + "21 Beesley, Mr. Lawrence 34.0 0 0 \n", + "22 McGowan, Miss. Anna \"Annie\" 15.0 0 0 \n", + "23 Sloper, Mr. William Thompson 28.0 0 0 \n", + "24 Palsson, Miss. Torborg Danira 8.0 3 1 \n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... 38.0 1 5 \n", + "26 Emir, Mr. Farred Chehab 25.0 0 0 \n", + "27 Fortune, Mr. Charles Alexander 19.0 3 2 \n", + "28 O'Dwyer, Miss. Ellen \"Nellie\" 21.5 0 0 \n", + "29 Todoroff, Mr. Lalio 25.0 0 0 \n", + ".. ... ... ... ... \n", + "861 Giles, Mr. Frederick Edward 21.0 1 0 \n", + "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... 48.0 0 0 \n", + "863 Sage, Miss. Dorothy Edith \"Dolly\" 21.5 8 2 \n", + "864 Gill, Mr. John William 24.0 0 0 \n", + "865 Bystrom, Mrs. (Karolina) 42.0 0 0 \n", + "866 Duran y More, Miss. Asuncion 27.0 1 0 \n", + "867 Roebling, Mr. Washington Augustus II 31.0 0 0 \n", + "868 van Melkebeke, Mr. Philemon 25.0 0 0 \n", + "869 Johnson, Master. Harold Theodor 4.0 1 1 \n", + "870 Balkic, Mr. Cerin 26.0 0 0 \n", + "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 47.0 1 1 \n", + "872 Carlsson, Mr. Frans Olof 33.0 0 0 \n", + "873 Vander Cruyssen, Mr. Victor 47.0 0 0 \n", + "874 Abelson, Mrs. Samuel (Hannah Wizosky) 28.0 1 0 \n", + "875 Najib, Miss. Adele Kiamie \"Jane\" 15.0 0 0 \n", + "876 Gustafsson, Mr. Alfred Ossian 20.0 0 0 \n", + "877 Petroff, Mr. Nedelio 19.0 0 0 \n", + "878 Laleff, Mr. Kristo 25.0 0 0 \n", + "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) 56.0 0 1 \n", + "880 Shelley, Mrs. William (Imanita Parrish Hall) 25.0 0 1 \n", + "881 Markun, Mr. Johann 33.0 0 0 \n", + "882 Dahlberg, Miss. Gerda Ulrika 22.0 0 0 \n", + "883 Banfield, Mr. Frederick James 28.0 0 0 \n", + "884 Sutehall, Mr. Henry Jr 25.0 0 0 \n", + "885 Rice, Mrs. William (Margaret Norton) 39.0 0 5 \n", + "886 Montvila, Rev. Juozas 27.0 0 0 \n", + "887 Graham, Miss. Margaret Edith 19.0 0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" 21.5 1 2 \n", + "889 Behr, Mr. Karl Howell 26.0 0 0 \n", + "890 Dooley, Mr. Patrick 32.0 0 0 \n", + "\n", + " Fare Embarked Gender GuessedAge EmbarcationNum \n", + "0 7.2500 S 1 0 2 \n", + "1 71.2833 C 0 0 1 \n", + "2 7.9250 S 0 0 2 \n", + "3 53.1000 S 0 0 2 \n", + "4 8.0500 S 1 0 2 \n", + "5 8.4583 Q 1 0 0 \n", + "6 51.8625 S 1 0 2 \n", + "7 21.0750 S 1 0 2 \n", + "8 11.1333 S 0 0 2 \n", + "9 30.0708 C 0 0 1 \n", + "10 16.7000 S 0 0 2 \n", + "11 26.5500 S 0 0 2 \n", + "12 8.0500 S 1 0 2 \n", + "13 31.2750 S 1 0 2 \n", + "14 7.8542 S 0 0 2 \n", + "15 16.0000 S 0 0 2 \n", + "16 29.1250 Q 1 0 0 \n", + "17 13.0000 S 1 0 2 \n", + "18 18.0000 S 0 0 2 \n", + "19 7.2250 C 0 0 1 \n", + "20 26.0000 S 1 0 2 \n", + "21 13.0000 S 1 0 2 \n", + "22 8.0292 Q 0 0 0 \n", + "23 35.5000 S 1 0 2 \n", + "24 21.0750 S 0 0 2 \n", + "25 31.3875 S 0 0 2 \n", + "26 7.2250 C 1 0 1 \n", + "27 263.0000 S 1 0 2 \n", + "28 7.8792 Q 0 0 0 \n", + "29 7.8958 S 1 0 2 \n", + ".. ... ... ... ... ... \n", + "861 11.5000 S 1 0 2 \n", + "862 25.9292 S 0 0 2 \n", + "863 69.5500 S 0 0 2 \n", + "864 13.0000 S 1 0 2 \n", + "865 13.0000 S 0 0 2 \n", + "866 13.8583 C 0 0 1 \n", + "867 50.4958 S 1 0 2 \n", + "868 9.5000 S 1 0 2 \n", + "869 11.1333 S 1 0 2 \n", + "870 7.8958 S 1 0 2 \n", + "871 52.5542 S 0 0 2 \n", + "872 5.0000 S 1 0 2 \n", + "873 9.0000 S 1 0 2 \n", + "874 24.0000 C 0 0 1 \n", + "875 7.2250 C 0 0 1 \n", + "876 9.8458 S 1 0 2 \n", + "877 7.8958 S 1 0 2 \n", + "878 7.8958 S 1 0 2 \n", + "879 83.1583 C 0 0 1 \n", + "880 26.0000 S 0 0 2 \n", + "881 7.8958 S 1 0 2 \n", + "882 10.5167 S 0 0 2 \n", + "883 10.5000 S 1 0 2 \n", + "884 7.0500 S 1 0 2 \n", + "885 29.1250 Q 0 0 0 \n", + "886 13.0000 S 1 0 2 \n", + "887 30.0000 S 0 0 2 \n", + "888 23.4500 S 0 0 2 \n", + "889 30.0000 C 1 0 1 \n", + "890 7.7500 Q 1 0 0 \n", + "\n", + "[891 rows x 12 columns]" + ] + } + ], + "prompt_number": 20 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + 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DvNfFNjVZx8pufwvQwwEiUnS1tVVkMtWFN4ygr46TtDiLTBMwLFxeCuxoZsOB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\nFzLu/+ToA4AAAAAASUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test = pd.read_csv(\"test.csv\")\n", + "\n", + "clean(test)\n", + "test[\"Survived\"] = 0\n", + "test.loc[test[\"Sex\"] == \"female\", \"Survived\"] = 1\n", + "test.loc[(test[\"Pclass\"] == 3) & (test[\"Fare\"] > 20), \"Survived\"] = 0\n", + "test.loc[(test[\"EmbarcationNum\"] == 2), \"Survived\"] = 0\n", + "test = test[[\"PassengerId\", \"Survived\"]]\n", + "test.to_csv(\"genderclassembark.csv\", index=False)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 35 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file