From 0394852ba587ce0d64b33f85dff016bc23155fe7 Mon Sep 17 00:00:00 2001 From: Donne Martin Date: Sat, 14 Mar 2015 20:03:48 -0400 Subject: [PATCH] Added snippets to analyze the Titanic Sex (Gender) feature. --- kaggle/titanic.ipynb | 302 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 298 insertions(+), 4 deletions(-) diff --git a/kaggle/titanic.ipynb b/kaggle/titanic.ipynb index ca14ee5..841956e 100644 --- a/kaggle/titanic.ipynb +++ b/kaggle/titanic.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:65b853762ab4a84c820902849d99ff6205fb1cc37e8c4b9b84d15cfd3ce9ecab" + "signature": "sha256:c2fcf83578d0c021bd7138a746b44b1dfc462c3e8963594da8dc2234fb6f8447" }, "nbformat": 3, "nbformat_minor": 0, @@ -651,7 +651,7 @@ "output_type": "display_data", "png": 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"text": [ - "" + "" ] } ], @@ -763,7 +763,7 @@ "output_type": "pyout", "prompt_number": 11, "text": [ - "" + "" ] }, { @@ -771,7 +771,7 @@ "output_type": "display_data", "png": 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"text": [ - "" + "" ] } ], @@ -783,6 +783,300 @@ "source": [ "We can see that passenger class seems to have a significant impact on whether a passenger survived." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature: Sex (Gender)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll need to map Sex from a string to a number to prepare it for machine learning algorithms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prepare to map Sex from a string to a number representation:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "genders = sort(df['Sex'].unique())\n", + "genders_mapping = dict(zip(genders, range(0, len(genders) + 1)))\n", + "genders_mapping" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 12, + "text": [ + "{'female': 0, 'male': 1}" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transform Sex from a string to a number representation:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df['Sex_Val'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df.head(3)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSex_Val
0 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.2500 NaN S 1
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 0 PC 17599 71.2833 C85 C 0
2 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.9250 NaN S 0
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 14, + "text": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n", + "2 Heikkinen, Miss. Laina female 26 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked Sex_Val \n", + "0 0 A/5 21171 7.2500 NaN S 1 \n", + "1 0 PC 17599 71.2833 C85 C 0 \n", + "2 0 STON/O2. 3101282 7.9250 NaN S 0 " + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "genders = sort(df['Sex_Val'].unique())\n", + "genders" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 15, + "text": [ + "array([0, 1])" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot a histogram of the Sex_Val:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df['Sex_Val'].hist(bins=len(genders))\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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MhvCgT1eyA1SkZAeoSMkOUJGSHWDqedCbmXWcO3qzFnf0Vjd39GZmNoQHfbqSHaAiJTtA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\nHedBb2bWcR70ZmYd93/OoxplCDN6/wAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot a normalized cross tab for Sex_Val and Survived:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "sex_val_xt = pd.crosstab(df['Sex_Val'], df['Survived'])\n", + "sex_val_xt_pct = sex_val_xt.div(sex_val_xt.sum(1).astype(float), axis=0)\n", + "sex_val_xt_pct.plot(kind='bar', stacked=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 17, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The majority of females survived, whereas the majority of males did not." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Count males and females in each Pclass:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "for p_class in passenger_classes:\n", + " print 'M: ', p_class, len(df[(df['Sex'] == 'male') & (df['Pclass'] == p_class)])\n", + " print 'F: ', p_class, len(df[(df['Sex'] == 'female') & (df['Pclass'] == p_class)])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "M: 1 122\n", + "F: 1 94\n", + "M: 2 108\n", + "F: 2 76\n", + "M: 3 347\n", + "F: 3 144\n" + ] + } + ], + "prompt_number": 18 } ], "metadata": {}