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akiniwa/titanic_01_k.ipynb

Last active December 20, 2015 19:29
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titanic_01_k.ipynb
{
"metadata": {
"name": "titanic_01_k"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"train = pd.read_csv('train.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(train)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 5,
"text": [
"pandas.core.frame.DataFrame"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"train\u306fDataFrame\u30aa\u30d6\u30b8\u30a7\u30af\u30c8"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"train.columns",
"type(train.Age)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 6,
"text": [
"pandas.core.series.Series"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"train\u306f\u30d8\u30c3\u30c0\u30fc\u306e\u540d\u524d\u3067\u5404\u30ab\u30e9\u30e0\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u308b\u3002\n",
"\u3061\u306a\u307f\u306btrain.Age\u306fSeries\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3068\u3044\u3046\u3082\u306e\u3067\u3001\u30ea\u30b9\u30c8\u307f\u305f\u3044\u3060\u3051\u3069\u3001\u7d71\u8a08\u51e6\u7406\u306b\u4fbf\u5229\u306a\u30e1\u30bd\u30c3\u30c9\u3092\u3082\u3063\u3066\u308b\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"train.Age.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 7,
"text": [
"0 22\n",
"1 38\n",
"2 26\n",
"3 35\n",
"4 35\n",
"Name: Age, dtype: float64"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"R\u3067\u4fbf\u5229\u306ahead\u3082\u4f7f\u3048\u308b\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print train.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 7.910400 \n",
"50% 0.000000 14.454200 \n",
"75% 0.000000 31.000000 \n",
"max 6.000000 512.329200 \n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u3053\u308c\u306f\u3001summary()\u7684\u306a\u3084\u3064\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"train.Age[0:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 12,
"text": [
"0 22\n",
"1 38\n",
"2 26\n",
"3 35\n",
"4 35\n",
"5 NaN\n",
"6 54\n",
"7 2\n",
"8 27\n",
"9 14\n",
"Name: Age, dtype: float64"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"train.Age[5]\u304cNaN\u306b\u306a\u3063\u3066\u308b\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"train.Age[0:10].isnull()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 14,
"text": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
"5 True\n",
"6 False\n",
"7 False\n",
"8 False\n",
"9 False\n",
"Name: Age, dtype: bool"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"isnull()\u3092\u4f7f\u3046\u3068\u3001True\u304c\u8fd4\u308b\u3002"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"train.Age = train.Age.fillna(train.Age.mean())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 39,
"text": [
"0 22.000000\n",
"1 38.000000\n",
"2 26.000000\n",
"3 35.000000\n",
"4 35.000000\n",
"5 29.699118\n",
"6 54.000000\n",
"7 2.000000\n",
"8 27.000000\n",
"9 14.000000\n",
"10 4.000000\n",
"11 58.000000\n",
"12 20.000000\n",
"13 39.000000\n",
"14 14.000000\n",
"...\n",
"876 20.000000\n",
"877 19.000000\n",
"878 29.699118\n",
"879 56.000000\n",
"880 25.000000\n",
"881 33.000000\n",
"882 22.000000\n",
"883 28.000000\n",
"884 25.000000\n",
"885 39.000000\n",
"886 27.000000\n",
"887 19.000000\n",
"888 29.699118\n",
"889 26.000000\n",
"890 32.000000\n",
"Name: Age, Length: 891, dtype: float64"
]
}
],
"prompt_number": 39
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"fillna('NaN\u3092\u57cb\u3081\u308b\u6570\u5024')\u304c\u4fbf\u5229!!"
]
}
],
"metadata": {}
}
]
}
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