From 43c147dad13e9304c9fa957a83660c55f45ee0c3 Mon Sep 17 00:00:00 2001 From: softpo <455098435@qq.com> Date: Thu, 16 May 2019 11:59:06 +0800 Subject: [PATCH] =?UTF-8?q?'pandas=E4=BD=BF=E7=94=A8'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...as\347\232\204\345\272\224\347\224\250.md" | 23 + .../1-pandas\345\205\245\351\227\250.ipynb" | 631 ++ .../2-pandas-\347\264\242\345\274\225.ipynb" | 2082 ++++++ ...347\251\272\346\225\260\346\215\256.ipynb" | 6285 +++++++++++++++++ ...345\261\202\347\264\242\345\274\225.ipynb" | 568 ++ ...345\274\225\350\256\241\347\256\227.ipynb" | 1000 +++ ...346\215\256\351\233\206\346\210\220.ipynb" | 1209 ++++ ...15\256\351\233\206\346\210\220merge.ipynb" | 1272 ++++ ...345\220\210\346\223\215\344\275\234.ipynb" | 877 +++ ...346\210\220\345\256\236\346\210\230.ipynb" | 6213 ++++++++++++++++ Day76-90/code/cancer_predict.npy | Bin 0 -> 450 bytes Day76-90/code/cancer_true.npy | Bin 0 -> 450 bytes Day76-90/code/state-abbrevs.csv | 52 + Day76-90/code/state-areas.csv | 53 + Day76-90/code/state-population.csv | 2545 +++++++ 15 files changed, 22810 insertions(+) create mode 100644 "Day76-90/code/1-pandas\345\205\245\351\227\250.ipynb" create mode 100644 "Day76-90/code/2-pandas-\347\264\242\345\274\225.ipynb" create mode 100644 "Day76-90/code/3-pandas\346\225\260\346\215\256\346\270\205\346\264\227\344\271\213\347\251\272\346\225\260\346\215\256.ipynb" create mode 100644 "Day76-90/code/4-pandas\345\244\232\345\261\202\347\264\242\345\274\225.ipynb" create mode 100644 "Day76-90/code/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227.ipynb" create mode 100644 "Day76-90/code/6-pandas\346\225\260\346\215\256\351\233\206\346\210\220.ipynb" create mode 100644 "Day76-90/code/7-pandas\346\225\260\346\215\256\351\233\206\346\210\220merge.ipynb" create mode 100644 "Day76-90/code/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234.ipynb" create mode 100644 "Day76-90/code/9-pandas\346\225\260\346\215\256\351\233\206\346\210\220\345\256\236\346\210\230.ipynb" create mode 100644 Day76-90/code/cancer_predict.npy create mode 100644 Day76-90/code/cancer_true.npy create mode 100644 Day76-90/code/state-abbrevs.csv create mode 100644 Day76-90/code/state-areas.csv create mode 100644 Day76-90/code/state-population.csv diff --git "a/Day76-90/02.Pandas\347\232\204\345\272\224\347\224\250.md" "b/Day76-90/02.Pandas\347\232\204\345\272\224\347\224\250.md" index 9a101cf..3115ad5 100644 --- "a/Day76-90/02.Pandas\347\232\204\345\272\224\347\224\250.md" +++ "b/Day76-90/02.Pandas\347\232\204\345\272\224\347\224\250.md" @@ -1,2 +1,25 @@ ## Pandas的应用 +### 1、pandas入门 + +### 2、pandas索引 + +### 3、pandas数据清洗之空数据 + +[数据挖掘之空数据处理（有史以来最全面）]: https://blog.csdn.net/Soft_Po/article/details/89302887 + +### 4、pandas多层索引 + +### 5、pandas多层索引计算 + +### 6、pandas数据集成concat + +### 7、pandas数据集成merge + +### 8、pandas分组聚合操作 + +### 9、pandas数据集成实战 + +### 10、美国大选项目 + +[2012美国大选政治献金项目数据分析（有史以来最全面）]: https://blog.csdn.net/Soft_Po/article/details/89283382 \ No newline at end of file diff --git "a/Day76-90/code/1-pandas\345\205\245\351\227\250.ipynb" "b/Day76-90/code/1-pandas\345\205\245\351\227\250.ipynb" new file mode 100644 index 0000000..2f25670 --- /dev/null +++ "b/Day76-90/code/1-pandas\345\205\245\351\227\250.ipynb" @@ -0,0 +1,631 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Math 120\n", + "Python 136\n", + "En 128\n", + "Chinese 99\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 创建\n", + "# Series是一维的数据\n", + "s = Series(data = [120,136,128,99],index = ['Math','Python','En','Chinese'])\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4,)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([120, 136, 128, 99], dtype=int64)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v = s.values\n", + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "120.75" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "136" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15.903353943953666" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Math 14400\n", + "Python 18496\n", + "En 16384\n", + "Chinese 9801\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.pow(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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66.000000\n", + "k 32.333333\n", + "dtype: float64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.mean(axis = 1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Day76-90/code/2-pandas-\347\264\242\345\274\225.ipynb" "b/Day76-90/code/2-pandas-\347\264\242\345\274\225.ipynb" new file mode 100644 index 0000000..ddbde0a --- /dev/null +++ "b/Day76-90/code/2-pandas-\347\264\242\345\274\225.ipynb" @@ -0,0 +1,2082 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "\n", + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10 34\n", + "11 111\n", + "12 113\n", + "13 103\n", + "14 147\n", + "15 63\n", + "16 11\n", + "17 130\n", + "18 38\n", + "19 17\n", + "20 32\n", + "21 112\n", + "22 75\n", + "23 68\n", + "24 124\n", + "25 138\n", + "26 56\n", + "27 1\n", + "28 88\n", + "29 113\n", + "30 63\n", + "31 42\n", + "32 65\n", + "33 104\n", + "34 105\n", + "35 0\n", + "36 95\n", + "37 119\n", + "38 86\n", + "39 124\n", + " ... \n", + "80 127\n", + "81 139\n", + "82 110\n", + "83 65\n", + "84 127\n", + "85 108\n", + "86 33\n", + "87 91\n", + "88 134\n", + "89 65\n", + "90 110\n", + "91 144\n", + "92 40\n", + "93 3\n", + "94 3\n", + "95 59\n", + "96 97\n", + "97 64\n", + "98 126\n", + "99 94\n", + "100 20\n", + "101 107\n", + "102 59\n", + "103 146\n", + "104 83\n", + "105 59\n", + "106 25\n", + "107 0\n", + "108 78\n", + "109 93\n", + "Name: Python, Length: 100, dtype: int16" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = Series(np.random.randint(0,150,size = 100),index = np.arange(10,110),dtype=np.int16,name = 'Python')\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "0", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 索引操作\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m 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"\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 0" + ] + } + ], + "source": [ + "# 索引操作\n", + "s[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "34" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[10]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10 34\n", + "20 32\n", + "Name: Python, dtype: int16" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[[10,20]]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20 32\n", + "21 112\n", + "22 75\n", + "23 68\n", + "24 124\n", + "25 138\n", + "26 56\n", + "27 1\n", + "28 88\n", + "29 113\n", + "Name: Python, dtype: int16" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 切片操作\n", + "s[10:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10 34\n", + "12 113\n", + "14 147\n", + "16 11\n", + "18 38\n", + "20 32\n", + "22 75\n", + "24 124\n", + "26 56\n", + "28 88\n", + "30 63\n", + "32 65\n", + "34 105\n", + "36 95\n", + "38 86\n", + "40 6\n", + "42 57\n", + "44 72\n", + "46 43\n", + "48 87\n", + "50 83\n", + "52 99\n", + "54 132\n", + "56 17\n", + "58 116\n", + "60 33\n", + "62 51\n", + "64 80\n", + "66 121\n", + "68 81\n", + "70 0\n", + "72 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+ ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10 34\n", + "11 111\n", + "12 113\n", + "13 103\n", + "14 147\n", + "15 63\n", + "16 11\n", + "17 130\n", + "18 38\n", + "19 17\n", + "20 32\n", + "Name: Python, dtype: int16" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.loc[10:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10 34\n", + "12 113\n", + "14 147\n", + "16 11\n", + "18 38\n", + "20 32\n", + "22 75\n", + "24 124\n", + "26 56\n", + "28 88\n", + "30 63\n", + "32 65\n", + "34 105\n", + "36 95\n", + "38 86\n", + "40 6\n", + "42 57\n", + "44 72\n", + "46 43\n", + "48 87\n", + "50 83\n", + "52 99\n", + "54 132\n", + "56 17\n", + "58 116\n", + "60 33\n", + "62 51\n", + "64 80\n", + "66 121\n", + "68 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106, 107, 108, 109],\n", + " dtype='int64')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.index" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "34" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# iloc 索引从0开始，数字化自然索引\n", + "s.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10 34\n", + "20 32\n", + "Name: Python, dtype: int16" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.iloc[[0,10]]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10 34\n", + "11 111\n", + "12 113\n", + "13 103\n", + "14 147\n", + "15 63\n", + 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PythonEnMath
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\u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2656\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2657\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2658\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'A'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'A'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2925\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2926\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2927\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2928\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2929\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2658\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2660\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2661\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'A'" + ] + } + ], + "source": [ + "df['A']" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "A 103\n", + "B 135\n", + "C 13\n", + "D 47\n", + "E 89\n", + "F 64\n", + "H 48\n", + "I 16\n", + "J 122\n", + "K 60\n", + "Name: Python, dtype: int32" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Python']" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2658\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'Python'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Python'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1499\u001b[0m \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1500\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1501\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1502\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1911\u001b[0m \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1912\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1913\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m 139\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'no slices here, handle elsewhere'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 141\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 143\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m 3583\u001b[0m drop_level=drop_level)\n\u001b[0;32m 3584\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3585\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3586\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3587\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2658\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2660\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2661\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in 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"source": [ + "en = df['En'].value_counts()\n", + "en" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.0" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "en.index[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 75.0\n", + "En 74.0\n", + "Math 77.5\n", + "Physic 73.0\n", + "Chem 72.0\n", + "dtype: float64 \n" + ] + } + ], + "source": [ + "s = df.median()\n", + "print(s,type(s))" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "zhongshu = []\n", + "for col in df.columns:\n", + " zhongshu.append(df[col].value_counts().index[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { 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" + ], + "text/plain": [ + " Python En Math\n", + "张三 期中 A 15 31 17\n", + " B 82 56 123\n", + " 期末 A 14 2 78\n", + " B 69 50 17\n", + "李四 期中 A 91 87 143\n", + " B 120 118 39\n", + " 期末 A 56 76 55\n", + " B 11 105 121\n", + "王五 期中 A 147 78 1\n", + " B 128 126 146\n", + " 期末 A 49 45 114\n", + " B 121 26 77" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 三层索引\n", + "df3 = DataFrame(np.random.randint(0,150,size = (12,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末'],['A','B']]) )\n", + "\n", + "df3" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "73" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 先获取列后获取行\n", + "df['Python']['张三']['期中']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df2 = df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PythonEnMath
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PythonEnMath
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" + ], + "text/plain": [ + " Python En Math\n", + "张三 期中 73 5 25\n", + " 期末 37 36 56" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[[0,1]]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Day76-90/code/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227.ipynb" "b/Day76-90/code/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227.ipynb" new file mode 100644 index 0000000..22e1c8e --- /dev/null +++ "b/Day76-90/code/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227.ipynb" @@ -0,0 +1,1000 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "\n", + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PythonEnMath
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A1311011731517
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PythonEnMath
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PythonEnMath
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PythonEnMath
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EnMathPython
A期中115131
期末7317101
B期中532462
期末1015734
C期中3612324
期末11710576
D期中7946112
期末4212246
E期中1041066
期末45108113
F期中4132111
期末4121108
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" + ], + "text/plain": [ + " En Math Python\n", + "A 期中 1 15 131\n", + " 期末 73 17 101\n", + "B 期中 53 24 62\n", + " 期末 101 57 34\n", + "C 期中 36 123 24\n", + " 期末 117 105 76\n", + "D 期中 79 46 112\n", + " 期末 42 122 46\n", + "E 期中 104 10 66\n", + " 期末 45 108 113\n", + "F 期中 4 132 111\n", + " 期末 41 21 108" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 行和列的多层索引，进行转换\n", + "# Stack the prescribed level(s) from columns to index.\n", + "# 从列变成行\n", + "df2 = df.stack(level = 1)\n", + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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EnMathPython
ABCDEFABCDEFABCDEF
期中1533679104415241234610132131622411266111
期末73101117424541175710512210821101347646113108
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" + ], + "text/plain": [ + " En Math Python \n", + " A B C D E F A B C D E F A B C D E F\n", + "期中 1 53 36 79 104 4 15 24 123 46 10 132 131 62 24 112 66 111\n", + "期末 73 101 117 42 45 41 17 57 105 122 108 21 101 34 76 46 113 108" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 从行变成列\n", + "df2.unstack(level= 0 )" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EnMathPython
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100644 index 0000000..e128ee4 --- /dev/null +++ "b/Day76-90/code/6-pandas\346\225\260\346\215\256\351\233\206\346\210\220.ipynb" @@ -0,0 +1,1209 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 数据分析数据挖掘\n", + "# 有数据情况下：\n", + "# 数据预处理\n", + "# 数据清洗（空数据，异常值）\n", + "# 数据集成（多个数据合并到一起，级联）数据可能存放在多个表中\n", + "# 数据转化\n", + "# 数据规约（属性减少（不重要的属性删除），数据减少去重操作）" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 12, 67, 29, 46, 103, 53, 53, 139, 87],\n", + " [126, 33, 55, 104, 45, 70, 96, 133, 116, 43],\n", + " [ 84, 45, 17, 42, 19, 11, 125, 43, 54, 39],\n", + " [ 97, 68, 99, 90, 28, 60, 135, 84, 111, 63],\n", + " [114, 56, 30, 81, 48, 73, 119, 65, 20, 22]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([[115, 128, 122, 127, 4, 135, 26, 25, 131, 139],\n", + " [ 66, 119, 37, 136, 101, 40, 102, 127, 148, 127],\n", + " [ 89, 80, 140, 133, 51, 142, 47, 27, 54, 23],\n", + " [ 64, 127, 33, 128, 60, 106, 67, 94, 110, 76],\n", + " [ 6, 21, 23, 96, 10, 62, 26, 79, 149, 43],\n", + " [116, 143, 132, 118, 68, 21, 57, 133, 124, 124]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 首先看numpy数组的集成\n", + "nd1 = np.random.randint(0,150,size = (5,10))\n", + "\n", + "nd2 = np.random.randint(0,150,size = (6,10))\n", + "display(nd1,nd2)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 12, 67, 29, 46, 103, 53, 53, 139, 87],\n", + " [126, 33, 55, 104, 45, 70, 96, 133, 116, 43],\n", + " [ 84, 45, 17, 42, 19, 11, 125, 43, 54, 39],\n", + " [ 97, 68, 99, 90, 28, 60, 135, 84, 111, 63],\n", + " [114, 56, 30, 81, 48, 73, 119, 65, 20, 22],\n", + " [115, 128, 122, 127, 4, 135, 26, 25, 131, 139],\n", + " [ 66, 119, 37, 136, 101, 40, 102, 127, 148, 127],\n", + " [ 89, 80, 140, 133, 51, 142, 47, 27, 54, 23],\n", + " [ 64, 127, 33, 128, 60, 106, 67, 94, 110, 76],\n", + " [ 6, 21, 23, 96, 10, 62, 26, 79, 149, 43],\n", + " [116, 143, 132, 118, 68, 21, 57, 133, 124, 124]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 原来数据一个5行，一个是6行，级联之后变成了11行\n", + "nd3 = np.concatenate([nd1,nd2],axis = 0)\n", + "nd3" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[110, 38, 144, 92, 38, 2, 67, 2, 103, 81],\n", + " [ 56, 61, 61, 22, 108, 145, 95, 44, 40, 100],\n", + " [ 65, 74, 85, 123, 47, 117, 35, 55, 120, 20],\n", + " [ 15, 9, 4, 84, 71, 133, 140, 13, 71, 91],\n", + " [ 94, 31, 41, 5, 7, 32, 50, 24, 18, 120]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([[ 65, 149, 86, 138, 98],\n", + " [136, 49, 102, 45, 140],\n", + " [ 13, 124, 94, 81, 73],\n", + " [ 82, 38, 0, 75, 94],\n", + " [146, 28, 143, 61, 49]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nd1 = np.random.randint(0,150,size = (5,10))\n", + "\n", + "nd2 = np.random.randint(0,150,size = (5,5))\n", + "display(nd1,nd2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[110, 38, 144, 92, 38, 2, 67, 2, 103, 81, 65, 149, 86,\n", + " 138, 98],\n", + " [ 56, 61, 61, 22, 108, 145, 95, 44, 40, 100, 136, 49, 102,\n", + " 45, 140],\n", + " [ 65, 74, 85, 123, 47, 117, 35, 55, 120, 20, 13, 124, 94,\n", + " 81, 73],\n", + " [ 15, 9, 4, 84, 71, 133, 140, 13, 71, 91, 82, 38, 0,\n", + " 75, 94],\n", + " [ 94, 31, 41, 5, 7, 32, 50, 24, 18, 120, 146, 28, 143,\n", + " 61, 49]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# axis = 0行级联（第一维度的级联），axis = 1（第二个维度的级联，列的级联）\n", + "np.concatenate((nd1,nd2),axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pandas级联操作，pandas基于numpy\n", + "# pandas的级联类似" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Python Math En score_mean\n", + "A 40.0 15.0 90.0 48.3\n", + "B 59.0 52.0 83.0 64.7\n", + "C 14.0 138.0 137.0 96.3\n", + "D 89.0 78.0 53.0 73.3\n", + "E 81.0 101.0 3.0 61.7\n", + "F 75.0 79.0 86.0 80.0\n", + "score_mean 59.7 77.2 75.3 70.7" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4.merge(df5,left_index=True,right_index=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Day76-90/code/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234.ipynb" "b/Day76-90/code/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234.ipynb" new file mode 100644 index 0000000..7c16aff --- /dev/null +++ "b/Day76-90/code/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234.ipynb" @@ -0,0 +1,877 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# 分组聚合透视\n", + "# 很多时候属性是相似的\n", + "\n", + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "\n", + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HandSmokesexweightIQ
0rightyesmale80100
1leftyesfemale50120
2leftnofemale4890
3rightnomale75130
4rightyesmale68140
5rightnomale10080
6rightnofemale4094
7rightnofemale90110
8leftnomale88100
9rightyesfemale76160
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" + ], + "text/plain": [ + " Hand Smoke sex weight IQ\n", + "0 right yes male 80 100\n", + "1 left yes female 50 120\n", + "2 left no female 48 90\n", + "3 right no male 75 130\n", + "4 right yes male 68 140\n", + "5 right no male 100 80\n", + "6 right no female 40 94\n", + "7 right no female 90 110\n", + "8 left no male 88 100\n", + "9 right yes female 76 160" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 走右手习惯，是否抽烟，性别，对体重，智商，有一定影响\n", + "\n", + "df = DataFrame({'Hand':['right','left','left','right','right','right','right','right','left','right'],\n", + " 'Smoke':['yes','yes','no','no','yes','no','no','no','no','yes'],\n", + " 'sex':['male','female','female','male','male','male','female','female','male','female'],\n", + " 'weight':[80,50,48,75,68,100,40,90,88,76],\n", + " 'IQ':[100,120,90,130,140,80,94,110,100,160]})\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 分组聚合查看规律，某一条件下规律" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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3rightnomale7513075.6
4rightyesmale6814075.6
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" + ], + "text/plain": [ + " Hand Smoke sex weight IQ weight_mean\n", + "0 right yes male 80 100 75.6\n", + "1 left yes female 50 120 62.0\n", + "2 left no female 48 90 62.0\n", + "3 right no male 75 130 75.6\n", + "4 right yes male 68 140 75.6\n", + "5 right no male 100 80 75.6\n", + "6 right no female 40 94 75.6\n", + "7 right no female 90 110 75.6\n", + "8 left no male 88 100 62.0\n", + "9 right yes female 76 160 75.6" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3 = df.merge(df2,left_index=True,right_index=True)\n", + "df3" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Hand\n", + "left ([3, 3], [62.0, 103.3])\n", + "right ([7, 7], [75.6, 116.3])\n", + "dtype: object" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def count(x):\n", + " \n", + " return (x.count(),x.mean().round(1))\n", + "\n", + "df.groupby(by = ['Hand'])[['weight','IQ']].apply(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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statearea (sq. mi)
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1Alaska656425
2Arizona114006
3Arkansas53182
4California163707
5Colorado104100
6Connecticut5544
7Delaware1954
8Florida65758
9Georgia59441
10Hawaii10932
11Idaho83574
12Illinois57918
13Indiana36420
14Iowa56276
15Kansas82282
16Kentucky40411
17Louisiana51843
18Maine35387
19Maryland12407
20Massachusetts10555
21Michigan96810
22Minnesota86943
23Mississippi48434
24Missouri69709
25Montana147046
26Nebraska77358
27Nevada110567
28New Hampshire9351
29New Jersey8722
30New Mexico121593
31New York54475
32North Carolina53821
33North Dakota70704
34Ohio44828
35Oklahoma69903
36Oregon98386
37Pennsylvania46058
38Rhode Island1545
39South Carolina32007
40South Dakota77121
41Tennessee42146
42Texas268601
43Utah84904
44Vermont9615
45Virginia42769
46Washington71303
47West Virginia24231
48Wisconsin65503
49Wyoming97818
50District of Columbia68
51Puerto Rico3515
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" + ], + "text/plain": [ + " state area (sq. mi)\n", + "0 Alabama 52423\n", + "1 Alaska 656425\n", + "2 Arizona 114006\n", + "3 Arkansas 53182\n", + "4 California 163707\n", + "5 Colorado 104100\n", + "6 Connecticut 5544\n", + "7 Delaware 1954\n", + "8 Florida 65758\n", + "9 Georgia 59441\n", + "10 Hawaii 10932\n", + "11 Idaho 83574\n", + "12 Illinois 57918\n", + "13 Indiana 36420\n", + "14 Iowa 56276\n", + "15 Kansas 82282\n", + "16 Kentucky 40411\n", + "17 Louisiana 51843\n", + "18 Maine 35387\n", + "19 Maryland 12407\n", + "20 Massachusetts 10555\n", + "21 Michigan 96810\n", + "22 Minnesota 86943\n", + "23 Mississippi 48434\n", + "24 Missouri 69709\n", + "25 Montana 147046\n", + "26 Nebraska 77358\n", + "27 Nevada 110567\n", + "28 New Hampshire 9351\n", + "29 New Jersey 8722\n", + "30 New Mexico 121593\n", + "31 New York 54475\n", + "32 North Carolina 53821\n", + "33 North Dakota 70704\n", + "34 Ohio 44828\n", + "35 Oklahoma 69903\n", + "36 Oregon 98386\n", + "37 Pennsylvania 46058\n", + "38 Rhode Island 1545\n", + "39 South Carolina 32007\n", + "40 South Dakota 77121\n", + "41 Tennessee 42146\n", + "42 Texas 268601\n", + "43 Utah 84904\n", + "44 Vermont 9615\n", + "45 Virginia 42769\n", + "46 Washington 71303\n", + "47 West Virginia 24231\n", + "48 Wisconsin 65503\n", + "49 Wyoming 97818\n", + "50 District of Columbia 68\n", + "51 Puerto Rico 3515" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 美国各州的面积\n", + "areas = pd.read_csv('./state-areas.csv')\n", + "areas" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(52, 2)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "areas.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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stateabbreviation
0AlabamaAL
1AlaskaAK
2ArizonaAZ
3ArkansasAR
4CaliforniaCA
5ColoradoCO
6ConnecticutCT
7DelawareDE
8District of ColumbiaDC
9FloridaFL
10GeorgiaGA
11HawaiiHI
12IdahoID
13IllinoisIL
14IndianaIN
15IowaIA
16KansasKS
17KentuckyKY
18LouisianaLA
19MaineME
20MontanaMT
21NebraskaNE
22NevadaNV
23New HampshireNH
24New JerseyNJ
25New MexicoNM
26New YorkNY
27North CarolinaNC
28North DakotaND
29OhioOH
30OklahomaOK
31OregonOR
32MarylandMD
33MassachusettsMA
34MichiganMI
35MinnesotaMN
36MississippiMS
37MissouriMO
38PennsylvaniaPA
39Rhode IslandRI
40South CarolinaSC
41South DakotaSD
42TennesseeTN
43TexasTX
44UtahUT
45VermontVT
46VirginiaVA
47WashingtonWA
48West VirginiaWV
49WisconsinWI
50WyomingWY
\n", + "
" + ], + "text/plain": [ + " state abbreviation\n", + "0 Alabama AL\n", + "1 Alaska AK\n", + "2 Arizona AZ\n", + "3 Arkansas AR\n", + "4 California CA\n", + "5 Colorado CO\n", + "6 Connecticut CT\n", + "7 Delaware DE\n", + "8 District of Columbia DC\n", + "9 Florida FL\n", + "10 Georgia GA\n", + "11 Hawaii HI\n", + "12 Idaho ID\n", + "13 Illinois IL\n", + "14 Indiana IN\n", + "15 Iowa IA\n", + "16 Kansas KS\n", + "17 Kentucky KY\n", + "18 Louisiana LA\n", + "19 Maine ME\n", + "20 Montana MT\n", + "21 Nebraska NE\n", + "22 Nevada NV\n", + "23 New Hampshire NH\n", + "24 New Jersey NJ\n", + "25 New Mexico NM\n", + "26 New York NY\n", + "27 North Carolina NC\n", + "28 North Dakota ND\n", + "29 Ohio OH\n", + "30 Oklahoma OK\n", + "31 Oregon OR\n", + "32 Maryland MD\n", + "33 Massachusetts MA\n", + "34 Michigan MI\n", + "35 Minnesota MN\n", + "36 Mississippi MS\n", + "37 Missouri MO\n", + "38 Pennsylvania PA\n", + "39 Rhode Island RI\n", + "40 South Carolina SC\n", + "41 South Dakota SD\n", + "42 Tennessee TN\n", + "43 Texas TX\n", + "44 Utah UT\n", + "45 Vermont VT\n", + "46 Virginia VA\n", + "47 Washington WA\n", + "48 West Virginia WV\n", + "49 Wisconsin WI\n", + "50 Wyoming WY" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 美国各州 缩写\n", + "abbrevs = pd.read_csv('./state-abbrevs.csv')\n", + "abbrevs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(51, 2)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abbrevs.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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state/regionagesyearpopulation
0ALunder1820121117489.0
1ALtotal20124817528.0
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6ALtotal20094757938.0
7ALunder1820091134192.0
8ALunder1820131111481.0
9ALtotal20134833722.0
10ALtotal20074672840.0
11ALunder1820071132296.0
12ALtotal20084718206.0
13ALunder1820081134927.0
14ALtotal20054569805.0
15ALunder1820051117229.0
16ALtotal20064628981.0
17ALunder1820061126798.0
18ALtotal20044530729.0
19ALunder1820041113662.0
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26ALunder1819991121287.0
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28ALtotal20004452173.0
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2514USAunder18199971946051.0
2515USAtotal2000282162411.0
2516USAunder18200072376189.0
2517USAtotal1999279040181.0
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stateabbreviation
0AlabamaAL
1AlaskaAK
2ArizonaAZ
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4CaliforniaCA
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" + ], + "text/plain": [ + " state abbreviation\n", + "0 Alabama AL\n", + "1 Alaska AK\n", + "2 Arizona AZ\n", + "3 Arkansas AR\n", + "4 California CA" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abbrevs.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2544, 4)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(51, 2)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(pop.shape,abbrevs.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2544, 6)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 级联时，数据变少了96个，哪些数据变少\n", + "# inner内连接，outer叫做外连接\n", + "pop2 = pop.merge(abbrevs,how = 'outer',left_on='state/region',right_on='abbreviation')\n", + "pop2.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state/region False\n", + "ages False\n", + "year False\n", + "population True\n", + "state True\n", + "abbreviation True\n", + "dtype: bool" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 前三列没有空值\n", + "pop2.isnull().any()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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state/regionagesyearpopulationstateabbreviation
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state/regionagesyearpopulationstate
0ALunder1820121117489.0Alabama
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statearea (sq. mi)
0Alabama52423
1Alaska656425
2Arizona114006
3Arkansas53182
4California163707
5Colorado104100
6Connecticut5544
7Delaware1954
8Florida65758
9Georgia59441
10Hawaii10932
11Idaho83574
12Illinois57918
13Indiana36420
14Iowa56276
15Kansas82282
16Kentucky40411
17Louisiana51843
18Maine35387
19Maryland12407
20Massachusetts10555
21Michigan96810
22Minnesota86943
23Mississippi48434
24Missouri69709
25Montana147046
26Nebraska77358
27Nevada110567
28New Hampshire9351
29New Jersey8722
30New Mexico121593
31New York54475
32North Carolina53821
33North Dakota70704
34Ohio44828
35Oklahoma69903
36Oregon98386
37Pennsylvania46058
38Rhode Island1545
39South Carolina32007
40South Dakota77121
41Tennessee42146
42Texas268601
43Utah84904
44Vermont9615
45Virginia42769
46Washington71303
47West Virginia24231
48Wisconsin65503
49Wyoming97818
50District of Columbia68
51Puerto Rico3515
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"stderr", + "output_type": "stream", + "text": [ + "d:\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], + "source": [ + "pop2['state'][cond] = 'Puerto Rico'" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\python36\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " \n" + ] + } + ], + "source": [ + "cond = pop2['state/region'] == 'USA'\n", + "pop2['state'][cond] = 'United State'" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state/region False\n", + "ages False\n", + "year False\n", + "population True\n", + "state False\n", + "dtype: bool" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop2.isnull().any()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(20, 5)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cond = pop2['population'].isnull()\n", + "pop2[cond].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2544, 5)" + ] + }, + "execution_count": 43, + 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+ "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "state/region True\n", + "ages True\n", + "year True\n", + "population True\n", + "state True\n", + "dtype: bool" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop2.notnull().all()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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191ARtotal20122949828.0Arkansas53182.055.5
193CAtotal201237999878.0California163707.0232.1
287COtotal20125189458.0Colorado104100.049.9
289CTtotal20123591765.0Connecticut5544.0647.9
383DEtotal2012917053.0Delaware1954.0469.3
385DCtotal2012633427.0District of Columbia68.09315.1
479FLtotal201219320749.0Florida65758.0293.8
480GAtotal20129915646.0Georgia59441.0166.8
575HItotal20121390090.0Hawaii10932.0127.2
576IDtotal20121595590.0Idaho83574.019.1
671ILtotal201212868192.0Illinois57918.0222.2
672INtotal20126537782.0Indiana36420.0179.5
767IAtotal20123075039.0Iowa56276.054.6
768KStotal20122885398.0Kansas82282.035.1
863KYtotal20124379730.0Kentucky40411.0108.4
864LAtotal20124602134.0Louisiana51843.088.8
959MEtotal20121328501.0Maine35387.037.5
960MDtotal20125884868.0Maryland12407.0474.3
1055MAtotal20126645303.0Massachusetts10555.0629.6
1056MItotal20129882519.0Michigan96810.0102.1
1151MNtotal20125379646.0Minnesota86943.061.9
1152MStotal20122986450.0Mississippi48434.061.7
1247MOtotal20126024522.0Missouri69709.086.4
1248MTtotal20121005494.0Montana147046.06.8
1343NEtotal20121855350.0Nebraska77358.024.0
1344NVtotal20122754354.0Nevada110567.024.9
1439NHtotal20121321617.0New Hampshire9351.0141.3
1440NJtotal20128867749.0New Jersey8722.01016.7
1535NMtotal20122083540.0New Mexico121593.017.1
1536NYtotal201219576125.0New York54475.0359.4
1631NCtotal20129748364.0North Carolina53821.0181.1
1632NDtotal2012701345.0North Dakota70704.09.9
1727OHtotal201211553031.0Ohio44828.0257.7
1728OKtotal20123815780.0Oklahoma69903.054.6
1823ORtotal20123899801.0Oregon98386.039.6
1824PAtotal201212764475.0Pennsylvania46058.0277.1
1919RItotal20121050304.0Rhode Island1545.0679.8
1920SCtotal20124723417.0South Carolina32007.0147.6
2015SDtotal2012834047.0South Dakota77121.010.8
2016TNtotal20126454914.0Tennessee42146.0153.2
2111TXtotal201226060796.0Texas268601.097.0
2112UTtotal20122854871.0Utah84904.033.6
2207VTtotal2012625953.0Vermont9615.065.1
2208VAtotal20128186628.0Virginia42769.0191.4
2303WAtotal20126895318.0Washington71303.096.7
2304WVtotal20121856680.0West Virginia24231.076.6
2399WItotal20125724554.0Wisconsin65503.087.4
2400WYtotal2012576626.0Wyoming97818.05.9
2475PRtotal20123651545.0Puerto Rico3515.01038.8
2523USAtotal2012313873685.0United State3790399.082.8
\n", + "
" + ], + "text/plain": [ + " state/region ages year population state area (sq. mi) pop_density\n", + "1 AL total 2012 4817528.0 Alabama 52423.0 91.9\n", + "95 AK total 2012 730307.0 Alaska 656425.0 1.1\n", + "97 AZ total 2012 6551149.0 Arizona 114006.0 57.5\n", + "191 AR total 2012 2949828.0 Arkansas 53182.0 55.5\n", + "193 CA total 2012 37999878.0 California 163707.0 232.1\n", + "287 CO total 2012 5189458.0 Colorado 104100.0 49.9\n", + "289 CT total 2012 3591765.0 Connecticut 5544.0 647.9\n", + "383 DE total 2012 917053.0 Delaware 1954.0 469.3\n", + "385 DC total 2012 633427.0 District of Columbia 68.0 9315.1\n", + "479 FL total 2012 19320749.0 Florida 65758.0 293.8\n", + "480 GA total 2012 9915646.0 Georgia 59441.0 166.8\n", + "575 HI total 2012 1390090.0 Hawaii 10932.0 127.2\n", + "576 ID total 2012 1595590.0 Idaho 83574.0 19.1\n", + "671 IL total 2012 12868192.0 Illinois 57918.0 222.2\n", + "672 IN total 2012 6537782.0 Indiana 36420.0 179.5\n", + "767 IA total 2012 3075039.0 Iowa 56276.0 54.6\n", + "768 KS total 2012 2885398.0 Kansas 82282.0 35.1\n", + "863 KY total 2012 4379730.0 Kentucky 40411.0 108.4\n", + "864 LA total 2012 4602134.0 Louisiana 51843.0 88.8\n", + "959 ME total 2012 1328501.0 Maine 35387.0 37.5\n", + "960 MD total 2012 5884868.0 Maryland 12407.0 474.3\n", + "1055 MA total 2012 6645303.0 Massachusetts 10555.0 629.6\n", + "1056 MI total 2012 9882519.0 Michigan 96810.0 102.1\n", + "1151 MN total 2012 5379646.0 Minnesota 86943.0 61.9\n", + "1152 MS total 2012 2986450.0 Mississippi 48434.0 61.7\n", + "1247 MO total 2012 6024522.0 Missouri 69709.0 86.4\n", + "1248 MT total 2012 1005494.0 Montana 147046.0 6.8\n", + "1343 NE total 2012 1855350.0 Nebraska 77358.0 24.0\n", + "1344 NV total 2012 2754354.0 Nevada 110567.0 24.9\n", + "1439 NH total 2012 1321617.0 New Hampshire 9351.0 141.3\n", + "1440 NJ total 2012 8867749.0 New Jersey 8722.0 1016.7\n", + "1535 NM total 2012 2083540.0 New Mexico 121593.0 17.1\n", + "1536 NY total 2012 19576125.0 New York 54475.0 359.4\n", + "1631 NC total 2012 9748364.0 North Carolina 53821.0 181.1\n", + "1632 ND total 2012 701345.0 North Dakota 70704.0 9.9\n", + "1727 OH total 2012 11553031.0 Ohio 44828.0 257.7\n", + "1728 OK total 2012 3815780.0 Oklahoma 69903.0 54.6\n", + "1823 OR total 2012 3899801.0 Oregon 98386.0 39.6\n", + "1824 PA total 2012 12764475.0 Pennsylvania 46058.0 277.1\n", + "1919 RI total 2012 1050304.0 Rhode Island 1545.0 679.8\n", + "1920 SC total 2012 4723417.0 South Carolina 32007.0 147.6\n", + "2015 SD total 2012 834047.0 South Dakota 77121.0 10.8\n", + "2016 TN total 2012 6454914.0 Tennessee 42146.0 153.2\n", + "2111 TX total 2012 26060796.0 Texas 268601.0 97.0\n", + "2112 UT total 2012 2854871.0 Utah 84904.0 33.6\n", + "2207 VT total 2012 625953.0 Vermont 9615.0 65.1\n", + "2208 VA total 2012 8186628.0 Virginia 42769.0 191.4\n", + "2303 WA total 2012 6895318.0 Washington 71303.0 96.7\n", + "2304 WV total 2012 1856680.0 West Virginia 24231.0 76.6\n", + "2399 WI total 2012 5724554.0 Wisconsin 65503.0 87.4\n", + "2400 WY total 2012 576626.0 Wyoming 97818.0 5.9\n", + "2475 PR total 2012 3651545.0 Puerto Rico 3515.0 1038.8\n", + "2523 USA total 2012 313873685.0 United State 3790399.0 82.8" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 查找2012年美国各州的全民人口数据\n", + "\n", + "# pandas非常强大的，可以像查询数据库一样进行数据查询\n", + "\n", + "pop5 = pop4.query(\"year == 2012 and ages == 'total'\")\n", + "pop5" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "pop5.set_index(keys = 'state/region',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesyearpopulationstatearea (sq. mi)pop_density
state/region
AKtotal2012730307.0Alaska656425.01.1
WYtotal2012576626.0Wyoming97818.05.9
MTtotal20121005494.0Montana147046.06.8
NDtotal2012701345.0North Dakota70704.09.9
SDtotal2012834047.0South Dakota77121.010.8
NMtotal20122083540.0New Mexico121593.017.1
IDtotal20121595590.0Idaho83574.019.1
NEtotal20121855350.0Nebraska77358.024.0
NVtotal20122754354.0Nevada110567.024.9
UTtotal20122854871.0Utah84904.033.6
KStotal20122885398.0Kansas82282.035.1
MEtotal20121328501.0Maine35387.037.5
ORtotal20123899801.0Oregon98386.039.6
COtotal20125189458.0Colorado104100.049.9
IAtotal20123075039.0Iowa56276.054.6
OKtotal20123815780.0Oklahoma69903.054.6
ARtotal20122949828.0Arkansas53182.055.5
AZtotal20126551149.0Arizona114006.057.5
MStotal20122986450.0Mississippi48434.061.7
MNtotal20125379646.0Minnesota86943.061.9
VTtotal2012625953.0Vermont9615.065.1
WVtotal20121856680.0West Virginia24231.076.6
USAtotal2012313873685.0United State3790399.082.8
MOtotal20126024522.0Missouri69709.086.4
WItotal20125724554.0Wisconsin65503.087.4
LAtotal20124602134.0Louisiana51843.088.8
ALtotal20124817528.0Alabama52423.091.9
WAtotal20126895318.0Washington71303.096.7
TXtotal201226060796.0Texas268601.097.0
MItotal20129882519.0Michigan96810.0102.1
KYtotal20124379730.0Kentucky40411.0108.4
HItotal20121390090.0Hawaii10932.0127.2
NHtotal20121321617.0New Hampshire9351.0141.3
SCtotal20124723417.0South Carolina32007.0147.6
TNtotal20126454914.0Tennessee42146.0153.2
GAtotal20129915646.0Georgia59441.0166.8
INtotal20126537782.0Indiana36420.0179.5
NCtotal20129748364.0North Carolina53821.0181.1
VAtotal20128186628.0Virginia42769.0191.4
ILtotal201212868192.0Illinois57918.0222.2
CAtotal201237999878.0California163707.0232.1
OHtotal201211553031.0Ohio44828.0257.7
PAtotal201212764475.0Pennsylvania46058.0277.1
FLtotal201219320749.0Florida65758.0293.8
NYtotal201219576125.0New York54475.0359.4
DEtotal2012917053.0Delaware1954.0469.3
MDtotal20125884868.0Maryland12407.0474.3
MAtotal20126645303.0Massachusetts10555.0629.6
CTtotal20123591765.0Connecticut5544.0647.9
RItotal20121050304.0Rhode Island1545.0679.8
NJtotal20128867749.0New Jersey8722.01016.7
PRtotal20123651545.0Puerto Rico3515.01038.8
DCtotal2012633427.0District of Columbia68.09315.1
\n", + "
" + ], + "text/plain": [ + " ages year population state area (sq. mi) pop_density\n", + "state/region \n", + "AK total 2012 730307.0 Alaska 656425.0 1.1\n", + "WY total 2012 576626.0 Wyoming 97818.0 5.9\n", + "MT total 2012 1005494.0 Montana 147046.0 6.8\n", + "ND total 2012 701345.0 North Dakota 70704.0 9.9\n", + "SD total 2012 834047.0 South Dakota 77121.0 10.8\n", + "NM total 2012 2083540.0 New Mexico 121593.0 17.1\n", + "ID total 2012 1595590.0 Idaho 83574.0 19.1\n", + "NE total 2012 1855350.0 Nebraska 77358.0 24.0\n", + "NV total 2012 2754354.0 Nevada 110567.0 24.9\n", + "UT total 2012 2854871.0 Utah 84904.0 33.6\n", + "KS total 2012 2885398.0 Kansas 82282.0 35.1\n", + "ME total 2012 1328501.0 Maine 35387.0 37.5\n", + "OR total 2012 3899801.0 Oregon 98386.0 39.6\n", + "CO total 2012 5189458.0 Colorado 104100.0 49.9\n", + "IA total 2012 3075039.0 Iowa 56276.0 54.6\n", + "OK total 2012 3815780.0 Oklahoma 69903.0 54.6\n", + "AR total 2012 2949828.0 Arkansas 53182.0 55.5\n", + "AZ total 2012 6551149.0 Arizona 114006.0 57.5\n", + "MS total 2012 2986450.0 Mississippi 48434.0 61.7\n", + "MN total 2012 5379646.0 Minnesota 86943.0 61.9\n", + "VT total 2012 625953.0 Vermont 9615.0 65.1\n", + "WV total 2012 1856680.0 West Virginia 24231.0 76.6\n", + "USA total 2012 313873685.0 United State 3790399.0 82.8\n", + "MO total 2012 6024522.0 Missouri 69709.0 86.4\n", + "WI total 2012 5724554.0 Wisconsin 65503.0 87.4\n", + "LA total 2012 4602134.0 Louisiana 51843.0 88.8\n", + "AL total 2012 4817528.0 Alabama 52423.0 91.9\n", + "WA total 2012 6895318.0 Washington 71303.0 96.7\n", + "TX total 2012 26060796.0 Texas 268601.0 97.0\n", + "MI total 2012 9882519.0 Michigan 96810.0 102.1\n", + "KY total 2012 4379730.0 Kentucky 40411.0 108.4\n", + "HI total 2012 1390090.0 Hawaii 10932.0 127.2\n", + "NH total 2012 1321617.0 New Hampshire 9351.0 141.3\n", + "SC total 2012 4723417.0 South Carolina 32007.0 147.6\n", + "TN total 2012 6454914.0 Tennessee 42146.0 153.2\n", + "GA total 2012 9915646.0 Georgia 59441.0 166.8\n", + "IN total 2012 6537782.0 Indiana 36420.0 179.5\n", + "NC total 2012 9748364.0 North Carolina 53821.0 181.1\n", + "VA total 2012 8186628.0 Virginia 42769.0 191.4\n", + "IL total 2012 12868192.0 Illinois 57918.0 222.2\n", + "CA total 2012 37999878.0 California 163707.0 232.1\n", + "OH total 2012 11553031.0 Ohio 44828.0 257.7\n", + "PA total 2012 12764475.0 Pennsylvania 46058.0 277.1\n", + "FL total 2012 19320749.0 Florida 65758.0 293.8\n", + "NY total 2012 19576125.0 New York 54475.0 359.4\n", + "DE total 2012 917053.0 Delaware 1954.0 469.3\n", + "MD total 2012 5884868.0 Maryland 12407.0 474.3\n", + "MA total 2012 6645303.0 Massachusetts 10555.0 629.6\n", + "CT total 2012 3591765.0 Connecticut 5544.0 647.9\n", + "RI total 2012 1050304.0 Rhode Island 1545.0 679.8\n", + "NJ total 2012 8867749.0 New Jersey 8722.0 1016.7\n", + "PR total 2012 3651545.0 Puerto Rico 3515.0 1038.8\n", + "DC total 2012 633427.0 District of Columbia 68.0 9315.1" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop5.sort_values(by = 'pop_density')" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesyearpopulationstatearea (sq. mi)pop_density
state/region
DCtotal2012633427.0District of Columbia68.09315.1
PRtotal20123651545.0Puerto Rico3515.01038.8
NJtotal20128867749.0New Jersey8722.01016.7
RItotal20121050304.0Rhode Island1545.0679.8
CTtotal20123591765.0Connecticut5544.0647.9
MAtotal20126645303.0Massachusetts10555.0629.6
MDtotal20125884868.0Maryland12407.0474.3
DEtotal2012917053.0Delaware1954.0469.3
NYtotal201219576125.0New York54475.0359.4
FLtotal201219320749.0Florida65758.0293.8
PAtotal201212764475.0Pennsylvania46058.0277.1
OHtotal201211553031.0Ohio44828.0257.7
CAtotal201237999878.0California163707.0232.1
ILtotal201212868192.0Illinois57918.0222.2
VAtotal20128186628.0Virginia42769.0191.4
NCtotal20129748364.0North Carolina53821.0181.1
INtotal20126537782.0Indiana36420.0179.5
GAtotal20129915646.0Georgia59441.0166.8
TNtotal20126454914.0Tennessee42146.0153.2
SCtotal20124723417.0South Carolina32007.0147.6
NHtotal20121321617.0New Hampshire9351.0141.3
HItotal20121390090.0Hawaii10932.0127.2
KYtotal20124379730.0Kentucky40411.0108.4
MItotal20129882519.0Michigan96810.0102.1
TXtotal201226060796.0Texas268601.097.0
WAtotal20126895318.0Washington71303.096.7
ALtotal20124817528.0Alabama52423.091.9
LAtotal20124602134.0Louisiana51843.088.8
WItotal20125724554.0Wisconsin65503.087.4
MOtotal20126024522.0Missouri69709.086.4
USAtotal2012313873685.0United State3790399.082.8
WVtotal20121856680.0West Virginia24231.076.6
VTtotal2012625953.0Vermont9615.065.1
MNtotal20125379646.0Minnesota86943.061.9
MStotal20122986450.0Mississippi48434.061.7
AZtotal20126551149.0Arizona114006.057.5
ARtotal20122949828.0Arkansas53182.055.5
OKtotal20123815780.0Oklahoma69903.054.6
IAtotal20123075039.0Iowa56276.054.6
COtotal20125189458.0Colorado104100.049.9
ORtotal20123899801.0Oregon98386.039.6
MEtotal20121328501.0Maine35387.037.5
KStotal20122885398.0Kansas82282.035.1
UTtotal20122854871.0Utah84904.033.6
NVtotal20122754354.0Nevada110567.024.9
NEtotal20121855350.0Nebraska77358.024.0
IDtotal20121595590.0Idaho83574.019.1
NMtotal20122083540.0New Mexico121593.017.1
SDtotal2012834047.0South Dakota77121.010.8
NDtotal2012701345.0North Dakota70704.09.9
MTtotal20121005494.0Montana147046.06.8
WYtotal2012576626.0Wyoming97818.05.9
AKtotal2012730307.0Alaska656425.01.1
\n", + "
" + ], + "text/plain": [ + " ages year population state area (sq. mi) pop_density\n", + "state/region \n", + "DC total 2012 633427.0 District of Columbia 68.0 9315.1\n", + "PR total 2012 3651545.0 Puerto Rico 3515.0 1038.8\n", + "NJ total 2012 8867749.0 New Jersey 8722.0 1016.7\n", + "RI total 2012 1050304.0 Rhode Island 1545.0 679.8\n", + "CT total 2012 3591765.0 Connecticut 5544.0 647.9\n", + "MA total 2012 6645303.0 Massachusetts 10555.0 629.6\n", + "MD total 2012 5884868.0 Maryland 12407.0 474.3\n", + "DE total 2012 917053.0 Delaware 1954.0 469.3\n", + "NY total 2012 19576125.0 New York 54475.0 359.4\n", + "FL total 2012 19320749.0 Florida 65758.0 293.8\n", + "PA total 2012 12764475.0 Pennsylvania 46058.0 277.1\n", + "OH total 2012 11553031.0 Ohio 44828.0 257.7\n", + "CA total 2012 37999878.0 California 163707.0 232.1\n", + "IL total 2012 12868192.0 Illinois 57918.0 222.2\n", + "VA total 2012 8186628.0 Virginia 42769.0 191.4\n", + "NC total 2012 9748364.0 North Carolina 53821.0 181.1\n", + "IN total 2012 6537782.0 Indiana 36420.0 179.5\n", + "GA total 2012 9915646.0 Georgia 59441.0 166.8\n", + "TN total 2012 6454914.0 Tennessee 42146.0 153.2\n", + "SC total 2012 4723417.0 South Carolina 32007.0 147.6\n", + "NH total 2012 1321617.0 New Hampshire 9351.0 141.3\n", + "HI total 2012 1390090.0 Hawaii 10932.0 127.2\n", + "KY total 2012 4379730.0 Kentucky 40411.0 108.4\n", + "MI total 2012 9882519.0 Michigan 96810.0 102.1\n", + "TX total 2012 26060796.0 Texas 268601.0 97.0\n", + "WA total 2012 6895318.0 Washington 71303.0 96.7\n", + "AL total 2012 4817528.0 Alabama 52423.0 91.9\n", + "LA total 2012 4602134.0 Louisiana 51843.0 88.8\n", + "WI total 2012 5724554.0 Wisconsin 65503.0 87.4\n", + "MO total 2012 6024522.0 Missouri 69709.0 86.4\n", + "USA total 2012 313873685.0 United State 3790399.0 82.8\n", + "WV total 2012 1856680.0 West Virginia 24231.0 76.6\n", + "VT total 2012 625953.0 Vermont 9615.0 65.1\n", + "MN total 2012 5379646.0 Minnesota 86943.0 61.9\n", + "MS total 2012 2986450.0 Mississippi 48434.0 61.7\n", + "AZ total 2012 6551149.0 Arizona 114006.0 57.5\n", + "AR total 2012 2949828.0 Arkansas 53182.0 55.5\n", + "OK total 2012 3815780.0 Oklahoma 69903.0 54.6\n", + "IA total 2012 3075039.0 Iowa 56276.0 54.6\n", + "CO total 2012 5189458.0 Colorado 104100.0 49.9\n", + "OR total 2012 3899801.0 Oregon 98386.0 39.6\n", + "ME total 2012 1328501.0 Maine 35387.0 37.5\n", + "KS total 2012 2885398.0 Kansas 82282.0 35.1\n", + "UT total 2012 2854871.0 Utah 84904.0 33.6\n", + "NV total 2012 2754354.0 Nevada 110567.0 24.9\n", + "NE total 2012 1855350.0 Nebraska 77358.0 24.0\n", + "ID total 2012 1595590.0 Idaho 83574.0 19.1\n", + "NM total 2012 2083540.0 New Mexico 121593.0 17.1\n", + "SD total 2012 834047.0 South Dakota 77121.0 10.8\n", + "ND total 2012 701345.0 North Dakota 70704.0 9.9\n", + "MT total 2012 1005494.0 Montana 147046.0 6.8\n", + "WY total 2012 576626.0 Wyoming 97818.0 5.9\n", + "AK total 2012 730307.0 Alaska 656425.0 1.1" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop5.sort_values(by='pop_density',ascending=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Day76-90/code/cancer_predict.npy b/Day76-90/code/cancer_predict.npy new file mode 100644 index 0000000000000000000000000000000000000000..a6bf034d44f5d207081381c00677db8ce4bcd7da GIT binary patch literal 450 zcmbu2O-sZu5QfvLtH$rHKcKf(CRb1&+N=vS| zb9U~YxYnEXkHYUgme-TA@mL|aH>oo@rb5sLM}o5=ucWA%#DH`P=ZSW~AZP|X7O^7X ztin8R2MlYWfe?yeJ?tyYs2t~~u<-%?05<2a6~MMb401UG{ChBwO{w;F!k)^HE?}1+ zV$5MLfPIJPYg0JzegE!TRoBS1gv0pr367lZw;M1!9Ks+Sq-OWbKh)Y%ZJn02$;GtF Sw5GjfUrj7FO)lX$_I?1@n0Pq= literal 0 HcmV?d00001 diff --git a/Day76-90/code/state-abbrevs.csv b/Day76-90/code/state-abbrevs.csv new file mode 100644 index 0000000..6d4db36 --- /dev/null +++ b/Day76-90/code/state-abbrevs.csv @@ -0,0 +1,52 @@ +"state","abbreviation" +"Alabama","AL" +"Alaska","AK" +"Arizona","AZ" +"Arkansas","AR" +"California","CA" +"Colorado","CO" +"Connecticut","CT" +"Delaware","DE" +"District of Columbia","DC" +"Florida","FL" +"Georgia","GA" +"Hawaii","HI" +"Idaho","ID" +"Illinois","IL" +"Indiana","IN" +"Iowa","IA" +"Kansas","KS" +"Kentucky","KY" +"Louisiana","LA" +"Maine","ME" +"Montana","MT" +"Nebraska","NE" +"Nevada","NV" +"New Hampshire","NH" +"New Jersey","NJ" +"New Mexico","NM" +"New York","NY" +"North Carolina","NC" +"North Dakota","ND" +"Ohio","OH" +"Oklahoma","OK" +"Oregon","OR" +"Maryland","MD" +"Massachusetts","MA" +"Michigan","MI" +"Minnesota","MN" +"Mississippi","MS" +"Missouri","MO" +"Pennsylvania","PA" +"Rhode Island","RI" +"South Carolina","SC" +"South Dakota","SD" +"Tennessee","TN" +"Texas","TX" +"Utah","UT" +"Vermont","VT" +"Virginia","VA" +"Washington","WA" +"West Virginia","WV" +"Wisconsin","WI" +"Wyoming","WY" \ No newline at end of file diff --git a/Day76-90/code/state-areas.csv b/Day76-90/code/state-areas.csv new file mode 100644 index 0000000..322345c --- /dev/null +++ b/Day76-90/code/state-areas.csv @@ -0,0 +1,53 @@ +state,area (sq. mi) +Alabama,52423 +Alaska,656425 +Arizona,114006 +Arkansas,53182 +California,163707 +Colorado,104100 +Connecticut,5544 +Delaware,1954 +Florida,65758 +Georgia,59441 +Hawaii,10932 +Idaho,83574 +Illinois,57918 +Indiana,36420 +Iowa,56276 +Kansas,82282 +Kentucky,40411 +Louisiana,51843 +Maine,35387 +Maryland,12407 +Massachusetts,10555 +Michigan,96810 +Minnesota,86943 +Mississippi,48434 +Missouri,69709 +Montana,147046 +Nebraska,77358 +Nevada,110567 +New Hampshire,9351 +New Jersey,8722 +New Mexico,121593 +New York,54475 +North 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