From a2a6a3f1694c5eab8ed81eda6e15b61a05fa40e9 Mon Sep 17 00:00:00 2001 From: HenryGuo Date: Wed, 14 Mar 2018 15:21:24 +0800 Subject: [PATCH] Add files via upload --- intro_to_pandas.ipynb | 2164 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2164 insertions(+) create mode 100644 intro_to_pandas.ipynb diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb new file mode 100644 index 00000000..fb20c1c3 --- /dev/null +++ b/intro_to_pandas.ipynb @@ -0,0 +1,2164 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "intro_to_pandas.ipynb", + "version": "0.3.2", + "views": {}, + "default_view": {}, + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "YHIWvc9Ms-Ll", + "TJffr5_Jwqvd" + ] + } + }, + "cells": [ + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [], + "base_uri": "https://localhost:8080/", + "height": 10 + }, + "cellView": "both", + "outputId": "05f2545d-b3d6-48dd-ffdd-af3910b78f45", + "executionInfo": { + "status": "ok", + "timestamp": 1521010393734, + "user_tz": -480, + "elapsed": 1017, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 1, + "outputs": [] + }, + { + "metadata": { + "id": "rHLcriKWLRe4", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " # Pandas 简介" + ] + }, + { + "metadata": { + "id": "QvJBqX8_Bctk", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**学习目标：**\n", + " * 大致了解 *pandas* 库的 `DataFrame` 和 `Series` 数据结构\n", + " * 存取和处理 `DataFrame` 和 `Series` 中的数据\n", + " * 将 CSV 数据导入 pandas 库的 `DataFrame`\n", + " * 对 `DataFrame` 重建索引来随机打乱数据" + ] + }, + { + "metadata": { + "id": "TIFJ83ZTBctl", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " [*pandas*](http://pandas.pydata.org/) 是一种列存数据分析 API。它是用于处理和分析输入数据的强大工具，很多机器学习框架都支持将 *pandas* 数据结构作为输入。\n", + "虽然全方位介绍 *pandas* API 会占据很长篇幅，但它的核心概念非常简单，我们会在下文中进行说明。有关更完整的参考，请访问 [*pandas* 文档网站](http://pandas.pydata.org/pandas-docs/stable/index.html)，其中包含丰富的文档和教程资源。" + ] + }, + { + "metadata": { + "id": "s_JOISVgmn9v", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " ## 基本概念\n", + "\n", + "以下行导入了 *pandas* API 并输出了相应的 API 版本：" + ] + }, + { + "metadata": { + "id": "aSRYu62xUi3g", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "9a9a623b-d844-4589-82b6-3d77576392b0", + "executionInfo": { + "status": "ok", + "timestamp": 1521010399674, + "user_tz": -480, + "elapsed": 680, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "u'0.22.0'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "id": "daQreKXIUslr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " *pandas* 中的主要数据结构被实现为以下两类：\n", + "\n", + " * **`DataFrame`**，您可以将它想象成一个关系型数据表格，其中包含多个行和已命名的列。\n", + " * **`Series`**，它是单一列。`DataFrame` 中包含一个或多个 `Series`，每个 `Series` 均有一个名称。\n", + "\n", + "数据框架是用于数据操控的一种常用抽象实现形式。[Spark](https://spark.apache.org/) 和 [R](https://www.r-project.org/about.html) 中也有类似的实现。" + ] + }, + { + "metadata": { + "id": "fjnAk1xcU0yc", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " 创建 `Series` 的一种方法是构建 `Series` 对象。例如：" + ] + }, + { + "metadata": { + "id": "DFZ42Uq7UFDj", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "outputId": "adfba658-149f-49a9-fdea-f2b9731415d0", + "executionInfo": { + "status": "ok", + "timestamp": 1521010401070, + "user_tz": -480, + "elapsed": 1136, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "U5ouUp1cU6pC", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " 您可以将映射 `string` 列名称的 `dict` 传递到它们各自的 `Series`，从而创建`DataFrame`对象。如果 `Series` 在长度上不一致，系统会用特殊的 [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) 值填充缺失的值。例如：" + ] + }, + { + "metadata": { + "id": "avgr6GfiUh8t", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "8d24a6a0-3389-43c8-d82e-fbf4380b14a5", + "executionInfo": { + "status": "ok", + "timestamp": 1521010401875, + "user_tz": -480, + "elapsed": 638, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n", + "population = pd.Series([852469, 1015785, 485199])\n", + "\n", + "pd.DataFrame({ 'City name': city_names, 'Population': population })" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785\n", + "2 Sacramento 485199" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "oa5wfZT7VHJl", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " 但是在大多数情况下，您需要将整个文件加载到 `DataFrame` 中。下面的示例加载了一个包含加利福尼亚州住房数据的文件。请运行以下单元格以加载数据，并创建特征定义：" + ] + }, + { + "metadata": { + "id": "av6RYOraVG1V", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 317 + }, + "outputId": "f2133493-5b8f-46c1-cabf-8781faac8b76", + "executionInfo": { + "status": "ok", + "timestamp": 1521010403215, + "user_tz": -480, + "elapsed": 1145, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://storage.googleapis.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "california_housing_dataframe.describe()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -114.31 34.19 15.0 5612.0 1283.0 \n", + "1 -114.47 34.40 19.0 7650.0 1901.0 \n", + "2 -114.56 33.69 17.0 720.0 174.0 \n", + "3 -114.57 33.64 14.0 1501.0 337.0 \n", + "4 -114.57 33.57 20.0 1454.0 326.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 1015.0 472.0 1.4936 66900.0 \n", + "1 1129.0 463.0 1.8200 80100.0 \n", + "2 333.0 117.0 1.6509 85700.0 \n", + "3 515.0 226.0 3.1917 73400.0 \n", + "4 624.0 262.0 1.9250 65500.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "metadata": { + "id": "w9-Es5Y6laGd", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " *pandas* 的另一个强大功能是绘制图表。例如，借助 `DataFrame.hist`，您可以快速了解一个列中值的分布：" + ] + }, + { + "metadata": { + "id": "nqndFVXVlbPN", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "XtYZ7114n3b-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " ## 访问数据\n", + "\n", + "您可以使用熟悉的 Python dict/list 指令访问 `DataFrame` 数据：" + ] + }, + { + "metadata": { + "id": "_TFm7-looBFF", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + }, + { + "item_id": 2 + } + ], + "base_uri": "https://localhost:8080/", + "height": 103 + }, + "outputId": "bbe17a44-79e3-461f-c0a7-e8a223ddfd51", + "executionInfo": { + "status": "ok", + "timestamp": 1521010406315, + "user_tz": -480, + "elapsed": 862, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n", + "print type(cities['City name'])\n", + "cities['City name']" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "Name: City name, dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "id": "V5L6xacLoxyv", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + }, + { + "item_id": 2 + } + ], + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "outputId": "a1e39b8f-2cb2-4d78-9db0-def04b01601b", + "executionInfo": { + "status": "ok", + "timestamp": 1521010407120, + "user_tz": -480, + "elapsed": 674, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "print type(cities['City name'][1])\n", + "cities['City name'][1]" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'San Jose'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "id": "gcYX1tBPugZl", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + }, + { + "item_id": 2 + } + ], + "base_uri": "https://localhost:8080/", + "height": 128 + }, + "outputId": "a3b3266a-5ec3-4845-a783-22efb1ce0442", + "executionInfo": { + "status": "ok", + "timestamp": 1521010408033, + "user_tz": -480, + "elapsed": 731, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "print type(cities[0:2])\n", + "cities[0:2]" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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1San Jose1015785
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "65g1ZdGVjXsQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " 此外，*pandas* 针对高级[索引和选择](http://pandas.pydata.org/pandas-docs/stable/indexing.html)提供了极其丰富的 API（数量过多，此处无法逐一列出）。" + ] + }, + { + "metadata": { + "id": "RM1iaD-ka3Y1", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " ## 操控数据\n", + "\n", + "您可以向 `Series` 应用 Python 的基本运算指令。例如：" + ] + }, + { + "metadata": { + "id": "XWmyCFJ5bOv-", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "outputId": "d589778a-9330-43a7-9e1c-168a1683753f", + "executionInfo": { + "status": "ok", + "timestamp": 1521010408873, + "user_tz": -480, + "elapsed": 706, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 852.469\n", + "1 1015.785\n", + "2 485.199\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "id": "TQzIVnbnmWGM", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " [NumPy](http://www.numpy.org/) 是一种用于进行科学计算的常用工具包。*pandas* `Series` 可用作大多数 NumPy 函数的参数：" + ] + }, + { + "metadata": { + "id": "ko6pLK6JmkYP", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "outputId": "b5167c98-2a6d-450d-d1bf-bf25b9391553", + "executionInfo": { + "status": "ok", + "timestamp": 1521010409849, + "user_tz": -480, + "elapsed": 729, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 13.655892\n", + "1 13.831172\n", + "2 13.092314\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "id": "xmxFuQmurr6d", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " 对于更复杂的单列转换，您可以使用 `Series.apply`。像 Python [映射函数](https://docs.python.org/2/library/functions.html#map)一样，`Series.apply` 将以参数形式接受 [lambda 函数](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions)，而该函数会应用于每个值。\n", + "\n", + "下面的示例创建了一个指明 `population` 是否超过 100 万的新 `Series`：" + ] + }, + { + "metadata": { + "id": "Fc1DvPAbstjI", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "outputId": "c08ade5b-0c08-4894-dc98-fbc948489e4e", + "executionInfo": { + "status": "ok", + "timestamp": 1521010410894, + "user_tz": -480, + "elapsed": 892, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "id": "ZeYYLoV9b9fB", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " \n", + "`DataFrames` 的修改方式也非常简单。例如，以下代码向现有 `DataFrame` 添加了两个 `Series`：" + ] + }, + { + "metadata": { + "id": "0gCEX99Hb8LR", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "428e6abf-fa8b-4f28-c1c6-0aecaacaf5f7", + "executionInfo": { + "status": "ok", + "timestamp": 1521010412133, + "user_tz": -480, + "elapsed": 1101, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n", + "cities['Population density'] = cities['Population'] / cities['Area square miles']\n", + "cities" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation density
0San Francisco85246946.8718187.945381
1San Jose1015785176.535754.177760
2Sacramento48519997.924955.055147
\n", + "
" + ], + "text/plain": [ + " City name Population Area square miles Population density\n", + "0 San Francisco 852469 46.87 18187.945381\n", + "1 San Jose 1015785 176.53 5754.177760\n", + "2 Sacramento 485199 97.92 4955.055147" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "metadata": { + "id": "6qh63m-ayb-c", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " ## 练习 1\n", + "\n", + "通过添加一个新的布尔值列（当且仅当以下*两项*均为 True 时为 True）修改 `cities` 表格：\n", + "\n", + " * 城市以圣人命名。\n", + " * 城市面积大于 50 平方英里。\n", + "\n", + "**注意：**布尔值 `Series` 是使用“按位”而非传统布尔值“运算符”组合的。例如，执行*逻辑与*时，应使用 `&`，而不是 `and`。\n", + "\n", + "**提示：**\"San\" 在西班牙语中意为 \"saint\"。" + ] + }, + { + "metadata": { + "id": "zCOn8ftSyddH", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [], + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "bf6b298a-27f7-4c00-dc77-1e0017ed5a47", + "executionInfo": { + "status": "ok", + "timestamp": 1521010412986, + "user_tz": -480, + "elapsed": 684, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "# Your code here" + ], + "execution_count": 15, + "outputs": [] + }, + { + "metadata": { + "id": "YHIWvc9Ms-Ll", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " ### 解决方案\n", + "\n", + "点击下方，查看解决方案。" + ] + }, + { + "metadata": { + "id": "T5OlrqtdtCIb", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 10 + }, + "outputId": "a31b153b-b64b-4ba3-e1e5-fae5deda18a3", + "executionInfo": { + "status": "ok", + "timestamp": 1521010414046, + "user_tz": -480, + "elapsed": 930, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs wide and has saint name
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
2Sacramento48519997.924955.055147False
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "\n", + " Is wide and has saint name \n", + "0 False \n", + "1 True \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "id": "f-xAOJeMiXFB", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " ## 索引\n", + "`Series` 和 `DataFrame` 对象也定义了 `index` 属性，该属性会向每个 `Series` 项或 `DataFrame` 行赋一个标识符值。\n", + "\n", + "默认情况下，在构造时，*pandas* 会赋可反映源数据顺序的索引值。索引值在创建后是稳定的；也就是说，它们不会因为数据重新排序而发生改变。" + ] + }, + { + "metadata": { + "id": "2684gsWNinq9", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "75170199-ca02-4ca6-a8b5-bdd159f90475", + "executionInfo": { + "status": "ok", + "timestamp": 1521010415086, + "user_tz": -480, + "elapsed": 931, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "city_names.index" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "metadata": { + "id": "F_qPe2TBjfWd", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "41b493bf-81e5-4474-b195-d7ee456ddf22", + "executionInfo": { + "status": "ok", + "timestamp": 1521010416219, + "user_tz": -480, + "elapsed": 1016, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "cities.index" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "metadata": { + "id": "hp2oWY9Slo_h", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " 调用 `DataFrame.reindex` 以手动重新排列各行的顺序。例如，以下方式与按城市名称排序具有相同的效果：" + ] + }, + { + "metadata": { + "id": "sN0zUzSAj-U1", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "5096cd4a-7348-4365-9994-5ee21ecd37b5", + "executionInfo": { + "status": "ok", + "timestamp": 1521010417285, + "user_tz": -480, + "elapsed": 937, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "cities.reindex([2, 0, 1])" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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2Sacramento48519997.924955.055147False
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "\n", + " Is wide and has saint name \n", + "2 False \n", + "0 False \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "metadata": { + "id": "-GQFz8NZuS06", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " 重建索引是一种随机排列 `DataFrame` 的绝佳方式。在下面的示例中，我们会取用类似数组的索引，然后将其传递至 NumPy 的 `random.permutation` 函数，该函数会随机排列其值的位置。如果使用此重新随机排列的数组调用 `reindex`，会导致 `DataFrame` 行以同样的方式随机排列。\n", + "尝试多次运行以下单元格！" + ] + }, + { + "metadata": { + "id": "mF8GC0k8uYhz", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "98adbf2b-3793-49e1-f280-f133ef3e7c96", + "executionInfo": { + "status": "ok", + "timestamp": 1521010418890, + "user_tz": -480, + "elapsed": 1426, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "cities.reindex(np.random.permutation(cities.index))" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityIs wide and has saint name
0San Francisco85246946.8718187.945381False
2Sacramento48519997.924955.055147False
1San Jose1015785176.535754.177760True
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "\n", + " Is wide and has saint name \n", + "0 False \n", + "2 False \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "metadata": { + "id": "fSso35fQmGKb", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " 有关详情，请参阅[索引文档](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)。" + ] + }, + { + "metadata": { + "id": "8UngIdVhz8C0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " ## 练习 2\n", + "\n", + "`reindex` 方法允许使用未包含在原始 `DataFrame` 索引值中的索引值。请试一下，看看如果使用此类值会发生什么！您认为允许此类值的原因是什么？" + ] + }, + { + "metadata": { + "id": "PN55GrDX0jzO", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "d8b8e1cc-7704-4dc0-e39b-3d26e5b412f7", + "executionInfo": { + "status": "ok", + "timestamp": 1521010457101, + "user_tz": -480, + "elapsed": 1118, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "print 'hello'" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "hello\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "SQ6zmohATKVV", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TJffr5_Jwqvd", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " ### 解决方案\n", + "\n", + "点击下方，查看解决方案。" + ] + }, + { + "metadata": { + "id": "8oSvi2QWwuDH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " 如果您的 `reindex` 输入数组包含原始 `DataFrame` 索引值中没有的值，`reindex` 会为此类“丢失的”索引添加新行，并在所有对应列中填充 `NaN` 值：" + ] + }, + { + "metadata": { + "id": "yBdkucKCwy4x", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ], + "base_uri": "https://localhost:8080/", + "height": 173 + }, + "outputId": "b6524051-6112-4a88-a269-8370d6ae015a", + "executionInfo": { + "status": "ok", + "timestamp": 1521010420678, + "user_tz": -480, + "elapsed": 896, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + } + } + }, + "cell_type": "code", + "source": [ + "cities.reindex([0, 4, 5, 2])" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469.0 46.87 18187.945381 \n", + "4 NaN NaN NaN NaN \n", + "5 NaN NaN NaN NaN \n", + "2 Sacramento 485199.0 97.92 4955.055147 \n", + "\n", + " Is wide and has saint name \n", + "0 False \n", + "4 NaN \n", + "5 NaN \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "metadata": { + "id": "2l82PhPbwz7g", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + " 这种行为是可取的，因为索引通常是从实际数据中提取的字符串（请参阅 [*pandas* reindex 文档](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html)，查看索引值是浏览器名称的示例）。\n", + "\n", + "在这种情况下，如果允许出现“丢失的”索引，您将可以轻松使用外部列表重建索引，因为您不必担心会将输入清理掉。" + ] + } + ] +} \ No newline at end of file