{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 快速查看整体信息\n", "上一章讲到了控制DataFrame显示的一些参数，本章则具体讲解一下如何获得对DataFrame的整体认知。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "E:\\ML\\实战\\pandas实用教程 - 副本\n" ] } ], "source": [ "!cd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. `.info()`\n", "这是DataFrame才可用的API，快捷查看多种信息：总行数和列数、每列元素类型和non-NaN的个数，总内存。\n", "#### `DataFrame.info(verbose=None, memory_usage=True, null_counts=True)`\n", "- verbose：True or False，字面意思是冗长的，也就说如何DataFrame有很多列，是否显示所有列的信息，如果为否，那么会省略一部分；\n", "- memory_usage：True or False，默认为True，是否查看DataFrame的内存使用情况；\n", "- null_counts：True or False，默认为True，是否统计NaN值的个数。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " A B\n", "0 NaN 2.0\n", "1 3.0 NaN" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame( [[np.nan, 2],[3,np.nan]], columns = ['A','B'])\n", "df" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ndim # 返回维度数，Series一维，DataFrame两维，平时很少用到，不过有时会在循环中用到" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2, 2)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape # (行数，列数)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.size # 元素个数，rows×cols" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# 3. `.head(), .tail()`\n", "默认分别查看头5行和后5行。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### `Series/DataFrame.head(n=5)`\n", "#### `Series/DataFrame.tail(n=5)`" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 1\n", "2 2\n", "3 3\n", "4 4\n", "dtype: int32" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series( range(0,5))\n", "s" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 1\n", "2 2\n", "dtype: int32" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.head(3)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2 2\n", "3 3\n", "4 4\n", "dtype: int32" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.tail(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# 4. `.memory_usage()`\n", "比info中内存显示更可控一些，单位是**字节**。\n", "#### `Series/DataFrame.memory_usage(index=True, deep=False)`\n", "- index：是否显示索引占用的内存，毫无疑问索引也占用内存；\n", "- deep：是否显示object类型的列消耗的系统资源，由于pandas中object元素只是一个引用，我估计这个deep是指显示真实的内存占用。" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index 80\n", "numeric 24\n", "object 24\n", "dtype: int64" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.memory_usage(deep = False) # Index即索引占用内存" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index 80\n", "numeric 24\n", "object 186\n", "dtype: int64" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.memory_usage(deep = True) # object 型占用的内存变大" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# 5. `.describe()`\n", "快速查看每一列的统计信息，默认排除所有NaN元素。\n", "#### `DataFrame.describe( include= [np.number])`\n", "- include：'all'或者[np.number 或 np.object]。numberic只对元素属性为数值的列做数值统计，object只对元素属性为object的列做类字符串统计。" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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