{
"cells": [
{
"cell_type": "markdown",
"id": "ed1f74e7",
"metadata": {},
"source": [
"# 二维数据结构DataFrame对象"
]
},
{
"cell_type": "markdown",
"id": "717b29b8",
"metadata": {},
"source": [
"DataFrame对象是一种二维带标记数据结构，不同列的数据类型可以不同。为了方便理解，可以将DataFrame对象看成一张Excel电子表格，或者是一个由多列Series对象构成的字典。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b0182569",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "79178b2b",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "698481f5",
"metadata": {},
"source": [
"## DataFrame对象的生成\n",
"\n",
"与Series类似，DataFrame对象也可以由多种类型的数据生成：\n",
"- 由Series对象为值构成的字典。\n",
"- 由一维数组或列表构成的字典。\n",
"- 由字典构成的列表或数组。"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "2e7cb22e",
"metadata": {},
"source": [
"### 使用Series对象构成的字典生成"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "821beb00",
"metadata": {},
"source": [
"DataFrame对象可以从一组由Series对象为值构成的字典中生成。字典中的值除了Series对象，也可以是另一个字典，因为字典被转换为Series对象。\n",
"\n",
"假设有一个包含两个Series对象的字典d："
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "98d20646",
"metadata": {},
"outputs": [],
"source": [
"s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "315ddecb",
"metadata": {},
"outputs": [],
"source": [
"s2 = pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1e13fa34",
"metadata": {},
"outputs": [],
"source": [
"d = {\"one\": s1, \"two\": s2}"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "49f79ba0",
"metadata": {},
"source": [
"可以用字典d构造一个DataFrame对象："
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "79c735da",
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(d)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d65d98a8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two\n",
"a 1.0 1.0\n",
"b 2.0 2.0\n",
"c 3.0 3.0\n",
"d NaN 4.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e1f02d1b",
"metadata": {},
"source": [
"与Series相比，DataFrame对象要区分不同的行和列，因此有行标记和列标记之分。默认情况下，df的列标记是传入字典的键，可以用属性`.columns`查看："
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "fa4a04fb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['one', 'two'], dtype='object')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "ff9acb82",
"metadata": {},
"source": [
"行标记是两个Series对象标记的并集，Pandas会自动将两个Series对象的标记进行对齐："
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "34b63bf8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['a', 'b', 'c', 'd'], dtype='object')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.index"
]
},
{
"cell_type": "markdown",
"id": "741871ba",
"metadata": {},
"source": [
"在生成DataFrame时，也可以指定index和columns参数："
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "227afaa7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two\n",
"d NaN 4.0\n",
"b 2.0 2.0\n",
"a 1.0 1.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(d, index=[\"d\", \"b\", \"a\"])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a50f5bc2",
"metadata": {},
"source": [
"Pandas会按照给定的顺序从传入的数据中寻找对应的值，如果该值不存在，则使用缺省值`np.nan`："
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "492f3b05",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>two</th>\n",
" <th>three</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" two three\n",
"d 4.0 NaN\n",
"b 2.0 NaN\n",
"a 1.0 NaN"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "2cef3d5d",
"metadata": {},
"source": [
"### 使用一维数组构成的字典生成"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "eb0bb12a",
"metadata": {},
"source": [
"DataFrame对象还可以使用由一维数组或列表构成的字典生成，这些数组和列表必须是等长的："
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a097d435",
"metadata": {},
"outputs": [],
"source": [
"d = {'one' : [1., 2., 3., 4.],\n",
" 'two' : [4., 3., 2., 1.]}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8d58e2f8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two\n",
"0 1.0 4.0\n",
"1 2.0 3.0\n",
"2 3.0 2.0\n",
"3 4.0 1.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(d)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "0333d58f",
"metadata": {},
"source": [
"传入index参数时，该参数的长度也必须与列表长度一致："
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "21661d1e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two\n",
"a 1.0 4.0\n",
"b 2.0 3.0\n",
"c 3.0 2.0\n",
"d 4.0 1.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(d, index=['a', 'b', 'c', 'd'])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9b2c149f",
"metadata": {},
"source": [
"### 使用字典数组生成"
]
},
{
"cell_type": "markdown",
"id": "3f5efc2a",
"metadata": {},
"source": [
"还可以使用字典构成的数组或列表进行构建："
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "57d4e5b2",
"metadata": {},
"outputs": [],
"source": [
"data = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "fd002d26",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 1 2 NaN\n",
"1 5 10 20.0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(data)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e6448551",
"metadata": {},
"source": [
"与Series不同的是，字典的键对应的是列标记，行标记由数组或列表的大小决定。"
]
},
{
"cell_type": "markdown",
"id": "28b0a139",
"metadata": {},
"source": [
"### 使用二维数组生成"
]
},
{
"cell_type": "markdown",
"id": "e73d4783",
"metadata": {},
"source": [
"还可以使用NumPy的二维数组生成："
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "5e70aa1e",
"metadata": {},
"outputs": [],
"source": [
"a = np.array([[1,2,3], [4,5,6]])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "a6fa5ce0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2\n",
"0 1 2 3\n",
"1 4 5 6"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(a)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "6aed9e8c",
"metadata": {},
"source": [
"## DataFrame对象的使用\n",
"\n",
"DataFrame对象不是二维NumPy数组，在使用方法上存在很大差异："
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "9f32f6c3",
"metadata": {},
"outputs": [],
"source": [
"s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "239d3b03",
"metadata": {},
"outputs": [],
"source": [
"s2 = pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "e8776dc6",
"metadata": {},
"outputs": [],
"source": [
"d = {\"one\": s1, \"two\": s2}"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "cdde7d53",
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(d)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "5e17d0aa",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two\n",
"a 1.0 1.0\n",
"b 2.0 2.0\n",
"c 3.0 3.0\n",
"d NaN 4.0"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "23e62817",
"metadata": {},
"source": [
"### 列相关的操作"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "3950a0b2",
"metadata": {},
"source": [
"DataFrame对象可以看成是一个由Series对象构成的字典，.columns属性对应字典的键，每一列对应字典的值："
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "a3c973ea",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1.0\n",
"b 2.0\n",
"c 3.0\n",
"d NaN\n",
"Name: one, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['one']"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "55ef0692",
"metadata": {},
"source": [
"可以像字典一样增加新列："
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "0f2ef75c",
"metadata": {},
"outputs": [],
"source": [
"df[\"three\"] = df[\"one\"] * df[\"two\"]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "05ac44bb",
"metadata": {},
"outputs": [],
"source": [
"df[\"flag\"] = df[\"one\"] > 2"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "7ecb4c9f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" <th>three</th>\n",
" <th>flag</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>9.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two three flag\n",
"a 1.0 1.0 1.0 False\n",
"b 2.0 2.0 4.0 False\n",
"c 3.0 3.0 9.0 True\n",
"d NaN 4.0 NaN False"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "1f62216a",
"metadata": {},
"source": [
"增加新列时，如果新列的值是单一值，Pandas会按照行标记自动进行扩展："
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "e156535d",
"metadata": {},
"outputs": [],
"source": [
"df[\"four\"] = 4"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e220dded",
"metadata": {},
"source": [
"DataFrame对象支持用del关键字或者.pop()方法删除列："
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "8e744653",
"metadata": {},
"outputs": [],
"source": [
"del df[\"two\"]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "4770ad42",
"metadata": {},
"outputs": [],
"source": [
"three = df.pop(\"three\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "d20663a2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1.0\n",
"b 4.0\n",
"c 9.0\n",
"d NaN\n",
"Name: three, dtype: float64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"three"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "8713ea5e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>flag</th>\n",
" <th>four</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3.0</td>\n",
" <td>True</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one flag four\n",
"a 1.0 False 4\n",
"b 2.0 False 4\n",
"c 3.0 True 4\n",
"d NaN False 4"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "18ec10aa",
"metadata": {},
"source": [
"增加一个行标记不完全相同的新列时，Pandas只会保留该列中与原有行标记相同的部分，以保证原DataFrame对象的行标记不变化："
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "07b61507",
"metadata": {},
"outputs": [],
"source": [
"df[\"foo\"] = pd.Series([1,2,3], index=[\"a\", \"d\", \"e\"])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "e45141a6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>flag</th>\n",
" <th>four</th>\n",
" <th>foo</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3.0</td>\n",
" <td>True</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one flag four foo\n",
"a 1.0 False 4 1.0\n",
"b 2.0 False 4 NaN\n",
"c 3.0 True 4 NaN\n",
"d NaN False 4 2.0"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "f53b8dd1",
"metadata": {},
"source": [
"默认情况下，新列的插入位置都在DataFrame对象的最后。可以使用.insert()方法将其插入指定的位置："
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "edad10b3",
"metadata": {},
"outputs": [],
"source": [
"df.insert(1, \"bar\", df[\"one\"])"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "5b1967c2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>bar</th>\n",
" <th>flag</th>\n",
" <th>four</th>\n",
" <th>foo</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>True</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>4</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one bar flag four foo\n",
"a 1.0 1.0 False 4 1.0\n",
"b 2.0 2.0 False 4 NaN\n",
"c 3.0 3.0 True 4 NaN\n",
"d NaN NaN False 4 2.0"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "2441cfd5",
"metadata": {},
"source": [
"### 行相关的操作\n",
"\n",
"DataFrame对象有两种常用的索引行的方式。可以用`.loc`属性索引行标记，返回一个Series对象："
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "737b173b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"one 2.0\n",
"bar 2.0\n",
"flag False\n",
"four 4\n",
"foo NaN\n",
"Name: b, dtype: object"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[\"b\"]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cb199e17",
"metadata": {},
"source": [
"也可以用.iloc属性索引位置，得到第二行数据："
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "80f5c2ae",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"one 2.0\n",
"bar 2.0\n",
"flag False\n",
"four 4\n",
"foo NaN\n",
"Name: b, dtype: object"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[1]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a41ed5e1",
"metadata": {},
"source": [
"### 加法与减法操作\n",
"\n",
"DataFrame对象支持加法和减法的操作，并且按照行列标记对齐的原则进行计算："
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "9b6e727a",
"metadata": {},
"outputs": [],
"source": [
"df1 = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D'])"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "f2b0db7d",
"metadata": {},
"outputs": [],
"source": [
"df2 = pd.DataFrame(np.random.randn(7, 3), columns=['A', 'B', 'C'])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "ed6e0274",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1.906552</td>\n",
" <td>-2.428495</td>\n",
" <td>1.131278</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.955872</td>\n",
" <td>-1.476556</td>\n",
" <td>-1.523796</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.766210</td>\n",
" <td>-0.162112</td>\n",
" <td>0.190370</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-2.866838</td>\n",
" <td>0.866281</td>\n",
" <td>1.340097</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-2.027247</td>\n",
" <td>0.972097</td>\n",
" <td>-0.807422</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.841079</td>\n",
" <td>0.101313</td>\n",
" <td>-1.701630</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.318099</td>\n",
" <td>-0.037061</td>\n",
" <td>-1.878293</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"0 -1.906552 -2.428495 1.131278 NaN\n",
"1 -0.955872 -1.476556 -1.523796 NaN\n",
"2 0.766210 -0.162112 0.190370 NaN\n",
"3 -2.866838 0.866281 1.340097 NaN\n",
"4 -2.027247 0.972097 -0.807422 NaN\n",
"5 0.841079 0.101313 -1.701630 NaN\n",
"6 0.318099 -0.037061 -1.878293 NaN\n",
"7 NaN NaN NaN NaN\n",
"8 NaN NaN NaN NaN\n",
"9 NaN NaN NaN NaN"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 + df2"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "f0a10763",
"metadata": {},
"source": [
"DataFrame对象还可以与Series对象进行加减操作。与NumPy中的广播机制类似，Pandas会先将Series对象的标记与DataFrame对象的列标记中对应的部分拿出来，然后使用广播机制将Series对象沿着行标记进行扩展："
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "898bd8b9",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.034677</td>\n",
" <td>-1.447889</td>\n",
" <td>0.239673</td>\n",
" <td>0.897156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.216450</td>\n",
" <td>-0.052522</td>\n",
" <td>0.237849</td>\n",
" <td>0.806303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.260522</td>\n",
" <td>0.590821</td>\n",
" <td>0.231546</td>\n",
" <td>-2.164184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.264539</td>\n",
" <td>0.947130</td>\n",
" <td>0.601591</td>\n",
" <td>-0.753204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.113126</td>\n",
" <td>0.063686</td>\n",
" <td>-0.379063</td>\n",
" <td>-0.275933</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.596109</td>\n",
" <td>-0.516650</td>\n",
" <td>-1.177866</td>\n",
" <td>0.075800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.386725</td>\n",
" <td>-0.328219</td>\n",
" <td>-1.303265</td>\n",
" <td>-0.790358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1.225454</td>\n",
" <td>0.923503</td>\n",
" <td>0.715214</td>\n",
" <td>-0.144048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-0.982050</td>\n",
" <td>-0.026315</td>\n",
" <td>1.963732</td>\n",
" <td>0.638793</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.715773</td>\n",
" <td>-0.767911</td>\n",
" <td>-0.379927</td>\n",
" <td>-1.533615</td>\n",
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" A B C D\n",
"0 0.034677 -1.447889 0.239673 0.897156\n",
"1 -0.216450 -0.052522 0.237849 0.806303\n",
"2 0.260522 0.590821 0.231546 -2.164184\n",
"3 -1.264539 0.947130 0.601591 -0.753204\n",
"4 -1.113126 0.063686 -0.379063 -0.275933\n",
"5 0.596109 -0.516650 -1.177866 0.075800\n",
"6 1.386725 -0.328219 -1.303265 -0.790358\n",
"7 1.225454 0.923503 0.715214 -0.144048\n",
"8 -0.982050 -0.026315 1.963732 0.638793\n",
"9 0.715773 -0.767911 -0.379927 -1.533615"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "0726b1fc",
"metadata": {},
"outputs": [
{
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" <td>-0.008127</td>\n",
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" <td>-0.619600</td>\n",
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"7 1.190778 2.371392 0.475542 -1.041204\n",
"8 -1.016727 1.421574 1.724059 -0.258363\n",
"9 0.681096 0.679978 -0.619600 -2.430771"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 - df1.iloc[0]"
]
},
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"id": "0fe7dcf0",
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