{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# pandas层次化索引"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. 创建多层行索引"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1) 隐式构造\n",
"\n",
"最常见的方法是给DataFrame构造函数的index参数传递两个或更多的数组"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Series也可以创建多层索引"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"张三 期中 40\n",
" 期末 79\n",
"李四 期中 88\n",
" 期末 109\n",
"Michael 期中 146\n",
" 期末 51\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = Series(data = np.random.randint(0,150,size = 6),\n",
" index =[['张三','张三','李四','李四','Michael','Michael'],['期中','期末','期中','期末','期中','期末']] )\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from pandas import Series,DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Python</th>\n",
" <th>Java</th>\n",
" <th>PHP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">张三</th>\n",
" <th>期中</th>\n",
" <td>87</td>\n",
" <td>65</td>\n",
" <td>129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>147</td>\n",
" <td>31</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">李四</th>\n",
" <th>期中</th>\n",
" <td>38</td>\n",
" <td>31</td>\n",
" <td>74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>38</td>\n",
" <td>86</td>\n",
" <td>99</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Michael</th>\n",
" <th>期中</th>\n",
" <td>107</td>\n",
" <td>123</td>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>21</td>\n",
" <td>149</td>\n",
" <td>72</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Python Java PHP\n",
"张三 期中 87 65 129\n",
" 期末 147 31 120\n",
"李四 期中 38 31 74\n",
" 期末 38 86 99\n",
"Michael 期中 107 123 46\n",
" 期末 21 149 72"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = DataFrame(data = np.random.randint(0,150,size = (6,3)),\n",
" columns = ['Python','Java','PHP'],\n",
" index = [['张三','张三','李四','李四','Michael','Michael'],['期中','期末','期中','期末','期中','期末']])\n",
"#隐式创建\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2) 显示构造pd.MultiIndex"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 使用数组"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Spring</th>\n",
" <th>Summer</th>\n",
" <th>Autumn</th>\n",
" <th>Winter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">张三</th>\n",
" <th>期中</th>\n",
" <td>33</td>\n",
" <td>61</td>\n",
" <td>104</td>\n",
" <td>117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>45</td>\n",
" <td>20</td>\n",
" <td>94</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">李四</th>\n",
" <th>期中</th>\n",
" <td>39</td>\n",
" <td>94</td>\n",
" <td>144</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>38</td>\n",
" <td>67</td>\n",
" <td>113</td>\n",
" <td>111</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Michael</th>\n",
" <th>期中</th>\n",
" <td>33</td>\n",
" <td>5</td>\n",
" <td>39</td>\n",
" <td>135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>117</td>\n",
" <td>87</td>\n",
" <td>5</td>\n",
" <td>118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Spring Summer Autumn Winter\n",
"张三 期中 33 61 104 117\n",
" 期末 45 20 94 61\n",
"李四 期中 39 94 144 4\n",
" 期末 38 67 113 111\n",
"Michael 期中 33 5 39 135\n",
" 期末 117 87 5 118"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = DataFrame(data = np.random.randint(0,150,size = (6,4)),\n",
" columns = [\"Spring\",'Summer','Autumn','Winter'],\n",
" index = pd.MultiIndex.from_arrays([['张三','张三','李四','李四','Michael','Michael'],['期中','期末','期中','期末','期中','期末']]))\n",
"\n",
"df2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 使用tuple"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Spring</th>\n",
" <th>Summer</th>\n",
" <th>Autumn</th>\n",
" <th>Winter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">张三</th>\n",
" <th>期中</th>\n",
" <td>31</td>\n",
" <td>57</td>\n",
" <td>124</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>134</td>\n",
" <td>20</td>\n",
" <td>60</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">李四</th>\n",
" <th>期中</th>\n",
" <td>33</td>\n",
" <td>70</td>\n",
" <td>128</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>9</td>\n",
" <td>55</td>\n",
" <td>101</td>\n",
" <td>117</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Sara</th>\n",
" <th>期中</th>\n",
" <td>14</td>\n",
" <td>114</td>\n",
" <td>146</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>108</td>\n",
" <td>114</td>\n",
" <td>73</td>\n",
" <td>86</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Spring Summer Autumn Winter\n",
"张三 期中 31 57 124 34\n",
" 期末 134 20 60 55\n",
"李四 期中 33 70 128 47\n",
" 期末 9 55 101 117\n",
"Sara 期中 14 114 146 147\n",
" 期末 108 114 73 86"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3 = DataFrame(data = np.random.randint(0,150,size = (6,4)),\n",
" columns = [\"Spring\",'Summer','Autumn','Winter'],\n",
" index = pd.MultiIndex.from_tuples([('张三','期中'),('张三','期末'),('李四','期中'),('李四','期末'),('Sara','期中'),('Sara','期末')]))\n",
"\n",
"df3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 使用product\n",
"\n",
" 最简单，推荐使用"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Spring</th>\n",
" <th>Summer</th>\n",
" <th>Autumn</th>\n",
" <th>Winter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">张三</th>\n",
" <th rowspan=\"2\" valign=\"top\">middle</th>\n",
" <th>A</th>\n",
" <td>61</td>\n",
" <td>58</td>\n",
" <td>64</td>\n",
" <td>105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>118</td>\n",
" <td>121</td>\n",
" <td>8</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">end</th>\n",
" <th>A</th>\n",
" <td>135</td>\n",
" <td>147</td>\n",
" <td>141</td>\n",
" <td>107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>97</td>\n",
" <td>29</td>\n",
" <td>31</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">Sara</th>\n",
" <th rowspan=\"2\" valign=\"top\">middle</th>\n",
" <th>A</th>\n",
" <td>111</td>\n",
" <td>90</td>\n",
" <td>9</td>\n",
" <td>128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>40</td>\n",
" <td>95</td>\n",
" <td>149</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">end</th>\n",
" <th>A</th>\n",
" <td>9</td>\n",
" <td>8</td>\n",
" <td>97</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>70</td>\n",
" <td>16</td>\n",
" <td>60</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">Lisa</th>\n",
" <th rowspan=\"2\" valign=\"top\">middle</th>\n",
" <th>A</th>\n",
" <td>23</td>\n",
" <td>142</td>\n",
" <td>65</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>75</td>\n",
" <td>113</td>\n",
" <td>122</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">end</th>\n",
" <th>A</th>\n",
" <td>126</td>\n",
" <td>15</td>\n",
" <td>138</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>125</td>\n",
" <td>118</td>\n",
" <td>1</td>\n",
" <td>110</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Spring Summer Autumn Winter\n",
"张三 middle A 61 58 64 105\n",
" B 118 121 8 81\n",
" end A 135 147 141 107\n",
" B 97 29 31 56\n",
"Sara middle A 111 90 9 128\n",
" B 40 95 149 44\n",
" end A 9 8 97 0\n",
" B 70 16 60 10\n",
"Lisa middle A 23 142 65 67\n",
" B 75 113 122 0\n",
" end A 126 15 138 28\n",
" B 125 118 1 110"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df4 = DataFrame(data = np.random.randint(0,150,size = (12,4)),\n",
" columns = [\"Spring\",'Summer','Autumn','Winter'],\n",
" index = pd.MultiIndex.from_product([['张三','Sara','Lisa'],['middle','end'],list('AB')]))\n",
"\n",
"df4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"============================================\n",
"\n",
"练习8：\n",
"\n",
"1. 创建一个DataFrame，表示出张三李四期中期末各科成绩\n",
"\n",
"============================================"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. 多层列索引"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"除了行索引index，列索引columns也能用同样的方法创建多层索引"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">张三</th>\n",
" <th colspan=\"4\" halign=\"left\">Sara</th>\n",
" <th colspan=\"4\" halign=\"left\">Lisa</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">middle</th>\n",
" <th colspan=\"2\" halign=\"left\">end</th>\n",
" <th colspan=\"2\" halign=\"left\">middle</th>\n",
" <th colspan=\"2\" halign=\"left\">end</th>\n",
" <th colspan=\"2\" halign=\"left\">middle</th>\n",
" <th colspan=\"2\" halign=\"left\">end</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Spring</th>\n",
" <td>131</td>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>21</td>\n",
" <td>119</td>\n",
" <td>72</td>\n",
" <td>92</td>\n",
" <td>139</td>\n",
" <td>132</td>\n",
" <td>63</td>\n",
" <td>142</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Summer</th>\n",
" <td>105</td>\n",
" <td>118</td>\n",
" <td>122</td>\n",
" <td>19</td>\n",
" <td>17</td>\n",
" <td>133</td>\n",
" <td>100</td>\n",
" <td>36</td>\n",
" <td>120</td>\n",
" <td>20</td>\n",
" <td>129</td>\n",
" <td>136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Autumn</th>\n",
" <td>100</td>\n",
" <td>121</td>\n",
" <td>32</td>\n",
" <td>53</td>\n",
" <td>109</td>\n",
" <td>57</td>\n",
" <td>104</td>\n",
" <td>125</td>\n",
" <td>96</td>\n",
" <td>68</td>\n",
" <td>76</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Winter</th>\n",
" <td>73</td>\n",
" <td>16</td>\n",
" <td>54</td>\n",
" <td>42</td>\n",
" <td>74</td>\n",
" <td>48</td>\n",
" <td>129</td>\n",
" <td>25</td>\n",
" <td>56</td>\n",
" <td>39</td>\n",
" <td>28</td>\n",
" <td>113</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 张三 Sara Lisa \n",
" middle end middle end middle end \n",
" A B A B A B A B A B A B\n",
"Spring 131 7 14 21 119 72 92 139 132 63 142 56\n",
"Summer 105 118 122 19 17 133 100 36 120 20 129 136\n",
"Autumn 100 121 32 53 109 57 104 125 96 68 76 89\n",
"Winter 73 16 54 42 74 48 129 25 56 39 28 113"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df5 = DataFrame(data = np.random.randint(0,150,size = (4,12)),\n",
" columns = pd.MultiIndex.from_product([['张三','Sara','Lisa'],['middle','end'],list('AB')]),\n",
" index = [\"Spring\",'Summer','Autumn','Winter'])\n",
"\n",
"df5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. 多层索引对象的索引与切片操作"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1）Series的操作"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"【重要】对于Series来说，直接中括号[]与使用.loc()完全一样，因此，推荐使用中括号索引和切片。"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"张三 期中 40\n",
" 期末 79\n",
"李四 期中 88\n",
" 期末 109\n",
"Michael 期中 146\n",
" 期末 51\n",
"dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"张三 期中 40\n",
" 期末 79\n",
"李四 期中 88\n",
" 期末 109\n",
"静静 期中 146\n",
" 期末 51\n",
"dtype: int64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#多层索引进行切片时，有些汉字，或者英文，不识别，运行异常，并不是代码的问题，自身的bug\n",
"\n",
"s.index = pd.MultiIndex.from_product([['张三','李四','静静'],['期中','期末']])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"张三 期中 40\n",
" 期末 79\n",
"李四 期中 88\n",
" 期末 109\n",
"dtype: int64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s['张三':'李四']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(1) 索引"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"期中 40\n",
"期末 79\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s['张三']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"40"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s['张三','期中']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "KeyError",
"evalue": "'the label [期中] is not in the [index]'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1433\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1434\u001b[0;31m \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1435\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1428\u001b[0m raise KeyError(\"the label [%s] is not in the [%s]\" %\n\u001b[0;32m-> 1429\u001b[0;31m (key, self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m 1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'the label [期中] is not in the [index]'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-52a5135a3116>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'期中'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1326\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1327\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1328\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1330\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1550\u001b[0m \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1551\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1440\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1441\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1444\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1427\u001b[0m \"key\")\n\u001b[1;32m 1428\u001b[0m raise KeyError(\"the label [%s] is not in the [%s]\" %\n\u001b[0;32m-> 1429\u001b[0;31m (key, self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m 1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1431\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'the label [期中] is not in the [index]'"
]
}
],
"source": [
"#多层索引，有层的概念\n",
"s.loc['期中']"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"109"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.loc['李四','期末']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(2) 切片"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"张三 期中 40\n",
" 期末 79\n",
"李四 期中 88\n",
"dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.iloc[0:3]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "UnsortedIndexError",
"evalue": "'Key length (1) was greater than MultiIndex lexsort depth (0)'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mUnsortedIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-20-bc2dcaf84d2d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'张三'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'Michael'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 642\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;31m# other: fancy integer or otherwise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 646\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 647\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_slice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'getitem'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 648\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 649\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_convert_slice_indexer\u001b[0;34m(self, key, kind)\u001b[0m\n\u001b[1;32m 1405\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1406\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1408\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1409\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_index_slice\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m 3348\u001b[0m \"\"\"\n\u001b[1;32m 3349\u001b[0m start_slice, end_slice = self.slice_locs(start, end, step=step,\n\u001b[0;32m-> 3350\u001b[0;31m kind=kind)\n\u001b[0m\u001b[1;32m 3351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3352\u001b[0m \u001b[0;31m# return a slice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mslice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m 1908\u001b[0m \u001b[0;31m# This function adds nothing to its parent implementation (the magic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1909\u001b[0m \u001b[0;31m# happens in get_slice_bound method), but it adds meaningful doc.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1910\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1912\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_partial_tup_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m 3536\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3537\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3538\u001b[0;31m \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'left'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3539\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3540\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m 1879\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1880\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1881\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial_tup_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mside\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1882\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1883\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mslice_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36m_partial_tup_index\u001b[0;34m(self, tup, side)\u001b[0m\n\u001b[1;32m 1915\u001b[0m \u001b[0;34m'Key length (%d) was greater than MultiIndex'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1916\u001b[0m \u001b[0;34m' lexsort depth (%d)'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1917\u001b[0;31m (len(tup), self.lexsort_depth))\n\u001b[0m\u001b[1;32m 1918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1919\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mUnsortedIndexError\u001b[0m: 'Key length (1) was greater than MultiIndex lexsort depth (0)'"
]
}
],
"source": [
"s['张三':'Michael']"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "UnsortedIndexError",
"evalue": "'Key length (1) was greater than MultiIndex lexsort depth (0)'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mUnsortedIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-c37724b918c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'张三'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'Michael'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1326\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1327\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1328\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1330\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1504\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1505\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1506\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1507\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1508\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getbool_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_slice_axis\u001b[0;34m(self, slice_obj, axis)\u001b[0m\n\u001b[1;32m 1354\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1355\u001b[0m indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop,\n\u001b[0;32m-> 1356\u001b[0;31m slice_obj.step, kind=self.name)\n\u001b[0m\u001b[1;32m 1357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1358\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m 3348\u001b[0m \"\"\"\n\u001b[1;32m 3349\u001b[0m start_slice, end_slice = self.slice_locs(start, end, step=step,\n\u001b[0;32m-> 3350\u001b[0;31m kind=kind)\n\u001b[0m\u001b[1;32m 3351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3352\u001b[0m \u001b[0;31m# return a slice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mslice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m 1908\u001b[0m \u001b[0;31m# This function adds nothing to its parent implementation (the magic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1909\u001b[0m \u001b[0;31m# happens in get_slice_bound method), but it adds meaningful doc.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1910\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1912\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_partial_tup_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m 3536\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3537\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3538\u001b[0;31m \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'left'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3539\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3540\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m 1879\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1880\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1881\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial_tup_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mside\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1882\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1883\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mslice_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36m_partial_tup_index\u001b[0;34m(self, tup, side)\u001b[0m\n\u001b[1;32m 1915\u001b[0m \u001b[0;34m'Key length (%d) was greater than MultiIndex'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1916\u001b[0m \u001b[0;34m' lexsort depth (%d)'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1917\u001b[0;31m (len(tup), self.lexsort_depth))\n\u001b[0m\u001b[1;32m 1918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1919\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mUnsortedIndexError\u001b[0m: 'Key length (1) was greater than MultiIndex lexsort depth (0)'"
]
}
],
"source": [
"s.loc['张三':'Michael']"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A e 46\n",
" f 93\n",
"B e 76\n",
" f 77\n",
"C e 61\n",
" f 125\n",
"dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2 = Series(data = np.random.randint(0,150,size = 6),index = pd.MultiIndex.from_product([list('ABC'),['e','f']]))\n",
"s2"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"A e 46\n",
" f 93\n",
"B e 76\n",
" f 77\n",
"C e 61\n",
" f 125\n",
"dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2['A':'C']"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A e 46\n",
" f 93\n",
"B e 76\n",
" f 77\n",
"dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2.loc['A':'B']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2）DataFrame的操作"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(1) 可以直接使用列名称来进行列索引"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Spring</th>\n",
" <th>Summer</th>\n",
" <th>Autumn</th>\n",
" <th>Winter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">张三</th>\n",
" <th>期中</th>\n",
" <td>31</td>\n",
" <td>57</td>\n",
" <td>124</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>134</td>\n",
" <td>20</td>\n",
" <td>60</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">李四</th>\n",
" <th>期中</th>\n",
" <td>33</td>\n",
" <td>70</td>\n",
" <td>128</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>9</td>\n",
" <td>55</td>\n",
" <td>101</td>\n",
" <td>117</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Sara</th>\n",
" <th>期中</th>\n",
" <td>14</td>\n",
" <td>114</td>\n",
" <td>146</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>108</td>\n",
" <td>114</td>\n",
" <td>73</td>\n",
" <td>86</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Spring Summer Autumn Winter\n",
"张三 期中 31 57 124 34\n",
" 期末 134 20 60 55\n",
"李四 期中 33 70 128 47\n",
" 期末 9 55 101 117\n",
"Sara 期中 14 114 146 147\n",
" 期末 108 114 73 86"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"31"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3['Spring']['张三','期中']"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "KeyError",
"evalue": "('Spring', '李四')",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2441\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2442\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2443\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: ('Spring', '李四')",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-42-fa2185c7b00c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Spring'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'李四'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1962\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1963\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1964\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1966\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1969\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1970\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1971\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1973\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1643\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1644\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1645\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1646\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1647\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m 3588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3589\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3590\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3591\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3592\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2442\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2443\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2444\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2446\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: ('Spring', '李四')"
]
}
],
"source": [
"#DataFrame多层索引和之前索引类似\n",
"#df [] 代表着列索引\n",
"df3['Spring','李四']"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Spring</th>\n",
" <th>Summer</th>\n",
" <th>Autumn</th>\n",
" <th>Winter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">A</th>\n",
" <th>a</th>\n",
" <td>31</td>\n",
" <td>57</td>\n",
" <td>124</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>134</td>\n",
" <td>20</td>\n",
" <td>60</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">B</th>\n",
" <th>a</th>\n",
" <td>33</td>\n",
" <td>70</td>\n",
" <td>128</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>9</td>\n",
" <td>55</td>\n",
" <td>101</td>\n",
" <td>117</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">C</th>\n",
" <th>a</th>\n",
" <td>14</td>\n",
" <td>114</td>\n",
" <td>146</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>108</td>\n",
" <td>114</td>\n",
" <td>73</td>\n",
" <td>86</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Spring Summer Autumn Winter\n",
"A a 31 57 124 34\n",
" b 134 20 60 55\n",
"B a 33 70 128 47\n",
" b 9 55 101 117\n",
"C a 14 114 146 147\n",
" b 108 114 73 86"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.index = pd.MultiIndex.from_product([list('ABC'),list('ab')])\n",
"df3"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Spring</th>\n",
" <th>Summer</th>\n",
" <th>Autumn</th>\n",
" <th>Winter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">A</th>\n",
" <th>a</th>\n",
" <td>31</td>\n",
" <td>57</td>\n",
" <td>124</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>134</td>\n",
" <td>20</td>\n",
" <td>60</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">B</th>\n",
" <th>a</th>\n",
" <td>33</td>\n",
" <td>70</td>\n",
" <td>128</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>9</td>\n",
" <td>55</td>\n",
" <td>101</td>\n",
" <td>117</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Spring Summer Autumn Winter\n",
"A a 31 57 124 34\n",
" b 134 20 60 55\n",
"B a 33 70 128 47\n",
" b 9 55 101 117"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#使用切片\n",
"df3['A':'B']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(2) 使用行索引需要用loc()等函数\n",
"\n",
"【极其重要】推荐使用loc()函数"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"注意在对行索引的时候，若一级行索引还有多个，对二级行索引会遇到问题！也就是说，无法直接对二级索引进行索引，必须让二级索引变成一级索引后才能对其进行索引！"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Spring</th>\n",
" <th>Summer</th>\n",
" <th>Autumn</th>\n",
" <th>Winter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>31</td>\n",
" <td>57</td>\n",
" <td>124</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>134</td>\n",
" <td>20</td>\n",
" <td>60</td>\n",
" <td>55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Spring Summer Autumn Winter\n",
"a 31 57 124 34\n",
"b 134 20 60 55"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.loc['A']"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"Spring 33\n",
"Summer 70\n",
"Autumn 128\n",
"Winter 47\n",
"Name: (B, a), dtype: int64"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.loc['B','a']"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "KeyError",
"evalue": "'the label [a] is not in the [index]'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1433\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1434\u001b[0;31m \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1435\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1428\u001b[0m raise KeyError(\"the label [%s] is not in the [%s]\" %\n\u001b[0;32m-> 1429\u001b[0;31m (key, self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m 1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'the label [a] is not in the [index]'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-51-178fc9e30662>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1326\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1327\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1328\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1330\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1550\u001b[0m \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1551\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1440\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1441\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1444\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1427\u001b[0m \"key\")\n\u001b[1;32m 1428\u001b[0m raise KeyError(\"the label [%s] is not in the [%s]\" %\n\u001b[0;32m-> 1429\u001b[0;31m (key, self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m 1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1431\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'the label [a] is not in the [index]'"
]
}
],
"source": [
"df3.loc['a']"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
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" Spring Summer Autumn Winter\n",
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},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.loc[['A','B']]"
]
},
{
"cell_type": "code",
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"metadata": {
"collapsed": true
},
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"source": []
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{
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" Spring Summer Autumn Winter\n",
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]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.loc['A':'B']"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
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"text/plain": [
" Spring Summer Autumn Winter\n",
"A a 31 57 124 34\n",
" b 134 20 60 55\n",
"B a 33 70 128 47"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.iloc[0:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"============================================\n",
"\n",
"练习9：\n",
"\n",
"1. 分析比较Series和DataFrame各种索引的方式，熟练掌握.loc()方法\n",
"\n",
"2. 假设张三再一次在期中考试的时候因为特殊原因放弃英语考试，如何实现？\n",
"\n",
"============================================"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 4. 索引的堆（stack）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- ``stack()``\n",
"- ``unstack()``"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"小技巧】使用stack()的时候，level等于哪一个，哪一个就消失，出现在行里。"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
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"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
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" </tr>\n",
" <tr>\n",
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" <td>19</td>\n",
" <td>17</td>\n",
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" <td>136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Autumn</th>\n",
" <td>100</td>\n",
" <td>121</td>\n",
" <td>32</td>\n",
" <td>53</td>\n",
" <td>109</td>\n",
" <td>57</td>\n",
" <td>104</td>\n",
" <td>125</td>\n",
" <td>96</td>\n",
" <td>68</td>\n",
" <td>76</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Winter</th>\n",
" <td>73</td>\n",
" <td>16</td>\n",
" <td>54</td>\n",
" <td>42</td>\n",
" <td>74</td>\n",
" <td>48</td>\n",
" <td>129</td>\n",
" <td>25</td>\n",
" <td>56</td>\n",
" <td>39</td>\n",
" <td>28</td>\n",
" <td>113</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" 张三 Sara Lisa \n",
" middle end middle end middle end \n",
" A B A B A B A B A B A B\n",
"Spring 131 7 14 21 119 72 92 139 132 63 142 56\n",
"Summer 105 118 122 19 17 133 100 36 120 20 129 136\n",
"Autumn 100 121 32 53 109 57 104 125 96 68 76 89\n",
"Winter 73 16 54 42 74 48 129 25 56 39 28 113"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df5"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
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" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Spring</th>\n",
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" <td>119</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>张三</th>\n",
" <td>14</td>\n",
" <td>21</td>\n",
" <td>131</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Summer</th>\n",
" <th>Lisa</th>\n",
" <td>129</td>\n",
" <td>136</td>\n",
" <td>120</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sara</th>\n",
" <td>100</td>\n",
" <td>36</td>\n",
" <td>17</td>\n",
" <td>133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>张三</th>\n",
" <td>122</td>\n",
" <td>19</td>\n",
" <td>105</td>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Autumn</th>\n",
" <th>Lisa</th>\n",
" <td>76</td>\n",
" <td>89</td>\n",
" <td>96</td>\n",
" <td>68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sara</th>\n",
" <td>104</td>\n",
" <td>125</td>\n",
" <td>109</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>张三</th>\n",
" <td>32</td>\n",
" <td>53</td>\n",
" <td>100</td>\n",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Winter</th>\n",
" <th>Lisa</th>\n",
" <td>28</td>\n",
" <td>113</td>\n",
" <td>56</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sara</th>\n",
" <td>129</td>\n",
" <td>25</td>\n",
" <td>74</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>张三</th>\n",
" <td>54</td>\n",
" <td>42</td>\n",
" <td>73</td>\n",
" <td>16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" end middle \n",
" A B A B\n",
"Spring Lisa 142 56 132 63\n",
" Sara 92 139 119 72\n",
" 张三 14 21 131 7\n",
"Summer Lisa 129 136 120 20\n",
" Sara 100 36 17 133\n",
" 张三 122 19 105 118\n",
"Autumn Lisa 76 89 96 68\n",
" Sara 104 125 109 57\n",
" 张三 32 53 100 121\n",
"Winter Lisa 28 113 56 39\n",
" Sara 129 25 74 48\n",
" 张三 54 42 73 16"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df5.stack(level = 0)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">Spring</th>\n",
" <th colspan=\"2\" halign=\"left\">Summer</th>\n",
" <th colspan=\"2\" halign=\"left\">Autumn</th>\n",
" <th colspan=\"2\" halign=\"left\">Winter</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>end</th>\n",
" <th>middle</th>\n",
" <th>end</th>\n",
" <th>middle</th>\n",
" <th>end</th>\n",
" <th>middle</th>\n",
" <th>end</th>\n",
" <th>middle</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Lisa</th>\n",
" <th>A</th>\n",
" <td>126</td>\n",
" <td>23</td>\n",
" <td>15</td>\n",
" <td>142</td>\n",
" <td>138</td>\n",
" <td>65</td>\n",
" <td>28</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>125</td>\n",
" <td>75</td>\n",
" <td>118</td>\n",
" <td>113</td>\n",
" <td>1</td>\n",
" <td>122</td>\n",
" <td>110</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Sara</th>\n",
" <th>A</th>\n",
" <td>9</td>\n",
" <td>111</td>\n",
" <td>8</td>\n",
" <td>90</td>\n",
" <td>97</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>70</td>\n",
" <td>40</td>\n",
" <td>16</td>\n",
" <td>95</td>\n",
" <td>60</td>\n",
" <td>149</td>\n",
" <td>10</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">张三</th>\n",
" <th>A</th>\n",
" <td>135</td>\n",
" <td>61</td>\n",
" <td>147</td>\n",
" <td>58</td>\n",
" <td>141</td>\n",
" <td>64</td>\n",
" <td>107</td>\n",
" <td>105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>97</td>\n",
" <td>118</td>\n",
" <td>29</td>\n",
" <td>121</td>\n",
" <td>31</td>\n",
" <td>8</td>\n",
" <td>56</td>\n",
" <td>81</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Spring Summer Autumn Winter \n",
" end middle end middle end middle end middle\n",
"Lisa A 126 23 15 142 138 65 28 67\n",
" B 125 75 118 113 1 122 110 0\n",
"Sara A 9 111 8 90 97 9 0 128\n",
" B 70 40 16 95 60 149 10 44\n",
"张三 A 135 61 147 58 141 64 107 105\n",
" B 97 118 29 121 31 8 56 81"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df4.unstack(level=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"【小技巧】使用unstack()的时候，level等于哪一个，哪一个就消失，出现在列里。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"============================================\n",
"\n",
"练习10：\n",
"\n",
"1. 使用unstack()将ddd变为两行，分别为期中期末\n",
"\n",
"2. 使用unstack()将ddd变为四行，分别为四个科目\n",
"\n",
"============================================"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 5. 聚合操作"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"【注意】\n",
"\n",
"- 需要指定axis\n",
"\n",
"- 【小技巧】和unstack()相反，聚合的时候，axis等于哪一个，哪一个就会进行计算。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"所谓的聚合操作：平均数，方差，最大值，最小值……"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Spring</th>\n",
" <th>Summer</th>\n",
" <th>Autumn</th>\n",
" <th>Winter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">A</th>\n",
" <th>a</th>\n",
" <td>31</td>\n",
" <td>57</td>\n",
" <td>124</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>134</td>\n",
" <td>20</td>\n",
" <td>60</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">B</th>\n",
" <th>a</th>\n",
" <td>33</td>\n",
" <td>70</td>\n",
" <td>128</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>9</td>\n",
" <td>55</td>\n",
" <td>101</td>\n",
" <td>117</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">C</th>\n",
" <th>a</th>\n",
" <td>14</td>\n",
" <td>114</td>\n",
" <td>146</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>108</td>\n",
" <td>114</td>\n",
" <td>73</td>\n",
" <td>86</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Spring Summer Autumn Winter\n",
"A a 31 57 124 34\n",
" b 134 20 60 55\n",
"B a 33 70 128 47\n",
" b 9 55 101 117\n",
"C a 14 114 146 147\n",
" b 108 114 73 86"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"A a 61.50\n",
" b 67.25\n",
"B a 69.50\n",
" b 70.50\n",
"C a 105.25\n",
" b 95.25\n",
"dtype: float64"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.mean(axis = 'columns')"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Spring</th>\n",
" <th>Summer</th>\n",
" <th>Autumn</th>\n",
" <th>Winter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">A</th>\n",
" <th>a</th>\n",
" <td>31</td>\n",
" <td>57</td>\n",
" <td>124</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>134</td>\n",
" <td>20</td>\n",
" <td>60</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">B</th>\n",
" <th>a</th>\n",
" <td>33</td>\n",
" <td>70</td>\n",
" <td>128</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>9</td>\n",
" <td>55</td>\n",
" <td>101</td>\n",
" <td>117</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">C</th>\n",
" <th>a</th>\n",
" <td>14</td>\n",
" <td>114</td>\n",
" <td>146</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>108</td>\n",
" <td>114</td>\n",
" <td>73</td>\n",
" <td>86</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Spring Summer Autumn Winter\n",
"A a 31 57 124 34\n",
" b 134 20 60 55\n",
"B a 33 70 128 47\n",
" b 9 55 101 117\n",
"C a 14 114 146 147\n",
" b 108 114 73 86"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"Spring 52.738664\n",
"Summer 36.740532\n",
"Autumn 33.571814\n",
"Winter 44.086279\n",
"dtype: float64"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#数据离散的程度，\n",
"df3.std()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"============================================\n",
"\n",
"练习11：\n",
"\n",
"1. 计算各个科目期中期末平均成绩\n",
"\n",
"2. 计算各科目张三李四的最高分\n",
"\n",
"============================================"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
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