Pandas-数据结构-DataFrame（四）：行索引【df[1:3]；单标签：loc/iloc[-1]；多标签：loc/iloc[[3, 2]]；多行索引：loc/iloc[1:3]】

超、凢脫俗 2023-09-29 09:29 10阅读 0赞

只选择一行输出Series，选择多行输出Dataframe

## 一、df\[1:3\]：按“位置下标”切片提取行数据 ##

按照行切片索引：`df[row_index_start : row_index_end]`

*  `df[]`中为数字时，默认选择行，且只能进行切片的选择，不能单独选择（`df[0]`是错误的）
 *  输出结果为Dataframe，即便只选择一行
 *  `df[]`不能通过索引标签名来选择行(`df['one']`)
 *  df\[\]：利用 **默认位置下标** 来获取想要的行【末端不包含】

import numpy as np
    import pandas as pd
    
    # df[index_start : index_end]
    
    df1 = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                       index=['one', 'two', 'four', 'three'],
                       columns=['a', 'b', 'c', 'd'])
    
    print("df1 = \n{0}\ntype(df1) = {1}".format(df1, type(df1)))
    print('-' * 50)
    # 多行索引
    df2 = df1[1:3]
    # df2 = df1[0] # 错误
    # df2 = df1['one'] # 错误
    print("多行索引：df2 = df1[1:3] = \n{0}\ntype(df2) = {1}".format(df2, type(df2)))
    print('-' * 100)

打印结果：

df1 = 
                   a          b          c          d
    one    14.697748  84.130102  75.636127  65.541925
    two    25.242130  53.488123  45.072336  24.906057
    four   94.686317  88.176227  67.092432  35.897882
    three  36.527603  70.150568  27.110961  45.964728
    type(df1) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------
    多行索引：df2 = df1[1:3] = 
                  a          b          c          d
    two   25.242130  53.488123  45.072336  24.906057
    four  94.686317  88.176227  67.092432  35.897882
    type(df2) = <class 'pandas.core.frame.DataFrame'>
    ----------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

## 二、df.loc\[\]：按“索引名”提取行数据 ##

### 1、单标签索引：df.loc\[1\]、df.loc\[‘one’\] ###

*  按照行名索引：df.loc\[row\_name\]
 *  按照行下标索引：df.loc\[row\_index\]

import numpy as np
    import pandas as pd
    
    # df.loc[] - 按index选择行
    # 核心：df.loc[label]主要针对index选择行，同时支持指定index，及默认数字index
    
    df1 = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                       index=['one', 'two', 'three', 'four'],
                       columns=['a', 'b', 'c', 'd'])
    
    df2 = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                       columns=['a', 'b', 'c', 'd'])
    
    print("df1 = \n{0}\ntype(df1) = {1}".format(df1, type(df1)))
    print('-' * 50)
    print("df2 = \n{0}\ntype(df2) = {1}".format(df2, type(df2)))
    print('-' * 100)
    
    # 单个标签索引，返回Series
    data1 = df1.loc['one']
    data2 = df2.loc[1]
    print("单标签索引：data1 = \ndf1.loc['one'] = \n{0}\ntype(data1) = {1}".format(data1, type(data1)))
    print('-' * 50)
    print("单标签索引：data2 = \ndf2.loc[1] = \n{0}\ntype(data2) = {1}".format(data2, type(data2)))
    print('-' * 100)

打印结果：

df1 = 
                   a          b          c          d
    one    93.037642  52.895322  42.547540  95.435676
    two    24.088954  56.966169  79.185705  48.582922
    three  76.162602  32.962263  41.853371  99.138612
    four   24.979909  10.191909  27.335317  20.452524
    type(df1) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------
    df2 = 
               a          b          c          d
    0  21.656858  31.404614  88.520987  41.839721
    1  26.884644   9.943081  91.739139  81.479288
    2  96.522109  71.673956  55.843560  38.131336
    3  73.574839  93.350715  89.358183  45.521198
    type(df2) = <class 'pandas.core.frame.DataFrame'>
    ----------------------------------------------------------------------------------------------------
    单标签索引：data1 = 
    df1.loc['one'] = 
    a    93.037642
    b    52.895322
    c    42.547540
    d    95.435676
    Name: one, dtype: float64
    type(data1) = <class 'pandas.core.series.Series'>
    --------------------------------------------------
    单标签索引：data2 = 
    df2.loc[1] = 
    a    26.884644
    b     9.943081
    c    91.739139
    d    81.479288
    Name: 1, dtype: float64
    type(data2) = <class 'pandas.core.series.Series'>

### 2 多标签索引：df.loc\[\[3, 2, 1\]\]、df.loc\[\[‘one’, ‘three’\]\] ###

df.loc\[\]

*  df.loc\[\]：利用index的名称来获取想要的行【末端包含】
 *  核心：df.loc\[label\]主要针对index选择行，同时支持指定index，及默认数字index
 *  其中的int类型的索引时索引的名称，而非下标位置信息；

import numpy as np 
    import pandas as pd
    
    # df.loc[] - 按index选择行
    # 核心：df.loc[label]主要针对index选择行，同时支持指定index，及默认数字index
    
    df1 = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                       index=['one', 'two', 'three', 'four'],
                       columns=['a', 'b', 'c', 'd'])
    
    df2 = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                       columns=['a', 'b', 'c', 'd'])
    
    print("df1 = \n{0}\ntype(df1) = {1}".format(df1, type(df1)))
    print('-' * 50)
    print("df2 = \n{0}\ntype(df2) = {1}".format(df2, type(df2)))
    print('-' * 100)
    
    # 多个标签索引【顺序可变】
    data3 = df1.loc[['two', 'three']]
    data4 = df2.loc[[3, 2, 1]]
    print("多标签索引：data3 = \ndf1.loc[['two', 'three']] = \n{0}\ntype(data3) = {1}".format(data3, type(data3)))
    print('-' * 50)
    print("多标签索引：data4 = \ndf2.loc[[3, 2, 1]] = \n{0}\ntype(data4) = {1}".format(data4, type(data4)))
    print('-' * 100)

打印结果：

df1 = 
                   a          b          c          d
    one    93.037642  52.895322  42.547540  95.435676
    two    24.088954  56.966169  79.185705  48.582922
    three  76.162602  32.962263  41.853371  99.138612
    four   24.979909  10.191909  27.335317  20.452524
    type(df1) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------
    df2 = 
               a          b          c          d
    0  21.656858  31.404614  88.520987  41.839721
    1  26.884644   9.943081  91.739139  81.479288
    2  96.522109  71.673956  55.843560  38.131336
    3  73.574839  93.350715  89.358183  45.521198
    type(df2) = <class 'pandas.core.frame.DataFrame'>
    ----------------------------------------------------------------------------------------------------
    多标签索引：data3 = 
    df1.loc[['two', 'three']] = 
                   a          b          c          d
    two    24.088954  56.966169  79.185705  48.582922
    three  76.162602  32.962263  41.853371  99.138612
    type(data3) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------
    多标签索引：data4 = 
    df2.loc[[3, 2, 1]] = 
               a          b          c          d
    3  73.574839  93.350715  89.358183  45.521198
    2  96.522109  71.673956  55.843560  38.131336
    1  26.884644   9.943081  91.739139  81.479288
    type(data4) = <class 'pandas.core.frame.DataFrame'>

### 3、多行索引：df.loc\[1:3\]、df.loc\[‘one’ : ‘three’\] ###

df.loc\[\]

*  df.loc\[\]：利用 **index的名称** 来获取想要的行【末端包含】
 *  核心：df.loc\[label\]主要针对index选择行，同时支持指定index，及默认数字index
 *  其中的int类型的索引时索引的名称，而非下标位置信息；

import numpy as np
    import pandas as pd
    
    # df.loc[label_start : label_end]
    
    df1 = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                       index=['one', 'two', 'four', 'three'],
                       columns=['a', 'b', 'c', 'd'])
    
    print("df1 = \n{0}\ntype(df1) = {1}".format(df1, type(df1)))
    print('-' * 50)
    # 多行索引【顺序可变】
    data3 = df1.loc['one':'three']
    print("多行索引：data3 = \ndf1.loc['one':'three'] = \n{0}\ntype(data3) = {1}".format(data3, type(data3)))
    print('-' * 100)
    
    df2 = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                       index=[2, 3, 1, 0],
                       columns=['a', 'b', 'c', 'd'])
    print("df2 = \n{0}\ntype(df2) = {1}".format(df2, type(df2)))
    print('-' * 50)
    data4 = df2.loc[1:0]
    print("多行索引：data4 = \ndf2.loc[1:0] = \n{0}\ntype(data4) = {1}".format(data4, type(data4)))
    print('-' * 100)

打印结果：

df1 = 
                   a          b          c          d
    one    23.078737   9.156431   0.439799  64.906356
    two    91.265745  12.581287  96.020470  95.584070
    four   95.501825  57.346484  73.247475  58.338678
    three  29.789496  95.718276  30.426301  94.037233
    type(df1) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------
    多行索引：data3 = 
    df1.loc['one':'three'] = 
                   a          b          c          d
    one    23.078737   9.156431   0.439799  64.906356
    two    91.265745  12.581287  96.020470  95.584070
    four   95.501825  57.346484  73.247475  58.338678
    three  29.789496  95.718276  30.426301  94.037233
    type(data3) = <class 'pandas.core.frame.DataFrame'>
    ----------------------------------------------------------------------------------------------------
    df2 = 
               a          b          c          d
    2  56.269699  50.504353  25.417596  65.251456
    3  93.865887  64.941123  92.814477  36.161766
    1  45.549343  58.922568  84.581590  75.418238
    0  38.015140  24.555141  41.455598  92.127194
    type(df2) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------
    多行索引：data4 = 
    df2.loc[1:0] = 
               a          b          c          d
    1  45.549343  58.922568  84.581590  75.418238
    0  38.015140  24.555141  41.455598  92.127194
    type(data4) = <class 'pandas.core.frame.DataFrame'>
    ----------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

## 三、df.iloc\[\]：按“位置下标”提取行数据 ##

*  df.iloc\[\] - 按照整数位置（从轴的0到length-1）选择行
 *  类似list的索引，其顺序就是dataframe的整数位置，从0开始计

#### 3.1 单标签索引：df.iloc\[-1\] ####

import numpy as np
    import pandas as pd
    
    # df.iloc[] - 按位置下标选择行
    # iloc和loc索引不同，不能索引超出数据行数的整数位置
    
    df = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                       index=['two', 'three', 'one', 'four'],
                       columns=['a', 'b', 'c', 'd'])
    
    
    print("df = \n{0}\ntype(df) = {1}".format(df, type(df)))
    print('-' * 100)
    
    # 单位置索引
    data1 = df.iloc[0]
    data2 = df.iloc[-1]
    print("data1 = df.iloc[0] = \n{0}\ntype(data1) = {1}".format(data1, type(data1)))
    print('-' * 50)
    print("data2 = df.iloc[-1] = \n{0}\ntype(data2) = {1}".format(data2, type(data2)))

打印结果：

df = 
                   a          b          c          d
    two    24.860960  54.227202  18.018653  38.724716
    three  69.652166  97.651980  19.959022  24.155129
    one    31.995642  59.591356  76.431234  44.830302
    four   33.716382  32.102688  20.937836  73.288219
    type(df) = <class 'pandas.core.frame.DataFrame'>
    ----------------------------------------------------------------------------------------------------
    data1 = df.iloc[0] = 
    a    24.860960
    b    54.227202
    c    18.018653
    d    38.724716
    Name: two, dtype: float64
    type(data1) = <class 'pandas.core.series.Series'>
    --------------------------------------------------
    data2 = df.iloc[-1] = 
    a    33.716382
    b    32.102688
    c    20.937836
    d    73.288219
    Name: four, dtype: float64
    type(data2) = <class 'pandas.core.series.Series'>
    
    Process finished with exit code 0

#### 3.2 多标签索引：df.iloc\[\[3, 2, 1\]\] ####

import numpy as np
    import pandas as pd
    
    # df.iloc[] - 按位置下标选择行
    # iloc和loc索引不同，不能索引超出数据行数的整数位置
    
    df = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                      index=['two', 'three', 'one', 'four'],
                      columns=['a', 'b', 'c', 'd'])
    
    print("df = \n{0}\ntype(df) = {1}".format(df, type(df)))
    print('-' * 100)
    
    # 多位置索引【顺序可变】
    data1 = df.iloc[[0, 2]]
    data2 = df.iloc[[3, 2, 1]]
    print("data1 = df.iloc[[0, 2]] = \n{0}\ntype(data1) = {1}".format(data1, type(data1)))
    print('-' * 50)
    print("data2 = df.iloc[[3, 2, 1]] = \n{0}\ntype(data2) = {1}".format(data2, type(data2)))

打印结果：

df = 
                   a          b          c          d
    two     4.708440  22.863487  26.806435   3.948613
    three  43.713656  75.754603  54.269785  64.708510
    one    49.566989  89.956527  26.388450  54.651651
    four   31.750995  81.558108  45.912672  40.851126
    type(df) = <class 'pandas.core.frame.DataFrame'>
    ----------------------------------------------------------------------------------------------------
    data1 = df.iloc[[0, 2]] = 
                 a          b          c          d
    two   4.708440  22.863487  26.806435   3.948613
    one  49.566989  89.956527  26.388450  54.651651
    type(data1) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------
    data2 = df.iloc[[3, 2, 1]] = 
                   a          b          c          d
    four   31.750995  81.558108  45.912672  40.851126
    one    49.566989  89.956527  26.388450  54.651651
    three  43.713656  75.754603  54.269785  64.708510
    type(data2) = <class 'pandas.core.frame.DataFrame'>
    
    Process finished with exit code 0

#### 3.3 多行索引：df.iloc\[1:3\] ####

import numpy as np
    import pandas as pd
    
    # df.iloc[] - 按位置下标选择行
    # iloc和loc索引不同，不能索引超出数据行数的整数位置
    
    df = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                      index=['two', 'three', 'one', 'four'],
                      columns=['a', 'b', 'c', 'd'])
    
    print("df = \n{0}\ntype(df) = {1}".format(df, type(df)))
    print('-' * 100)
    
    # 多位置索引
    data1 = df.iloc[0:2]
    data2 = df.iloc[1:3]
    print("data1 = df.iloc[0:2] = \n{0}\ntype(data1) = {1}".format(data1, type(data1)))
    print('-' * 50)
    print("data2 = df.iloc[1:3] = \n{0}\ntype(data2) = {1}".format(data2, type(data2)))

打印结果：

df = 
                   a          b          c          d
    two    58.579340  15.748046  11.903897  90.569674
    three  64.781174  49.745905   5.778577  99.143819
    one    96.295298  61.041770  61.024144   9.930110
    four   36.892635  26.641423  16.890470  43.553212
    type(df) = <class 'pandas.core.frame.DataFrame'>
    ----------------------------------------------------------------------------------------------------
    data1 = df.iloc[0:2] = 
                   a          b          c          d
    two    58.579340  15.748046  11.903897  90.569674
    three  64.781174  49.745905   5.778577  99.143819
    type(data1) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------
    data2 = df.iloc[1:3] = 
                   a          b          c          d
    three  64.781174  49.745905   5.778577  99.143819
    one    96.295298  61.041770  61.024144   9.930110
    type(data2) = <class 'pandas.core.frame.DataFrame'>
    
    Process finished with exit code 0

## 四、df.loc\[\] 和 df.iloc\[\] 的区别 ##

前提，简单介绍一下它俩：

*  loc利用 index的名称，来获取想要的行（或列）【名称导向】
 *  iloc利用 index的具体位置（所以它只能是整数型参数），来获取想要的行（或列）。

import numpy as np
    import pandas as pd
    
    s = pd.Series(np.nan, index=[49, 48, 47, 46, 45, 1, 2, 3, 4, 5])
    print("s = \n", s)

打印结果：

s = 
    49   NaN
    48   NaN
    47   NaN
    46   NaN
    45   NaN
    1    NaN
    2    NaN
    3    NaN
    4    NaN
    5    NaN
    dtype: float64

**让我们用整数3来试着提数**

*  s.iloc\[:3\]返回给我们的是前3行的数（因为把3当作位置信息做的处理）；
 *  s.loc\[:3\]返回前8行得数（因为把3当作名称对象做的处理）；

import numpy as np
    import pandas as pd
    
    s = pd.Series(np.nan, index=[49, 48, 47, 46, 45, 1, 2, 3, 4, 5])
    print("s.iloc[:3] = \n", s.iloc[:3])
    print("-" * 50)
    print("s.loc[:3] = \n", s.loc[:3])

打印结果：

s.iloc[:3] = 
    49   NaN
    48   NaN
    47   NaN
    dtype: float64
    --------------------------------------------------
    s.loc[:3] = 
    49   NaN
    48   NaN
    47   NaN
    46   NaN
    45   NaN
    1    NaN
    2    NaN
    3    NaN
    dtype: float64

如果我们试着用一个不在index里的整数，比如6会出现什么结果呢？

*  当然s.iloc\[:6\]返回的是前6行的数。
    
        import numpy as np
        import pandas as pd
        
        s = pd.Series(np.nan, index=[49, 48, 47, 46, 45, 1, 2, 3, 4, 5])
        print("s.iloc[:6] = \n", s.iloc[:6])
    
    打印结果：
    
        s.iloc[:6] = 
         49   NaN
        48   NaN
        47   NaN
        46   NaN
        45   NaN
        1    NaN
        dtype: float64
 *  但是，s.loc\[:6\]会被挂起提示KeyError，这是因为6不是index的元素。