Pandas-数据结构-DataFrame（五）：行&列同时索引【①：df.loc[[‘b‘, ‘c‘], [‘y‘, 8]]；②：先列索引，再行索引；③：df.iloc[1:3, 2:6]】

深藏阁楼爱情的钟 2023-09-29 09:09 39阅读 0赞

Dataframe既有行索引也有列索引，可以被看做由Series组成的字典（共用一个索引）。

## 一、直接索引（先列后行，直接索引时只能通过索引名进行索引，不能通过下标） ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center]  
获取’2018-02-27’这天的’close’的结果

# 直接使用行列索引名字的方式（先列后行）
    data['open']['2018-02-27']
    23.53
    
    # 不支持的操作
    # 错误
    data['2018-02-27']['open']
    # 错误
    data[:1, :2]

## 二、提取 “目标行 & 目标列”：df.loc\[\[‘b’, ‘c’\], \[‘y’, 8\]\] ##

df = 
         x   y   z   8   9
    a NaN NaN NaN NaN NaN
    b NaN NaN NaN NaN NaN
    c NaN NaN NaN NaN NaN
    d NaN NaN NaN NaN NaN
    e NaN NaN NaN NaN NaN

*  df.loc\[:, \[‘y’, 8\]\]：提取第‘y’、8列的所有行；
 *  df.loc\[:, ‘y’:8\]：错误表达；

import numpy as np
    import pandas as pd
    
    df = pd.DataFrame(np.nan,
                      index=list('abcde'),
                      columns=['x', 'y', 'z', 8, 9])
    print("df = \n", df)
    
    print("-" * 100)
    
    data1 = df.loc[['b', 'c']]
    print("data1 = \n", data1)
    print("-" * 50)
    data2 = df.loc[:, ['y', 8]]
    print("data2 = \n", data2)
    print("-" * 50)
    data3 = df.loc[['b', 'c'], ['y', 8]]
    print("data3 = \n", data3)

打印结果：

df = 
         x   y   z   8   9
    a NaN NaN NaN NaN NaN
    b NaN NaN NaN NaN NaN
    c NaN NaN NaN NaN NaN
    d NaN NaN NaN NaN NaN
    e NaN NaN NaN NaN NaN
    ----------------------------------------------------------------------------------------------------
    data1 = 
         x   y   z   8   9
    b NaN NaN NaN NaN NaN
    c NaN NaN NaN NaN NaN
    --------------------------------------------------
    data2 = 
         y   8
    a NaN NaN
    b NaN NaN
    c NaN NaN
    d NaN NaN
    e NaN NaN
    --------------------------------------------------
    data3 = 
         y   8
    b NaN NaN
    c NaN NaN
    
    Process finished with exit code 0

## 三、先列索引，再行索引 ##

先选择列再选择行：相当于对于一个数据，先筛选字段，再选择数据量

import numpy as np
    import pandas as pd
    
    # 多重索引：比如同时索引行和列
    # 先选择列再选择行 —— 相当于对于一个数据，先筛选字段，再选择数据量
    
    df = pd.DataFrame(np.random.rand(16).reshape(4, 4) * 100,
                      index=['one', 'two', 'three', 'four'],
                      columns=['a', 'b', 'c', 'd'])
    print("df = \n", df)
    print('-' * 100)
    
    data1 = df['a'].loc[['one', 'three']]
    print("data1 = df['a'].loc[['one', 'three']] = \n", df['a'].loc[['one', 'three']])  # 选择a列的one，three行
    print('-' * 50)
    
    data2 = df[['b', 'c', 'd']]
    print("data2 = df[['b', 'c', 'd']] = \n", data2)
    print('-' * 100)
    
    data = df['a'] < 50
    print("data = df['a'] < 50 = \n", data)
    print('-' * 50)
    data3 = df[df['a'] < 50]
    print("data3 = df[df['a'] < 50] = \n", data3)  # 选择b，c，d列的one，three行
    print('-' * 50)
    
    data4 = df[df['a'] < 50].iloc[1]
    print("data4 = df[df['a'] < 50].iloc[:2] = \n", data4)  # 选择满足判断索引的前两行数据

打印结果：

df = 
                    a          b          c          d
    one     9.835341  90.198909  41.946498  57.696927
    two    42.118455  92.361098  12.128027  58.962167
    three  57.007146  18.977019  92.999803  47.113144
    four   97.706270  99.227877   4.032991  27.748419
    ----------------------------------------------------------------------------------------------------
    data1 = df['a'].loc[['one', 'three']] = 
     one       9.835341
    three    57.007146
    Name: a, dtype: float64
    --------------------------------------------------
    data2 = df[['b', 'c', 'd']] = 
                    b          c          d
    one    90.198909  41.946498  57.696927
    two    92.361098  12.128027  58.962167
    three  18.977019  92.999803  47.113144
    four   99.227877   4.032991  27.748419
    ----------------------------------------------------------------------------------------------------
    data = df['a'] < 50 = 
     one       True
    two       True
    three    False
    four     False
    Name: a, dtype: bool
    --------------------------------------------------
    data3 = df[df['a'] < 50] = 
                  a          b          c          d
    one   9.835341  90.198909  41.946498  57.696927
    two  42.118455  92.361098  12.128027  58.962167
    --------------------------------------------------
    data4 = df[df['a'] < 50].iloc[:2] = 
     a    42.118455
    b    92.361098
    c    12.128027
    d    58.962167
    Name: two, dtype: float64
    
    Process finished with exit code 0

## 四、行切片&列切片：df.iloc\[1:3, 2:6\] ##

根据位置和名称信息混搭的取数：对于一个DaraFrame，如果我想提取c行及其之前所有的，同时属于前4列的数呢？

`iloc[num_of_row_start : num_of_row_end, num_of_column_start : num_of_column_end]`

import numpy as np
    import pandas as pd
    
    df = pd.DataFrame(np.nan,
                      index=list('abcde'),
                      columns=['x', 'y', 'z', 8, 9])
    print("df = \n", df)
    
    print("-" * 100)
    
    df_select = df.iloc[:df.index.get_loc('c') + 1, :4]
    print("df_select = \n", df_select)

打印结果：

get\_loc(pandas 0.24.1)是一个应用在index的工具，即“获取名称对象在index的位置（整数）”。注意，因为不包含num\_of\_end，所以需要 +1才能包含c行。

--------------------

参考资料：  
[Python笔记：df.loc\[\]和df.iloc\[\]的区别][Python_df.loc_df.iloc]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center]: https://img-blog.csdnimg.cn/20201024234502464.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE=,size_16,color_FFFFFF,t_70#pic_center
[Python_df.loc_df.iloc]: https://blog.csdn.net/htuhxf/article/details/88221418