Pandas-数据操作-字符串型（一）：常用方法【str（自动过滤NaN值）、索引】

偏执的太偏执、 2023-09-29 09:39 7阅读 0赞

Pandas针对字符串配备的一套方法，使其易于对数组的每个元素进行操作。

## 一、str：通过str访问，且自动排除丢失/ NA值 ##

通过str访问，且自动排除丢失/ NA值

*  直接通过.str调用字符串方法
 *  可以对Series、Dataframe使用
 *  自动过滤NaN值

import numpy as np
    import pandas as pd
    
    # 通过str访问，且自动排除丢失/ NA值
    # 直接通过.str调用字符串方法
    # 可以对Series、Dataframe使用
    # 自动过滤NaN值
    
    s = pd.Series(['A', 'b', 'C', 'bbhello', '123', np.nan, 'hj'])
    df = pd.DataFrame({
        'key1': list('abcdef'),
                       'key2': ['hee', 'fv', 'w', 'hija', '123', np.nan]})
    print("s = \n", s)
    print('-' * 50)
    print("df = \n", df)
    print('-' * 200)
    
    print("s.str.count('b') = \n", s.str.count('b'))
    print('-' * 50)
    print("df['key2'].str.upper() = \n", df['key2'].str.upper())
    print('-' * 200)
    
    # df.columns是一个Index对象，也可使用.str
    df.columns = df.columns.str.upper()
    print("df = \n", df)
    print('-' * 200)

打印结果：

s = 
    0          A
    1          b
    2          C
    3    bbhello
    4        123
    5        NaN
    6         hj
    dtype: object
    --------------------------------------------------
    df = 
       key1  key2
    0    a   hee
    1    b    fv
    2    c     w
    3    d  hija
    4    e   123
    5    f   NaN
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    s.str.count('b') = 
     0    0.0
    1    1.0
    2    0.0
    3    2.0
    4    0.0
    5    NaN
    6    0.0
    dtype: float64
    --------------------------------------------------
    df['key2'].str.upper() = 
     0     HEE
    1      FV
    2       W
    3    HIJA
    4     123
    5     NaN
    Name: key2, dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    df = 
       KEY1  KEY2
    0    a   hee
    1    b    fv
    2    c     w
    3    d  hija
    4    e   123
    5    f   NaN
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

## 二、字符串索引 ##

import numpy as np
    import pandas as pd
    
    # 字符串索引
    
    s = pd.Series(['A', 'b', 'C', 'bbhello', '123', np.nan, 'hj'])
    df = pd.DataFrame({
        'key1': list('abcdef'),
                       'key2': ['hee', 'fv', 'w', 'hija', '123', np.nan]})
    
    # 取第一个字符
    data1 = s.str[0]
    print("取第一个字符: data1 = s.str[0] = \n", data1)
    print('-' * 200)
    # 取前两个字符
    data2 = s.str[:2]
    print("取前两个字符: data2 = s.str[:2] = \n", data2)
    print('-' * 200)
    
    # str之后和字符串本身索引方式相同
    data3 = df['key2'].str[:2]
    print("data3 = df['key2'].str[:2] = \n", data3)
    print('-' * 200)

打印结果：

取第一个字符: data1 = s.str[0] = 
     0      A
    1      b
    2      C
    3      b
    4      1
    5    NaN
    6      h
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    取前两个字符: data2 = s.str[:2] = 
     0      A
    1      b
    2      C
    3     bb
    4     12
    5    NaN
    6     hj
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    data3 = df['key2'].str[:2] = 
     0     he
    1     fv
    2      w
    3     hi
    4     12
    5    NaN
    Name: key2, dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0