Pandas-数据操作-字符串型（二）：常用方法【lower、upper、len、startswith、endswith、strip、lstrip、replace、split、rsplit】

川长思鸟来 2023-09-29 07:16 22阅读 0赞

## 一、字符串常用方法：lower，upper，len，startswith，endswith ##

import numpy as np
    import pandas as pd
    
    s = pd.Series(['A', 'b', 'bbhello', '123', np.nan])
    print("s = \n", s)
    print('-' * 200)
    print("lower小写: s.str.lower() = \n", s.str.lower())
    print('-' * 200)
    print("upper大写: s.str.upper() = \n", s.str.upper())
    print('-' * 200)
    print("len字符长度: s.str.len() = \n", s.str.len())
    print('-' * 200)
    print("判断起始是否为b: s.str.startswith('b') = \n", s.str.startswith('b'))
    print('-' * 200)
    print("判断结束是否为3: s.str.endswith('3') = \n", s.str.endswith('3'))

打印结果：

s = 
    0          A
    1          b
    2    bbhello
    3        123
    4        NaN
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    lower小写: s.str.lower() = 
    0          a
    1          b
    2    bbhello
    3        123
    4        NaN
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    upper大写: s.str.upper() = 
     0          A
    1          B
    2    BBHELLO
    3        123
    4        NaN
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    len字符长度: s.str.len() = 
    0    1.0
    1    1.0
    2    7.0
    3    3.0
    4    NaN
    dtype: float64
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    判断起始是否为b: s.str.startswith('b') = 
    0    False
    1     True
    2     True
    3    False
    4      NaN
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    判断结束是否为3: s.str.endswith('3') = 
    0    False
    1    False
    2    False
    3     True
    4      NaN
    dtype: object
    
    Process finished with exit code 0

## 二、字符串常用方法：strip ##

import numpy as np
    import pandas as pd
    
    # 字符串常用方法（2） - strip
    
    s = pd.Series([' jack', 'jill ', ' je sse ', 'frank'])
    df = pd.DataFrame(np.random.randn(3, 2),
                      columns=[' Column A ', ' Column B '],
                      index=range(3))
    print("s = \n", s)
    print('-' * 200)
    print("df = \n", df)
    print('-' * 200)
    
    # 去除字符串左右的空格
    print("去除字符串左右的空格: s.str.strip() = \n", s.str.strip())
    # 去除字符串中的左空格
    print("去除字符串中的左空格: s.str.lstrip() = \n", s.str.lstrip())
    # 去除字符串中的右空格
    print("去除字符串中的右空格: s.str.rstrip() = \n", s.str.rstrip())
    
    # 这里去掉了columns的前后空格，但没有去掉中间空格
    df.columns = df.columns.str.strip()
    print("df = \n", df)
    print('-' * 200)

打印结果：

s = 
    0        jack
    1       jill 
    2     je sse 
    3       frank
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    df = 
         Column A    Column B 
    0   -1.318646   -0.831649
    1   -0.339870   -1.141231
    2   -0.024364   -2.163961
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    去除字符串左右的空格: s.str.strip() = 
    0      jack
    1      jill
    2    je sse
    3     frank
    dtype: object
    去除字符串中的左空格: s.str.lstrip() = 
    0       jack
    1      jill 
    2    je sse 
    3      frank
    dtype: object
    去除字符串中的右空格: s.str.rstrip() = 
     0       jack
    1       jill
    2     je sse
    3      frank
    dtype: object
    df = 
        Column A  Column B
    0 -1.318646 -0.831649
    1 -0.339870 -1.141231
    2 -0.024364 -2.163961
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

## 三、字符串常用方法：replace ##

import numpy as np
    import pandas as pd
    
    # 字符串常用方法（3） - replace
    
    df = pd.DataFrame(np.random.randn(3, 2),
                      columns=[' Column A ', ' Column B '],
                      index=range(3))
    # 替换
    df.columns = df.columns.str.replace(' ', '-')
    print("df = \n", df)
    print('-' * 200)
    
    # n：替换个数
    df.columns = df.columns.str.replace('-', '*', n=1)
    print("df = \n", df)

打印结果：

df = 
        -Column-A-  -Column-B-
    0    0.704728   -0.835929
    1    1.478930   -2.708538
    2    0.585825   -1.395908
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    df = 
        *Column-A-  *Column-B-
    0    0.704728   -0.835929
    1    1.478930   -2.708538
    2    0.585825   -1.395908
    
    Process finished with exit code 0

## 四、字符串常用方法：split、rsplit ##

import numpy as np
    import pandas as pd
    
    # 字符串常用方法（4） - split、rsplit
    
    
    s = pd.Series(['a,b,c', '1,2,3', ['a,,,c'], np.nan])
    print("s = \n", s)
    print('-' * 200)
    
    # 类似字符串的split
    data1 = s.str.split(',')
    print("data1 = s.str.split(',') = \n{0} \ntype(data1) = {1}".format(data1, type(data1)))
    print('-' * 100)
    
    # 直接索引得到一个list
    data2 = data1[0]
    print("data2 = data1[0] = s.str.split(',')[0] = \n{0} \ntype(data2) = {1}".format(data2, type(data2)))
    print('-' * 100)
    
    # 可以使用get或[]符号访问拆分列表中的元素
    data3 = s.str.split(',').str.get(1)
    print("data3 = s.str.split(',').str.get(1) = \n{0} \ntype(data3) = {1}".format(data3, type(data3)))
    print('-' * 200)
    
    # 可以使用expand可以轻松扩展此操作以返回DataFrame
    # n参数限制分割数
    # rsplit类似于split，反向工作，即从字符串的末尾到字符串的开头
    data4 = s.str.split(',', expand=True)
    print("data4 = s.str.split(',', expand=True) = \n{0} \ntype(data4) = {1}".format(data4, type(data4)))
    print('-' * 100)
    data5 = s.str.split(',', expand=True, n=1)
    print("data5 = s.str.split(',', expand=True, n=1) = \n{0} \ntype(data5) = {1}".format(data5, type(data5)))
    print('-' * 100)
    data6 = s.str.rsplit(',', expand=True, n=1)
    print("data6 = s.str.rsplit(',', expand=True, n=1) = \n{0} \ntype(data6) = {1}".format(data6, type(data6)))
    print('-' * 200)
    
    # Dataframe使用split
    df = pd.DataFrame({
        'key1': ['a,b,c', '1,2,3', [':,., ']],
                       'key2': ['a-b-c', '1-2-3', [':-.- ']]})
    print("df = \n", df)
    print('-' * 100)
    data7 = df['key2'].str.split('-')
    print("data7 = df['key2'].str.split('-') = \n{0} \ntype(data7) = {1}".format(data7, type(data7)))
    print('-' * 200)

打印结果：

s = 
    0      a,b,c
    1      1,2,3
    2    [a,,,c]
    3        NaN
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    data1 = s.str.split(',') = 
    0    [a, b, c]
    1    [1, 2, 3]
    2          NaN
    3          NaN
    dtype: object 
    type(data1) = <class 'pandas.core.series.Series'>
    ----------------------------------------------------------------------------------------------------
    data2 = data1[0] = s.str.split(',')[0] = 
    ['a', 'b', 'c'] 
    type(data2) = <class 'list'>
    ----------------------------------------------------------------------------------------------------
    data3 = s.str.split(',').str.get(1) = 
    0      b
    1      2
    2    NaN
    3    NaN
    dtype: object 
    type(data3) = <class 'pandas.core.series.Series'>
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    data4 = s.str.split(',', expand=True) = 
         0    1    2
    0    a    b    c
    1    1    2    3
    2  NaN  NaN  NaN
    3  NaN  NaN  NaN 
    type(data4) = <class 'pandas.core.frame.DataFrame'>
    ----------------------------------------------------------------------------------------------------
    data5 = s.str.split(',', expand=True, n=1) = 
         0    1
    0    a  b,c
    1    1  2,3
    2  NaN  NaN
    3  NaN  NaN 
    type(data5) = <class 'pandas.core.frame.DataFrame'>
    ----------------------------------------------------------------------------------------------------
    data6 = s.str.rsplit(',', expand=True, n=1) = 
         0    1
    0  a,b    c
    1  1,2    3
    2  NaN  NaN
    3  NaN  NaN 
    type(data6) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    df = 
           key1     key2
    0    a,b,c    a-b-c
    1    1,2,3    1-2-3
    2  [:,., ]  [:-.- ]
    ----------------------------------------------------------------------------------------------------
    data7 = df['key2'].str.split('-') = 
    0    [a, b, c]
    1    [1, 2, 3]
    2          NaN
    Name: key2, dtype: object 
    type(data7) = <class 'pandas.core.series.Series'>
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0