本文主要是介绍Pandas-数据操作-字符串型（二）：常用方法【lower、upper、len、startswith、endswith、strip、lstrip、replace、split、rsplit】，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
一、字符串常用方法：lower，upper，len，startswith，endswith
import numpy as np
import pandas as pds = 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: objectProcess 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） - strips = 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） - replacedf = 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 -1.395908Process 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、rsplits = 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
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