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 77阅读 0赞

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

  1. import numpy as np
  2. import pandas as pd
  4. s = pd.Series(['A', 'b', 'bbhello', '123', np.nan])
  5. print("s = \n", s)
  6. print('-' * 200)
  7. print("lower小写: s.str.lower() = \n", s.str.lower())
  8. print('-' * 200)
  9. print("upper大写: s.str.upper() = \n", s.str.upper())
  10. print('-' * 200)
  11. print("len字符长度: s.str.len() = \n", s.str.len())
  12. print('-' * 200)
  13. print("判断起始是否为b: s.str.startswith('b') = \n", s.str.startswith('b'))
  14. print('-' * 200)
  15. print("判断结束是否为3: s.str.endswith('3') = \n", s.str.endswith('3'))

打印结果：

  1. s = 
  2. 0          A
  3. 1          b
  4. 2    bbhello
  5. 3        123
  6. 4        NaN
  7. dtype: object
  8. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  9. lower小写: s.str.lower() = 
  10. 0          a
  11. 1          b
  12. 2    bbhello
  13. 3        123
  14. 4        NaN
  15. dtype: object
  16. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  17. upper大写: s.str.upper() = 
  18.  0          A
  19. 1          B
  20. 2    BBHELLO
  21. 3        123
  22. 4        NaN
  23. dtype: object
  24. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  25. len字符长度: s.str.len() = 
  26. 0    1.0
  27. 1    1.0
  28. 2    7.0
  29. 3    3.0
  30. 4    NaN
  31. dtype: float64
  32. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  33. 判断起始是否为b: s.str.startswith('b') = 
  34. 0    False
  35. 1     True
  36. 2     True
  37. 3    False
  38. 4      NaN
  39. dtype: object
  40. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  41. 判断结束是否为3: s.str.endswith('3') = 
  42. 0    False
  43. 1    False
  44. 2    False
  45. 3     True
  46. 4      NaN
  47. dtype: object
  49. Process finished with exit code 0

二、字符串常用方法：strip

  1. import numpy as np
  2. import pandas as pd
  4. # 字符串常用方法（2） - strip
  6. s = pd.Series([' jack', 'jill ', ' je sse ', 'frank'])
  7. df = pd.DataFrame(np.random.randn(3, 2),
  8.                   columns=[' Column A ', ' Column B '],
  9.                   index=range(3))
  10. print("s = \n", s)
  11. print('-' * 200)
  12. print("df = \n", df)
  13. print('-' * 200)
  15. # 去除字符串左右的空格
  16. print("去除字符串左右的空格: s.str.strip() = \n", s.str.strip())
  17. # 去除字符串中的左空格
  18. print("去除字符串中的左空格: s.str.lstrip() = \n", s.str.lstrip())
  19. # 去除字符串中的右空格
  20. print("去除字符串中的右空格: s.str.rstrip() = \n", s.str.rstrip())
  22. # 这里去掉了columns的前后空格，但没有去掉中间空格
  23. df.columns = df.columns.str.strip()
  24. print("df = \n", df)
  25. print('-' * 200)

打印结果：

  1. s = 
  2. 0        jack
  3. 1       jill 
  4. 2     je sse 
  5. 3       frank
  6. dtype: object
  7. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  8. df = 
  9.      Column A    Column B 
  10. 0   -1.318646   -0.831649
  11. 1   -0.339870   -1.141231
  12. 2   -0.024364   -2.163961
  13. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  14. 去除字符串左右的空格: s.str.strip() = 
  15. 0      jack
  16. 1      jill
  17. 2    je sse
  18. 3     frank
  19. dtype: object
  20. 去除字符串中的左空格: s.str.lstrip() = 
  21. 0       jack
  22. 1      jill 
  23. 2    je sse 
  24. 3      frank
  25. dtype: object
  26. 去除字符串中的右空格: s.str.rstrip() = 
  27.  0       jack
  28. 1       jill
  29. 2     je sse
  30. 3      frank
  31. dtype: object
  32. df = 
  33.     Column A  Column B
  34. 0 -1.318646 -0.831649
  35. 1 -0.339870 -1.141231
  36. 2 -0.024364 -2.163961
  37. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  39. Process finished with exit code 0

三、字符串常用方法：replace

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

打印结果：

  1. df = 
  2.     -Column-A-  -Column-B-
  3. 0    0.704728   -0.835929
  4. 1    1.478930   -2.708538
  5. 2    0.585825   -1.395908
  6. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  7. df = 
  8.     *Column-A-  *Column-B-
  9. 0    0.704728   -0.835929
  10. 1    1.478930   -2.708538
  11. 2    0.585825   -1.395908
  13. Process finished with exit code 0

四、字符串常用方法：split、rsplit

  1. import numpy as np
  2. import pandas as pd
  4. # 字符串常用方法（4） - split、rsplit
  7. s = pd.Series(['a,b,c', '1,2,3', ['a,,,c'], np.nan])
  8. print("s = \n", s)
  9. print('-' * 200)
  11. # 类似字符串的split
  12. data1 = s.str.split(',')
  13. print("data1 = s.str.split(',') = \n{0} \ntype(data1) = {1}".format(data1, type(data1)))
  14. print('-' * 100)
  16. # 直接索引得到一个list
  17. data2 = data1[0]
  18. print("data2 = data1[0] = s.str.split(',')[0] = \n{0} \ntype(data2) = {1}".format(data2, type(data2)))
  19. print('-' * 100)
  21. # 可以使用get或[]符号访问拆分列表中的元素
  22. data3 = s.str.split(',').str.get(1)
  23. print("data3 = s.str.split(',').str.get(1) = \n{0} \ntype(data3) = {1}".format(data3, type(data3)))
  24. print('-' * 200)
  26. # 可以使用expand可以轻松扩展此操作以返回DataFrame
  27. # n参数限制分割数
  28. # rsplit类似于split，反向工作，即从字符串的末尾到字符串的开头
  29. data4 = s.str.split(',', expand=True)
  30. print("data4 = s.str.split(',', expand=True) = \n{0} \ntype(data4) = {1}".format(data4, type(data4)))
  31. print('-' * 100)
  32. data5 = s.str.split(',', expand=True, n=1)
  33. print("data5 = s.str.split(',', expand=True, n=1) = \n{0} \ntype(data5) = {1}".format(data5, type(data5)))
  34. print('-' * 100)
  35. data6 = s.str.rsplit(',', expand=True, n=1)
  36. print("data6 = s.str.rsplit(',', expand=True, n=1) = \n{0} \ntype(data6) = {1}".format(data6, type(data6)))
  37. print('-' * 200)
  39. # Dataframe使用split
  40. df = pd.DataFrame({
  41.     'key1': ['a,b,c', '1,2,3', [':,., ']],
  42.                    'key2': ['a-b-c', '1-2-3', [':-.- ']]})
  43. print("df = \n", df)
  44. print('-' * 100)
  45. data7 = df['key2'].str.split('-')
  46. print("data7 = df['key2'].str.split('-') = \n{0} \ntype(data7) = {1}".format(data7, type(data7)))
  47. print('-' * 200)

打印结果：

  1. s = 
  2. 0      a,b,c
  3. 1      1,2,3
  4. 2    [a,,,c]
  5. 3        NaN
  6. dtype: object
  7. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  8. data1 = s.str.split(',') = 
  9. 0    [a, b, c]
  10. 1    [1, 2, 3]
  11. 2          NaN
  12. 3          NaN
  13. dtype: object 
  14. type(data1) = <class 'pandas.core.series.Series'>
  15. ----------------------------------------------------------------------------------------------------
  16. data2 = data1[0] = s.str.split(',')[0] = 
  17. ['a', 'b', 'c'] 
  18. type(data2) = <class 'list'>
  19. ----------------------------------------------------------------------------------------------------
  20. data3 = s.str.split(',').str.get(1) = 
  21. 0      b
  22. 1      2
  23. 2    NaN
  24. 3    NaN
  25. dtype: object 
  26. type(data3) = <class 'pandas.core.series.Series'>
  27. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  28. data4 = s.str.split(',', expand=True) = 
  29.      0    1    2
  30. 0    a    b    c
  31. 1    1    2    3
  32. 2  NaN  NaN  NaN
  33. 3  NaN  NaN  NaN 
  34. type(data4) = <class 'pandas.core.frame.DataFrame'>
  35. ----------------------------------------------------------------------------------------------------
  36. data5 = s.str.split(',', expand=True, n=1) = 
  37.      0    1
  38. 0    a  b,c
  39. 1    1  2,3
  40. 2  NaN  NaN
  41. 3  NaN  NaN 
  42. type(data5) = <class 'pandas.core.frame.DataFrame'>
  43. ----------------------------------------------------------------------------------------------------
  44. data6 = s.str.rsplit(',', expand=True, n=1) = 
  45.      0    1
  46. 0  a,b    c
  47. 1  1,2    3
  48. 2  NaN  NaN
  49. 3  NaN  NaN 
  50. type(data6) = <class 'pandas.core.frame.DataFrame'>
  51. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  52. df = 
  53.        key1     key2
  54. 0    a,b,c    a-b-c
  55. 1    1,2,3    1-2-3
  56. 2  [:,., ]  [:-.- ]
  57. ----------------------------------------------------------------------------------------------------
  58. data7 = df['key2'].str.split('-') = 
  59. 0    [a, b, c]
  60. 1    [1, 2, 3]
  61. 2          NaN
  62. Name: key2, dtype: object 
  63. type(data7) = <class 'pandas.core.series.Series'>
  64. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  66. Process finished with exit code 0

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