devbox
小丑西瓜9
这个屌丝很懒，什么也没留下！
关注作者
热门标签
热门文章
当前位置:   article > 正文

Python pandas中DataFrame添加列、获取行列、获取元素值_dataframe增加列和相应的值

作者：小丑西瓜9 | 2024-02-27 12:58:47

赞

踩

dataframe增加列和相应的值

一、DataFrame添加列

直接通过赋值为空，添加一列。

  1. >>> import pandas as pd
  3. >>> df = pd.DataFrame(np.arange(12).reshape(3, 4), index = ['row1', 'row2', 'row3'], columns=['col1', 'col2', 'col3', 'col4'])
  5. >>> df
  6.       col1  col2  col3  col4
  7. row1     0     1     2     3
  8. row2     4     5     6     7
  9. row3     8     9    10    11
  10. >>> 
  11. >>> df['col5']=''
  12. >>> 
  13. >>> df
  14.       col1  col2  col3  col4 col5
  15. row1     0     1     2     3     
  16. row2     4     5     6     7     
  17. row3     8     9    10    11

通过一个list给新加的列赋值，添加一列。注意，list里的元素个数要跟dataframe的行数一致，否则回报长度对不齐的错误。

  1. >>> df['col6']=[1,1,1]
  2. >>> 
  3. >>> df
  4.       col1  col2  col3  col4 col5  col6
  5. row1     0     1     2     3          1
  6. row2     4     5     6     7          1
  7. row3     8     9    10    11          1
  8.  
  9. >>> df['col7']=[]
  10. Traceback (most recent call last):
  11.   File "<stdin>", line 1, in <module>
  12.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 3119, in __setitem__
  13.     self._set_item(key, value)
  14.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 3194, in _set_item
  15.     value = self._sanitize_column(key, value)
  16.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 3391, in _sanitize_column
  17.     value = _sanitize_index(value, self.index, copy=False)
  18.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/series.py", line 4001, in _sanitize_index
  19.     raise ValueError('Length of values does not match length of ' 'index')
  20. ValueError: Length of values does not match length of index
  21. >>> 
  22. >>> df['col7']=[1]
  23. Traceback (most recent call last):
  24.   File "<stdin>", line 1, in <module>
  25.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 3119, in __setitem__
  26.     self._set_item(key, value)
  27.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 3194, in _set_item
  28.     value = self._sanitize_column(key, value)
  29.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 3391, in _sanitize_column
  30.     value = _sanitize_index(value, self.index, copy=False)
  31.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/series.py", line 4001, in _sanitize_index
  32.     raise ValueError('Length of values does not match length of ' 'index')
  33. ValueError: Length of values does not match length of index

如果需要在指定位置处添加一列，用insert方法实现。DataFrame.insert(loc, column, value, allow_duplicates=False)，loc-位置，column-列名，value-元素值，allow_duplicates-是否允许重列。

  1. >>> df
  2.       col1  col2  col3  col4 col5  col6
  3. row1     0     1     2     3          1
  4. row2     4     5     6     7          1
  5. row3     8     9    10    11          1
  6. >>> 
  7. >>> df.insert(1,'col7','')
  8. >>> 
  9. >>> df
  10.       col1 col7  col2  col3  col4 col5  col6
  11. row1     0          1     2     3          1
  12. row2     4          5     6     7          1
  13. row3     8          9    10    11          1
  14. >>> 
  15. >>> df.insert(4,'col8',[2,2,2])
  16. >>> 
  17. >>> df
  18.       col1 col7  col2  col3  col8  col4 col5  col6
  19. row1     0          1     2     2     3          1
  20. row2     4          5     6     2     7          1
  21. row3     8          9    10     2    11          1

二、DataFrame取行列

1、按索引或者行列名选取DataFrame的具体行和列

  1. >>> df
  2.       col1 col7  col2  col3  col8  col4 col5  col6
  3. row1     0          1     2     2     3          1
  4. row2     4          5     6     2     7          1
  5. row3     8          9    10     2    11          1
  6. >>>  
  7. >>> col1=df['col1']
  8. >>> 
  9. >>> col1
  10. row1    0
  11. row2    4
  12. row3    8
  13. Name: col1, dtype: int64
  14. >>> 
  15. >>> type(col1)
  16. <class 'pandas.core.series.Series'>

注意，不能用行名取行，也不能用数字索引取行列，以下方式都会报错。

  1. >>> row1=df['row1']
  2. Traceback (most recent call last):
  3.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 3078, in get_loc
  4.     return self._engine.get_loc(key)
  5.   File "pandas/_libs/index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
  6.   File "pandas/_libs/index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
  7.   File "pandas/_libs/hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
  8.   File "pandas/_libs/hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
  9. KeyError: 'row1'
  10.  
  11. During handling of the above exception, another exception occurred:
  12.  
  13. Traceback (most recent call last):
  14.   File "<stdin>", line 1, in <module>
  15.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 2688, in __getitem__
  16.     return self._getitem_column(key)
  17.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 2695, in _getitem_column
  18.     return self._get_item_cache(key)
  19.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/generic.py", line 2489, in _get_item_cache
  20.     values = self._data.get(item)
  21.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/internals.py", line 4115, in get
  22.     loc = self.items.get_loc(item)
  23.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 3080, in get_loc
  24.     return self._engine.get_loc(self._maybe_cast_indexer(key))
  25.   File "pandas/_libs/index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
  26.   File "pandas/_libs/index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
  27.   File "pandas/_libs/hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
  28.   File "pandas/_libs/hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
  29. KeyError: 'row1'
  30. >>> 
  31. >>> row1=df[0]
  32. Traceback (most recent call last):
  33.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 3078, in get_loc
  34.     return self._engine.get_loc(key)
  35.   File "pandas/_libs/index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
  36.   File "pandas/_libs/index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
  37.   File "pandas/_libs/hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
  38.   File "pandas/_libs/hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
  39. KeyError: 0
  40.  
  41. During handling of the above exception, another exception occurred:
  42.  
  43. Traceback (most recent call last):
  44.   File "<stdin>", line 1, in <module>
  45.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 2688, in __getitem__
  46.     return self._getitem_column(key)
  47.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 2695, in _getitem_column
  48.     return self._get_item_cache(key)
  49.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/generic.py", line 2489, in _get_item_cache
  50.     values = self._data.get(item)
  51.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/internals.py", line 4115, in get
  52.     loc = self.items.get_loc(item)
  53.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 3080, in get_loc
  54.     return self._engine.get_loc(self._maybe_cast_indexer(key))
  55.   File "pandas/_libs/index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
  56.   File "pandas/_libs/index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
  57.   File "pandas/_libs/hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
  58.   File "pandas/_libs/hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
  59. KeyError: 0
  60. >>> 
  61. >>> row1=df[0,:]
  62. Traceback (most recent call last):
  63.   File "<stdin>", line 1, in <module>
  64.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 2688, in __getitem__
  65.     return self._getitem_column(key)
  66.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 2695, in _getitem_column
  67.     return self._get_item_cache(key)
  68.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/generic.py", line 2487, in _get_item_cache
  69.     res = cache.get(item)
  70. TypeError: unhashable type: 'slice'
  71. >>> 
  72. >>> col1=df[:,0]
  73. Traceback (most recent call last):
  74.   File "<stdin>", line 1, in <module>
  75.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 2688, in __getitem__
  76.     return self._getitem_column(key)
  77.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 2695, in _getitem_column
  78.     return self._get_item_cache(key)
  79.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/generic.py", line 2487, in _get_item_cache
  80.     res = cache.get(item)
  81. TypeError: unhashable type: 'slice'

取某些行可以用限定行号的方式。

  1. >>> row1=df[0:1]
  2. >>> 
  3. >>> row1
  4.       col1 col7  col2  col3  col8  col4 col5  col6
  5. row1     0          1     2     2     3          1
  6. >>> 
  7. >>> row2=df[1:2]
  8. >>> 
  9. >>> row2
  10.       col1 col7  col2  col3  col8  col4 col5  col6
  11. row2     4          5     6     2     7          1
  12. >>> 
  13. >>> row23=df[1:3]
  14. >>> 
  15. >>> row23
  16.       col1 col7  col2  col3  col8  col4 col5  col6
  17. row2     4          5     6     2     7          1
  18. row3     8          9    10     2    11          1

2、使用iloc按行号和列号取相应的行列

  1. >>> df
  2.       col1 col7  col2  col3  col8  col4 col5  col6
  3. row1     0          1     2     2     3          1
  4. row2     4          5     6     2     7          1
  5. row3     8          9    10     2    11          1
  6.  
  7. # 取第一行
  8. >>> row1=df.iloc[0]
  9. >>> 
  10. >>> row1
  11. col1    0
  12. col7     
  13. col2    1
  14. col3    2
  15. col8    2
  16. col4    3
  17. col5     
  18. col6    1
  19. Name: row1, dtype: object
  20.  
  21. # 取第1，2，3行
  22. >>> row123=df.iloc[[0,1,2]]
  23. >>> 
  24. >>> row123
  25.       col1 col7  col2  col3  col8  col4 col5  col6
  26. row1     0          1     2     2     3          1
  27. row2     4          5     6     2     7          1
  28. row3     8          9    10     2    11          1
  29. >>> 
  30. >>> row12=df.iloc[[0,1]]
  31. >>> 
  32. >>> row12
  33.       col1 col7  col2  col3  col8  col4 col5  col6
  34. row1     0          1     2     2     3          1
  35. row2     4          5     6     2     7          1
  36.  
  37. # 连续取多行
  38. >>> row12=df.iloc[0:2]
  39. >>> 
  40. >>> row12
  41.       col1 col7  col2  col3  col8  col4 col5  col6
  42. row1     0          1     2     2     3          1
  43. row2     4          5     6     2     7          1
  44. >>> 
  45. >>> row12=df.iloc[0:2,:]
  46. >>> 
  47. >>> row12
  48.       col1 col7  col2  col3  col8  col4 col5  col6
  49. row1     0          1     2     2     3          1
  50. row2     4          5     6     2     7          1
  1. >>> df
  2.       col1 col7  col2  col3  col8  col4 col5  col6
  3. row1     0          1     2     2     3          1
  4. row2     4          5     6     2     7          1
  5. row3     8          9    10     2    11          1
  6.  
  7. # 取行时可以不指定列，但取列时必须用:,来指定全行 
  8. >>> col1=df.iloc[:,0]
  9. >>> 
  10. >>> col1
  11. row1    0
  12. row2    4
  13. row3    8
  14. Name: col1, dtype: int64
  15.  
  16. # 用list[0,2]指定取第1，3列
  17. >>> col13=df.iloc[:,[0,2]]
  18. >>> 
  19. >>> col13
  20.       col1  col2
  21. row1     0     1
  22. row2     4     5
  23. row3     8     9
  24. >>> 
  25. >>> col123=df.iloc[:,[0,1,2]]
  26. >>> 
  27. >>> col123
  28.       col1 col7  col2
  29. row1     0          1
  30. row2     4          5
  31. row3     8          9
  32.  
  33. # 用0:2指定连续的多列
  34. >>> col12=df.iloc[:,0:2]
  35. >>> 
  36. >>> col12
  37.       col1 col7
  38. row1     0     
  39. row2     4     
  40. row3     8

3、使用loc按行名和列名取相应的行列

  1. >>> df
  2.       col1 col7  col2  col3  col8  col4 col5  col6
  3. row1     0          1     2     2     3          1
  4. row2     4          5     6     2     7          1
  5. row3     8          9    10     2    11          1
  6. >>> 
  7. >>> row1=df.loc['row1']
  8. >>> 
  9. >>> row1
  10. col1    0
  11. col7     
  12. col2    1
  13. col3    2
  14. col8    2
  15. col4    3
  16. col5     
  17. col6    1
  18. Name: row1, dtype: object
  19. >>> 
  20. >>> row2=df.loc['row2',:]
  21. >>> 
  22. >>> row2
  23. col1    4
  24. col7     
  25. col2    5
  26. col3    6
  27. col8    2
  28. col4    7
  29. col5     
  30. col6    1
  31. Name: row2, dtype: object
  32. >>> 
  33. >>> row12=df.loc[['row1','row2']]
  34. >>> 
  35. >>> row12
  36.       col1 col7  col2  col3  col8  col4 col5  col6
  37. row1     0          1     2     2     3          1
  38. row2     4          5     6     2     7          1
  39. >>> 
  40. >>> row123=df.loc['row1':'row3',:]
  41. >>> 
  42. >>> row123
  43.       col1 col7  col2  col3  col8  col4 col5  col6
  44. row1     0          1     2     2     3          1
  45. row2     4          5     6     2     7          1
  46. row3     8          9    10     2    11          1
  1. >>> df
  2.       col1 col7  col2  col3  col8  col4 col5  col6
  3. row1     0          1     2     2     3          1
  4. row2     4          5     6     2     7          1
  5. row3     8          9    10     2    11          1
  6. >>> 
  7. >>> col1=df.loc[:,'col1']
  8. >>> 
  9. >>> col1
  10. row1    0
  11. row2    4
  12. row3    8
  13. Name: col1, dtype: int64
  14.  
  15. >>> col128=df.loc[:,['col1','col2','col8']]
  16. >>> 
  17. >>> col128
  18.       col1  col2  col8
  19. row1     0     1     2
  20. row2     4     5     2
  21. row3     8     9     2
  22. >>> 
  23. >>> col1723=df.loc[:,'col1':'col3']
  24. >>> 
  25. >>> col1723
  26.       col1 col7  col2  col3
  27. row1     0          1     2
  28. row2     4          5     6
  29. row3     8          9    10

4、使用条件取相应的行列

  1. >>> df
  2.       col1 col7  col2  col3  col8  col4 col5  col6
  3. row1     0          1     2     2     3          1
  4. row2     4          5     6     2     7          1
  5. row3     8          9    10     2    11          1
  6.  
  7. # 取出第一列，有对应数字为4的所有行 
  8. >>> df.loc[df['col1']==4]
  9.       col1 col7  col2  col3  col8  col4 col5  col6
  10. row2     4          5     6     2     7          1
  11. >>> 
  12. >>> df.loc[df['col8']==2]
  13.       col1 col7  col2  col3  col8  col4 col5  col6
  14. row1     0          1     2     2     3          1
  15. row2     4          5     6     2     7          1
  16. row3     8          9    10     2    11          1
  17.  
  18. >>> df.loc[~(df['col1']!=4)]
  19.       col1 col7  col2  col3  col8  col4 col5  col6
  20. row2     4          5     6     2     7          1
  21. >>> 
  22. >>> df.loc[~(df['col1']==4)]
  23.       col1 col7  col2  col3  col8  col4 col5  col6
  24. row1     0          1     2     2     3          1
  25. row3     8          9    10     2    11          1

5、使用条件替换行列的值

  1. >>> df
  2.       col1 col7  col2  col3  col8  col4 col5  col6
  3. row1     0          1     2     2     3          1
  4. row2     4          5     6     2     7          1
  5. row3     8          9    10     2    11          1
  6.  
  7. # 把第一列中值为4的行选取出来，再选择第'col1'列的元素赋值成新的44
  8. >>> df.loc[df['col1']==4,'col1']=44
  9. >>> 
  10. >>> df
  11.       col1 col7  col2  col3  col8  col4 col5  col6
  12. row1     0          1     2     2     3          1
  13. row2    44          5     6     2     7          1
  14. row3     8          9    10     2    11          1
  15.  
  16. #  把第一列中值为4的行选取出来，再选择第'col4'列的元素赋值成新的44
  17. >>> df.loc[df['col1']==44,'col4']=44
  18. >>> 
  19. >>> df
  20.       col1 col7  col2  col3  col8  col4 col5  col6
  21. row1     0          1     2     2     3          1
  22. row2    44          5     6     2    44          1
  23. row3     8          9    10     2    11          1
  24.  
  25. # 选择'row3'行，其中有值为2的元素赋值成新的44
  26. >>> df.loc['row3',df.loc['row3',:]==2]=44
  27. >>> 
  28. >>> df
  29.       col1 col7  col2  col3  col8  col4 col5  col6
  30. row1     0          1     2     2     3          1
  31. row2    44          5     6     2    44          1
  32. row3     8          9    10    44    11          1
  33.  
  34. # 改变某行某列的元素值
  35. >>> df
  36.       col1  col2  col3  col4
  37. row1     0     1     2     3
  38. row2     4     5     6     7
  39. row3     8     9    10    11
  40. >>> 
  41. >>> df.at['row1','col1']=100
  42. >>> 
  43. >>> df
  44.       col1  col2  col3  col4
  45. row1   100     1     2     3
  46. row2     4     5     6     7
  47. row3     8     9    10    11

6、使用条件替换整个矩阵的部分元素的值

  1. >>> df
  2.       col1 col7  col2  col3  col8  col4 col5  col6
  3. row1     0          1     2     2     3          1
  4. row2    44          5     6     2    44          1
  5. row3     8          9    10    44    11          1
  6.  
  7. # 将所有值大于40的元素赋值为新的40，注意包含np.nan空值时会报错，需要先去除或者赋值
  8. >>> df[df>40]=40
  9. Traceback (most recent call last):
  10.   File "<stdin>", line 1, in <module>
  11.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 3114, in __setitem__
  12.     self._setitem_frame(key, value)
  13.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 3161, in _setitem_frame
  14.     self._check_inplace_setting(value)
  15.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/generic.py", line 4503, in _check_inplace_setting
  16.     raise TypeError('Cannot do inplace boolean setting on '
  17. TypeError: Cannot do inplace boolean setting on mixed-types with a non np.nan value
  18. >>> 
  19. >>> 
  20. >>> df>40
  21.        col1  col7   col2   col3   col8   col4  col5   col6
  22. row1  False  True  False  False  False  False  True  False
  23. row2   True  True  False  False  False   True  True  False
  24. row3  False  True  False  False   True  False  True  False
  25.  
  26. >>> df=df.drop(['col7'], axis=1)
  27. >>> df=df.drop(['col5'], axis=1)
  28. >>> 
  29. >>> df
  30.       col1  col2  col3  col8  col4  col6
  31. row1     0     1     2     2     3     1
  32. row2    44     5     6     2    44     1
  33. row3     8     9    10    44    11     1
  34. >>> 
  35. >>> df[df>40]=40
  36. >>> 
  37. >>> df
  38.       col1  col2  col3  col8  col4  col6
  39. row1     0     1     2     2     3     1
  40. row2    40     5     6     2    40     1
  41. row3     8     9    10    40    11     1

三、DataFrame取元素

1、选择列然后遍历获取元素的值

  1. >>> df
  2.       col1  col2  col3  col8  col4  col6
  3. row1     0     1     2     2     3     1
  4. row2    40     5     6     2    40     1
  5. row3     8     9    10    40    11     1
  6.  
  7. >>> for index in df['col1'].index:
  8. ...     idx=df['col1'].get(index)
  9. ...     print(idx)
  10. ... 
  11. 0
  12. 40
  13. 8

2、使用pandas.DataFrame.at的行索引和列名获取元素的值

  1. >>> df
  2.       col1  col2  col3  col8  col4  col6
  3. row1     0     1     2     2     3     1
  4. row2    40     5     6     2    40     1
  5. row3     8     9    10    40    11     1
  6. >>> 
  7. >>> df.at[4,'col1']
  8. Traceback (most recent call last):
  9.   File "<stdin>", line 1, in <module>
  10.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/indexing.py", line 2141, in __getitem__
  11.     key = self._convert_key(key)
  12.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/indexing.py", line 2227, in _convert_key
  13.     raise ValueError("At based indexing on an non-integer "
  14. ValueError: At based indexing on an non-integer index can only have non-integer indexers
  15. >>> 
  16. >>> df.at['row4','col1']
  17. Traceback (most recent call last):
  18.   File "<stdin>", line 1, in <module>
  19.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/indexing.py", line 2142, in __getitem__
  20.     return self.obj._get_value(*key, takeable=self._takeable)
  21.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/frame.py", line 2539, in _get_value
  22.     return engine.get_value(series._values, index)
  23.   File "pandas/_libs/index.pyx", line 106, in pandas._libs.index.IndexEngine.get_value
  24.   File "pandas/_libs/index.pyx", line 114, in pandas._libs.index.IndexEngine.get_value
  25.   File "pandas/_libs/index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
  26.   File "pandas/_libs/hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
  27.   File "pandas/_libs/hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
  28. KeyError: 'row4'
  29. >>> 
  30. >>> df.at['row2','col1']
  31. 40
  32.  
  33. >>> df.iloc[1].at['col1']
  34. 40

3、使用pandas.DataFrame.iat的行索引和列索引获取元素的值

  1. >>> df
  2.       col1  col2  col3  col8  col4  col6
  3. row1     0     1     2     2     3     1
  4. row2    40     5     6     2    40     1
  5. row3     8     9    10    40    11     1
  6. >>> 
  7. >>> df.iat[1,2]
  8. 6
  9. >>> 
  10. >>> df.iloc[3].iat[4]
  11. Traceback (most recent call last):
  12.   File "<stdin>", line 1, in <module>
  13.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/indexing.py", line 1478, in __getitem__
  14.     return self._getitem_axis(maybe_callable, axis=axis)
  15.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/indexing.py", line 2102, in _getitem_axis
  16.     self._validate_integer(key, axis)
  17.   File "/root/miniconda3/lib/python3.6/site-packages/pandas/core/indexing.py", line 2009, in _validate_integer
  18.     raise IndexError("single positional indexer is out-of-bounds")
  19. IndexError: single positional indexer is out-of-bounds
  20. >>> 
  21. >>> df.iloc[2].iat[4]
  22. 11

4、使用pandas.DataFrame.loc获取元素的值或者行

  1. >>> df
  2.       col1  col2  col3  col8  col4  col6
  3. row1     0     1     2     2     3     1
  4. row2    40     5     6     2    40     1
  5. row3     8     9    10    40    11     1
  6. >>> 
  7. >>> 
  8. >>> df.loc['row1','col1']
  9. 0
  10. >>> 
  11. >>> df.loc['row1']
  12. col1    0
  13. col2    1
  14. col3    2
  15. col8    2
  16. col4    3
  17. col6    1
  18. Name: row1, dtype: int64
  19. >>> 
  20. >>> df.loc[['row1','row3']]
  21.       col1  col2  col3  col8  col4  col6
  22. row1     0     1     2     2     3     1
  23. row3     8     9    10    40    11     1

5、使用pandas.DataFrame.iloc获取元素的值或者行

  1. >>> df
  2.       col1  col2  col3  col8  col4  col6
  3. row1     0     1     2     2     3     1
  4. row2    40     5     6     2    40     1
  5. row3     8     9    10    40    11     1
  6. >>> 
  7. >>> df.iloc[0,2]
  8. 2
  9. >>> 
  10. >>> df.iloc[2]
  11. col1     8
  12. col2     9
  13. col3    10
  14. col8    40
  15. col4    11
  16. col6     1
  17. Name: row3, dtype: int64

引用

【1】https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.insert.html

声明：本文内容由网友自发贡献，不代表【wpsshop博客】立场，版权归原作者所有，本站不承担相应法律责任。如您发现有侵权的内容，请联系我们。转载请注明出处：https://www.wpsshop.cn/w/小丑西瓜9/article/detail/153123
推荐阅读
相关标签

Copyright © 2003-2013 www.wpsshop.cn 版权所有，并保留所有权利。

  

闽ICP备14008679号