2 Contents

Explanation of the phenomenon

The error we're talking about right now is the one that happens when you try to extract an impossible column name. In the example below, columns B, C, and D are provided as columns, but column F does not exist. Despite not existing I am getting an error because I am trying to extract column F.

sample.py

import pandas as pd
import numpy as np

dat = [
    ['2019-07-01','9997','740','78'],
    ['2019-07-02','9997','749','45'],
    ['2019-07-03','9997','757','12'],
    ['2019-07-04','9997','769','45'],
    ['2019-07-05','9997','762','8'],
    ['2019-07-08','9997','760','8']
]
df0 = pd.DataFrame(dat,columns=["DATE","B","C","D"])

#Specify column A as index.
df0.set_index("DATE",inplace=True) 
print(df0)


#Columns from df0'B','F'However, since column F does not exist in df0, an error is output.
df2 = df0.loc[:,['B', 'F']]

Execution result

console

               B    C   D
DATE                     
2019-07-01  9997  740  78
2019-07-02  9997  749  45
2019-07-03  9997  757  12
2019-07-04  9997  769  45
2019-07-05  9997  762   8
2019-07-08  9997  760   8
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-100-4f9da40f40c4> in <module>
     18 
     19 #Columns from df0'B','F'However, since column F does not exist in df0, an error is output.
---> 20 df2 = df0.loc[:,['B', 'F']]
     21 
     22 
Omitted

~\anaconda3\lib\site-packages\pandas\core\indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
   1653             if not (ax.is_categorical() or ax.is_interval()):
   1654                 raise KeyError(
-> 1655                     "Passing list-likes to .loc or [] with any missing labels "
   1656                     "is no longer supported, see "
   1657                     "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike"  # noqa:E501

KeyError: 'Passing list-likes to .loc or [] with any missing labels is no longer supported, see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike'

Countermeasures

Use reindex to add column F that didn't exist before.

sample.py

import pandas as pd
import numpy as np

dat = [
    ['2019-07-01','9997','740','78'],
    ['2019-07-02','9997','749','45'],
    ['2019-07-03','9997','757','12'],
    ['2019-07-04','9997','769','45'],
    ['2019-07-05','9997','762','8'],
    ['2019-07-08','9997','760','8']
]
df0 = pd.DataFrame(dat,columns=["DATE","B","C","D"])

#Specify column A as index.
df0.set_index("DATE",inplace=True) 
print(df0)


#Columns from df0'F'With reindex.
print("Redefine the column name with column F added using reindex.")
df3 = df0.reindex(["B","C","D","F"], axis=1)
print(df3)

print("Column B has been added, so column B,Column F has been extracted correctly.")
df4 = df3.loc[:,['B', 'F']]
print(df4)

Execution result

console

                B    C   D
DATE                     
2019-07-01  9997  740  78
2019-07-02  9997  749  45
2019-07-03  9997  757  12
2019-07-04  9997  769  45
2019-07-05  9997  762   8
2019-07-08  9997  760   8

Redefine the column name with column F added using reindex.
               B    C   D   F
DATE                         
2019-07-01  9997  740  78 NaN
2019-07-02  9997  749  45 NaN
2019-07-03  9997  757  12 NaN
2019-07-04  9997  769  45 NaN
2019-07-05  9997  762   8 NaN
2019-07-08  9997  760   8 NaN

Column B has been added, so column B,Column F has been extracted correctly.
               B   F
DATE                
2019-07-01  9997 NaN
2019-07-02  9997 NaN
2019-07-03  9997 NaN
2019-07-04  9997 NaN
2019-07-05  9997 NaN
2019-07-08  9997 NaN