Pandas - select column using other column value as column name

问题

I have a dataframe that contains a column, let's call it "names". "names" has the name of other columns. I would like to add a new column that would have for each row the value based on the column name contained on that "names" column.

Example:

Input dataframe: pd.DataFrame.from_dict({"a": [1, 2, 3,4], "b": [-1,-2,-3,-4], "names":['a','b','a','b']})

  a  |  b  | names |
 --- | --- | ----  |
  1  |  -1 | 'a'   |
  2  |  -2 | 'b'   |
  3  |  -3 | 'a'   |
  4  |  -4 | 'b'   |

Output dataframe: pd.DataFrame.from_dict({"a": [1, 2, 3,4], "b": [-1,-2,-3,-4], "names":['a','b','a','b'], "new_col":[1,-2,3,-4]})

  a  |  b  | names | new_col | 
 --- | --- | ----  | ------  |
  1  |  -1 | 'a'   |    1    |
  2  |  -2 | 'b'   |   -2    |
  3  |  -3 | 'a'   |    3    |
  4  |  -4 | 'b'   |   -4    |

回答1:

You can use lookup:

df['new_col'] = df.lookup(df.index, df.names)
df
#   a    b  names   new_col
#0  1   -1      a   1
#1  2   -2      b   -2
#2  3   -3      a   3
#3  4   -4      b   -4

回答2:

Because DataFrame.lookup is deprecated as of Pandas 1.2.0, the following is what I came up with using DataFrame.melt:

df['new_col'] = df.melt(id_vars='names', value_vars=['a', 'b'], ignore_index=False).query('names == variable').loc[df.index, 'value']

Output:

>>> df
   a  b names  new_col
0  1 -1     a        1
1  2 -2     b       -2
2  3 -3     a        3
3  4 -4     b       -4

Can this be simplified? For correctness, the index must not be ignored.

Additional reference:

• Looking up values by index/column labels (archive)

来源：https://stackoverflow.com/questions/45487312/pandas-select-column-using-other-column-value-as-column-name