How to get column index which is matching with specific value in Pandas?-CodePudding

I have the following dataframe as below.

     0      1      2      3      4      5      6      7
    True  False  False  False  False  False  False  False
    [1 rows * 8 columns]

As you can see, there is one True value which is the first column.

Therefore, I want to get the 0 index which is True element in the dataframe. In other case, there is True in the 4th column index, then I would like to get the 4 as 4th column has the True value for below dataframe.

     0      1      2      3      4      5      6      7
    False  False  False  False  True  False  False  False
    [1 rows * 8 columns]

I tried to google it but failed to get what I want. And for assumption, there is no designated column name in the case.

Look forward to your help.

Thanks.

CodePudding user response：

if you want every field that is true:

cols_true = []
for idx, row in df.iterrows():
    for i in cols:
        if row[i]:
            cols_true.append(i)
print(cols_true)

CodePudding user response：

Use boolean indexing:

df.columns[df.iloc[0]]

output:

Index(['0'], dtype='object')

Or numpy.where

np.where(df)[1]

CodePudding user response：

IIUC, you are looking for idxmax:

>>> df
      0      1      2      3      4      5      6      7
0  True  False  False  False  False  False  False  False

>>> df.idxmax(axis=1)
0    0
dtype: object

>>> df
       0      1      2      3     4      5      6      7
0  False  False  False  False  True  False  False  False

>>> df.idxmax(axis=1)
0    4
dtype: object

Caveat: if all values are False, Pandas returns the first index because index 0 is the lowest index of the highest value:

>>> df
       0      1      2      3      4      5      6      7
0  False  False  False  False  False  False  False  False

>>> df.idxmax(axis=1)
0    0
dtype: object

Workaround: replace False by np.nan:

>>> df.replace(False, np.nan).idxmax(axis=1)
0   NaN
dtype: float64

CodePudding user response：

You may want to index the dataframe's index by a column itself (0 in this case), as follows:

df.index[df[0]]

You'll get:

Int64Index([0], dtype='int64')