如何在大 Pandas DataFrame 中找到“True”值对应的索引和列？

Question

I have a large DataFrame df whose values are mostly False .

About 1% of the values of df are True .

How can I display the True values' corresponding index and column?

Here's the index of df

df.index
DatetimeIndex(['2007-04-23', '2007-04-24', '2007-04-25', '2007-04-26',
               '2007-04-27', '2007-04-30', '2007-05-02', '2007-05-03',
               '2007-05-04', '2007-05-07',
               ...
               '2021-02-24', '2021-02-25', '2021-02-26', '2021-03-02',
               '2021-03-03', '2021-03-04', '2021-03-05', '2021-03-08',
               '2021-03-09', '2021-03-10'],
              dtype='datetime64[ns]', name='date', length=3426, freq=None)

Here's the columns of df

df.columns
Index(['0015', '0050', '0051', '0052', '0053', '0054', '0055', '0056', '0057',
       '0058',
       ...
       '9944', '9945', '9946', '9949', '9950', '9951', '9955', '9958', '9960',
       '9962'],
      dtype='object', name='stock_id', length=1947)

And df.shape returns (3426, 1947) .

Suppose only the values of df['1234']['2020-01-05'] , and df['4321']['2020-03-07'] are true.

How can I write a function whose input is df and whose output are df['1234']['2020-01-05'] and df['4321']['2020-03-07'] ?

Answer 1

If need DataFrame use DataFrame.stack for MultiIndex Series and then filter True s with convert MultiIndex to DataFrame by Index.to_frame :

#data from @Quang Hoang answer
s = df.stack()

df1 = s[s].index.to_frame(index=False).set_axis(['idx','cols'], axis=1)
print (df1)
    idx cols
0  2010    a
1  2011    c

Answer 2

Suppose we have this:

# Test data
          a      b      c
2010   True  False  False
2011  False  False   True

You can try np.where :

x,y = np.where(df)
indexes = df.index[x]
columns = df.columns[y]
print(indexes, columns)

Output:

Index(['2010', '2011'], dtype='object') Index(['a', 'c'], dtype='object')