What's Happening In This Piece Of Code From Documentation?

A Noob question, I am going through documentation and found this sample example, I could not understand the conditions for AAA,BBB,CCC here in snippet df: AAA BBB CCC 0

Solution 1:

You can check DataFrame.where:

cond : boolean Series/DataFrame, array-like, or callable Where cond is True, keep the original value. Where False, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

other : scalar, Series/DataFrame, or callable Entries where cond is False are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

So it means it replace False value of mask by other, here -1000.

Sample:

df = pd.DataFrame({'AAA': [4, 5, 6, 7], 'BBB': [4, 11, 0, 8], 'CCC': [2000, 45, 555, 85]})
print (df)
   AAA  BBB   CCC
044200015114526055537885

df_mask = pd.DataFrame({'AAA': [True] * 4,
                        'BBB': [False] * 4,
                        'CCC': [True, False] * 2})

print (df.where(df_mask, -1000))
   AAA   BBB   CCC
04 -1000200015 -1000 -100026 -100055537 -1000 -1000

If no values in other there is replacement to NaNs:

print (df.where(df_mask))
   AAA  BBB     CCC
04NaN2000.015NaNNaN26NaN555.037NaNNaN

You can also pass mask with compare values, e.g.:

print (df.where(df > 10, -1000))
    AAA   BBB   CCC
0 -1000 -1000  2000
1 -1000    11    45
2 -1000 -1000   555
3 -1000 -1000    85

Solution 2:

do print(df_mask) You will get the dataframe as below

    AAA     BBB     CCC
0TrueFalseTrue1TrueFalseFalse2TrueFalseTrue3TrueFalseFalse

with df.where(df_mask, -1000), you are replacing False values with -1000 with final out put as below

AAABBBCCC04-1000200015-1000-100026-100055537-1000-1000