Easy way to distinguish between 0 and False in a dataframe with mixed values

Question

I have a column in my dataframe where the values take on either 1, 0, False but the rows with False or O are functionally different.

I would therefore like to convert either the False or 0 values to something else

What would be an good way to do this?

Using replace has not worked well

df["col_name"] = df["col_name"].replace(0,2) converts the False values too

and

df["col_name"] = df["col_name"].replace(False,2) converts the 0 values too

Answer 1

You can use mask to replace values with a boolean mask - the advantage of this solution is no original types are changed:

df = pd.DataFrame({'Col':[1, False, 0]})

df['Col'] = df['Col'].mask(df['Col'].astype(str) == '0', 2).replace(False, 3)
print (df)
   Col
0    1
1    3
2    2

Solution with Series.replace by dict, but first converting to str by astype works too, but generally it convert all values to str what with real data can be problem.

d = {'0':'Zero', 'False':False}
df = df['Col'].astype(str).replace(d)
print (df)
0        1
1    False
2     Zero
Name: Col, dtype: object

I try create more general solution with map and checking bools by isinstance:

df = pd.DataFrame({'Col':[1, False, 0, True,5]})
print (df)
     Col
0      1
1  False
2      0
3   True
4      5

m = df['Col'].apply(lambda x: isinstance(x, bool))
df['Col'] = df['Col'].mask(m, df['Col'].map({False:2, True:3}))

print (df)
  Col
0   1
1   2
2   0
3   3
4   5

Answer 2

You can convert to str type and then use df.str.replace:

In [223]: df = pd.DataFrame({'Col':[1, False, 0]})

In [224]: df.Col.astype(str).replace('0', 'Zero').replace('False', np.nan)
Out[224]: 
0       1
1     NaN
2    Zero