Converting all occurrence of True/False to 1/0 in a dataframe with mixed datatype [duplicate]

Question

I have a dataframe that has about 100 columns, There are some Boolean columns and some chars. I want to replace all Boolean having values True/False and also -1 with 1/0. I want to apply it on whole dataframe instead of single column.

I saw some solutions here, like converting the column to integer. But I want to avoid the exercise of going through 100s of columns.

Here is something I tried unsuccessfully:

test.applymap(lambda x: 1 if x=='True' else x)
test.applymap(lambda x: 0 if x=='False' else x)

But the dataframe test still has True/False

Answer 1

applymap is not in-place by default, it will return a new dataframe.

The correct way:

test = test.applymap(lambda x: 1 if x == True else x)
test = test.applymap(lambda x: 0 if x == False else x)

or

test = test.applymap(lambda x: 1 if x == True else x).test.applymap(lambda x: 0 if x=='False' else x)

or simply

test.applymap(lambda x: 1 if x == True else x, inplace=True)
test.applymap(lambda x: 0 if x == False else x, inplace=True)

Although replace seems the best way of achieving this:

test.replace(False, 0, inplace=True)

Answer 2

For a single column, the simplest way by far is to convert the column type. Pandas is smart enough to map boolean to int correctly.

df.column_name = df.column_name.astype(int)

If df.column_name starts as Boolean, it will become zeros and ones after converting to type int

Answer 3

Define a function that loops the .replace() through each column of the Dataframe:

def replace_boolean(data):
    for col in data:
        data[col].replace(True, 1, inplace=True)
        data[col].replace(False, 0, inplace=True)

replace_boolean(test)