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How can I map True/False to 1/0 in a Pandas DataFrame?

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How do you change true/false to 1 0 in Python?

In Python True and False are equivalent to 1 and 0. Use the int() method on a boolean to get its int values. int() turns the boolean into 1 or 0. Note: that any value not equal to 'true' will result in 0 being returned.

Can we apply map on DataFrame?

You cannot apply map on DataFrame.

How do you set Index false in DataFrame?

pandas DataFrame to CSV with no index can be done by using index=False param of to_csv() method. With this, you can specify ignore index while writing/exporting DataFrame to CSV file.


A succinct way to convert a single column of boolean values to a column of integers 1 or 0:

df["somecolumn"] = df["somecolumn"].astype(int)

Just multiply your Dataframe by 1 (int)

[1]: data = pd.DataFrame([[True, False, True], [False, False, True]])
[2]: print data
          0      1     2
     0   True  False  True
     1   False False  True

[3]: print data*1
         0  1  2
     0   1  0  1
     1   0  0  1

True is 1 in Python, and likewise False is 0*:

>>> True == 1
True
>>> False == 0
True

You should be able to perform any operations you want on them by just treating them as though they were numbers, as they are numbers:

>>> issubclass(bool, int)
True
>>> True * 5
5

So to answer your question, no work necessary - you already have what you are looking for.

* Note I use is as an English word, not the Python keyword is - True will not be the same object as any random 1.


You also can do this directly on Frames

In [104]: df = DataFrame(dict(A = True, B = False),index=range(3))

In [105]: df
Out[105]: 
      A      B
0  True  False
1  True  False
2  True  False

In [106]: df.dtypes
Out[106]: 
A    bool
B    bool
dtype: object

In [107]: df.astype(int)
Out[107]: 
   A  B
0  1  0
1  1  0
2  1  0

In [108]: df.astype(int).dtypes
Out[108]: 
A    int64
B    int64
dtype: object

This question specifically mentions a single column, so the currently accepted answer works. However, it doesn't generalize to multiple columns. For those interested in a general solution, use the following:

df.replace({False: 0, True: 1}, inplace=True)

This works for a DataFrame that contains columns of many different types, regardless of how many are boolean.


You can use a transformation for your data frame:

df = pd.DataFrame(my_data condition)

transforming True/False in 1/0

df = df*1