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

Question

I have a column in python pandas DataFrame that has boolean True/False values, but for further calculations I need 1/0 representation. Is there a quick pandas/numpy way to do that?

Solution 1

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)

Solution 2

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

Solution 3

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.}

Solution 4

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.

Solution 5

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

View more solutions

Comments

[Jon Clements]

What further calculations are required?

[cs95]

To parrot @JonClements, why do you need to convert bool to int to use in calculation? bool works with arithmetic directly (since it is internally an int).

[sql_knievel]

@cs95 - Pandas uses numpy bools internally, and they can behave a little differently. In plain Python, True + True = 2, but in Pandas, numpy.bool_(True) + numpy.bool_(True) = True, which may not be the desired behavior on your particular calculation.

[jorgeca]

Just be careful with data types if doing floating point math: np.sin(True).dtype is float16 for me.

[dwanderson]

I've got a dataframe with a boolean column, and I can call df.my_column.mean() just fine (as you imply), but when I try: df.groupby("some_other_column").agg({"my_column":"mean"}) I get DataError: No numeric types to aggregate, so it appears they are NOT always the same. Just FYI.

[BallpointBen]

In pandas version 24 (and maybe earlier) you can aggregate bool columns just fine.

[Amadou Kone]

It looks like numpy also throws errors with boolean types: TypeError: numpy boolean subtract, the -` operator, is deprecated, use the bitwise_xor, the ^ operator, or the logical_xor function instead.` Using @User's answer fixes this.

[colorlace]

Another reason it's not the same: df.col1 + df.col2 + df.col3 doesn't work for bool columns as it does for int columns

[DustByte]

The corner case is if there are NaN values in somecolumn. Using astype(int) will then fail. Another approach, which converts True to 1.0 and False to 0.0 (floats) while preserving NaN-values is to do: df.somecolumn = df.somecolumn.replace({True: 1, False: 0})

[Homunculus Reticulli]

@DustByte Good catch!

[AMC]

@DustByte Couldn't you just use astype(float) and get the same result?

[AMC]

What are the advantages of this solution?

[AMC]

This is identical to this solution, posted 3 years earlier.

[Golden Lion]

if the value is text and a lowercase "true" or "false" then first do a astype(bool].astype(int) and the conversion will work. Sas outputs is bools as lowercase true and false.

[Phillip Copley]

@AMC There are none, it's a hacky way to do it.

[AMC]

Much simpler: df['type'] = df['type'].map({'REAL': 1, 'FAKE': 0}). In any case, I'm not sure it's too relevant to this question.

[kaishu]

Thanks for providing simpler solution. As I mentioned in answer, I was trying to find solution for slightly different question, and only similar questions like this were available. Hope my answer and your solution will help someone in future.

[AMC]

There are other questions which already cover that, though, like stackoverflow.com/q/20250771.

[Dmitriy Work]

@AMC if your dataframe has float types beside booleans this method won't ruin them, df.astype(int) does. And since it's hacky it's probably a good idea to make intention clear with comment like # bool -> int.

[Dmitriy Work]

There is an advantage of using data * 1 against data + 0 with mixed types – it works on strings as well, where data + 0 throws an error. Equivalent performance-wise.

[unaied]

how can this be applied to a number of columns?

[Avv]

Thank you. Should I do this to all columns or there is a command without specifying column name?

[qwr]

advantage: slightly shorter