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Mapping NaN values to 0 and non-NaN values to 1 in Pandas DataFrame

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Handling Missing Values
schedule Mar 10, 2022
Last updated
local_offer PythonPandas
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Solution

To map NaN values to 0 and non-NaN values to 1 in Pandas DataFrame, use the following:

df.notnull().astype("int")

Example

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,None],"B":[5,6]})
df
A B
0 3.0 5
1 NaN 6

To map NaN values to 0 and non-NaN values to 1:

df.notnull().astype("int")
A B
0 1 1
1 0 1

Explanation

We first map the NaN values to False and non-NaN values to True using the notnull() method:

df.notnull()
A B
0 True True
1 False True

We then map False to 0 and True to 1 using the astype() method:

df.notnull().astype("int")
A B
0 1 1
1 0 1

Supplement - mapping certain columns

The solution presented performs the mapping for all columns, that is, every value in the DataFrame. To map certain columns only:

df = pd.DataFrame({"A":[3,None],"B":[5,6]})
df["A"] = df["A"].notnull().astype("int")
df
A B
0 1 5
1 0 6

Notice how only column A's values were mapped, while column B was left intact.

robocat
Published by Isshin Inada
Edited by 0 others
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