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chevron_leftHandling Missing Values
Adding missing dates in Datetime IndexChecking if a certain value in a DataFrame is NaNChecking if a DataFrame contains any missing valuesConverting a column with missing values to integer typeCounting non-missing valuesCounting number of rows with missing valuesCounting the number of NaN in each row of a DataFrameCounting number of NaN values in each column of a DataFrameCounting the total number of NaN values of a DataFrameFilling missing values using another columnFilling missing values with the mean of the columnFinding columns with missing valuesGetting integer indexes of rows with NaNGetting rows with missing valuesGetting rows with missing values in certain columnsGetting index of rows with missing values (NaNs)Getting index of rows without missing valuesMapping NaN values to 0 and non-NaN values to 1Mapping NaN values to False and non-NaN values to TrueRemoving columns where some rows contain missing valuesRemoving rows from a DataFrame with missing valuesReplacing all NaN values of a DataFrameReplacing all NaN values with zeros in a DataFrameReplacing missing valuesReplacing missing values with constantsReplacing NaN with blank stringReplacing NaNs for certain columnsReplacing NaNs with preceding valuesReplacing values with NaNsUsing interpolation to fill missing values
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Mapping NaN values to 0 and non-NaN values to 1 in Pandas DataFrame

schedule Aug 12, 2023
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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.

Related

Mapping NaN values to False and non-NaN values to True in Pandas DataFrame
To map NaN values to False and non-NaN values to True in Pandas DataFrame, use the notnull() method.
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Published by Isshin Inada
Edited by 0 others
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