Pandas DataFrame mask() Method
Example
Set to NaN, all values where the age IS over 30:
import pandas as pd
data = {
"age": [50, 40, 30, 40, 20,
10, 30],
"qualified": [True, False, False, False, False, True,
True]
}
df =
pd.DataFrame(data)
newdf = df.mask(df["age"] > 30)
Try it Yourself »
Definition and Usage
The mask() method replaces the values of
the rows where the condition evaluates to True.
The mask() method is the opposite of the The
where() method.
Syntax
dataframe.mask(cond, other, inplace, axis, level, errors, try_cast)
Parameters
The other, inplace,
axis,
level,
errors,
try_cast parameters are
keyword arguments.
| Parameter | Value | Description |
|---|---|---|
| cond | Required. An expression or function that evacuates to either True or False | |
| other | String Number Series DataFrame |
Optional. A set of values to replace the rows that evaluates to True with |
| inplace | True |
Optional, default False. Specifies whether to perform the operation on the original DataFrame or not, if not, which is default, this method returns a new DataFrame |
| axis | NumberNone |
Optional, default None. Specifies the alignment axis |
| level | NumberNone |
Optional, default None. Specifies the alignment level |
| errors | 'raise' |
Optional, default 'raise'. Specifies what to do with exceptions |
| try_cast | True |
Optional, default False. Specifies whether to try to cast the result back to the input type or not |
Return Value
A DataFrame with the result, or None if the inplace parameter is set to True.
