Pandas Series: dropna() function

Last update on September 15 2022 12:57:50 (UTC/GMT +8 hours)

Analyze and drop Rows/Columns with Null values in a Pandas series

The dropna() function is used to return a new Series with missing values removed.

Syntax:

Series.dropna(self, axis=0, inplace=False, **kwargs)

Parameters:

Name	Description	Type/Default Value	Required / Optional
axis	There is only one axis to drop values from.	{0 or ‘index’} Default Value: 0	Required
inplace	If True, do operation inplace and return None.	bool Default Value: False	Required
inplace	Whether to perform the operation in place on the data.	bool Default Value: False	Required
**kwargs	Not in use.		Required

Returns: Series- Series with NA entries dropped from it.

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2., 3., np.nan])
s

Output:

0    2.0
1    3.0
2    NaN
dtype: float64

Example - Drop NA values from a Series:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2., 3., np.nan])
s.dropna()

Output:

0    2.0
1    3.0
dtype: float64

Example - Keep the Series with valid entries in the same variable:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2., 3., np.nan])
s.dropna(inplace=True)
s

Output:

0    2.0
1    3.0
dtype: float64

Example - Empty strings are not considered NA values. None is considered an NA value:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2., 3., np.nan])
s = pd.Series([np.NaN, 2, pd.NaT, '', None, 'I am'])
s

Output:

0     NaN
1       2
2     NaT
3        
4    None
5    I am
dtype: object

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2., 3., np.nan])
s = pd.Series([np.NaN, 2, pd.NaT, '', None, 'I am'])
s.dropna()

Output:

1       2
3        
5    I am
dtype: object

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