Python | Pandas Series.dropna()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.dropna() function return a new Series with missing values in the given series object removed.

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

Parameter :
axis : There is only one axis to drop values from.
inplace : If True, do operation inplace and return None.

Returns : Series

Example #1: Use Series.dropna() function to drop the missing values in the given series object.

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series(['New York', 'Chicago', 'Toronto', None, 'Rio'])

# Create the Index
index_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5']

# set the index
sr.index = index_

# Print the series
print(sr)

Output :

Now we will use Series.dropna() function to drop all the missing values in the given series object.

# drop the missing values
result = sr.dropna()

# Print the result
print(result)

Output :

As we can see in the output, the Series.dropna() function has successfully dropped all the missing values in the given series object.

Example #2 : Use Series.dropna() function to drop the missing values in the given series object.

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series([100, None, None, 18, 65, None, 32, 10, 5, 24, None])

# Create the Index
index_ = pd.date_range('2010-10-09', periods = 11, freq ='M')

# set the index
sr.index = index_

# Print the series
print(sr)

Output :

Now we will use Series.dropna() function to drop all the missing values in the given series object.

# drop the missing values
result = sr.dropna()

# Print the result
print(result)

Output :

As we can see in the output, the Series.dropna() function has successfully dropped all the missing values in the given series object.