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 fillna() Syntax
Pandas Series.fillna() function is used to fill Pandas NA/NaN values using the specified method.
Syntax: Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)
Parameter :
- value : Value to use to fill holes
- method : Method to use for filling holes in reindexed Series pad / ffill
- axis : {0 or ‘index’}
- inplace : If True, fill in place.
- limit : If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill
- downcast : dict, default is None
Returns : filled : Series
Pandas DataFrame fillna() Examples
Example 1: Use Series.fillna() function to fill out the missing values in the given series object. Use a dictionary to pass the values to be filled corresponding to the different index labels in the series object.
Python3
# importing pandas as pdimport pandas as pd# Creating the Seriessr = pd.Series(['New York', 'Chicago', 'Toronto', None, 'Rio'])# Create the Indexsr.index = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5']# set the indexsr.index = index_# Print the seriesprint(sr) |
Output :
Now we will use Series.fillna() function to fill out the missing values in the given series object.
Python3
# fill the values using dictionaryresult = sr.fillna(value={'City 4': 'Lisbon', 'City 1': 'Dublin'})# Print the resultprint(result) |
Output :
As we can see in the output, the Series.fillna() function has successfully filled out the missing values in the given series object.
Example 2: Use Series.fillna() function to fill out the missing values in the given series object using forward fill (ffill) method.
Python3
# importing pandas as pdimport pandas as pd# Creating the Seriessr = pd.Series([100, None, None, 18, 65, None, 32, 10, 5, 24, None])# Create the Indexindex_ = pd.date_range('2010-10-09', periods = 11, freq ='M')# set the indexsr.index = index_# Print the seriesprint(sr) |
Output :
Now we will use Series.fillna() function to fill out the missing values in the given series object. We will use forward fill method to fill out the missing values.
Python3
# fill the values using forward fill methodresult = sr.fillna(method = 'ffill')# Print the resultprint(result) |
Output :
As we can see in the output, the Series.fillna() function has successfully filled out the missing values in the given series object.
