# How to use pandas series.fillna() to replace missing values?

The pandas series.fillna() method is used to replace missing values with a specified value. This method replaces the Nan or NA values in the entire series object.

The parameters of pandas fillna are as follows −

• Value − it allows us to specify a particular value to replace Nan’s, by default it takes None.

• Method − it is used to fill the missing values in the reindexed Series. It takes any of these values like ‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, and None(default).

• Inplace − this parameter takes a boolean value. If it takesTrue, then the modifications are applied to the original series object itself, otherwise, it will create a new series with updated missing values as result. The default value is False.

• Limit − This parameter takes an integer value, which is used to specify how many NA values you wish to fill forward/backward. The default value of this parameter is None.

• Axis − it takes 0 or index label.

• Downcast − It takes a dictionary which specifies the downcast of data types.

Here we will see how the series.fillna() method replaces the missing value.

## Example 1

In the following example, we will replace the missing values with an integer value 10.

# importing pandas package
import pandas as pd
import numpy as np

# create a series
s = pd.Series([np.nan, np.nan, 89, 64, np.nan], index=["a", "b", "c", "d", "e"])
print(s)

# replace Missing values with 10
result = s.fillna(10)
print('Result:')
print(result)

## Explanation

Initially, we have created the pandas series object with some missing values. Then applied the fillna() method with value 10. Here, the default parameters are not changed.

## Output

The output is as follows −

a     NaN
b     NaN
c    89.0
d    64.0
e     NaN
dtype: float64

Result:
a    10.0
b    10.0
c    89.0
d    64.0
e    10.0
dtype: float64

In the above output block, we can see that all Nan values in the entire series object are nicely being replaced with value 10.

## Example 2

This time, we will replace the missing values by specifying the bfill value to the method parameter. So that, we do not need to specify any particular value to fill the missing values, it will take the value after Nan for replacement.

# importing pandas package
import pandas as pd
import numpy as np

# create a series
s = pd.Series([np.nan, np.nan, 89, 64, np.nan], index=["a", "b", "c", "d", "e"])
print(s)

# replace Missing values with bfill
result = s.fillna(method='bfill')
print('Result:')
print(result)

## Output

The output is given below −

a     NaN
b     NaN
c    89.0
d    64.0
e     NaN
dtype: float64

Result:
a    89.0
b    89.0
c    89.0
d    64.0
e     NaN
dtype: float64

In the above output block, We can see that Nan values at index positions a, b are replaced with a value 89, which is due to the fact that we have mentioned the bfill value to the method parameter. The Nan value at index position e remains the same.