# Pandas fillna() : Replace NaN Values in the DataFrame

**Pandas fillna()** : In this tutorial we will learn how to use the fillna() function of the pandas python module to replace the NaN values of a pandas dataframe.

## Introduction

The **Pandas **module is a **python**-based **toolkit **for **data analysis** that is widely used by **data scientists **and** data analysts**. It simplifies **data import **and** data cleaning**. Pandas also offers several ways to create a type of data structure called **dataframe **(It is a data structure that contains *rows *and *columns*).

The most frequent problem when cleaning the data is managing the case of missing values in our dataframe. There are a lot of factors for a value to be missing (for example, in a survey, a person has not filled in a field of the form).

To overcome this problem, the **fillna**() method in the **pandas **module will help us to manage these missing values. It will replace all the ** None **or

*values by the value of your choice.*

**NaN**### Pandas fillna() Syntax

The syntax of the Dataframe.fillna() function is as follows:

```
#Pandas fillna() Syntax
DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)
```

**value**: scalar, dictionary, pandas Series or a DataFrame**method**: {‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}.**axis**: 0 or ‘index’, 1 or ‘columns’.**inplace**: it is a boolean argument. If*True*, the DataFrame is modified inplace, and if*False*a new DataFrame with resulting contents is returned.**limit**: takes integer or None. This is the maximum number of consecutive NaN values to forward/backward fill. This argument is used : only if*method*is specified.**downcast**: can be a dictionary or None.****kwargs**: Any other Keyword arguments

These arguments are pretty straightforward to use. We will then give some examples to understand how to use the fillna() function.

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