## Filling NaN Values in Pandas DataFrame: A Comprehensive Guide

Pandas is an excellent library in Python for easy and efficient data analysis. One of the recurrent problems that people encounter when working with data is missing values or NaN values.

In this article, we will demonstrate how to use the `fillna()`

function in Pandas to replace the NaN values in a DataFrame with different values. We will use a dictionary containing the values for replacement.

### Example: Filling NaN Values in Pandas Using a Dictionary

Let us consider an example that will help us understand how to fill NaN values in a Pandas DataFrame. Let’s suppose that we have a DataFrame that contains the sales of different stores in the months of January, February, and March.

Store | Months | Sales |
---|---|---|

A | Jan | 500 |

B | Jan | NaN |

A | Feb | 350 |

B | Feb | NaN |

A | Mar | NaN |

B | Mar | 150 |

We can see that there are NaN values in the sales column. To fix this, we will create a dictionary that contains the replacement values for each store.

The keys in the dictionary will be the stores, and the values will be the replacement values. In the above example, we want to replace NaN values with the mean of the sales in each store.

To create such a dictionary, we can leverage Python’s dictionary comprehension.

`replacement_dict = {store: df[df['Store']==store]['Sales'].mean() for store in df.Store.unique()}`

Here, we created a dictionary which has store names as keys and their corresponding average sales as values.

Using this dictionary, we will perform the `fillna()`

operation on the DataFrame.

`df['Sales'] = df.apply(lambda x: replacement_dict[x['Store']] if pd.isna(x['Sales']) else x['Sales'], axis=1)`

Above code replaces the NaN values in the sales column with the corresponding average sales for that store.

Here, `apply()`

is used to apply replacement function to each row of the DataFrame. Also, the lambda function is used to provide conditions when to use replacement values.

## df looks like this after filling NaN values:

Store | Months | Sales |
---|---|---|

A | Jan | 500 |

B | Jan | 150.0 |

A | Feb | 350 |

B | Feb | 150.0 |

A | Mar | 425.0 |

B | Mar | 150 |

We can now see that all the NaN values in the sales column have been successfully replaced with the corresponding average sales for the store.

## Conclusion

In this article, we illustrated how to fill NaN values in a Pandas DataFrame using a dictionary. By using replacement values for NANs, we can ensure that analysis or model-building isn’t affected by the presence of missing data.

It is always essential to have a clean and complete dataset to achieve better results. We hope this article helps you in resolving NAN values in your DataFrames.

Happy coding!

## Additional Resources for Filling NaN Values in Pandas DataFrame

In this addition, we will discuss additional resources available for filling NaN values in a Pandas DataFrame. It is essential to be knowledgeable not just about how to fill NaN values but also about all the options that are available to you.

One of the most valuable resources that you can use are the complete online documentation for the `fillna()`

function.

## Link to Complete Online Documentation for `fillna()`

Function

The online documentation for Pandas is an extensive resource that provides detailed information about the `fillna()`

function. To access the online documentation, you can visit the official Pandas website at https://pandas.pydata.org/.

The documentation provides an in-depth analysis of the `fillna()`

function and how it can be used to fill NaN values in a DataFrame. The documentation explains the various parameters that can be used with the `fillna()`

function in detail.

Let’s briefly discuss some of the important parameters of the `fillna()`

function:

`value`

: This parameter is used to provide the replacement value that will be used to fill the NaN values.`method`

: This parameter is used when the NaN values are to be filled using a certain method like`ffill`

,`bfill`

,`interpolate`

, etc.`axis`

: This parameter is used to specify the axis to use.`inplace`

: This parameter is used to specify whether to make changes in the original DataFrame or not.

The documentation also provides examples of how to use the `fillna()`

function to fill NaN values in different scenarios. It is recommended to go through the examples in detail to understand the full potential of the `fillna()`

function.

## A few examples from the documentation are mentioned below:

### 1. Filling NaN Values with a Specific Value:

In this example, a DataFrame with NaN values is created, and then the `fillna()`

function is used to fill those NaN values with a specific value.

```
import pandas as pd
import numpy as np
data = {'A': [25, 50, np.nan, 40, 65],
'B': [40, 75, 60, np.nan, 63],
'C': [np.nan, 80, np.nan, 70, 93]}
df = pd.DataFrame(data)
df.fillna(0, inplace=True)
print(df)
```

### The output of the code above is as follows:

```
A B C
0 25.0 40.0 0.0
1 50.0 75.0 80.0
2 0.0 60.0 0.0
3 40.0 0.0 70.0
4 65.0 63.0 93.0
```

### 2. Filling NaN Values with a Forward Fill Method:

In this example, a DataFrame is created with NaN values, and then the `fillna()`

function is used along with the forward fill method to fill the NaN values.

```
import pandas as pd
import numpy as np
data = {'A': [25, 50, np.nan, 40, np.nan],
'B': [40, np.nan, 60, np.nan, 63],
'C': [np.nan, 80, np.nan, 70, np.nan]}
df = pd.DataFrame(data)
df.fillna(method='ffill', limit=1, inplace=True)
print(df)
```

### The output of the above code is as follows:

```
A B C
0 25.0 40.0 NaN
1 50.0 40.0 80.0
2 50.0 60.0 80.0
3 40.0 60.0 70.0
4 40.0 63.0 70.0
```

This example illustrates how we can use the `fillna()`

function with the forward fill method to fill NaN values in a DataFrame. We can also use other methods like ‘bfill’ for backward fill or ‘interpolate’ for interpolation.

## Conclusion

In this addition, we explored the importance of having additional resources to help us work efficiently with Pandas DataFrames. We specifically discussed how online documentation for `fillna()`

function can be an invaluable resource while working with missing data in Pandas.

Additionally, we went through important parameters and examples provided in the online documentation, which will hopefully help you better understand and utilize the `fillna()`

function. Remember, the more knowledge you have, the better equipped you are to tackle real-world data analysis problems.

In conclusion, this article has provided a comprehensive guide to filling NaN values in Pandas DataFrames using a dictionary. We have demonstrated how to use the `fillna()`

function with a dictionary, and how to create a dictionary for replacement values.

Additionally, we have emphasized the importance of online documentation for the `fillna()`

function, which provides an extensive resource that can help with filling NaN values in different scenarios. By filling NaN values with appropriate replacement values, we can ensure that our data analysis or model-building is not negatively impacted by the presence of missing data.

Takeaway is that having a deep understanding of the `fillna()`

function and its parameters, along with the ability to leverage online documentation, can help us work efficiently with Pandas DataFrames, especially when dealing with missing data.