# How to deal with missing values in Pandas DataFrame in Python

Dealing with missing values is a common task when working with datasets in Pandas. Missing values can occur for many reasons, such as data entry errors, missing data in the source, or outliers. It’s important to handle missing values effectively to ensure the accuracy and reliability of your data analysis.

Pandas provides several options for handling missing values in a DataFrame. One common strategy is to simply drop any rows or columns that contain missing values. This can be done using the `dropna()`

function.

`df = df.dropna()`

This will drop any rows that contain at least one missing value.

Another strategy is to fill in missing values with a specific value, such as the mean or median of the column. This can be done using the `fillna()`

function.

`df = df.fillna(value)`

This will fill in all missing values in the DataFrame with the specified value.

In the case you know the missing values are just for some specific columns, you can use the `fillna()`

function with the specific columns name as well.

`df[["column_name1","column_name2"]] = df[["column_name1","column_name2"]].fillna(value)`

Another strategy is to fill the missing values with a value calculated based on some other values within the dataset. This can be done using the `interpolate()`

function. This function will interpolate the missing values based on the method specified. Some common methods are ‘linear’, ‘time’, ‘index’, and ‘values’.

`df = df.interpolate(method='linear')`

It will fill the missing values linearly based on the other values present.

Additionally, you can drop missing value based on a specific condition, using the `dropna()`

function with the thresold parameter, which is a numeric value that represent the minimum number of non-NA values to be retained.

`df = df.dropna(thresold=n)`

where `n`

is the number of non-NA values.

Finally, it’s also possible to create a Boolean mask indicating the presence of missing values using `isna()`

or `notna()`

function which can be then used to select specific subset of data based on the missing values.

Handling missing values is an important step in data preparation, and the choice of strategy will depend on the specific dataset and the desired outcome of the analysis. Understanding the different options provided by Pandas and when to use each one will help ensure accurate and reliable results in your data analysis.

In this Learn through Codes example, you will learn: How to deal with missing values in Pandas DataFrame in Python.

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