Pandas Read Excel NaN

When working with Excel files in Python using Pandas, it is not uncommon to encounter missing or null values represented by NaN (Not a Number). In this article, we will discuss how to handle NaN values when reading Excel files with Pandas.

Reading Excel Files with Pandas

Pandas provides a read_excel() function that allows us to read data from Excel files into a DataFrame.

import pandas as pd 

df = pd.read_excel('file.xlsx')

By default, read_excel() will replace all empty cells in the Excel file with NaN values.

Handling NaN Values

When we have missing or null values represented by NaN, we need to decide how to handle them. There are several options available to us:

1. Drop NaN Values

We can drop all rows containing NaN values from our DataFrame using the dropna() function.

df.dropna(inplace=True)

This will drop all rows containing NaN values from the DataFrame.

2. Fill NaN Values

We can fill NaN values with a specific value, such as the mean or median of the column.

df.fillna(df.mean(), inplace=True)

This will fill all NaN values in the DataFrame with the mean value of each column.

3. Interpolate NaN Values

We can interpolate NaN values using the interpolate() function.

df.interpolate(inplace=True)

This will interpolate all NaN values in the DataFrame using linear interpolation.

Conclusion

In this article, we discussed how to handle NaN values when reading Excel files with Pandas. We explored three different options for handling NaN values: dropping them, filling them, and interpolating them. Understanding how to handle missing or null values is an important skill when working with data in Pandas.