# How to Replace NaN Values by Zeros in a DataFrame?

NaN values are also called missing values and simply indicate the data we do not have. We do not like to have missing values in a dataset but it’s inevitable to have them in some cases. Therefore, we need to learn how to handle them properly.

There are different ways of handling missing values. In this how-to article, we will learn how to replace NaN values by zeros in Pandas and PySpark DataFrames.

## Pandas

The fillna function can be used for replacing missing values. We just need to write the value to be used as the replacement inside the function.

				
# Replace all missing values in the DataFrame
df = df.fillna(0)

# Replace missing values in a specific column
df["f2"] = df["f2"].fillna(0)




## PySpark

We can either use fillna or na.fill function. They are aliases and return the same results.

				
# Replace all missing values in the DataFrame
df = df.na.fill(0)

# Replace missing values in a specific column
df = df.na.fill(0, subset=["f2"])




### This question is also being asked as:

• How to replace NaN values in Python?
• How to replace NaN value with some other value in Pandas?

How-Tos

### How to Convert a Dictionary to a DataFrame?

Dictionary is a built-in data structure of Python, which consists of key-value pairs. In this short …
Data Science

### How to Delete Rows Based on Column Values in a DataFrame?

A row in a DataFrame can be considered as an observation with several features that are represented …
Data Science

### How to Convert the Index of a DataFrame to a Column?

DataFrame is a two-dimensional data structure with labeled rows and columns. Row labels are also kno …
Start Monitoring Your Models in Minutes