## Introduction

In this tutorial, we want to **replace null values** in a **Pandas DataFrame**. In order to do this, we use the the **fillna() **method** **of **Pandas**.

## Import Libraries

First, we import the following python modules**:**

```
import numpy as np
import pandas as pd
```

**Create Pandas DataFrame**

Next, we create a **Pandas DataFrame** with some example data from a **dictionary**:

```
mydict = {
"language": ["Python", np.nan, "Python", "Java"],
"framework": ["FastAPI", np.nan, "Django", np.nan],
"users": [np.nan, 7000, 20000, np.nan],
}
df = pd.DataFrame(mydict)
df
```

**Replace Missing Values with Constant Values**

Now, we would like to **replace all null values** of the DataFrame with **constant values**.

The **null values** of the columns **"language"** and **"framework"** should be replaced with the value **"unknown"**. The **null values** of the column **"users"** should be replaced with the value **0**.

To do this, we use **fillna()** method of **Pandas** and pass a **dictionary** with the new values as argument:

```
new_values = {
"language": "unknown",
"framework": "unknown",
"users": 0
}
df_cleaned = df.fillna(value=new_values)
df_cleaned
```

## Replace Missing Values with Aggregated Values

Next, we would like to **replace null values** of the DataFrame with **aggregated values**.

The **null values** of the column **"users" ** should be replaced with the **mean **of the column values.

To do this, we use the **mean() **method of **Pandas ** for calculating the mean of the column and the **fillna()** method of **Pandas **for replacing the null values with the mean:

```
users_mean = df['users'].mean()
df['users'] = df['users'].fillna(value=users_mean)
df
```

## Conclusion

Congratulations! Now you are one step closer to become an **AI Expert**. You have seen that it is very easy to** replace null values** in a **Pandas DataFrame**. We can simply use the **fillna() **method** **of **Pandas**. Try it yourself!

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