# Replace NaN with 0 Pandas

When conducting numerical operations, having 0 instead of NaN can be beneficial to ensure calculations proceed smoothly also in scenarios where NaN values should be represented visually, using 0 can help maintain continuity in plots and visualizations.

In this article, we will look at multiple ways to replace NaN values with 0 in Pandas DataFrame.

## 1. Replace using fillna()

The fillna() is a special function in Pandas that is used to replace NaN values with a specified value. It can be used to replace NaN values with 0 as well.

To replace NaN values with 0, apply fillna() on the DataFrame and pass value=0 parameter.

``````import pandas as pd

# Creating a sample DataFrame with NaN values
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Age': [25, np.nan, 35, np.nan],
'Score': [90, 85, np.nan, 92]}

df = pd.DataFrame(data)

# 👇 Replace NaN with 0 using fillna()
df.fillna(value=0, inplace=True)

print(df)``````

Output:

```      Name   Age  Score
0    Alice  25.0   90.0
1      Bob   0.0   85.0
2  Charlie  35.0    0.0
3    David   0.0   92.0```

Note: Here inplace=True modifies the original DataFrame.

## 2. Replace using replace()

The replace() function is used to replace values in a DataFrame. It accept two arguments:

• to_replace - The value to be replaced
• value - The value to replace with

To replace NaN with 0 use `replace(to_replace=np.nan, value=0, inplace=True)`

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

# Creating a sample DataFrame with NaN values
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Age': [25, np.nan, 35, np.nan],
'Score': [90, 85, np.nan, 92]}

df = pd.DataFrame(data)

# 👇 Replace NaN with 0 using replace()
df.replace(to_replace=np.nan, value=0, inplace=True)

print(df)``````

Output:

```      Name   Age  Score
0    Alice  25.0   90.0
1      Bob   0.0   85.0
2  Charlie  35.0    0.0
3    David   0.0   92.0```

## 3. Replace using applymap()

The following example uses applymap() to replace NaN values with 0. It applies a function to each element in dataframe.

Using this we can check and replace for each element in the DataFrame.

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

# Creating a sample DataFrame with NaN values
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Age': [25, np.nan, 35, np.nan],
'Score': [90, 85, np.nan, 92]}

df = pd.DataFrame(data)

# 👇 Replace NaN with 0 using applymap()
df = df.applymap(lambda x: 0 if pd.isnull(x) else x)

print(df)``````

Output:

```      Name   Age  Score
0    Alice  25.0   90.0
1      Bob   0.0   85.0
2  Charlie  35.0    0.0
3    David   0.0   92.0```

## 4. Replace using apply()

The apply() function is used to apply a function along an axis of the DataFrame.

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

# Creating a sample DataFrame with NaN values
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Age': [25, np.nan, 35, np.nan],
'Score': [90, 85, np.nan, 92]}
df = pd.DataFrame(data)

# 👇 Replace NaN with 0 using apply()
df = df.apply(lambda x: [0 if pd.isnull(x[i]) else x[i] for i in range(len(x))])

print(df)``````

Output:

```      Name   Age  Score
0    Alice  25.0   90.0
1      Bob   0.0   85.0
2  Charlie  35.0    0.0
3   David   0.0   92.0```

## Conclusion

Replacing NaN with 0 in Pandas is a basic skill. Whether you opt for the fillna() method, the replace() method (prefer these 2 methods over other), the goal is to enhance the consistency and suitability of your data for analysis.