# Drop and Replacing NaN values

In a previous post I talked about the need for replacing NaN values in my data in preparation for some machine learning models. Some other methods that I have used include dropping or filling them in with something else.

### drop the values

Sometimes the easiest thing to do is drop the values, if my data is large enough.

Dropping the rows or dropping the columns

```python
# Drop rows with NaN values
df_cleaned = df.dropna()

# Drop columns with NaN values in-place
df.dropna(axis=1, inplace=True)
```

### replacing values

I had a couple datasets that were small and I did not want to drop the rows of data.

Instead I utilized the `.fillna()` function to replace the NaN value with a number. Maybe replacing it with 0, or replacing it with the column mean. Another option is using [interpolation](https://dtucker.hashnode.dev/using-interpolation-to-replace-values-in-a-dataframe). Options depend on what makes sense for the dataset.

```python
# Replace NaN values with column mean
df_filled = df.fillna(df.mean())

# Replace NaN values with zero
df_filled = df.fillna(0)
```