To drop rows with NaN (null) values in Pandas DataFrame:

df.dropna()

To drop rows where all the values are NaN:

`df.dropna(how="all")`

## Steps to Drop Rows with NaN Values in Pandas DataFrame

### Step 1: Create a DataFrame with NaN Values

Create a DataFrame with NaN values:

import pandas as pd import numpy as np data = {"col_a": [1, 2, np.nan, 4], "col_b": [5, np.nan, np.nan, 8], "col_c": [9, 10, 11, 12] } df = pd.DataFrame(data) print(df)

As can be observed, the second and third rows now have NaN values:

```
col_a col_b col_c
0 1.0 5.0 9
1 2.0 NaN 10
2 NaN NaN 11
3 4.0 8.0 12
```

### Step 2: Drop the Rows with the NaN Values in Pandas DataFrame

Use **df.dropna()** to drop all the rows with the NaN values in the DataFrame:

import pandas as pd import numpy as np data = {"col_a": [1, 2, np.nan, 4], "col_b": [5, np.nan, np.nan, 8], "col_c": [9, 10, 11, 12] } df = pd.DataFrame(data) df_dropped = df.dropna() print(df_dropped)

There results are two rows without any NaN values:

```
col_a col_b col_c
0 1.0 5.0 9
3 4.0 8.0 12
```

Noticed that those two rows no longer have a sequential index. It’s currently 0 and 3. You can then reset the index to start from 0 and increase sequentially.

### Step 3 (Optional): Reset the Index

The general syntax to reset an index in Pandas DataFrame:

`df.reset_index(drop=True)`

The complete script to drop the rows with the NaN values, and then reset the index:

import pandas as pd import numpy as np data = {"col_a": [1, 2, np.nan, 4], "col_b": [5, np.nan, np.nan, 8], "col_c": [9, 10, 11, 12] } df = pd.DataFrame(data) df_dropped = df.dropna() df_reset = df_dropped.reset_index(drop=True) print(df_reset)

The index now starts from 0 and increases sequentially:

```
col_a col_b col_c
0 1.0 5.0 9
1 4.0 8.0 12
```

## Drop Rows Where all the Values are NaN

Here is an example of a DataFrame where all the values are NaN for the third row:

import pandas as pd import numpy as np data = {"col_a": [1, 2, np.nan, 4], "col_b": [5, np.nan, np.nan, 8], "col_c": [9, 10, np.nan, 12] } df = pd.DataFrame(data) print(df)

As can be seen, all the values are NaN for the third row:

```
col_a col_b col_c
0 1.0 5.0 9.0
1 2.0 NaN 10.0
2 NaN NaN NaN
3 4.0 8.0 12.0
```

Use **df.dropna(how=”all”)** to drop only the row/s where all the values are NaN:

import pandas as pd import numpy as np data = {"col_a": [1, 2, np.nan, 4], "col_b": [5, np.nan, np.nan, 8], "col_c": [9, 10, np.nan, 12] } df = pd.DataFrame(data) df_dropped = df.dropna(how="all") print(df_dropped)

The result:

```
col_a col_b col_c
0 1.0 5.0 9.0
1 2.0 NaN 10.0
3 4.0 8.0 12.0
```