How to drop missing values in pandas?

To drop missing values in pandas, you can use the dropna() method on a pandas DataFrame. By default, dropna() removes all rows that contain any missing values. Here is an example:

import pandas as pd

# create a sample dataframe with missing values
df = pd.DataFrame({'A': [1, 2, None, 4], 'B': [5, 6, 7, None]})

# drop all rows with missing values
df = df.dropna()

print(df)

This will output a new DataFrame with all rows containing missing values removed:

   A  B
0  1  5
1  2  6

If you want to remove columns with missing values instead of rows, you can set the axis parameter to 1:

df = df.dropna(axis=1)

This will remove all columns containing missing values.