data.dropna

()This method drops all rows that contain any missing values (NaNs). It returns a new DataFrame with the same columns as the original, but with rows containing NaNs removed. If the original DataFrame has no NaNs, it returns a copy of the original DataFrame.

Example:

import pandas as pd

# create a DataFrame with missing values
data = pd.DataFrame({'A': [1, 2, None, 4], 'B': [5, None, 7, 8], 'C': [9, 10, 11, None]})
print(data)

# drop all rows with missing values
clean_data = data.dropna()
print(clean_data)

Output:

     A    B     C
0  1.0  5.0   9.0
1  2.0  NaN  10.0
2  NaN  7.0  11.0
3  4.0  8.0   NaN

     A    B    C
0  1.0  5.0  9.0

In this example, the `dropna()` method removes the second and fourth rows because they contain NaNs, and returns a new DataFrame with only the first row.