Here’s a clear and simple answer you can use:


You can handle missing values in a Pandas dataframe in several common ways, depending on what you need:

1. Check for missing values

df.isnull().sum()

2. Drop rows or columns with missing data

df.dropna() # remove rows with missing values
df.dropna(axis=1) # remove columns with missing values

3. Fill missing values with a constant

df.fillna(0)
df.fillna("unknown")

4. Fill missing values with statistics

df["col"].fillna(df["col"].mean())
df["col"].fillna(df["col"].median())
df["col"].fillna(df["col"].mode()0)

5. Forward/backward fill for time-series

df.fillna(method="ffill") # use previous value
df.fillna(method="bfill") # use next value

The best method depends on your dataset and the meaning of the missing values.