About Lesson
In text data, missing or null values are common. Pandas handles these null values using the NaN (Not a Number) value from the NumPy library. You can use .fillna() to handle missing values in text columns.
# Introduce missing data
df.loc[2, 'City'] = None
# Fill missing values with a placeholder
df['City_filled'] = df['City'].fillna('Unknown')
print(df)
Output:
Name City Occupation City_upper City_length \
0 Alice New York Engineer NEW YORK 8
1 Bob Los Angeles Artist LOS ANGELES 11
2 Charlie None Scientist NaN NaN
3 David Chicago Chef CHICAGO 7
City_cleaned City_corrected City_lower City_title City_first_letter Has_LA City_part1 City_part2 City_filled
0 New York New York new york New York N False New York New York
1 Los Angeles L.A. Angeles los angeles Los Angeles L True Los Angeles Los Angeles
2 Charlie None Scientist NaN NaN Unknown Unknown
3 David Chicago Chef CHICAGO 7
Here, missing data in the City column is filled with the placeholder 'Unknown'.