← NumPy & Pandas learning pathNumPy & Pandas · Module 5 of 10
Cleaning data
Handle missing values, duplicates and wrong types.
📖 Key ideas
- Find missing values: df.isna().sum().
- Fill: df.fillna(0) or drop: df.dropna().
- Remove duplicates: df.drop_duplicates().
- Fix types: df["date"] = pd.to_datetime(df["date"]).
- Clean text: df["name"].str.strip().str.title().
💡 Real-life example: Data cleaning is washing vegetables before cooking: skip it and the dish (analysis) goes bad.
isna()Detects missing values
fillna()Fills missing values
drop_duplicates()Removes repeated rows
to_datetimeConverts to dates