Python Learning Series Part-5

Complete Python Topics for Data Analysis: https://t.me/sqlspecialist/548

Data Cleaning and Preprocessing:

1. Handling Missing Data:
- Identifying Missing Values:

     df.isnull()  # Boolean DataFrame indicating missing values
     

- Dropping Missing Values:

     df.dropna()  # Drop rows with missing values
     

- Filling Missing Values:

     df.fillna(value)  # Replace missing values with a specified value
     

2. Removing Duplicates:
- Identifying Duplicates:

     df.duplicated()  # Boolean Series indicating duplicate rows
     

- Removing Duplicates:

     df.drop_duplicates()  # Remove duplicate rows
     

3. Data Normalization and Scaling:
- Min-Max Scaling:

     from sklearn.preprocessing import MinMaxScaler

     scaler = MinMaxScaler()
     df_scaled = scaler.fit_transform(df[['feature']])
     

- Standardization:

     from sklearn.preprocessing import StandardScaler

     scaler = StandardScaler()
     df_standardized = scaler.fit_transform(df[['feature']])
     

4. Handling Categorical Data:
- One-Hot Encoding:

     pd.get_dummies(df['categorical_column'])
     

- Label Encoding:

     from sklearn.preprocessing import LabelEncoder

     label_encoder = LabelEncoder()
     df['encoded_column'] = label_encoder.fit_transform(df['categorical_column'])
     

Understanding data cleaning and preprocessing is crucial for ensuring the quality and suitability of your data for analysis.

Share with credits: https://t.me/sqlspecialist

Hope it helps :)