x̄ - > Data Cleaning - Code clinic

Data cleaning is a crucial step in the data analysis process. It involves identifying and correcting any errors, inconsistencies, or missing values in the dataset to ensure that the data is accurate and reliable. 

Here's an example code for a data-cleaning process: 

 1. Import the necessary libraries: import pandas as pdimport numpy as np 

 2. Load the dataset: df = pd.read_csv('dataset.csv') 

 3. Check for missing values: print(df.isnull().sum()) This will print the count of missing values in each column of the dataset. 

 4. Remove rows with missing values: df = df.dropna() This will remove all the rows with missing values from the dataset. 

 5. Check for duplicates: print(df.duplicated().sum()) This will print the count of duplicate rows in the dataset. 

 6. Remove duplicates: df = df.drop_duplicates() This will remove all the duplicate rows from the dataset. 

 7. Check for outliers: Q1 = df.quantile(0.25)Q3 = df.quantile(0.75)IQR = Q3 - Q1print(((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).sum()) This will print the count of outliers in each column of the dataset. 

 8. Remove outliers: df = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)] This will remove all the rows with outliers from the dataset. 

 9. Save the cleaned dataset: df.to_csv('cleaned_dataset.csv', index=False) This will save the cleaned dataset to a CSV file.