DATA CLEANING:

Pandas offers functions for handling missing data, removing duplicates, and transforming data to make it suitable for analysis.

Data cleaning is an essential step in the data preparation process, involving identifying and correcting errors, inconsistencies, and missing values in a dataset to improve its quality and reliability. Some common data cleaning tasks include:

a. Handling Missing Values: Identifying and dealing with missing or null values in the dataset by either removing them, filling them with appropriate values (e.g., mean, median, mode), or using advanced imputation techniques.

b. Removing Duplicates: Identifying and removing duplicate rows or columns from the dataset to avoid redundancy and ensure data integrity.

c. Standardizing Data: Standardizing or normalizing data to ensure consistency across different columns or features, such as converting categorical variables to a consistent format or scaling numerical variables.

d. Correcting Errors: Identifying and correcting errors or inconsistencies in the dataset, such as typos, incorrect data types, or outliers that may skew the analysis.

e. Handling Outliers: Identifying and dealing with outliers in the dataset, which are observations that significantly deviate from the rest of the data and may distort statistical analysis or machine learning models.

f. Parsing Dates and Times: Parsing and standardizing date and time formats in the dataset to facilitate temporal analysis and visualization.

g. Feature Engineering: Creating new features or transforming existing features to extract meaningful information from the dataset and improve model performance.

h. Handling Inconsistent Formats: Handling inconsistent formats or units across different columns, such as currency symbols, date formats, or measurement units.

i. Dealing with Special Characters: Handling special characters, encoding issues, or non-standard characters in text data to ensure proper processing and analysis.

j. Data Validation: Validating data against predefined rules or constraints to ensure its accuracy, completeness, and consistency.

Here’s an illustrative code example demonstrating some of these data cleaning tasks using Pandas:

“`python
import pandas as pd

# Load dataset
df = pd.read_csv(‘data.csv’)

# Handling missing values
df.fillna(0, inplace=True) # Fill missing values with 0

# Removing duplicates
df.drop_duplicates(inplace=True)

# Standardizing data
df[‘category’] = df[‘category’].str.lower() # Convert category names to lowercase

# Correcting errors
df.loc[df[‘age’] < 0, ‘age’] = 0 # Correct negative age values

# Handling outliers
q1 = df[‘salary’].quantile(0.25)
q3 = df[‘salary’].quantile(0.75)
iqr = q3 – q1
df = df[(df[‘salary’] >= q1 – 1.5 * iqr) & (df[‘salary’] <= q3 + 1.5 * iqr)] # Remove outliers

# Parsing dates and times
df[‘date’] = pd.to_datetime(df[‘date’], format=’%Y-%m-%d’)

# Feature engineering
df[‘year’] = df[‘date’].dt.year
df[‘month’] = df[‘date’].dt.month

# Data validation
df = df[df[‘age’] >= 18] # Filter out records with age less than 18

# Save cleaned dataset
df.to_csv(‘cleaned_data.csv’, index=False)
“`

This code demonstrates various data cleaning tasks such as handling missing values, removing duplicates, standardizing data, correcting errors, handling outliers, parsing dates and times, feature engineering, and data validation using Pandas. These operations help ensure that the dataset is accurate, consistent, and suitable for further analysis or modeling.