Introduction:

Within the field of data science and analytics, data cleaning and data wrangling are two crucial procedures that are critical to guaranteeing the quality and usefulness of data. Despite their frequent versatility, these phrases designate distinct phases of the data preparation pipeline. We will explain the differences between data wrangling and data cleaning in this article, along with some sample codes to help you understand each idea.

Understanding Data Cleaning:

Data cleaning, often referred to as data cleansing or data scrubbing, is the process of finding and fixing mistakes and inconsistencies in a dataset. These mistakes can include inaccuracy, outliers, and duplicate entries in addition to missing numbers. Enhancing the quality of the dataset by resolving problems that might compromise the analysis's correctness and dependability is the main objective of data cleaning.

Code snippet for data cleaning:

import pandas as pd

# Load the dataset
df = pd.read_csv('your_dataset.csv')
# Check for missing values
missing_values = df.isnull().sum()
# Remove duplicate entries
df = df.drop_duplicates()
# Handle missing values (e.g., fill with mean or median)
df['column_with_missing_values'].fillna(df['column_with_missing_values'].mean(), inplace=True)

For a better understanding of data cleaning, check out my other post on Unraveling the art of data cleaning from raw to refined.

Understanding data wrangling:

On the other hand, data wrangling is a more comprehensive procedure that includes data transformation and reshaping in addition to data cleansing. To prepare the data for analysis, it must be manipulated and arranged. This process is known as data wrangling. This covers operations like combining datasets, rearranging columns, and creating new features. Transforming data into a format that works with the models and analytical methods used in the subsequent stages of the data science process is the primary objective of data wrangling.

Code snippets for data wrangling:

import pandas as pd

# Merge two datasets
merged_df = pd.merge(df1, df2, on='common_column')
# Create a new feature
df['new_feature'] = df['feature1'] + df['feature2']
# Convert data types
df['numeric_column'] = pd.to_numeric(df['numeric_column'])
# Pivot the data
pivot_table = pd.pivot_table(df, values='value', index='index_column', columns='column_to_pivot')

In conclusion, while data cleaning and data wrangling share the common goal of preparing data for analysis, they involve distinct processes. The goal of data cleaning is to find and fix mistakes and inconsistencies so that the dataset is accurate. However, data wrangling includes a wider range of tasks, including data transformation, cleansing, and restructuring, with the goal of reshaping the data to enable efficient analysis. Understanding the distinctions between these two critical steps in the data science workflow is necessary to properly prepare and use datasets for analytical purposes.