Data Wrangling: What is Involved in Data Cleaning?

The process of cleaning data is a crucial step that ensures the accuracy and reliability of your analyses. Imagine you’re running a business or working as a decision-maker, and you rely heavily on data to make informed decisions. You want that data to be as clean and accurate as possible, right? So, let’s dive right in and learn about the different aspects involved in cleaning data.

Missing Values
Sometimes, data comes with holes, like a puzzle with missing pieces. Handling missing values is important because they can affect your analysis and conclusions.

There are several ways to deal with missing values, such as: 

  1. Removing rows with missing values can be helpful if you have a large dataset and only a small percentage of rows have missing values.
  2. Filling missing values with a specific value or the mean/median of the column can be useful when you need to maintain the size of your dataset.
  3. Interpolating based on surrounding values – if missing values have a linear correlation with nearby values, you can fill the gaps with a calculated value.

Imagine you’re running an e-commerce store, and you accidentally count the same sale twice. That will create an inaccurate picture of your revenue. Duplicate data can also come from human error or merging different data sources.

We need to identify and remove these duplicates using methods such as: 

  1. Deduplication based on unique identifiers: If each row has a unique identifier, we can easily remove duplicates by comparing these identifiers.
  2. Deduplication based on specific columns: If you don’t have unique identifiers, you can compare multiple columns to identify duplicates.

Inconsistencies and Errors
Inconsistent or erroneous data can be a major obstacle in making accurate analyses. For example, imagine you’re looking at customer data for a business and have different address or phone number formats.

Steps to address these issues include: 

  1. Checking for typos and misspellings: Pre-built dictionaries, domain-specific terminology, or string similarity algorithms can be used.
  2. Standardizing units and scales: Ensure that data in the same column is using the same units, like miles instead of kilometers or Fahrenheit instead of Celsius.
  3. Categorizing data to create homogeneity: Grouping similar values together can help to identify and resolve discrepancies, such as ‘USA,’ ‘United States,’ and ‘U.S.’

Standardizing and Transforming Data Types
Sometimes, data comes in different formats, which can affect the accuracy of analyses. For instance, when tracking shipment dates, ensure that all dates are in a consistent format (e.g., MM/DD/YYYY).

Standardization can be achieved through: 

  1. Converting data types: Ensure numerical values are in a numerical data type and categorical values are in a text or categorical data type.
  2. Standardizing date formats: Use a consistent format for dates and times, which helps in carrying out time-based analyses.
  3. Working with text data: Clean up and preprocess text data by removing extra spaces, punctuation and ensuring consistent capitalization.

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