Lawrence Maoka 2023-05-22 15:02:07

What is clean data?

If you’re new to data science, you might have heard the term “clean data” used quite a bit but maybe you’re not too familiar with what it means.

Clean data is data that has been carefully reviewed, validated, and processed to make sure it is accurate, consistent, and complete. To clean data, you have to identify and correct or remove errors, inconsistencies, and discrepancies within the dataset. It is an essential step in the data preparation process and comes before any analysis, modelling, or any other data-driven task.

Why is data cleaning important?

Here are a few reasons why cleaning data is important:

1. Accuracy and reliability: Clean data ensures that the information you are working with is accurate, reliable, and free from errors. It helps to minimize the risk of making incorrect assumptions, drawing flawed conclusions, or producing inaccurate results.
2. Consistency and uniformity: Cleaning data involves standardizing formats, resolving inconsistencies, and ensuring uniformity across the dataset. This enhances the compatibility and comparability of data from different sources, systems, or time periods, making it easier to merge or integrate datasets for analysis.
3. Elimination of duplicates: Duplicate records or entries can skew the analysis and lead to incorrect insights. By cleaning data, you can identify and remove duplicates, ensuring that each data point is unique and represents a distinct entity or observation.
4. Handling missing values: Datasets often have missing values, which can introduce bias and affect the analysis. Cleaning data involves dealing with missing values by imputing them or making informed decisions on how to handle them appropriately.
5. Efficient analysis and modelling: Clean data simplifies the analysis process by reducing the time spent on troubleshooting errors, fixing inconsistencies, or dealing with missing values. It allows you to focus on extracting insights, building models, and making informed decisions based on reliable information.
6. Data integrity and compliance: In certain industries or domains, data integrity and compliance with regulations or standards are crucial. Cleaning data helps maintain data integrity by ensuring that the information meets the required quality standards and adheres to relevant regulations.

By investing time and effort into cleaning data, you can improve the quality and reliability of your data, leading to more accurate analyses, better decision-making, and meaningful insights.

Data cleaning with Power Query

If you have already tried data cleaning in Microsoft Excel, CSV, or txt formats and struggled, you're not alone. Data cleaning can be a time-consuming and frustrating process, but it doesn't have to be. Power Query is a powerful tool that can help you clean your data quickly and easily.

Power Query is a part of Microsoft Excel and Microsoft Power BI, and it's designed specifically for data transformation and cleansing tasks. Power Query offers a wide range of features that make data cleaning easy. You can use it to:

• Remove duplicates
• Split columns
• Merge data from different sources
• Transform data types
• Apply complex transformations

Using the Power Query formula language called M Power Query also allows you to create reusable data cleaning steps. This means that you can save your transformations as a query and apply them to new data with just a few clicks.

To get started with Power Query, go to the "Data" tab in Excel and click on "Get Data." From there, you can choose the file format you're working with, whether it's Excel, CSV, or txt. Power Query will guide you through the steps to import the data into a new query.

Once your data is imported, you can start cleaning it up. Power Query offers a range of data-cleaning tools that can help you quickly and easily fix common data problems. When you're done cleaning the data, you can load it back into Excel or create a connection to the cleaned data that automatically refreshes whenever the source data changes. This ensures that your cleaned data is always up to date.

Tips for using Power Query

Here are some additional tips for using Power Query to clean your data:

• Start by importing your data into Power Query.
• Use the "Home" tab to identify and fix common data problems.
• Use the "Transform" tab to apply more advanced data transformations.
• Save your transformations as a query so that you can apply them to new data with just a few clicks.
• Connect your cleaned data to Excel so that you can easily access it for analysis.

Power Query is a powerful tool that can help you clean your data quickly and easily. With a little practice, you'll be able to use Power Query to get your data in shape for analysis. So, if you've struggled with data cleaning, I encourage you to give Power Query a try. You won't be disappointed.