What Is Data Wrangling? Benefits, Tools and Skills

Data wrangling is the process of transforming raw data into a neater format. This fast-growing amount of data from disparate sources is what raw data is all about.

This raw data contains various data types. For this reason, it is necessary to do data wrangling where the raw data will be uniform so that the data is easier to analyze.

One of the libraries in Python that is often used in the data-wrangling process is Pandas. The Pandas library can read various data types.

Besides that, it can also change data from files into data frames which can later be accessed and processed. So, it would help if you remembered that we first need to do data wrangling before doing data analysis.

There are six stages when carrying out the data wrangling process.

Data Wrangling Overview

Data wrangling is an activity that includes the process of collecting, selecting, and transforming data into a neater format to make it easier to read. No wonder it is also known as the data cleaning process.

Doing so will enable businesses to handle more complex data in less time, resulting in more accurate data processing results.

Ultimately, the decisions made based on the data are more appropriate. Data wrangling methods vary, depending on the data and project objectives to be achieved.

Data Wrangling Process

In carrying out the data wrangling process in general, six stages are carried out, namely as follows.

  1. Please get to know the data used. For example, when we want to do data wrangling of customer data, understand what was purchased and which branch was visited.
  2. Understand the raw data structure. With this, it will be easier for you to do the data-wrangling process.
  3. The next stage is cleaning the data. To achieve the goal of data wrangling, you have to clean up the raw data, for example, removing Null values in the data.
  4. Save the cleaned data. If necessary, you can add other data so that the goals achieved are more optimal.
  5. Validate the data used.
  6. Prepare data and document the data wrangling process for easy understanding.

Data Wrangling Importance for Data Science

Any analysis performed by the company is limited to data that complements the information. If the data is incomplete, unreliable, or incorrect, the results of the study will likely be erroneous.

This is where the role of data wrangling is needed. Data wrangling eliminates that risk by ensuring the data is in good condition and can be trusted before it is analyzed and used.

This activity also collects data from various sources in one location so that the data can be used. In addition, data wrangling is also important so that the raw data is well structured so that errors in data processing can be minimized.

Data Wrangling Stages

When you want to do data wrangling before processing your company data set, there are six data wrangling steps you need to do, namely discovery, structuring, cleaning, enriching, validating, and publishing. Here’s an explanation.

1. Discovery

Discovery refers to identifying data so you know how to use the data. This process is like looking at the refrigerator’s contents before cooking. Of course, before cooking, you need to know what ingredients are in the fridge, likewise with processing data.

In this process, you can identify data trends and find problems, such as missing or incomplete data. This data-wrangling step is important because this step will determine each activity in the next steps.

2. Structuring

Raw data cannot be used if it is still in a “raw” state because it is usually incomplete or formatting errors occur. In the structuring process, the raw data is converted into usable data.

3. Cleaning

Data cleaning or data cleaning removes incorrect data so that it does not affect the analysis. Data cleaning can be done in various ways: deleting cells or rows, removing outliers, and standardizing input.

4. Enriching

Once you understand what data you have and have converted that data into a usable format, you need to decide whether you have all the data you need to analyze a project.

If not, you need to enrich the data by entering additional data from different sources. If you need this process, repeat the previous three data-wrangling steps.

5. Validating

The next step is to perform data validation or data validation. This refers to the verification process where your data is consistent and high-quality.

In this process, you may encounter some problems you must solve. If there are no problems, you can conclude that the data is ready for analysis.

The validation process is generally carried out through programming, which means this process is carried out automatically.

6. Publishing

The last data wrangling step is publishing or publication. When the data has been validated, then you can publish it. This activity enables other company parties to view and analyze the data.

The format you use to publish data can vary, depending on the data and company goals.

Data Wrangling Function

According to Elder Research, a data scientist spends 80% of his time doing data wrangling. While the remaining 20% has just been used to explore and model the data.

With a high percentage, data wrangling certainly has roles and benefits for companies, namely: Generate accurate final data most efficiently.

Data processed and compiled accurately will be easier to interpret and create in data visualization. This data-wrangling process can also maximize the accuracy and quality of data.

This process can be made automatic in the future if you have found the right formula. Of course, this can greatly cut time and make it easier for data analysts when processing large amounts of data.

Methods in Pandas Python in Processing Data Wrangling

Python provides several methods in Pandas in the data-wrangling process. Please take a peek at some of the methods and their functions often used in data wrangling below.

  • .shape to see the data size of the dataset that we are using.
  • .info to see whether the dataset we are using has missing values.
  • .describe to see the statistics of the dataset, such as mean and std.
  • .columns to see the column names in the dataset that we are using.
  • .isnull().values.any() to check data one by one that is Null/NaN.

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