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What is Data Wrangling?
Table of Contents
Did you ever think how the huge data is converted into relevant form and made use of it?
What is Data Wrangling, and why do we need it?
If the above questions raise doubts in your mind, then this is the article for you, and we have the answer to all your questions.
What is Data Wrangling and its Goals
Data Wrangling is a process of converting raw data into such form so that it becomes more useful. This way, efforts are made to select the most relevant and useful information and thereby make analysis so that the most desired, dependable, and relevant outcomes are achieved that could support the decision-making process. The present-day businesses are so expanded, and data inputs are so huge that data wrangling has become important for it.
The goals of data wrangling include the following:
- It should give precise and applicable data to business analysts in a stipulated time frame.
- Minimize the time is being spent on the collection and the arrangement of the data.
- Approve data scientist to have a main focus on analysis than wrangling data.
- Make a better decision based on data in a short period
5 Steps in Data Wrangling
The major steps in the data wrangling process include the following.
Step 1: Acquiring Data
The first and the most major step is acquiring and sorting of data. Either we can claim that the most important step towards reaching your goal of answering your question is finding your data to investigate. Although, before finding data, you must be aware of the following properties, and you must be okay with that because this is just the first step of a boring process:
- Unequal data:
- Even though we would like to believe in the sincerity and quality of data we see, not every data we have stood up to our expectations.
- Questions that could help in understanding the Data Wrangling project:
The following are the set of questions that could help in understanding the Data Wrangling project.
- Is the creator of the source approachable if I have any questions or problems?
- Does the data seem to be routinely updated?
- Does it come with details as to how it was gained and what sort of samples were used in its achievement?
- Is there any other option where you can check the data?
Step 2: Fact-Checking in Data Wrangling
Fact-checking your data is, most of the time, quite irritating, is primary to the validity of your reporting. If you have an approach to some of the devices such as LexisNexis, Google's data search, you can learn what others have learned and used a project or research. Once you have gone through and fact-checked your data, it will be easy to prove its validity in the future.
You are not going to give a call on everyone's phone to get the data. Similarly, there are many sources to support your data. There are several sources from where you can get your data. These include government data, data from the NGOs, university or educational data, scientific or medical data, crowdsourced data, and many more. Perceive the best places to gain datasets for data science projects.
Step 3: Data Cleaning in Data Wrangling
Cleaning up data is not more of a fascinating task, but it is the necessary part of data wrangling. To become a data cleaning expert, you must have clearness, knowledge of a certain field, and on top of that, self-control, yes, self-control.
Taking a move towards the technical side, Python can help you to clean your data easily. Knowing that you have a simple knowledge of Python, in this chapter, we will go through at some of the Data Wrangling with Python.
With the help of web scraping skills, and small data wrangling with R, we can have the other CSV, which encloses headers with its English variant. We can find this file under the same archive (mn-headers.csv).
Step 4: Formatting Data in Data Wrangling
The most common goal of the data cleanup is to make your unreadable data or, in simple words, hard to read data to change it in a proper readable format. Python gives us several ways to format strings and numbers. We used %, which depicts the Python representation of the object in a string or Unicode to correct and display our results.
Python also consists of chain formatted %s and %d, which depicts chain and digits, respectively. We, again and again, use these with the print command. There is professional way, which refers to the format method of Python, which is claimed by the official documentation, let us describe a chain and pass the data as a debate or keyword debate into the chain. Let us take a near look at the format.
Step 5: Finding outliers in Data Wrangling
Finding bad data or outliers is likely to be the hardest task. You have to make sure that you need to clean the data and not control it. If we get to know that workers only interviewed families in urban areas and not the rural areas, this might be a problem in selection error or sampling error. Depending upon your sources, you should resolve what biases our dataset might have.
Rather than finding which data bias is utilized, you can find irregularity by easy if -not statements. But most of the time, they decline in huge data –sets. For example, if you cross-check your full data –set for missing data by if –not statements, it will appear like this. But you will not be able to find any clear missing data points.
Complete Data Wrangling & Data Visualisation With Python
API in Data Wrangling
An API is a uniform way of sharing data on the web. So many websites transfer data through API endpoints. Some of them are Twitter, Linkedin, World Bank, US Census.
An API can be as straight as a data response to a request, but it's hard to look for APIs with only that process. Many APIs have other practical features. Let's understand that with an illustration.
Twitter APIs are available in two forms: REST and STREAMING. REST stands for representational. State transfer and it is built to create stability in API architecture, whereas some real-time service offer is streaming APIs.
In conclusion, Data Wrangling can help in reducing the burden of the data analysis process. It helps in finding out the most relevant information and, thereafter, supports the data analysis process so that the lesser time is consumed in bringing out the most dependable outcomes.
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