Data Wrangling is basically the process where we are able to clean, and then unify, the messy and complex sets of data that we have, in order to make them easier to access and analyze when we would like. This may seem like part of the boring stuff when it comes to our data science proves, but it is going to be so important to the end results so we need to spend some time on seeing how this works.
With all of the vast amounts of data that are present in the world right now, and with all of the sources of that data growing at a rapid rate and always expanding, it is getting more and more essential for these large amounts of available data to get organized and ready to go before you try to accomplish any analysis. If you just leave the data in the messy form from before, then it is not going to provide you with an accurate analysis in the end, and you will be disappointed by the results.
Now, the process of data wrangling is typically going to include a few steps. We may find that we need to manually convert or map out data from one raw form into another format. The reason that this is done in the first place is that it allows us to have a more convenient consumption for the company who wants to use that data.
What Is Data Wrangling?
When you work with your own project in data science, there are going to be times when you gather a lot of data and it is incomplete or messy. This is pretty normal considering all of the types of data you have to collect, from a variety of sources overall. The raw data that we are going to gather from all of those different sources is often going to be hard to use in the beginning. And this is why we need to spend some time cleaning it. Without the data being cleaned properly, it will not work with the analytical algorithm that we want to create.
Our algorithm is going to be an important part of this process as well. It is able to take all of the data you collect over time and will turn it into some good insights and predictions that can then help to propel your business into the future with success. But if you are feeding the analytical data a lot of information that is unorganized or doesn’t make sense for your goals, then you are going to end up with a mess. To ensure that the algorithm works the way that you want, you need to make sure that you clean it first, and this is the process that we can call data wrangling.
If you as the programmer would like to create your own efficient ETL pipeline, which is going to include extract, transform and load, or if you would like to create some great looking data visualizations of your work when you are done, then just get prepared now for the data wrangling.
Like most data scientists, data analysts, and statisticians will admit, most of the time that they spend implementing an analysis is going to be devoted to cleaning or wrangling up the data on its own, rather than in actually coding or running the model or algorithm that they want to use with the data. According to the O’Reilly 2016 Data Science Salary Survey, almost 70 percent of data scientists will spend a big portion of their time dealing with a basic analysis of exploratory data, and then 53 percent will spend their time on the process of cleaning their data before using in an algorithm.
Data wrangling, as we can see here, is going to be an essential part of the data science process. And if you are able to gain some skills in data wrangling, and become more proficient with it, you will soon find that you are one of those people who can be trusted and relied on when it comes to some of the cutting-edge data science work.
Data Wrangling with Pandas
Pandas is seen as one of the most popular libraries in Python for data science, and specifically to help with data wrangling. Pandas is able to help us to learn a variety of techniques that work well with data wrangling, and when these come together to help us deal with some of the data formats that are the most common out there, along with some of their transformations.
We have already spent a good deal of time talking about what the Pandas library is all about. And when it comes to data science, Pandas can definitely step in and help get a ton of the work done. With that said, it is especially good at helping us to get a lot of the data wrangling process that we want doing as well. There may be a few other libraries out there that can do the job but none are going to be as efficient or as great to work with, as the Pandas library.
Pandas will have all of the functions and the tools that you need to really make your project stand out, and to ensure that we are going to see some great results in the process of data wrangling as well. So, when you are ready to work with data wrangling, make sure to download the Pandas library, and any of the other extensions that it needs.
Our Goals with Data Wrangling
When it comes to data wrangling, most data scientists are going to have a few goals that they would like to meet in order to get the best results. Some of the main goals that can come up with data wrangling, and should be high on the list of priorities, include:
- Reveal a deep intelligence inside of the data that you are working with. This is often going to be accomplished by gathering data from multiple sources.
- Provides us with accurate and actionable data and then puts it in the hands of n analyst for the business, in a timely manner so they can see what is there.
- Reduce the time that is spent collecting, and even organizing, some of the data that is really unruly, before it can be analyzed and utilized by that business.
- Enables the data scientists, and any other analyst to focus on the analysis of the data, rather than just the process of wrangling.
- Drives better skills for making decisions by senior leaders in that company.
The Key Steps with Data Wrangling
Just like with some of the other processes, there are a few key steps that need to come into play when it comes to data wrangling. There are three main steps that we can focus on for now, but depending on the goals you have and the data that you are trying to handle, there could be a few more that get added in as well. The three key steps that we are going to focus on here though will include data acquisition, joining data, and data cleansing.
First on the list is data acquisition. How are you meant to organize and get the data ready for your model if you don’t even have the data in the first place? In this part of the process, our goal is to first identify and then obtain, access to the data that is in your preferred sources so that you can use it as you need in the model.
The second step is going to be where we join together the data. You have already been able to gather in the data that you want to use from a variety of sources and even did a bit of editing in the process. Now it is time for us to combine together the edited data for further use and more analysis in the process.
And then we can end up with the process that is known as data cleansing. In the data cleansing process, we need to redesign the data into a format that is functional and usable, and then remove or correct any of the data that we consider as something that is bad.
What to Expect with Data Wrangling?
The process of data wrangling can be pretty complex, and we need to take some time to get through all of it and make sure that we have things in the right order. When people first get into the process of data wrangling, they are often surprised that there are a number of steps, but each of these is going to be important to ensure that we can see the results that we want.
To keep things simple for now, we are going to recognize that the data wrangling process is going to contain six iterative steps. These are going to include the following:
The process of discovering. Before you are able to dive into the data and the analysis that you want to do too deeply, we first need to gain a better understanding of what might be found in the data. This information is going to give you more guidance on how you would like to analyze the data. How you wrangle your customer data, as an example, maybe informed by where they are located, what the customer decided to buy, and what promotions they were sent and then used.
The second iterative step that comes with the data wrangling process is going to be structuring. This means that we need to organize the data. This is a necessary process because the raw data that we have collected may be useful, but it does come to us in a variety of shapes and sizes. A single column may actually turn into a few rows to make the analysis a bit easier to work within the end. One column can sometimes become two. No matter how we change up some of the work, remember that the movement of our data is necessary in order to allow our analysis and computation to become so much easier than before.
Then we can go on to the process of cleaning. We are not able to take that data and then just throw it at the model or the algorithm that we want to work with. We do not want to allow all of those outliers and errors into the data because they are likely to skew some of our data and ruin the results that we are going to get. This is why we want to clean off the data.
There are a number of things that are going to spend our time cleaning when it comes to the data in this step. We can get rid of some of the noise and the outliers we can take some of the null values and change this around to make them worth something. Sometimes it is as simple as adding in the standard format, changing the missing values, or handling some of the duplicates that show up in the data. The point of doing this though is to increase the quality of the data that you have, no matter what source you were able to find it from.
Next on the list is the process of enriching the data. Here we are going to take stock of the data that we are working with, and then we can strategize about how some other additional data might be able to augment it out. This is going to be a stage of questions to make sure that it works, so get ready to put on your thinking cap.
Some of the questions that you may want to ask during this step could include things like what new types of data can I derive from what I already have? What other information would better inform my decision making about this current data? This is the part where we will fill in some of the holes that may have found their way into the data, and then find the supplementation that is needed to make that data pop out.
From here we can move on to the step of validation. The validation rules that we are going to work with this step in the data science process are going to be repetitive programming sequences. The point of working with these is that we want to check out and verify the consistency, quality, and security of our data to make sure that it is going to do the work that we want.
There are a lot of examples that come with the validation stage. But this can include something like ensuring the uniform distribution of attributes that should be distributed in a normal way, such as birth dates. It can also be used as a way to confirm the accuracy of fields through a check across the data.
And the last stage is going to be publishing. Analysts are going to be able to prepare the wrangled data to use downstream, whether by a software or a particular user. This one also needs us to go through and document any of the special steps that were taken or the logic that we used to wrangle this data. Those who have spent some time wrangling data understand that implementation of the insights is going to rely upon the ease with which we are able to get others the information, and how easy it is for these others to access and utilize the data at hand.
Data wrangling is an important part of our process and ensures that we are able to get the best results with any process that we undertake. We need to remember that this is going to help us to get ahead with many of the aspects of our data science project, and without the proper steps being taken we are going to be disappointed in what we see as the result in the end. Make sure to understand what data wrangling is all about, and why it is so important so that it can be used to help with your data science project.
