We generate data through all of our actions not only from anything we do on the internet but also from anything we do in the offline world. The data we collect is in either numerical or textual format though, making it difficult to understand and find trends until it has been converted to visual forms such as charts or plots. This is where Data Visualization comes in.
Data visualization is a method that uses visuals, both static and interactive, to help people understand the large amount of data being collected. Data visualization is an important skill in applied statistics and machine learning. It can be helpful when you need to get information from some datasets; the information we can mine from datasets can be about finding patterns and around identifying outliers, and much more. With some prior domain knowledge, visualization can be used to find relationships between the data, which can be insightful to you and your audience.
Python has many visualization tools/libraries which provide excellent features and are easy to implement. It includes support for all types of visual, live, customized charts.
Worth mentioning, below are some of the most used python libraries for data visualization:
The first and most important step of data visualization is gathering data in large amounts. Only after we have substantial data, we can apply data visualization techniques on the collected data and get some helpful insights from it.
Data cleaning is an essential step to perform before creating a visualization. A bunch of data out of a large dateset which has inappropriate, empty or false values may lead to adding erroneous visuals with anomalies in it. The output received from a data cleaning process is usually a dateset that is free of errors and anomalies etc. which gives much more accuracy when data is processed. Data cleaning is pretty much dependent on the dateset domain that you’re working with.
Before choosing a visual chart or graph, it is important to understand your audience and then choose a chart or graph accordingly which will best communicate the message.
Choosing a chart totally depends on what findings you need to convey to your audience.
Choosing a couple of these can help to select the charts that will be best suitable for you. This usually requires some playing around with different charts before choosing the best.
To prepare the data before sending it further for visualization is to determine the type of graph, chart or any other visualizations you need to create and the supporting library you will be integrating for it. After the chart is finalized it may be necessary to transform the data as per requirements. Data preparation tasks include finding data columns that help make some decisions out of it, giving some meaningful insights about data, grouping data, creating aggregate values for groups, combining variables to create new columns, etc.
In the final step you’ll have the required data you need to create visualizations. Now you can apply all your visualizations skills on the prepared data and represent the data in charts or graphs with meaningful insights.
Now that we understand how the data visualization process works, we can now apply different data visualization types to their uses. As mentioned in the earlier section by using those visualizations libraries, we can create some visualizations as follows:
Line charts are used to display trends over time. The X-axis is usually used to represent a period, and the Y-axis is used to represent quantity associated with the time period on the X-axis. For e.g: A line chart can illustrate a shopping mall’s peak visit time for the day broken down by week days and hours.
An area chart is a line chart with the areas below the lines filled with colors. Use a stacked area chart to display each value’s contribution to a total over some time.
A bar chart also displays trends over time. In case of multiple variables, a bar chart can make it easier to compare the data for each variable, every moment in time. For e.g, a bar chart can be used to compare the company’s growth year wise.
A histogram represents data using bars of different heights. Usually, each bar group numbers into ranges in a histogram. Taller the bars more, the data falls in that range. It is used to display the shape and spread of continuous data set samples. For e.g, we can use a histogram to measure each answer’s frequencies in a survey question. The bars would be the answer: “bad,” “good,” and “best”.
When there is a need to find the correlations, Scatter plots are used. If there exists a data XY, then a Scatter plot is used to find the relationship between variables X and Y.
The bubble chart is evolved from a scatter plot. Where unlike scatter plots each data point is assigned a label or category and shown as a bubble. It is used to show and compare the relationship between the labelled circles. Bubble chart makes it hard to read the chart with multiple bubbles, so it has a limited data set size capacity.
A pie chart is a circular graph representing the data set in which the slices of pie are divided to represent a numeric proportion. Pie charts are used when there is a need to show the contribution of a data point inside a whole data set.
A gauge chart is evolved from a pie chart and doughnut chart. It is used to visualize the distance between intervals. Multiple gauge charts can be shown linearly to visualize the difference between multiple intervals.
Most of the data collected has a location variable, which makes it easy to plot on a map. An e.g, of a map visualization is mapping the number of customers all over the world country wise, where each country would represent a number of customers. Location information can help businesses to grow their business in a particular region where the business has not scattered compared to other regions.
A heat map is a visualization tool that uses color the way a bar chart uses its height and width. Two dimensions are shown as a magnitude of a phenomenon. The heat map illustrates it can be used to identify whether the phenomenon is clustered or varies over space.
It is difficult for humans to understand the data in numeric format because of its complexity and a large amount of data. That’s where data visualizations come into the picture as it makes it easy to understand the data, and it allows the decision-makers to act more quickly.
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