What is Python Data Visualization ? : A Complete guide

• In this article you will learn:
• 1.What is Data Visualization?
• 2.Data Visualization in Python.
• 3.Matplotlib and Seaborn.
• 4.Line Charts.
• 5.Bar graphs.
• 6.Histogram.
• 7.Scatter plots.
• 8.Heat maps.
• 9.Conclusion.

What is Data Visualization?

Data visualization is the field in data analysis that deals with a visual representation of data. It graphically plots a data and is an effective way to communicate inferences from a data.Using a data visualization can get a visual summary of a data. With pictures maps and graphs a human mind has an easier time processing and understanding to any given data in which it is impossible to see all of data let alone process and understand it be manually.

Data Visualization in Python:

Python provide a several plotting libraries, namely Matplotlib, Seaborn and many other like data visualization packages with various features for creating informative, customized and appealing plots to a present data in most simple and effective way.

Matplotlib and Seaborn:

Matplotlib and Seaborn are python libraries that are used for a data visualization. They have inbuilt modules for the plotting various graphs. While Matplotlib is used to embed a graphs into applications Seaborn is a primarily used for the statistical graphs.But when should use either of a two?

Matplotlib	Seaborn
It is used for a basic graph plotting like a line charts, bar graphs etc.	It is majorly used for statistics visualization and can perform a complex visualizations with some commands.
It majorly works with a datasets and arrays.	It works with an entire datasets.
A Seaborn is considerably much organized and functional than Matplotlib and treats the entire dataset as a solitary unit.	Matplotlib acts productively with a data arrays and frames. It regards aces and a figures as objects.
Seaborn has a more inbuilt themes and is mainly used for a statistical analysis.	A Matplotlib is more customizable and pairs well with a Pandas and Numpy for Exploratory Data Analysis.

Line Charts:

A Line chart is the graph that represents information as series of data points connected by the straight line. In line charts every data point or marker is plotted and connected with the line or curve. Let’s consider an apple yield in Kanto. Let’s plot a line graph using this data and see how a yield of apples changes over a time. start by importing a Matplotlib and Seaborn.

Using Matplotlib:

• Using a random data points to represent a yield of apples.
• To better understand a graph and its purpose and can add the x-axis values too.
• Let’s add labels to an axes so that can show what every axis represents.
• To plot the multiple datasets on a same graph just use a plt.plot function once for every dataset. Let’s use this to compare yields of apples vs. oranges on a same grap.
• Using Seaborn.
• An simple way to make a charts look beautiful is to use some default styles from a Seaborn library. These can be applied globally using a sns.set_style function.
• Can also use a darkgrid option to change background color to a darker shade.

Bar Graphs:

When have categorical data can represent it with the bar graph. A bar graph plots a data with the help of bars which represent a value on the y-axis and category on x-axis. Bar graphs use a bars with varying heights to show a data which belongs to specific category.Can also stack bars on a top of each other. Let’s plot a data for apples and oranges.Let’s use a tips dataset in a Seaborn next.

• Time of day
• Total bill
• Tips given by a customers visiting a restaurant for a week Can draw a bar chart to visualize be how average bill amount are varies across the different days of a week.
• Can do this by computing a day-wise averages and then using a plt.bar.
• The Seaborn library also provides the barplot function that can automatically compute an average.Want to compare a bar plots side-by-side and can use a hue argument.
• The comparison will be done based on a third feature specified in this argument.Can make a bars horizontal by switching taxes.

Histograms:

• A Histogram is the bar representation of data that varies be over a range. It plots a height of the data belonging to a range along y-axis and the range along the x-axis. Histograms are used to plot a data over a range of values.
• They use a bar representation to show data belonging to each range. Let’s again use ‘Iris’ data which contains an information about flowers to plot a histograms.let’s plot a histogram using a hist() function.
• Similar to the line charts can draw the multiple histograms in a single chart. can reduce every histogram’s opacity so that one histogram’s bars don’t hide others’. Let’s draw a separate histograms for every species of flowers.
• The Multiple histograms can be stacked on a top of one another by setting a stacked parameter to True.

Scatter Plots:

• Scatter plots are used when have to plot a two or more variables present at various coordinates. The data is scattered all over a graph and is not confined to a range. Two or more variables are plotted in the Scatter Plot with every variable being represented by different color. Let’s use the ‘Iris’ dataset to plot the Scatter Plot.
• This is not more informative and cannot figure out a relationship between different data point.
• This is more better but still cannot differentiate a different data points belonging to various categories. Can color the dots using a flower species as a hue.
• Since Seaborn uses the Matplotlib’s plotting functions internally and can use a functions like plt.figure and plt.title to modify figure.

Heat Maps:

Heatmaps are used to see changes in the behavior or gradual changes in data. It uses a different colors to represent the different values. Based on how these colors range in a hues, intensity etc tells us how phenomenon varies. Let’s use a heatmaps to visualize a monthly passenger footfall at an airport over 12 years from a flights dataset in a Seaborn.Above dataset, flights_df shows us monthly footfall in the airport for each year from 1949 to 1960. The values are represent the number of passengers (in thousands) that passed through an airport. Let’s use a heatmap to visualize a above data.The brighter the colour the more people who visit the airport. By looking at a graph we can deduce:

1. The annual footfall for any given year is the highest around July and August.

2. The footfall grows an annually. Any month in year will have a higher footfall when compared to a previous years.

Conclusion:

The Complete Guide to a Data Visualization in Python and gave an overview of a data visualization in python and discussed how to create a Line Charts, Bar Graphs, Histograms, Scatter Plot and Heat Maps using different data visualization packages offered by a Python like Matplotlib and Seaborn. Python offers the multiple other visualization packages which can be used to create a various types of visualizations and not just graphs and plots. It is therefore also important to understand a challenges and advantages of the various libraries and how to use them to their full potential.