In this article, we are going to use **Python** to visualize the data in a **Simple Line Chart**. Nowadays, the internet is being bombarded with a huge amount of data each second. According to the Sixth edition of Domo Inc. reports, over 2.5 quintillion bytes of data are generated each second. We can use the data of our interest to get insights about it and **Data Visualization** provides a way to see the data graphically which provides an easier way to see the trends, patterns, outliers etc. As a human, we understand easily the abstract things rather than getting into the details. So, data visualization allows the draw insights from data in an effective manner.

A visually appealing representation of data conveys more meaning to the user rather than in tabular or textual form. It allows the user to see patterns and trends in the data that they never knew before.

You don’t need a heavy machine to visualize the complex data. You can quickly explore data using **Python’s** efficient libraries available. **Python** is highly used for data-intensive tasks. It can be genetics data, economic analysis, social media trend analysis, and much more.

# Introducing **matplotlib**

**matplotlib** is one of the most popular mathematical plotting library available in **Python**. It is extensively used. We will use it to create different visualizations of data such as simple plots, line graphs, and scatter plots.

**Pre-requisites**

In order to follow this article series, we assume the following:

- You have some experience in
**Python**. - You have installed
**Python’s version 3.X** - You have
**pip**installed

## Installing **matplotlib**

In order to install **matplotlib** and it dependencies for
**macOS, Windows **and** Linux **distributions, open the command
prompt/shell and type the following command:

**Make sure you have latest pip version. In order to update
pip you can use the following command**

`> python –m pip install –-upgrade pip`

`> python –m pip install –U matplotlib`

## Testing **matplotlib**

In order to check whether **matplotlib** have successfully installed, open command prompt. Type following:

`> python`

`>>> import matplotlib`

If you don’t see any errors it means that **matplotlib is successfully installed on your system. Now you can move to the **next section of the article.

# Types of Visualizations in *matplotlib*

*matplotlib*

There are numerous visualizations available in matplotlib. You can see the full list at here. Let us enlist some of them:

- Lines, bars and markers
- Stacked Bar Graph

- Horizontal Bar Chart

- Grouped Bar Chart

- and many more

- Subplots, axes and figures
- Aligning Labels

- Geographic Projections

- Multiple Subplots

- and many more

- Pie and Polar Charts
- Statistics related graphs and charts

# Plotting a Simple Line Graph

A graph with points connected by lines is called a **line graph**. Let’s plot a simple line graph using **matplotlib**, and then modify it according to our needs to create a more informative visualization of our data.

We will use a function named **generate_square_series(n) **which
will generate square number sequence as data for the graph.

Create a file named **mpl_squares_plotting.py** in your
favorite editor.

def generate_square_series(n): squares = [] # i starts from 1 and ends at n-1 for i in range(1, n): squares.append(i * i) return squares

Now that we have written a function to generate data. Let’s use **matplotlib** to visualize it.

import matplotlib.pyplot as plt # Using the function we’ve created squares = generate_square_series(100) plt.plot(squares) plt.show()

**Explanation of Code**

We first imported the **pyplot** module as **plt** so
we don’t have to type **pyplot** repeatedly.

**pyplot** contains functions that help to generate
charts and plots.

Then we used the function **generate_square_series** that
returns a list and we have assigned it to **squares **object reference.

We then passed the **squares** list to the function **plot**()
which plots the number.

**plt.show()** opens matplotlib’s viewer and displays the
plot as shown in the figure below.

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