**What is data visualization?**

Human minds

are more flexible and adaptable to the graphic illustration of data than to

textual data. We can comprehend things when they are visualized. It is better

to depict the information through a graph in which we can analyze the data more

efficiently and make a specific decision on the basis of data analysis. Before

learning the matplotlib, we need to understand data visualization and why data

visualization is essential. Graphics provides an excellent approach to data

exploration that is necessary for the presentation of results. Data

visualization is a new term and expresses the idea that involves more than just

representing data in the graphical form (instead of using the textual form). It

is most frequently used to translate complex information into digestible

insights for non-technical audiences.

**Why
need data visualization?**

- It identifies areas that need improvement and attention.
- It clarifies the factors.
- It helps to understand which product to place where.
- Predict sales volumes.

**What is Matplotlib?**

It is a Python library that is defined as a multi-platform data visualization library built on a Numpy array. It can be used in python scripts, shell, web application, and another graphical user interface toolkit. There are thousands of libraries in Python, and Matplotlib is one of the most powerful tools for data visualization in Python. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, error charts, scatterplots, etc., with just a few lines of code. This can be very helpful when discovering and getting to know a dataset and can help with classifying patterns, corrupt data, outliers, and much more. With a little domain knowledge, data visualizations can be used to express and demonstrate key relationships in plots and charts. The static does indeed focus on quantitative description and estimations of data. It provides an important set of tools for gaining a qualitative understanding. There are five key plots that are used for data visualization which we discuss in this article are as follows,

- Line Graph / Plot
- Bar Graph / Plot
- Histogram Graph / Plot
- Scatter Graph / Plot
- Pie Graph / Plot

**Install
Matplotlib using with Anaconda Prompt**

Matplotlib can be installed using the Anaconda Prompt by typing the following command.

conda install matplotlib

**Install
Matplotlib with pip**

Matplotlib can be installed using the python command prompt by typing the following command.

pip install matplotlib

**Importing
the library**

To get matplotlib up and running in our environment, we need to import it.

import matplotlib.pyplot as plt

Whenever you plot with matplotlib, the two main code lines should be,

1) Type of graph — this is where you define a bar chart, line chart, etc.

2) Show the graph — this is to display the graph

**Line
Plot: **The line graph is
one of charts which shows information as a series of the line. The graph is
plotted by the plot() function.

**#
Example 1.1: Line plot:**

# importing matplotlib module

from matplotlib import pyplot as plt

# x-axis values

x = [5, 2, 9, 4, 7]

# Y-axis values

y = [10, 5, 8, 4, 2]

# Function to plot

plt.plot(x,y)

# function to show the plot

plt.show()

**#
Example 1.2: Line plot:**

#create data for plotting

from matplotlib import pyplot as plt

x_values = [0,1,2,3,4,5]

squares = [0,1,4,9,16,25]

#the default graph style for plot is a line

plt.plot(x_values, squares)

#display the graph

plt.show()

**#
Example 1.3: Line plot:**

from matplotlib import pyplot as plt

x = [5,2,7]

y = [2,16,4]

plt.plot(x,y)

plt.title(‘Info’)

plt.ylabel(‘Y axis’)

plt.xlabel(‘X axis’)

plt.show()

**Bar Plot: **Bar graphs are one of the most common types of graphs and are used to show data associated with the categorical variables. Matplotlib provides a bar() to make bar graphs which accepts arguments such as: categorical variables, their value and color.

**#
Example 2.1: Bar plot:**

# importing matplotlib module

from matplotlib import pyplot as plt

# x-axis values

x = [5, 2, 9, 4, 7]

# Y-axis values

y = [10, 5, 8, 4, 2]

# Function to plot the bar

plt.bar(x,y)

# function to show the plot

plt.show()

**#
Example 2.2: Bar graphs**

from matplotlib import pyplot as plt

#create data for plotting

x_values = [5,6,3,7,2]

y_values = [“A”, “B”, “C”, “D”, “E”]

plt.bar(y_values,x_values, color = “green”)

plt.show()

**#
Example 2.3: Bar graph**

#We can also flip the bar graph horizontally with the following,

from matplotlib import pyplot as plt

#create data for plotting

x_values = [5,6,3,7,2]

y_val = [“A”, “B”, “C”, “D”, “E”]

# Adding an “h” after bar will flip the graph

plt.barh(y_val,x_values, color =”yellowgreen”)

plt.show()

**Histogram
Plot: **A histogram is a
type of bar plot that shows the frequency of a number of values compared to a
set of values ranges.

**#
Example 3.1: Histogram:**

# importing matplotlib module

from matplotlib import pyplot as plt

# Y-axis values

y = [10, 5, 8, 4, 2]

# Function to plot histogram

plt.hist(y)

# Function to show the plot

plt.show()

**#
Example 3.2: Histograms**

from matplotlib import pyplot as plt

#generate fake data

x = [2,1,6,4,2,4,8,9,4,2,4,10,6,4,5,7,7,3,2,7,5,3,5,9,2,1]

#plot for a histogram

plt.hist(x, bins = 10, color=’blue’, alpha=0.5) #Where, Bins — is an argument specific to a histogram and allows the user to customize how many bins they want. Alpha — is an argument that displays the level of transparency of the data points.

plt.show()

**#
Example 3.3: Histograms**

from matplotlib import pyplot as plt

import numpy as np

import math #needed for definition of pi

x = np.arange(0, math.pi*2, 0.05)

y = np.sin(x)

plt.plot(x,y)

plt.xlabel(“angle”)

plt.ylabel(“sine”)

plt.title(‘sine wave’)

plt.show()

**Scatter Plot:** The scatter plots are mostly used for comparing variables when we need to define how much one variable is affected by another variable. The data is displayed as a collection of points. Each point has the value of one variable, which defines the position on the horizontal axes, and the value of other variable represents the position on the vertical axis.

**#
Example 4.1: Scatter Plot:**

# importing matplotlib module

from matplotlib import pyplot as plt

# x-axis values

x = [5, 2, 9, 4, 7]

# Y-axis values

y = [10, 5, 8, 4, 2]

# Function to plot scatter

plt.scatter(x, y)

# function to show the plot

plt.show()

**#
Example 4.2: Scatter Plot :**

import matplotlib.pyplot as plt

x = [1,1.5,2,2.5,3,3.5,3.6]

y = [7.5,8,8.5,9,9.5,10,10.5]

x1=[8,8.5,9,9.5,10,10.5,11]

y1=[3,3.5,3.7,4,4.5,5,5.2]

plt.scatter(x,y, label=’high income low saving’,color=’r’)

plt.scatter(x1,y1,label=’low income high savings’,color=’b’)

plt.xlabel(‘saving*100’)

plt.ylabel(‘income*1000’)

plt.title(‘Scatter Plot’)

plt.legend()

plt.show()

**Pie Plot:** A pie chart is a circular graph that is broken down in the segment or slices of pie. It is generally used to represent the percentage or proportional data where each slice of pie represents a particular category. Let’s have a look at the below example:

**#
Example 5.1: Pie Plot:**

from matplotlib import pyplot as plt

# Pie chart, where the slices will be ordered and plotted counter-clockwise:

Players = ‘Rohit’, ‘Virat’, ‘Shikhar’, ‘Yuvraj’

Runs = [45, 30, 15, 10]

explode = (0.1, 0, 0, 0) # it “explode” the 1st slice

fig1, ax1 = plt.subplots()

ax1.pie(Runs, explode=explode, labels=Players, autopct=’%1.1f%%’,

shadow=True, startangle=90)

ax1.axis(‘equal’) # Equal aspect ratio ensures that pie is drawn as a circle.

plt.show()