Plotting with Pyplot 1 - Bar Graphs and Scatter Plots (CLASS - XII) (PART-1)

PLOTTING WITH PYPLOT I - BAR GRAPHS AND SCATTER PLOTS

CHAPTER - 3 (PART-1)

CLASS - XII

Data Visualization: Data visualization basically refers to the graphical or visual representation of information and data using visual elements like charts, graphs, and maps etc. Data visualization is immensely useful in decision making.

Data visualization unveils pattern, trends, outlines, correlations etc. in the data, and thereby helps decision makers understand the meaning of data to drive business decision.

Using Pyplot of Matplotlib Library: For data visualization in Python, the Matplotlib library’s Pyplot interface is used.

The matplotlib is a Python library that provides many interfaces and functionality for 2D-graphics similar to MATLAB’s in various forms.  You can call matplotlib as a high-quality plotting library of Python. It provides both a very quick way to visualize data from Python and publication-quality figure in many formats. The matplotlib library offers many different named collections of methods; Pyplot is one such interface.

Note: MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.

Installing and Importing matplotlib: If you have installed Python using Anaconda, then matplotlib library is already installed on your computers.

If you have installed Python using standard official distribution, you may need to install matplotlib separately.

First you will need to download wheel package of matplotlib as per Python’s version installed.

Next you need to install it by giving following commands on the command prompt.

python -m pip install -U pip

python -m pip install -U matplotlib OR

using pip install matplotlib

Importing Pyplot: In order to use Pyplot on your computers for data visualization, you need to first import it in your Python environment by issuing one of the following commands:

import matplotlib.pyplotèThis would require you to refer to every command of pyplot as matplotlib.pyplot.<command>

import matplotlib.pyplot as pltè with this, you can refer to every command of pyplot as plt.<command> as you have given an alias name to matplotlib.pyplot as plt

Note: You can choose any legal identifier in place of plt.

Working with PyPlot Methods: The PyPlot interface provides many methods for 2D plotting of data. The matplotlib’s Pyplot interface lets one plot data in multiple ways such as line chart, bar chart, pie chart, scatter chart etc. You can easily plot the data available in the form of NumPy arrays (ndarrays) or dataframes etc.

Example

import numpy as np

import matplotlib.pyplot as plt

x=np.linspace(1,5,6)

y=np.log(x)

plt.plot(x,y)

plt.title('Plot Chart')

plt.show()

OUTPUT

Basics of Simple Plotting: You can create many different types of graphs and chart using PyPlot.

Line Chart: A line chart or line graph is a type of chart which displays information as a series of data points called ‘markers’ connected by straight line segment.

Bar Chart: A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically as well as horizontally.

Scatter Plot: The scatter plot is similar to line chart, the major difference is that while line graph connects the data points with a line, scatter chart simply plots the data points to show the trend in the data.

Example of Line Chart:

import numpy as np

import matplotlib.pyplot as plt

a = [1,2,3,4]

b = [2,4,6,8]

c = [1,4,9,16]

plt.xlabel("Some Values")

plt.ylabel("Double Values")

plt.plot(a,b)

plt.show()

OUTPUT

NOTE: This is the notes of chapter 3rd first part, soon I will upload the second part and who will complete the notes they will write their name in comment box.