Matplotlib,Python, Machine Learning, AI,ML

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Index  Matplotlib  Features of matplotlib  Matplotlib installation  Matplotlib importing  Matplotlib Functions  Formatting the style of plot  Line Style  Plotting with keyword String  Plotting with Categorical variables  Controlling line properties  Type of plots  Scatter plot  Scatter plot with groups  Bar Chart  Multiple Bar Chart  Stack Bar Chart  Comparing Bar Chart  Pie Chart  Box plot  Assignment 1

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Matplotlib  matplotlib.pyplot is a plotting library used for 2D graphics in python programming language. It can be used in python scripts, shell, web application servers and other graphical user interface toolkits.  Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. 2

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Features of matplotlib  Versatility: Matplotlib can generate a wide range of plots, including line plots, scatter plots, bar plots, histograms, pie charts, and more.  Customization: It offers extensive customization options to control every aspect of the plot, such as line styles, colors, markers, labels, and annotations.  Integration with NumPy: Matplotlib integrates seamlessly with NumPy, making it easy to plot data arrays directly.  Publication Quality: Matplotlib produces high-quality plots suitable for publication with fine-grained control over the plot aesthetics.  Interactive Plots: Matplotlib supports interactive plotting through the use of widgets and event handling, enabling users to explore data dynamically. 3

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Matplotlib Installation  Open cmd window and type below commands to install matplotlib: ◼ python –m pip install matplotlib Or ◼ pip install matplotlib 4

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Importing matplotlib  The package is imported into the Python script by adding the following statement − ◼ from matplotlib import pyplot as plt Or ◼ Import matplotlib.pyplot as plt 5

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example of matplotlib  import matplotlib.pyplot as plt #the x and y coordinates x = [ 'A','B','C','D','E'] y = [ 11,17,25,60,66] plt.title("My Graph") # Plot the points using matplotlib plt.plot(x, y) plt.show() 6

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png  import numpy as np import matplotlib.pyplot as plt # Compute the x and y coordinates for points on a sine curve x = np.arange(0, 3 * np.pi, 0.1) y = np.sin(x) plt.title("sine wave form") # Plot the points using matplotlib plt.plot(x, y) plt.show() 7

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Matplotlib Functions 8

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Formatting the style of your plot  For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot.  The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string.  The default format string is 'b-', which is a solid blue line.  For example, to plot with red circles, we write plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro') plt.axis([0, 6, 0, 20]) plt.show() Note: The axis( ) command takes a list of [xmin, xmax, ymin, ymax] 9

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png 10

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Line Style 11

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example of formatting the style of your plot plt.plot( [1, 2, 3, 4], [1, 4, 9, 16], 'ro' ) plt.axis( [0, 6, 0, 20]) plt.show( ) 12

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Plotting with keyword Strings  There are some instances where you have data in a format that lets you access particular variables with strings. For example, with numpy.recarray or pandas.DataFrame.  Matplotlib allows you provide such an object with the data keyword argument. If provided, then you may generate plots with the strings corresponding to these variables.  Example: data = {'a': np.arange(50), 'c': np.random.randint(0, 50, 50), 'd': np.random.randn(50)} data['b'] = data['a'] + 10 * np.random.randn(50) data['d'] = np.abs(data['d']) * 100 plt.scatter('a', 'b', c='c', s='d', data=data) plt.xlabel('entry a') plt.ylabel('entry b') plt.show() 13

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png  Output: 14

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png  Example : import pandas as pd import matplotlib.pyplot as plt Student1={ 'Monthly': [ 'Feb' , 'Apr' , 'June', 'Sep', 'Nov', 'Dec'], 'Eng' : [45,67,78,58,87,89], 'Maths': [55,87,98,88,97,69] } df1=pd.DataFrame(Student1) df1['Total']= df1['Eng']+df1['Maths'] df1['PCT']=df1['Total']/2 plt.scatter('Monthly','PCT', s=50,color='r' , data=df1) plt.xlabel('Monthly Exam') plt.ylabel('Percentage') plt.title('Compare Percentage of Two Students') plt.show( ) 15

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Plotting with categorical variables  It is also possible to create a plot using categorical variables. Matplotlib allows you to pass categorical variables directly to many plotting functions. For example: names = ['group_a', 'group_b', 'group_c'] values = [1, 10, 100] plt.figure(figsize=(9, 3)) plt.subplot(131) plt.bar(names, values) plt.subplot(132) plt.scatter(names, values) plt.subplot(133) plt.plot(names, values) plt.suptitle('Categorical Plotting') plt.show( ) 17

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Controlling line properties  Lines have many attributes that you can set: linewidth, dash style, antialiased, etc; see matplotlib.lines.Line2D. There are several ways to set line properties.  Use keyword args: plt.plot(x, y, linewidth=2.0)  Use the setter methods of a Line2D instance. plot returns a list of Line2D objects; line1= plt.plot(x1, y1). 19

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png  Use the setp( ) command. 1. The example below uses a MATLAB-style command to set multiple properties on a list of lines. 2. setp works transparently with a list of objects or a single object. You can either use python keyword arguments or MATLAB-style string/value pairs: lines = plt.plot(x1, y1) # use keyword args plt.setp(lines, color='r', linewidth=2.0) # or MATLAB style string value pairs plt.setp(lines, 'color', 'r', 'linewidth', 2.0) 20

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Matplotlib: Types of Plots  There are various plots which can be created using python matplotlib. Some of them are listed below: 21

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Scatter Plot  This type of plot shows all individual data points. Here, they aren’t connected with lines.  Each data point has the value of the x-axis value and the value from the y-axis values.  This type of plot can be used to display trends or correlations.  In data science, it shows how 2 variables compare.  To make a scatter plot with Matplotlib, we can use the plt.scatter( ) function.  Again, the first argument is used for the data on the horizontal axis, and the second - for the vertical axis. 22

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example import numpy as np import matplotlib.pyplot as plt # Create data Marks = [89, 90, 70, 89, 100, 80, 90, 100, 80, 34] Marks_Range=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100] colors = 'r' circle_area = 25 contrast=0.9 # has value from 0 - 1 # Plot plt.scatter(Marks,Marks_Range, s=circle_area, c=colors, alpha=contrast) plt.title('Scatter plot - Marks ') plt.xlabel('Marks Range') plt.ylabel('Marks Obtained') plt.show( ) 23

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Scatter plot with groups  Data can be classified in several groups. The code below demonstrates that: import numpy as np import matplotlib.pyplot as plt # Create data g1 = np.array( [ [89, 90, 70, 89, 100, 80, 90, 100, 80, 34], [1,2,3,4,5,6,7,8,9,10]] ) g2 = np.array([ [30, 29, 49, 48, 100, 48, 38, 45, 20, 30], [1,2,3,4,5,6,7,8,9,10]]) g3 = np.array([ [ 69, 90, 70, 59, 70, 80, 40, 80, 55, 24], [1,2,3,4,5,6,7,8,9,10]]) data = (g1, g2, g3) colors = ("red", "green", "blue") groups = ("Red House", "Green House", "Blue House") Code continue… 25

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png # Create plot fig = plt.figure() ax = fig.add_subplot(1,1,1) x, y = g1 ax.scatter(y, x, alpha=0.8, c='red', edgecolors='none', s=30, label='Red House') x, y = g2 ax.scatter(y, x, alpha=0.8, c='green', edgecolors='none', s=30, label='Green House') x, y = g3 ax.scatter(y, x, alpha=0.8, c='blue', edgecolors='none', s=30, label='Blue House') #for data, color, group in zip(data, colors, groups): # x, y = data # ax.scatter(y, x, alpha=0.8, c=color, edgecolors='none', s=30, label=group) plt.title('Matplot scatter plot') plt.legend(loc=2) plt.show() 26

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Bar Chart  Bar chart represents categorical data with rectangular bars. Each bar has a height corresponds to the value it represents.  To make a bar chart with Maplotlib, we’ll need the plt.bar() function. 28

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example import numpy as np import matplotlib.pyplot as plt subjects= ('Python', 'C++', 'Java', 'Perl', 'Scala', 'Lisp') y_pos = np.arange(len(subjects)) performance = [10,8,6,4,2,1] plt.grid(color=‘y', linestyle='--', linewidth=2, axis='y', alpha=0.7) plt.bar(y_pos, performance, align='center', alpha=0.5) plt.xticks(y_pos, subjects) plt.ylabel('Usage') plt.title('Programming language usage') plt.show() 29

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Multiple Bar Chart import numpy as np import matplotlib.pyplot as plt data1 = [23,85, 72, 43, 52] data2 = [42, 35, 21, 16, 9] width =0.3 plt.bar(np.arange(len(data1)), data1, color='r',width=width) plt.bar(np.arange(len(data2))+ width, data2, color='b', width=width) plt.show() 31

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Stack Bar Chart import numpy as np import matplotlib.pyplot as plt data1 = [23,85, 72, 43, 52] data2 = [42, 35, 21, 16, 9] plt.bar(range(len(data1)), data1) plt.bar(range(len(data2)), data2, bottom=data1) plt.show() 33

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Comparing Bar Charts  You can compare two data series using this Matplotlib code: import numpy as np import matplotlib.pyplot as plt # data to plot n_groups = 4 Sales_Voltas = (90, 55, 40, 65) Sales_Samsung = (85, 62, 54, 20) Year_Sales= ('2016', '2017', '2018', '2020') # create plot y_pos= np.arange(n_groups) bar_width = 0.35 opacity = 0.8 Code Continues … 34

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png rects1 = plt.bar(y_pos , Sales_Voltas , bar_width, alpha=opacity, color='b', label='Voltas') rects2 = plt.bar(y_pos + bar_width, Sales_Samsung , bar_width, alpha=opacity, color='g', label='Samsung') plt.xlabel('Year') plt.ylabel('Sales') plt.title('Sales Scores') plt.xticks(y_pos + bar_width, Year_Sales) plt.legend() plt.show() 35

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Pie Charts  Pie chart: a circular plot, divided into slices to show numerical proportion. They are widely used in the business world. import matplotlib.pyplot as plt labels = 'Python', 'C++', 'Ruby', 'Java' sizes = [215, 130, 245, 210] colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue'] explode = (0.1, 0, 0, 0) # explode 1st slice plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140) plt.axis('equal') plt.show() 37

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Add legend location to pie chart  To add a legend use the plt.legend() function: import matplotlib.pyplot as plt labels = ['Cookies', 'Jellybean', 'Milkshake', 'Cheesecake'] sizes = [38.4, 40.6, 20.7, 10.3] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] patches, texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90) plt.legend(patches, labels, loc="best") plt.axis('equal') plt.tight_layout() plt.show() 39

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png  Output: 40 Legend Location best upper right upper left lower left lower right right center left center right lower center upper center center

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Box Plot  A box plot is a good way to show many important features of quantitative (numerical) data.  It shows the median of the data. This is the middle value of the data and one type of an average value.  It also shows the range and the quartiles of the data. This tells us something about how spread out the data is.  Boxplots are a measure of how well distributed the data in a data set is. It divides the data set into three quartiles. This graph represents the minimum, maximum, median, first quartile and third quartile in the data set.  Box plot is another method to view the distribution of data in a numerical column df=pd.read_csv(‘train_loan.csv’) df.boxplot(column=‘ApplicantIncome') 41

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Box Plot 42

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png  The median is the red line through the middle of the 'box'. We can see that this is just above the number 60 on the number line below. So the middle value of age is 60 years.  The left side of the box is the 1st quartile. This is the value that separates the first quarter, or 25% of the data, from the rest. Here, this is 51 years.  The right side of the box is the 3rd quartile. This is the value that separates the first three quarters, or 75% of the data, from the rest. Here, this is 69 years. 43

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Box Plot  The distance between the sides of the box is called the inter-quartile range (IQR). This tells us where the 'middle half' of the values are. Here, half of the winners were between 51 and 69 years.  The ends of the lines from the box at the left and the right are the minimum and maximum values in the data. The distance between these is called the range.  The youngest winner was 17 years old, and the oldest was 97 years old. So the range of the age of winners was 80 years. 44

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png  In the box plot, those points which are out of range are called outliers. We can create the box plot of the data to determine the following: • The number of outliers in a dataset • Is the data skewed or not • The range of the data  Syntax: import matplotlib.pyplot as plt plt.boxplot(data, notch=none, vert=none, patch_artist,widths=none) 45

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png  In the boxplot() function, we have a lot of attributes which can be used to create a more attractive and amazing box plot of the data set. ◼ data: The data should be an array or sequence of arrays which will be plotted. ◼ vert: This attribute accepts a Boolean value. If it is set to true, then the graph will be vertical. Otherwise, it will be horizontal. ◼ position: It accepts the array of integers which defines the position of the box. ◼ widths: It accepts the array of integers which defines the width of the box. ◼ patch_artist: this parameter accepts Boolean values, either true or false, and this is an optional parameter. ◼ labels: This accepts the strings which define the labels for each data point. 46

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example  Example import matplotlib.pyplot as plot age_of_nobel_prize_winner =[40,42,45,55,60,62,70,75,75,77,80,85,200] plot.title("Age of Nobel Prize Winners") plot.boxplot(age_of_nobel_prize_winner,patch_artist=True,labels=['Age']) plot.show() 47

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png  Output : the outlier is showing. 48

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example # Library Import (matplotlib) import matplotlib.pyplot as plot India=[55,65,75,80,85] America =[40,42,45,55,60] China =[25,30,32,35,40] Russia =[60,67,69,72,75] Japan =[20,23,28,35,38] box_plot_data=[India,America,China,Russia,Japan] plot.title("Age of Nobel Prize Winners of different countries") plot.boxplot(box_plot_data,patch_artist=True,labels=['India','America','China','Russia','Japan']) plot.show() 49

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Assignment  Create a simple line graph to show the relationship between two lists:x = [1, 2, 3, 4, 5] and y = [2, 4, 6, 8, 10]. Label the x-axis as "X Values" and the y-axis as "Y Values". Add a title to your graph.  Using the following data, create a bar chart that shows the number of students in different grades:grades = ['A', 'B', 'C', 'D', 'F'] and students = [12, 18, 25, 10, 5]. Label the x-axis as "Grades" and the y-axis as "Number of Students". Add appropriate titles and labels.  Plot a scatter graph to show the following points:x = [10, 20, 30, 40, 50] and y = [5, 15, 25, 35, 45]. Customize the points to appear as red stars and give the graph a title.  Create a pie chart to show the market share of 4 companies:companies = ['Company A', 'Company B', 'Company C', 'Company D'] and market_share = [30, 25, 20, 25]. Add a legend and customize the colors of each section.  Generate a histogram using the following data to represent the age distribution in a group:ages = [23, 45, 56, 67, 34, 45, 29, 39, 50, 60, 72, 32]. Set the number of bins to 5 and label the axes accordingly. 51

Matplotlib,Python, Machine Learning, AI,ML

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