Matplotlib is a popular Python plotting library that plays a major role in subjects like data science, machine learning, etc. Visualization gives us access to huge amounts of data in easily digestible visuals.
pip install matplotlib
import matplotlib.pyplot as plt`
# importing required libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt # Generation of variables x=np.arange(0,10) #Array of range 0 to 9 y=x**3 # Printing the variables print(x) print(y) plt.plot(x,y) # Function to plot plt.title('Line Chart') # Function to give title # Functions to give x and y labels plt.xlabel('X-Axis') plt.ylabel('Y-Axis') # Functionn to show the graph plt.show()
# importing required libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt # Generation of 1 set of variables x = np.arange(0,11) y = x**3 # Generation of 1 set of variables x2 = np.arange(0,11) y2 = (x**3)/2 # Printing all variables print(x,y,x2,y2,sep="\n") # "linewidth" is used to specify the width of the lines # "color" is used to specify the colour of the lines # "label"is used to specify the name of axes to represent in the lengend plt.plot(x,y,color='r',label='first data', linewidth=5) plt.plot(x2,y2,color='y',linewidth=5,label='second data') plt.title('Multiline Chart') # Uses the label attribute to display reference in legend plt.ylabel('Y axis') plt.xlabel('X axis') # Shows the legend in the best postion with respect to the graph plt.legend() plt.show()
# Importing required libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt # Generation of variables x = ["India",'USA',"Japan",'Australia','Italy'] y = [6,7,8,9,2] # Printing the variables print(x) print(y) plt.bar(x,y, label='Bars1', color ='r') # Function to plot # Function to give x and y labels plt.xlabel("Country") plt.ylabel("Inflation Rate%") # Function to give heading of the chart plt.title("Bar Graph") # Function to show the chart plt.show()
# importing required libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt # Generation of 1 set of variables x = ["India",'USA',"Japan",'Australia','Italy'] y = [6,7,8,9,5] # Generation of 2 set of variables x2 = ["India",'USA',"Japan",'Australia','Italy'] y2 = [5,1,3,4,2] # Printing all variables print(x,y,x2,y2,sep="\n") # Functions to plot plt.bar(x,y, label='Inflation', color ='y') plt.bar(x2,y2, label='Growth', color ='g') # Functions to give x and y labels plt.xlabel("Country") plt.ylabel("Inflation & Growth Rate%") plt.title("Multiple Bar Graph") plt.legend() plt.show()
import numpy as np import pandas as pd import matplotlib.pyplot as plt # Generation of variable stock_prices = [32,67,43,56,45,43,42,46,48,53,73,55,54,56,43,55,54,20,33,65,62,51,79,31,27] # Function to show the chart plt.figure(figsize = (8,5)) plt.hist(stock_prices, bins = 5)
# Importing required libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt # Generation of x and y variables x = [1,2,3,4,5,6,7,8] y = [5,2,4,2,1,4,5,2] # Function to plot the graph plt.scatter(x,y) plt.xlabel('x') plt.ylabel('y') plt.title('Scatter Plot')
# Importing required libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt # Collection of raw data raw_data={'names':['Nick','Sani','John','Rubi','Maya'], 'jan_score':[123,124,125,126,128], 'feb_score':[23,24,25,27,29], 'march_score':[3,5,7,6,9]} # Segregating the raw data into usuable form/variables df=pd.DataFrame(raw_data,columns=['names','jan_score','feb_score','march_score']) df['total_score']=df['jan_score']+df['feb_score']+df['march_score'] # Printing the data print(df) # Function to plot the graph plt.pie(df['total_score'],labels=df['names'],autopct='%.2f%%') plt.axis('equal') plt.axis('equal') plt.show()
# Importing required libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt # Defining the sixe og the figures plt.figure(figsize=(10,10)) # Generation of variables x = np.array([1,2,3,4,5,6,7,8]) y = np.array([5,2,4,2,1,4,5,2]) # Generating 4 subplots in form of 2x2 matrix # In the line below the arguments of plt.subplot are as follows: # 2- no. of rows # 2- no. of columns # 1- position in matrix # Position (0,0) plt.subplot(2,2,1) plt.plot(x,y,'g') plt.title('Sub Plot 1') plt.xlabel('X-Axis') plt.ylabel('Y-Axis') # Position (0,1) plt.subplot(2,2,2) plt.plot(y,x,'b') plt.title('Sub Plot 2') plt.xlabel('X-Axis') plt.ylabel('Y-Axis') # Position (1,0) plt.subplot(2,2,3) plt.plot(y*2,x*2,'y') plt.title('Sub Plot 3') plt.xlabel('X-Axis') plt.ylabel('Y-Axis') # Position (1,1) plt.subplot(2,2,4) plt.plot(x*2,y*2,'m') plt.title('Sub Plot 4') plt.xlabel('X-Axis') plt.ylabel('Y-Axis') # Function for layout and spacing plt.tight_layout(h_pad=5, w_pad=10)
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