Python code for Graphs

To import dataset from google drive (for single dataset)




import numpy as np

import pandas as pd

from google.colab import drive

drive.mount('/content/drive')

%cd /content/drive/My Drive/

df = pd.read_csv('dataset.csv')

df

To import dataset from google drive (for multiple datasets)




import numpy as np

import pandas as pd

from google.colab import drive

drive.mount('/content/drive')

%cd /content/drive/My Drive/

directory = "foldername"

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Code 1 Scatter dots




# import modules

from bokeh.plotting import figure, output_notebook, show

  

# output to notebook

output_notebook()

  

# create figure

p = figure(plot_width = 400, plot_height = 400)

  

# add a circle renderer with

# size, color and alpha

p.circle([1, 2, 3, 4, 5], [4, 7, 1, 6, 3], 

         size = 10, color = "navy", alpha = 0.5)

  

# show the results

show(p) 

Code 2 line graph

# import modules

from bokeh.plotting import figure, output_notebook, show

  

# output to notebook

output_notebook()

  

# create figure

p = figure(plot_width = 400, plot_height = 400)

   

# add a line renderer

p.line([1, 2, 3, 4, 5], [3, 1, 2, 6, 5], 

        line_width = 2, color = "green")

  

# show the results

show(p)







Code 3 bar graph




# import necessary modules

import pandas as pd

from bokeh.charts import Bar, output_notebook, show

  

# output to notebook

output_notebook()

  

# read data in dataframe

df = pd.read_csv(r"D:/kaggle/mcdonald/menu.csv")

  

# create bar

p = Bar(df, "Category", values = "Calories",

        title = "Total Calories by Category", 

                        legend = "top_right")

  

# show the results

show(p)




Code 4 box plot graph

# import necessary modules

from bokeh.charts import BoxPlot, output_notebook, show

import pandas as pd

  

# output to notebook

output_notebook()

  

# read data in dataframe

df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv")

  

# create bar

p = BoxPlot(df, values = "Protein", label = "Category", 

            color = "yellow", title = "Protein Summary (grouped by category)",

             legend = "top_right")

  

# show the results

show(p)










Code 5 Histogram graph




# import necessary modules

from bokeh.charts import Histogram, output_notebook, show

import pandas as pd

  

# output to notebook

output_notebook()

  

# read data in dataframe

df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv")

  

# create histogram

p = Histogram(df, values = "Total Fat",

               title = "Total Fat Distribution", 

               color = "navy")

  

# show the results

show(p) 







Code 6 scatter plot

# import necessary modules

from bokeh.charts import Scatter, output_notebook, show

import pandas as pd

  

# output to notebook

output_notebook()

  

# read data in dataframe

df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv")

  

# create scatter plot

p = Scatter(df, x = "Carbohydrates", y = "Saturated Fat",

            title = "Saturated Fat vs Carbohydrates",

            xlabel = "Carbohydrates", ylabel = "Saturated Fat",

            color = "orange")

   

# show the results

show(p)







Dataset 1

 id product unit

1 apple 11

2 peas 22

3 grapes 33

4 bananas 44

import numpy as np

import pandas as pd

from google.colab import drive

drive.mount('/content/drive')

%cd /content/drive/My Drive/

df = pd.read_csv('dataset.csv')

df

Dataset 2

Company name	Market capital	PE ratio	Earnings per share	Dividend yield	Sector P/E	Price to Book	Debt to equity	Return on equity	Price to sales
0	Adani wilmar	672322	97.79	5.29	0.00	55.42	8.58	0.40	8.79	1.15
1	Happiest Minds	12432	55.15	15.35	0.43	26.60	16.65	0.53	29.06	9.20
2	Route mobile	7525	27.76	43.51	0.42	26.60	4.53	0.01	14.04	2.36
3	Avenue supermart	230834	98.44	36.20	0.00	89.37	15.38	0.05	15.38	5.63
4	Paras defence tech	2090	62.20	8.59	0.00	4.25	5.94	0.09	8.52	10.24
5	Laxmi organic	7236	45.87	5.95	0.26	27.11	5.34	0.19	15.69	2.46
6	KPIT	19131	60.21	11.59	0.44	26.60	13.70	0.13	22.83	7.07

Python Code for Charts

a simple graph

1. from matplotlib import pyplot as plt    
2. #ploting our canvas    
3. plt.plot([1,2,3],[4,5,1])    
4. #display the graph    
5. plt.show()  

Line Graph

1. from matplotlib import pyplot as plt    
2.     
3. x = [1,2,3]    
4. y = [10,11,12]    
5.     
6. plt.plot(x,y)    
7.     
8. plt.title("Line graph")    
9. plt.ylabel('Y axis')    
10. plt.xlabel('X axis')    
11. plt.show()    

Bar graph

1. from matplotlib import pyplot as plt    
2. Names = ['Arun','James','Ricky','Patrick']    
3. Marks = [51,87,45,67]    
4. plt.bar(Names,Marks,color = 'blue')    
5. plt.title('Result')    
6. plt.xlabel('Names')    
7. plt.ylabel('Marks')    
8. plt.show()   

Pie chart

1. from matplotlib import pyplot as plt    
2.     
3. # Pie chart, where the slices will be ordered and plotted counter-clockwise:    
4. Aus_Players = 'Smith', 'Finch', 'Warner', 'Lumberchane'    
5. Runs = [42, 32, 18, 24]    
6. explode = (0.1, 0, 0, 0)  # it "explode" the 1st slice     
7.     
8. fig1, ax1 = plt.subplots()    
9. ax1.pie(Runs, explode=explode, labels=Aus_Players, autopct='%1.1f%%',    
10.         shadow=True, startangle=90)    
11. ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.    
12.     
13. plt.show()    

Histogram

1. from matplotlib import pyplot as plt    
2. from matplotlib import pyplot as plt    
3. percentage = [97,54,45,10, 20, 10, 30,97,50,71,40,49,40,74,95,80,65,82,70,65,55,70,75,60,52,44,43,42,45]    
4. number_of_student = [0,10,20,30,40,50,60,70,80,90,100]    
5. plt.hist(percentage, number_of_student, histtype='bar', rwidth=0.8)    
6. plt.xlabel('percentage')    
7. plt.ylabel('Number of people')    
8. plt.title('Histogram')    
9. plt.show()  

Scatter Plot

1. from matplotlib import pyplot as plt    
2. from matplotlib import style    
3. style.use('ggplot')    
4.     
5. x = [4,8,12]    
6. y = [19,11,7]    
7.     
8. x2 = [7,10,12]    
9. y2 = [8,18,24]    
10.     
11. plt.scatter(x, y)    
12.     
13. plt.scatter(x2, y2, color='g')    
14.     
15. plt.title('Epic Info')    
16. plt.ylabel('Y axis')    
17. plt.xlabel('X axis')    
18.     
19. plt.show()    

1.