Plotting in Jupyter Notebook

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

Section 1: Plotting Math functions

#directions: plot two math functions together in the same plot, using two different colors and different line styles, and label the plots, axis. 
x = np.linspace(-2, 2, 100)
y1 = np.sin(x)     #two math functions
y2 = x**3

plt.plot(x, y1, color='orange', linestyle='-.', label='y = sin(x)')     #different line colors, styles, labels
plt.plot(x, y2, color='purple', linestyle=':', label='y = x³')

plt.title('y = sin(x) with y = x³')
plt.xlabel('x-axis')     #axis
plt.ylabel('y-axis')

plt.legend()

plt.show()

Section 2: Making Pie Charts of Data in Excel

male_new_cases = pd.read_excel(r"C:\Users\QBPAM\Downloads\BigData AI Cancer class by Yongmei Wang\Group-Training-Male-Cancer-statistics.xlsx", sheet_name = "Estimated New Cases")
male_new_cases

	Unnamed: 0	Male	%
0	Prostate	299010	29
1	Lung & bronchus	116310	11
2	Colon & rectum	81540	8
3	Urinary bladder	63070	6
4	Melanoma of the skin	59170	6
5	Kidney & renal pelvis	52380	5
6	Non-Hodgkin lymphoma	44590	4
7	Oral cavity & pharynx	41510	4
8	Leukemia	36450	4
9	Pancreas	34530	3

categories = male_new_cases['Unnamed: 0']
sizes = male_new_cases['%']

plt.figure(figsize = (8,8))
plt.pie(sizes, labels = categories, autopct = '%1.1f%%', startangle = 90)   #autopct formats the numbers
plt.axis('equal')    #makes the circle perfectly round
plt.title('Statistics of Estimated New Male Cases')
plt.show()

male_deaths = pd.read_excel(r"C:\Users\QBPAM\Downloads\BigData AI Cancer class by Yongmei Wang\Group-Training-Male-Cancer-statistics.xlsx", sheet_name = "Estimated Deaths")
male_deaths

	Unnamed: 0	Male	%
0	Lung & bronchus	65790	20
1	Prostate	35250	11
2	Colon & rectum	28700	9
3	Pancreas	27270	8
4	Liver & intrahepatic bile duct	19120	6
5	Leukemia	13640	4
6	Esophagus	12880	4
7	Urinary bladder	12290	4
8	Non-Hodgkin lymphoma	11780	4
9	Brain & other nervous system	10690	3

categories1 = male_deaths['Unnamed: 0']
sizes1 = male_deaths['%']

plt.figure(figsize = (8,8))
plt.pie(sizes1, labels = categories1, autopct = '%1.1f%%', startangle = 90)
plt.axis('equal')
plt.title('Statistics of Estimated Male Deaths')
plt.show()

female_new_cases = pd.read_excel(r"C:\Users\QBPAM\Downloads\BigData AI Cancer class by Yongmei Wang\Female-Cancer-Statistics.xlsx", sheet_name = "Estimated New Cases")
female_new_cases

	Unnamed: 0	Female	%
0	Breast	310720	32
1	Lung & bronchus	118270	12
2	Colon & rectum	71270	7
3	Uterine corpus	67880	7
4	Melanoma of the skin	41470	4
5	Non-Hodgkin lymphoma	36030	4
6	Pancreas	31910	3
7	Thyroid	31520	3
8	Kidney & renal pelvis	29230	3
9	Leukemia	26320	3

categories2 = female_new_cases['Unnamed: 0']
sizes2 = female_new_cases['%']

plt.figure(figsize = (8,8))
plt.pie(sizes2, labels = categories2, autopct = '%1.1f%%', startangle = 90)
plt.axis('equal')
plt.title('Statistics of Estimated New Female Cases')
plt.show()

female_deaths = pd.read_excel(r"C:\Users\QBPAM\Downloads\BigData AI Cancer class by Yongmei Wang\Female-Cancer-Statistics.xlsx", sheet_name = "Estimated Deaths")
female_deaths

	Unnamed: 0	Female	%
0	Lung & bronchus	59280	21
1	Breast	42250	15
2	Pancreas	24480	8
3	Colon & rectum	24310	8
4	Uterine corpus	13250	5
5	Ovary	12740	4
6	Liver & intrahepatic bile duct	10720	4
7	Leukemia	10030	3
8	Non-Hodgkin lymphoma	8360	3
9	Brain & other nervous system	8070	3

categories3 = female_deaths['Unnamed: 0']
sizes3 = female_deaths['%']

plt.figure(figsize = (8,8))
plt.pie(sizes3, labels = categories3, autopct = '%1.1f%%', startangle = 90)
plt.axis('equal')
plt.title('Statistics of Estimated Female Deaths')
plt.show()