Here is my code:
import codecademylib3
from sklearn import preprocessing
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
load in financial data
financial_data = pd.read_csv(‘financial_data.csv’)
code goes here
print(financial_data.head())
month = financial_data[‘Month’]
revenue = financial_data[‘Revenue’]
expenses = financial_data[‘Expenses’]
plt.plot(month,revenue)
plt.xlabel(‘Month’)
plt.ylabel(‘Amount ($)’)
plt.title(‘Revenue’)
plt.show()
plt.clf()
plt.plot(month,expenses)
plt.xlabel(‘Month’)
plt.ylabel(‘Amount ($)’)
plt.title(‘Expenses’)
plt.show()
expense_overview = pd.read_csv(‘expenses.csv’)
print(expense_overview.head(10))
expense_categories = expense_overview[‘Expense’]
proportions = expense_overview[‘Proportion’]
plt.clf()
plt.pie(proportions, labels = expense_categories)
plt.title(‘Expense Categories’)
plt.axis(‘Equal’)
plt.tight_layout()
plt.show()
expense_categories = [‘Salaries’, ‘Advertising’, ‘Office Rent’, ‘Other’]
proportions = [0.62, 0.15, 0.15, 0.08]
plt.clf()
plt.pie(proportions, labels = expense_categories)
plt.title(‘Expense Categories’)
plt.axis(‘Equal’)
plt.tight_layout()
plt.show()
expense_cut = max(expense_categories)
print("If the company wants to cut costs in a big way, they should focus on " + expense_cut)
employees = pd.read_csv(‘employees.csv’)
print(employees.head())
sorted_productivity = employees.sort_values(by=[‘Productivity’])
print(sorted_productivity)
employees_cut = sorted_productivity.head(100)
print(employees_cut)
transformation = “Sarah should transform the data by using standardization”
print(transformation)
commute_times = employees[‘Commute Time’]
print(commute_times.describe())
commute_times_log = np.log(commute_times)
plt.clf()
plt.hist(commute_times_log)
plt.title(“Employee Commute Times”)
plt.xlabel(“Commute Time”)
plt.ylabel(“Frequency”)
plt.show()
