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Data Analytics

BoxPlot Company Analysis

BoxPlot
import matplotlib.pyplot as plt
import numpy as np

# Sample data
data = [15, 20, 25, 30, 35, 40, 100] # 100 is an outlier

plt.boxplot(data,notch=True,patch_artist=True)
plt.title("Basic Box Plot")
plt.ylabel("Values")

plt.show()
Compare Companies 
if __name__ == "__main__":
    import pandas as pd

    data = {
        'Company': ['A', 'B'],
        'Revenue': [100000, 120000],
        'Profit': [20000, 25000],
        'Assets': [150000, 180000],
        'Equity': [80000, 90000]
    }

    df = pd.DataFrame(data)

    df['Profit Margin'] = df['Profit'] / df['Revenue']
    df['ROA'] = df['Profit'] / df['Assets']
    df['ROE'] = df['Profit'] / df['Equity']

    print(df)
Company Treand analysis 
import pandas as pd
import matplotlib.pyplot as plt
if __name__== "__main__":

#Trend Analysis (Revenue Growth)
    data = {
        'Year': [2020, 2021, 2022, 2023],
        'Revenue': [80000, 90000, 120000, 150000]
    }
    df = pd.DataFrame(data)
    # Calculate growth rate
    df['Growth Rate'] = df['Revenue'].pct_change()
    print(df)
#Expense Analysis (Cost Control)
    data = {
        'Revenue': [100000, 120000, 140000],
        'Expenses': [70000, 90000, 110000]
    }

    df = pd.DataFrame(data)
    df['Expense Ratio'] = df['Expenses'] / df['Revenue']
    print(df)
#Profit Trend Visualization
    years = [2020, 2021, 2022, 2023]
    profit = [10000, 15000, 20000, 18000]

    plt.plot(years, profit, marker='o')
    plt.title("Profit Trend")
    plt.xlabel("Year")
    plt.ylabel("Profit")
    plt.show()
#Break-even Analysis
    fixed_cost = 50000
    price_per_unit = 20
    variable_cost_per_unit = 10
    break_even_units = fixed_cost / (price_per_unit - variable_cost_per_unit)
    print("Break-even units:", break_even_units)
#Moving Average (Stock Analysis)
#Smooth out stock price fluctuations
    prices = [100, 105, 102, 110, 115]
    df = pd.DataFrame({'Price': prices})
    df['Moving Average'] = df['Price'].rolling(window=3).mean()
    print(df)
#Correlation Analysis
    data = {
        'Revenue': [100, 120, 140, 160],
        'Profit': [20, 25, 30, 35]
    }
    df = pd.DataFrame(data)
    correlation = df['Revenue'].corr(df['Profit'])
    print("Correlation:", correlation)
#Cash Flow Analysis
    cash_in = [50000, 60000, 55000]
    cash_out = [40000, 45000, 50000]
    net_cash_flow = [i - o for i, o in zip(cash_in, cash_out)]
    print("Net Cash Flow:", net_cash_flow)