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Informatics Practices

Given a DataFrame that stores the details of past 25 years' monthly sales. Some old data is however missing. Write a script to calculate average :

  • Monthly sales across years

  • Yearly sales

Make sure that missing values do not hamper the overall result.

Python Pandas

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Answer

import pandas as pd
data = {
    'Year': [],
    'Month': [],
    'Sales': []
}

n = int(input("Enter the number of rows in the DataFrame: "))
for i in range(n):
    year = int(input("Enter the year: "))
    month = int(input("Enter the month (1-12): "))
    sales = float(input("Enter the sales amount: "))
    data['Year'].append(year)
    data['Month'].append(month)
    data['Sales'].append(sales)

df = pd.DataFrame(data)

df.fillna(0, inplace=True)

avg_monthly_sales = df.groupby('Month')['Sales'].mean()

yearly_sales = df.groupby('Year')['Sales'].mean()

print("Average Monthly Sales Across Years:")
print(avg_monthly_sales)
print("\nYearly Sales:")
print(yearly_sales)

Output

Average Monthly Sales Across Years:
Month
1     543.25
2     644.25
3     486.50
4     583.50
5     376.50
6     644.75
7     847.00
8     497.75
9     564.50
10    701.00
11    524.75
12    522.25
Name: Sales, dtype: float64

Yearly Sales:
Year
2000    781.082333
2001    480.030233
2002    390.530000
2003    682.917767
2004    421.250050
2005    408.333333
2006    509.757500
2007    634.333667
2008    907.250000
2009    876.222222
2010    940.757578
2011    690.900067
2012    623.250043
2013    890.088888
2014    491.500000
2015    601.919997
2016    623.250055
2017    489.086633
2018    523.750000
2019    789.910067
2020    456.250000
2021    481.083333
2022    590.750000
2023    618.916667
2024    621.250000
Name: Sales, dtype: float64

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