Charts and Graphs Data Preparation Prompt

import pandas as pd
import matplotlib.pyplot as plt
Load the dataset
data = pd.read_csv("SalesData.csv")
Data preparation
Aggregating sales data by month
monthly_sales = data.groupby("Month")["Sales"].sum()
Data cleaning and handling missing values
monthlysales = monthlysales[monthlysales > 0]  # Remove negative values
monthlysales.fillna(monthly_sales.mean(), inplace=True)  # Fill missing values with the monthly average
Data aggregation for quarterly summarization
quarterlysales = monthlysales.resample('Q').sum()
Data labeling
monthlysales.index = monthlysales.index.strftime("%b %Y")  # Format month names
quarterlysales.index = quarterlysales.index.strftime("Q%q %Y")  # Format quarter names
Data scaling (Min-Max scaling)
monthlysales = (monthlysales - monthlysales.min()) / (monthlysales.max() - monthly_sales.min())
Data visualization
plt.figure(figsize=(10, 6))
plt.bar(monthlysales.index, monthlysales.values, label="Monthly Sales")
plt.bar(quarterlysales.index, quarterlysales.values, label="Quarterly Sales", alpha=0.5)
Labeling axes
plt.xlabel("Time")
plt.ylabel("Total Sales (USD)")
plt.xticks(rotation=45)
plt.legend()
plt.title("Monthly and Quarterly Sales Trends")
Show the bar chart
plt.tight_layout()
plt.show()
This code prepares, cleans, and visualizes the sales data using a bar chart, with monthly and quarterly aggregation.