Create charts in Python Pandas for Sales Data Part2

import matplotlib.pyplot as plt

import pandas as pd

import matplotlib.ticker as ticker

# Load the data from the CSV file

data_path = "E:/My Learning Stuff/AI/ML/sales_data.csv"

df = pd.read_csv(data_path)

df['Date'] = pd.to_datetime(df['Date'], format='%d/%m/%Y %H:%M')



# Extract year from 'Date'

df['Year'] = df['Date'].dt.year



# Group by Year for Bar Chart

yearly_sales = df.groupby('Year')['Sales_Amount'].sum()



# Group by Region for Pie Chart

region_sales = df.groupby('Region')['Sales_Amount'].sum()



# Create a figure and two subplots

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))



# Bar chart for Yearly Sales

bars = ax1.bar(yearly_sales.index, yearly_sales.values, color='b')

ax1.set_title('Yearly Sales Amount')

ax1.set_xlabel('Year')

ax1.set_ylabel('Sales Amount')



# Adding value labels to each bar

for bar in bars:

    height = bar.get_height()

    ax1.annotate(f'{height:.2f}',

                 xy=(bar.get_x() + bar.get_width() / 2, height),

                 xytext=(0, 3),  # 3 points vertical offset

                 textcoords="offset points",

                 ha='center', va='bottom')



# Format x-axis to display years as integers

ax1.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: int(x)))



# Pie chart for Sales Distribution by Year

ax2.pie(yearly_sales, labels=[str(year) for year in yearly_sales.index], autopct='%1.1f%%')

ax2.set_title('Sales Distribution by Year')



# Adjust layout

plt.tight_layout()



plt.show()