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Analysis_of_Sales_Data.py
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Analysis_of_Sales_Data.py
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"""
@author: Kianoush
GitHUb: https://github.com/Kianoush-h
YouTube: https://www.youtube.com/channel/UCvf9_53f6n3YjNEA4NxAkJA
LinkedIn: https://www.linkedin.com/in/kianoush-haratiannejadi/
Email: haratiank2@gmail.com
"""
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
# Load all CSV files into a list of DataFrames
file_path = 'data/sales_data_sample.csv'
# encoding = "utf-8"
encoding = "ISO-8859-1"
raw_data = pd.read_csv(file_path, encoding = encoding)
print(raw_data.shape)
# Display the first few rows
head = raw_data.head(5)
# Display the last few rows
raw_data.tail(5)
# Basic statistics of numerical columns
temp = raw_data.describe()
categorical_stats = raw_data.describe(include=['object'])
raw_data.info()
# raw_data.dropna(inplace=True)
raw_data['ORDERDATE'] = pd.to_datetime(raw_data['ORDERDATE'], infer_datetime_format=True)
# raw_data['year'] = raw_data['date_of_infraction'].dt.year
# raw_data['month'] = raw_data['date_of_infraction'].dt.month
raw_data['day_of_week'] = raw_data['ORDERDATE'].dt.dayofweek
# =============================================================================
# Data Visualization
# =============================================================================
raw_data.isnull().sum()/len(raw_data)*100
# =============================================================================
# PART 1
# =============================================================================
# Graph 1
month_name_mapping = {1: 'January', 2: 'February', 3: 'March', 4: 'April', 5: 'May', 6: 'June', 7: 'July', 8: 'August', 9: 'September', 10: 'October', 11: 'November', 12: 'December'}
raw_data['month_name'] = raw_data['MONTH_ID'].map(month_name_mapping)
months_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
raw_data['month_name'] = pd.Categorical(raw_data['month_name'], categories=months_order, ordered=True)
# Use a color palette from seaborn
sns.set_palette("viridis")
# Create the first subplot (top)
plt.figure(figsize=(12, 12))
plt.subplot(2, 1, 1)
# Line plot for Temporal Trends by Months with sorted month names
sns.lineplot(data=raw_data.groupby('month_name')['SALES'].sum().sort_index(), marker='o', linestyle='-')
# Labels and titles for the first subplot
plt.title('SALES Over the Months', fontsize=16)
plt.xlabel('Month', fontsize=14)
plt.ylabel('SALES', fontsize=14)
plt.grid(True)
# Data labels for the first subplot
for index, value in enumerate(raw_data.groupby('month_name')['SALES'].sum().sort_index()):
plt.text(index, value + 1, str(value), ha='center', va='bottom')
# Graph 2
# Use a color palette from seaborn
sns.set_palette("viridis")
# Create the second subplot (bottom)
plt.subplot(2, 1, 2)
# Line plot for Temporal Trends by Months with sorted month names
sns.lineplot(data=raw_data.groupby('month_name')['QUANTITYORDERED'].sum().sort_index(), marker='o', linestyle='-')
# Labels and titles for the second subplot
plt.title('QUANTITY ORDERED Over the Months', fontsize=16)
plt.xlabel('Month', fontsize=14)
plt.ylabel('QUANTITY ORDERED', fontsize=14)
plt.grid(True)
# Data labels for the second subplot
for index, value in enumerate(raw_data.groupby('month_name')['QUANTITYORDERED'].sum().sort_index()):
plt.text(index, value + 1, str(value), ha='center', va='bottom')
# Adjust layout to prevent clipping of titles and labels
plt.tight_layout()
# Show the plot
plt.show()
# =============================================================================
# PART 1-2
# =============================================================================
# Use a color palette from seaborn
sns.set_palette("pastel")
# Bar plot for Temporal Trends by Day of the Week with sorted day names
plt.figure(figsize=(12, 6))
sns.set_style("whitegrid") # Add grid lines
df = raw_data.groupby('YEAR_ID')['SALES'].sum().sort_index().plot(kind='bar')
# Labels and titles
plt.title('SALES Over the Years', fontsize=16)
plt.xlabel('Years', fontsize=14)
plt.ylabel('SALES', fontsize=14)
# Legend
plt.legend(['SALES'], loc='upper right')
# Data labels
for index, value in enumerate(raw_data.groupby('YEAR_ID')['SALES'].sum().sort_index()):
plt.text(index, value + 1, f'{value / 1e6:.2f}M', ha='center', va='bottom')
plt.grid(True)
plt.show()
# =============================================================================
# PART 1-3
# =============================================================================
# Use a color palette from seaborn
sns.set_palette("pastel")
# Bar plot for Temporal Trends by Day of the Week with sorted day names
plt.figure(figsize=(12, 6))
sns.set_style("whitegrid") # Add grid lines
df = raw_data.groupby('COUNTRY')['SALES'].sum().sort_index().plot(kind='bar')
# Labels and titles
plt.title('SALES by Countries', fontsize=16)
plt.xlabel('Countries', fontsize=14)
plt.ylabel('SALES', fontsize=14)
# Legend
# plt.legend(['SALES'], loc='upper right')
# Data labels
for index, value in enumerate(raw_data.groupby('COUNTRY')['SALES'].sum().sort_index()):
plt.text(index, value + 1, f'{value / 1e6:.2f}M', ha='center', va='bottom')
plt.grid(True)
plt.show()
# =============================================================================
# PART 1-4
# =============================================================================
unique_years = raw_data['YEAR_ID'].unique()
temp = pd.DataFrame()
# Create subplots for each year
for i, year in enumerate(unique_years):
year_data = raw_data[raw_data['YEAR_ID'] == year]
year_data = year_data.groupby('COUNTRY')['SALES'].sum()
new_data = year_data
new_data = pd.DataFrame(new_data.reset_index())
new_data['YEAR_ID'] = year
temp = pd.concat([temp,new_data], ignore_index=True)
plt.figure(figsize=(20,8))
plt.title("Year Revenue by Country") # add title
a= sns.barplot(x='COUNTRY',y='SALES',data=temp,hue='YEAR_ID',errorbar=None,palette=color)
plt.show()
# =============================================================================
# PART 2
# =============================================================================
# Map day_of_week numerical values to day names and sort them
day_name_mapping = {0: 'Monday', 1: 'Tuesday', 2: 'Wednesday', 3: 'Thursday', 4: 'Friday', 5: 'Saturday', 6: 'Sunday'}
raw_data['day_name'] = raw_data['day_of_week'].map(day_name_mapping)
# Sort the days of the week
days_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
raw_data['day_name'] = pd.Categorical(raw_data['day_name'], categories=days_order, ordered=True)
# Use a color palette from seaborn
sns.set_palette("pastel")
# Bar plot for Temporal Trends by Day of the Week with sorted day names
plt.figure(figsize=(12, 6))
sns.set_style("whitegrid") # Add grid lines
raw_data.groupby('day_name')['SALES'].sum().sort_index().plot(kind='bar')
# Labels and titles
plt.title('SALES Over the Days of the Week', fontsize=16)
plt.xlabel('Day of the Week', fontsize=14)
plt.ylabel('SALES', fontsize=14)
# Legend
plt.legend(['SALES'], loc='upper right')
# Data labels
for index, value in enumerate(raw_data.groupby('day_name')['SALES'].sum().sort_index()):
plt.text(index, value + 1, str(value), ha='center', va='bottom')
plt.grid(True)
plt.show()
# =============================================================================
# PART 3
# =============================================================================
# Adjust the number of top descriptions to show (e.g., top 10)
top_n = 10
top_descriptions = raw_data['PRODUCTCODE'].value_counts().nlargest(top_n).index
# Use a color palette from seaborn
sns.set_palette("pastel")
plt.figure(figsize=(12, 6))
sns.set_style("whitegrid") # Add grid lines
# Filter data for top PRODUCTCODE
filtered_data = raw_data[raw_data['PRODUCTCODE'].isin(top_descriptions)]
# Bar plot for sales by Top PRODUCTCODE
filtered_data.groupby('PRODUCTCODE')['SALES'].sum().plot(kind='bar')
# Labels and titles
plt.title('SALES by Top PRODUCTCODE', fontsize=16)
plt.xlabel('PRODUCTCODE', fontsize=14)
plt.ylabel('SALES', fontsize=14)
# Rotate x-axis labels for better readability
plt.xticks(rotation=45, ha='right')
# Data labels
for index, value in enumerate(filtered_data.groupby('PRODUCTCODE')['SALES'].sum()):
plt.text(index, value + 1, str(value), ha='center', va='bottom')
plt.show()
# =============================================================================
# PART 4
# =============================================================================
# Adjust the number of top descriptions to show (e.g., top 10)
top_n = 20
top_descriptions = raw_data['PRODUCTLINE'].value_counts().nlargest(top_n).index
# Use a color palette from seaborn
sns.set_palette("pastel")
plt.figure(figsize=(12, 6))
sns.set_style("whitegrid") # Add grid lines
# Filter data for top PRODUCTCODE
filtered_data = raw_data[raw_data['PRODUCTLINE'].isin(top_descriptions)]
# Bar plot for sales by Top PRODUCTCODE
filtered_data.groupby('PRODUCTLINE')['SALES'].sum().plot(kind='bar')
# Labels and titles
plt.title('SALES by Top PRODUCTLINE', fontsize=16)
plt.xlabel('PRODUCTLINE', fontsize=14)
plt.ylabel('SALES', fontsize=14)
# Rotate x-axis labels for better readability
plt.xticks(rotation=45, ha='right')
# Data labels
for index, value in enumerate(filtered_data.groupby('PRODUCTLINE')['SALES'].sum()):
plt.text(index, value + 1, str(value), ha='center', va='bottom')
plt.show()
# =============================================================================
# PART 5
# =============================================================================
# Calculate the ticket counts for each combined location
if len(set(raw_data['STATUS'])) < 10:
top_x = len(set(raw_data['STATUS']))
else:
top_x = 10
status = raw_data['STATUS'].value_counts().nlargest(top_x)
# Use a color palette from seaborn
colors = sns.color_palette("pastel")
# Explode and Shadow
explode = (0.1,) + tuple(0 for i in range( top_x-1)) # Highlight the first slice
shadow = True
# Plotting a pie chart for the top 10
plt.figure(figsize=(10, 10))
plt.pie(status, labels=status.index, autopct='%1.1f%%', startangle=140, colors=colors, explode=explode, shadow=shadow)
# Title and labels
plt.title(f'Top {top_x} Status', fontsize=16)
plt.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle
# Legend
# plt.legend(location_counts.index, title='Locations', loc='upper right')
plt.show()
# =============================================================================
# PART 6
# =============================================================================
# Calculate correlation matrix
correlation_matrix = raw_data.corr()
# Plot heatmap
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix')
plt.show()
# =============================================================================
# PART 7
# =============================================================================
# Example: Time series plot of parking tickets count by date
plt.figure(figsize=(16, 8))
raw_data['ORDERDATE'].value_counts().sort_index().plot(kind='line')
plt.title('SALES Over Time')
plt.xlabel('DATE')
plt.ylabel('SALES')
plt.show()