├── Basic_Data_Exploration_with_Pandas.py ├── README.md ├── bar.py ├── boxplot.py ├── heatmap.py ├── histoplot.py ├── lineplot.py ├── pie.py └── scatterplot.py /Basic_Data_Exploration_with_Pandas.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | import matplotlib.pyplot as plt 4 | 5 | import seaborn as sns 6 | 7 | data = pd.read_csv("C:\\Users\\prave\\OneDrive\\Desktop\\python12314539\\data.csv") 8 | 9 | #Printing the Basic Data Exploration with pandas 10 | 11 | print(data.head(5)) 12 | 13 | print(data.tail(5)) 14 | 15 | print(data.info()) 16 | 17 | print(data.describe()) 18 | 19 | print(data.isnull().sum()) 20 | 21 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Statistical-Analysis-and-Visualization-of-Adidas-US-Retail-Data 2 | The project enforces data cleaning and visualization techniques, using python libraries such as Pandas, Matplotlib, Seaborn and numpy, to provide meaningful insight into an employee attraction in a visually attractive format 3 | 4 | 5 | The visualizations presented here offer a detailed statistical analysis and graphical representation of Adidas US Retail data, using Python libraries such as Pandas, Matplotalib, Seaborn and Numpy. The dataset includes large variables including dealers -D, invoice date, area, state, city, product, unit sales, sales, total sales, operating benefits, operating margin and sales method. These analysis provide a comprehensive approach to sales performance in different product categories and regions. They reveal strong correlations and distribution patterns, highlighting trends in total sales and operational benefits. The inclusion of monthly sales trends and units sold distribution aids to identify seasonal variations and products popularity. This data driven approach supports strategic decision -making, which provides insight into retail and maximizes the profitability of adidas in the US market. 6 | 7 | 8 | # Bar Graph: 9 | 10 | This Python script visualizes sales data using Matplotlib, Pandas, and Seaborn libraries. It reads data from a CSV file and creates a bar chart showing total sales per product. The x-axis represents different products, while the y-axis shows their corresponding total sales. The bars are colored sky blue, and the product names are rotated for better readability. `plt.tight_layout()` is used to ensure the layout fits well before displaying the chart. 11 | -------------------------------------------------------------------------------- /bar.py: -------------------------------------------------------------------------------- 1 | # importing lib 2 | 3 | import pandas as pd 4 | 5 | import matplotlib.pyplot as plt 6 | 7 | import seaborn as sns 8 | 9 | data = pd.read_csv("C:\\Users\\prave\\OneDrive\\Desktop\\python12314539\\data.csv") 10 | 11 | # Using Matplotlib, pandas, seaborn lib to plot the bar graph. 12 | 13 | plt.title("Total Sales Per Product Using Bar-Chart") # title 14 | 15 | plt.xlabel("Product") # x-axis 16 | 17 | plt.ylabel("Total Sales") # y-axis 18 | 19 | plt.bar(data['Product'], data['Total Sales'], color = 'skyblue') 20 | 21 | plt.xticks(rotation=45) 22 | 23 | plt.tight_layout() 24 | 25 | plt.show() 26 | -------------------------------------------------------------------------------- /boxplot.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | import matplotlib.pyplot as plt 4 | 5 | import seaborn as sns 6 | 7 | data = pd.read_csv("C:\\Users\\prave\\OneDrive\\Desktop\\python12314539\\data.csv") 8 | 9 | #Using Us Adidas Sales Dataset, I Created a Box-Plot 10 | 11 | plt.figure(figsize =(10, 6)) 12 | 13 | sns.boxplot(x = data['Total Sales'], color = 'lightgreen') 14 | 15 | plt.title('BoxPlot of Total Sales', fontsize = 18) 16 | 17 | plt.xlabel('Total Sales', fontsize = 14) 18 | 19 | plt.show() 20 | -------------------------------------------------------------------------------- /heatmap.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | import matplotlib.pyplot as plt 4 | 5 | import seaborn as sns 6 | 7 | data = pd.read_csv("C:\\Users\\prave\\OneDrive\\Desktop\\python12314539\\data.csv") 8 | 9 | #Using US Adidas sales dataset, I Created a Heatmap 10 | 11 | num_data = data.select_dtypes(include = ['float64', 'int64']) 12 | 13 | corr = num_data.corr() 14 | 15 | plt.figure(figsize = (10, 8)) 16 | 17 | sns.heatmap(corr, annot = True, cmap = 'coolwarm', fmt = ".2f", linewidth = 0.5) 18 | 19 | plt.title("Correlation Heatmap", fontsize = 18) 20 | 21 | plt.show() 22 | -------------------------------------------------------------------------------- /histoplot.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | import matplotlib.pyplot as plt 4 | 5 | import seaborn as sns 6 | 7 | data = pd.read_csv("C:\\Users\\prave\\OneDrive\\Desktop\\python12314539\\data.csv") 8 | 9 | #Using US sales dataset, I Created a Histo-Plot 10 | 11 | plt.figure(figsize = (10, 6)) 12 | 13 | sns.histplot(data['Units Sold'], bins = 30, kde = True, color = 'skyblue') 14 | 15 | plt.title(f'Distribution of {"Units Sold"}', fontsize = 18) 16 | 17 | plt.xlabel('Units Sold', fontsize = 14) 18 | 19 | plt.ylabel('Frequency', fontsize = 14) 20 | 21 | plt.grid('True') 22 | 23 | plt.show() 24 | -------------------------------------------------------------------------------- /lineplot.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | import matplotlib.pyplot as plt 4 | 5 | import seaborn as sns 6 | 7 | data = pd.read_csv("C:\\Users\\prave\\OneDrive\\Desktop\\python12314539\\data.csv") 8 | 9 | #Using US Adidas Sales Dataset, I Created a Line-Chart 10 | 11 | data['Invoice Date'] = pd.to_datetime(data['Invoice Date'], format = '%d-%m-%Y') 12 | 13 | data['Month'] = data['Invoice Date'].dt.to_period('M') 14 | 15 | monthly_sales = data.groupby('Month')['Total Sales'].sum().reset_index() 16 | 17 | monthly_sales['Month'] = monthly_sales['Month'].dt.to_timestamp() 18 | 19 | plt.figure(figsize=(14, 7)) 20 | 21 | sns.lineplot(x = 'Month', y = 'Total Sales', data = monthly_sales, marker = 'o') 22 | 23 | plt.title('Monthly Total Sales Trend', fontsize = 18) 24 | 25 | plt.xlabel('Month', fontsize = 14) 26 | 27 | plt.ylabel('Total Sales', fontsize = 14) 28 | 29 | plt.xticks(rotation = 45) 30 | 31 | plt.grid(True) 32 | 33 | plt.show() 34 | -------------------------------------------------------------------------------- /pie.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | import matplotlib.pyplot as plt 4 | 5 | import seaborn as sns 6 | 7 | data = pd.read_csv("C:\\Users\\prave\\OneDrive\\Desktop\\python12314539\\data.csv") 8 | 9 | # Using US Adiada sales dataset I Created a Pie Chart 10 | 11 | sales_by_product = data.groupby('Product')['Units Sold'].sum() 12 | 13 | plt.figure(figsize = (8, 8)) 14 | 15 | plt.pie(sales_by_product, labels = sales_by_product.index, autopct = '%1.1f%%', startangle = 140) 16 | 17 | plt.title("Units Solds Percentage Per Product Using Pie-Chart") 18 | 19 | plt.axis('equal') 20 | 21 | plt.show() 22 | -------------------------------------------------------------------------------- /scatterplot.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | import matplotlib.pyplot as plt 4 | 5 | import seaborn as sns 6 | 7 | data = pd.read_csv("C:\\Users\\prave\\OneDrive\\Desktop\\python12314539\\data.csv") 8 | 9 | # Using Adidas US sales Dataset, I created a Scatter-Plot 10 | 11 | plt.figure(figsize=(10, 6)) 12 | 13 | plt.scatter(data['Total Sales'], data['Operating Profit'], alpha = 0.5,color = 'blue') 14 | 15 | plt.title('Scatter plot of Total Sales vs Operating profit') 16 | 17 | plt.xlabel('Total Sales ($)') 18 | 19 | plt.ylabel('Operating Sales ($)') 20 | 21 | plt.grid(True) 22 | 23 | plt.show() 24 | --------------------------------------------------------------------------------