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Office Supplies: Examining the popularity of products in each region: West, East, Central, South
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  • import matplotlib.pyplot as plt  
    import pandas as pd
    import matplotlib.pyplot as plt
    import seaborn as sns
    from matplotlib import rcParams
    
    office = pd.read_csv("office_supplies.csv")  
    df = pd.DataFrame(office)
    
    sns.set_style("darkgrid")
    
    ####
    Date = pd.to_datetime(df["Order Date"]).dt.year
    Total_Quantity_Year = df[df["Region"]=="West"]
    #print(Total_Quantity_Year)
    Total_Quantity = df["Quantity"]
    #print(Total_Quantity)
    hue_colors = {"Technology": "Blue",
                  "Office Supplies": "Orange",
                  "Furniture": "Green"}
    sns.relplot(x=Date,
            	y=Total_Quantity,
                data=Total_Quantity_Year,
                kind="line",
                height=3,
                aspect=2,
                hue="Category",
                ci=None,
                style="Category",
                style_order=["Technology", "Office Supplies", "Furniture"],
                estimator = sum,
                markers=True,
                palette=hue_colors,
                hue_order=["Technology", "Office Supplies", "Furniture"])
    plt.ylabel('Quantity',weight="bold")
    plt.xlabel('Year',weight="bold")
    plt.title('Quantity in each category over the years: West region',weight="bold")
    plt.ylim([0,3000])
    plt.xticks([2014,2015,2016,2017])
    plt.savefig('West_Quantity_Year.pdf')
    plt.show()
    
    
    Date = pd.to_datetime(df["Order Date"]).dt.year
    Total_Quantity_Year = df[df["Region"]=="South"]
    Total_Quantity = df["Quantity"]
    sns.relplot(x=Date,
            	y=Total_Quantity,
                data=Total_Quantity_Year,
                kind="line",
                height=3,
                aspect=2,
                hue="Category",
                ci=None,
                style="Category",
                style_order=["Technology", "Office Supplies", "Furniture"],
                estimator = sum,
                markers=True,
                palette=hue_colors,
                hue_order=["Technology", "Office Supplies", "Furniture"])
    plt.ylabel('Quantity',weight="bold") 
    plt.xlabel('Year',weight="bold")
    plt.title('Quantity in each category over the years: South region',weight="bold")
    plt.ylim([0,3000])
    plt.xticks([2014,2015,2016,2017])
    plt.show()
    plt.savefig('South_Quantity_Year', format = 'pdf')
    
    Date = pd.to_datetime(df["Order Date"]).dt.year
    Total_Quantity_Year = df[df["Region"]=="East"]
    Total_Quantity = df["Quantity"]
    sns.relplot(x=Date,
            	y=Total_Quantity,
                data=Total_Quantity_Year,
                kind="line",
                height=3,
                aspect=2,
                hue="Category",
                ci=None,
                style="Category",
                style_order=["Technology", "Office Supplies", "Furniture"],
                estimator = sum,
                markers=True,
                palette=hue_colors,
                hue_order=["Technology", "Office Supplies", "Furniture"])
    plt.ylabel('Quantity',weight="bold") 
    plt.xlabel('Year',weight="bold")
    plt.title('Quantity in each category over the years: East region',weight="bold")
    plt.ylim([0,3000])
    plt.xticks([2014,2015,2016,2017])
    plt.show()
    plt.savefig('East_Quantity_Year', format = 'pdf')
    
    Date = pd.to_datetime(df["Order Date"]).dt.year
    Total_Quantity_Year = df[df["Region"]=="Central"]
    Total_Quantity = df["Quantity"]
    sns.relplot(x=Date,
            	y=Total_Quantity,
                data=Total_Quantity_Year,
                kind="line",
                height=3,
                aspect=2,
                hue="Category",
                ci=None,
                style="Category",
                style_order=["Technology", "Office Supplies", "Furniture"],
                estimator = sum,
                markers=True,
                palette=hue_colors,
                hue_order=["Technology", "Office Supplies", "Furniture"])
    plt.ylabel('Quantity',weight="bold") 
    plt.xlabel('Year',weight="bold")
    plt.title('Quantity in each category over the years: Central region',weight="bold")
    plt.ylim([0,3000])
    plt.xticks([2014,2015,2016,2017])
    plt.show()
    plt.savefig('Central_Quantity_Year', format = 'pdf')
    
    #Data for West Region
    #https://datavizpyr.com/bar-plots-with-matplotlib-in-python/
    #Category = df[df["Region"]=="West"]["Sub-Category"]
    #data_sorted = df.sort_values("Sales", ascending=False)
    
    Total_Sales = df[df["Region"]=="West"].groupby("Sub-Category")["Sales"].sum()
    Total_Sales_Sorted = Total_Sales.sort_values(ascending = False)
    plt.figure(figsize=(20,5))
    plt.ylabel('Sales',weight="bold") 
    plt.xlabel('Sub-Category',weight="bold")
    plt.title('Sales of Sub-Category by Region: West',weight="bold")
    plt.ylim([0, 120000])
    Total_Sales_Sorted.plot(kind='bar')
    plt.xticks(rotation=0)
    plt.savefig('West_Sales', format = 'pdf')
    
    
    Total_Quantity = df[df["Region"]=="West"].groupby("Sub-Category")["Quantity"].sum()
    Total_Quantity_Sorted = Total_Quantity.sort_values(ascending = False)
    plt.figure(figsize=(20,5))
    plt.ylabel('Quantity',weight="bold") 
    plt.xlabel('Sub-Category',weight="bold")
    plt.title('Quantity in each sub-category: West region',weight="bold")
    plt.ylim([0, 2000])
    Total_Quantity_Sorted.plot(kind='bar')
    plt.xticks(rotation=0)
    plt.savefig('West_Quantity', format = 'pdf')
    
    #Data for East Region
    Total_Sales = df[df["Region"]=="East"].groupby("Sub-Category")["Sales"].sum()
    Total_Sales_Sorted = Total_Sales.sort_values(ascending = False)
    plt.figure(figsize=(20,5))
    plt.ylabel('Sales',weight="bold") 
    plt.xlabel('Sub-Category',weight="bold")
    plt.title('Sales of Sub-Category by Region: East',weight="bold")
    plt.ylim([0, 120000])
    Total_Sales_Sorted.plot(kind='bar')
    plt.xticks(rotation=0)
    plt.savefig('East_Sales', format = 'pdf')
    
    Total_Quantity = df[df["Region"]=="East"].groupby("Sub-Category")["Quantity"].sum()
    Total_Quantity_Sorted = Total_Quantity.sort_values(ascending = False)
    plt.figure(figsize=(20,5))
    plt.ylabel('Quantity',weight="bold") 
    plt.xlabel('Sub-Category',weight="bold")
    plt.title('Quantity in each sub-category: East region',weight="bold")
    plt.ylim([0, 2000])
    Total_Quantity_Sorted.plot(kind='bar')
    plt.xticks(rotation=0)
    plt.savefig('East_Quantity', format = 'pdf')
    
    #Data for South Region
    Total_Sales = df[df["Region"]=="South"].groupby("Sub-Category")["Sales"].sum()
    Total_Sales_Sorted = Total_Sales.sort_values(ascending = False)
    plt.figure(figsize=(20,5))
    plt.ylabel('Sales',weight="bold") 
    plt.xlabel('Sub-Category',weight="bold")
    plt.title('Sales of Sub-Category by Region: South',weight="bold")
    plt.ylim([0, 120000])
    Total_Sales_Sorted.plot(kind='bar')
    plt.xticks(rotation=0)
    plt.savefig('South_Sales', format = 'pdf')
    
    Total_Quantity = df[df["Region"]=="South"].groupby("Sub-Category")["Quantity"].sum()
    Total_Quantity_Sorted = Total_Quantity.sort_values(ascending = False)
    plt.figure(figsize=(20,5))
    plt.ylabel('Quantity',weight="bold") 
    plt.xlabel('Sub-Category',weight="bold")
    plt.title('Quantity in each sub-category: South region',weight="bold")
    plt.ylim([0, 2000])
    Total_Quantity_Sorted.plot(kind='bar')
    plt.xticks(rotation=0)
    plt.savefig('South_Quantity', format = 'pdf')
    
    #Data for Central Region
    Total_Sales = df[df["Region"]=="Central"].groupby("Sub-Category")["Sales"].sum()
    Total_Sales_Sorted = Total_Sales.sort_values(ascending = False)
    plt.figure(figsize=(20,5))
    plt.ylabel('Sales',weight="bold") 
    plt.xlabel('Sub-Category',weight="bold")
    plt.title('Sales of Sub-Category by Region: Central',weight="bold")
    plt.ylim([0, 120000])
    Total_Sales_Sorted.plot(kind='bar')
    plt.xticks(rotation=0)
    plt.savefig('Central_Sales', format = 'pdf')
    
    Total_Quantity = df[df["Region"]=="Central"].groupby("Sub-Category")["Quantity"].sum()
    Total_Quantity_Sorted = Total_Quantity.sort_values(ascending = False)
    plt.figure(figsize=(20,5))
    plt.ylabel('Quantity',weight="bold") 
    plt.xlabel('Sub-Category',weight="bold")
    plt.title('Quantity in each sub-category: Central region',weight="bold")
    plt.ylim([0, 2000])
    Total_Quantity_Sorted.plot(kind='bar')
    plt.xticks(rotation=0)
    plt.savefig('Central_Quantity', format = 'pdf')