!sudo apt-get update !sudo apt-get install -y libssl-dev libffi-dev

!pip install --upgrade snowflake-connector-python[pandas]

#We've included an example below to help you get started. Query_1 = """ SELECT * FROM All_Records_Joined;""" All_Records_Joined = pd.read_sql(Query_1, conn) Query_2 = """ SELECT * FROM COMPLETED_DATA_VIEW """ Completed_Data_View = pd.read_sql(Query_2, conn) Query_3 = """ SELECT * FROM Market_Awareness_monthly_growth """ Market_Awareness_monthly_growth = pd.read_sql(Query_3, conn) Query_4 = """ SELECT * FROM Market_Awareness_monthly_growth_rate_pct """ Market_Awareness_monthly_growth_rate_pct = pd.read_sql(Query_4, conn) Query_5 = """ SELECT * FROM Product_Monthly_Growth """ Product_Monthly_Growth = pd.read_sql(Query_5, conn) Query_6 = """ SELECT * FROM divestment_dealership_aggregation """ divestment_dealership_aggregation = pd.read_sql(Query_6, conn) # missing Product_Monthly_Growth_Qtr

import seaborn as sns import matplotlib.pyplot as plt

All_Records_Joined.columns

Completed_Data_View.columns

Market_Awareness_monthly_growth.columns

Market_Awareness_monthly_growth_rate_pct.columns

Product_Monthly_Growth.columns

divestment_dealership_aggregation.columns

popular_car1=Product_Monthly_Growth.groupby(['MARKETING_DIVISION','MODEL'],as_index=False).sum('PROFITABILITY') #as_index=False makes the things i grouped back into columns instead of the index popular_car1.sort_values('PROFITABILITY', ascending=False)

# i think this is best answer by the qtr analysis, #but for now i will use my monthly data # would this best be answered by profitabilty total or avg_profit_margin... and WHY? popular_szn=Product_Monthly_Growth.groupby(['MONTH'],as_index=False).sum('PROFITABILITY') #as_index=False makes the things i grouped back into columns instead of the index popular_szn.sort_values('PROFITABILITY', ascending=False)

# Are there any *car products* that have *low growth rates * # in certain market segments we should consider divesting? #car model or lux, growth rate, location-- aka marketing division Product_Monthly_Growth.columns Product_Monthly_Growth.head(4)

Divestment_geo=Product_Monthly_Growth.groupby(['MARKETING_DIVISION','MODEL'],as_index=False).sum('MONTHLY_GROWTH_RATE') Divestment_geo2=Divestment_geo.sort_values('MONTHLY_GROWTH_RATE', ascending=True).head(5) Divestment_geo2

sns.barplot(data=Divestment_geo2,x='MODEL',y='MONTHLY_GROWTH_RATE',hue='MARKETING_DIVISION') plt.show()

divestment_dealership_aggregation.columns

Divestment_Dealerships=divestment_dealership_aggregation.groupby(['MARKETING_DIVISION','STATE','DEALERSHIP_ID'],as_index=False).mean('AVG_PROFITABILITY_MARGIN_PCT') Divestment_Dealerships2=divestment_dealership_aggregation.sort_values('AVG_PROFITABILITY_MARGIN_PCT', ascending=True).head(10) Divestment_Dealerships2 sns.barplot(data=Divestment_Dealerships2,x='DEALERSHIP_ID',y='AVG_PROFITABILITY_MARGIN_PCT',hue='STATE') sns.set(font_scale = 0.60) plt.xticks(rotation=90) plt.show()