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| user_id | cart_time | |
|---|---|---|
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| import codecademylib | |
| import pandas as pd | |
| # -- inspecting the dataframes -- | |
| visits = pd.read_csv('visits.csv', | |
| parse_dates=[1]) | |
| #print(visits) | |
| cart = pd.read_csv('cart.csv', | |
| parse_dates=[1]) | |
| #print(cart) | |
| checkout = pd.read_csv('checkout.csv', | |
| parse_dates=[1]) | |
| #print(checkout) | |
| purchase = pd.read_csv('purchase.csv', | |
| parse_dates=[1]) | |
| #print(purchase) | |
| # ---------------------------------------------------- | |
| def mergeCal(df,df2): | |
| merged = pd.merge(df,df2,how='left') #left merge on df and df2 | |
| info_col = merged.columns[-1] # the _time column | |
| null_rows = merged[merged[info_col].isnull()] # null rows | |
| percentage = float(len(null_rows))/len(merged)*100 # dividing number of null rows by number of rows in merged df | |
| return merged,percentage,len(null_rows) | |
| visitsCart,visitsCart_per,visitsCart_null = mergeCal(visits,cart) | |
| print(visitsCart.shape) | |
| #print(visitsCart_per) 82.6% of users didn't place a t-shirt in their cart | |
| cartCheckout,cartCheckout_per,cartCheckout_null = mergeCal(cart,checkout) | |
| #print(cartCheckout_per) 25.3% of users did not proceed to checkout | |
| all_data = visits.merge(cart,how='left')\ | |
| .merge(checkout,how='left')\ | |
| .merge(purchase,how='left') | |
| checkoutPurchase,checkoutPurchase_per,checkoutPurchase_null = mergeCal(checkout,purchase) | |
| #print(checkoutPurchase_per) 16.8% of users did not purchase a t-shirt | |
| # the weakest step of the funnel is the purchase step. Cool T-Shirts could look at how well-recieved their prices are, and survey for best-selling t-shirt qualities | |
| all_data['time_to_purchase'] = \ | |
| all_data.purchase_time - \ | |
| all_data.visit_time | |
| print(all_data.time_to_purchase.mean()) |
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