Created
June 8, 2018 19:52
-
-
Save Kiwibp/c394655347458b03d5e4bdd23f96c8ac to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
locations_ten_or_more = all_items_df.groupby(['Location']).filter(lambda g: g.Location.value_counts() >= 10) \ | |
.loc[:,['Location','Description', 'Price', 'Title', 'Url']] | |
#checking the number of locations with less than 10 items | |
len_of_locs = len(locations_ten_or_more.groupby("Location").size()) | |
print(f'There are {len_of_locs} cities with 10 items or more.') | |
print('\n') | |
#checking the locations with the most items in this subset | |
print('Locations with the most amount of items in this subset:') | |
print(locations_ten_or_more.groupby(['Location']).size().sort_values(ascending=False).head(11)) | |
print('\n') | |
#sorting locations by largest total selling price | |
print('Locations with highest total selling price:') | |
print(locations_ten_or_more.groupby(['Location']).agg(['sum']).loc[:,'Price'].sort_values('sum', ascending=False).head(10)) | |
print('\n') | |
#sorting locations by largest average selling price totals | |
print('Locations with highest average selling price:') | |
print(locations_ten_or_more.groupby(['Location']).mean().sort_values(by='Price',ascending=False).head(11)) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment