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completed more groupby examples. #1

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24 changes: 15 additions & 9 deletions Python/wrangle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -202,22 +202,24 @@
))).reset_index()

# Group the transactions per year of the transaction date, measuring the number of transactions per year

t_year = transactions.TransactionDate.dt.year
transactions.groupby(t_year)['TransactionID'].nunique()
# Group the transactions per (user, transaction-year) pair, measuring the number of transactions per group

transactions.groupby(['UserID', t_year]).TransactionID.nunique()
# Group the transactions per user, measuring the max quantity each user made for a single transaction and the date of that transaction

transactions.sort_values(['Quantity', 'TransactionDate']).groupby('UserID').last()
# Group the transactions per (user, transaction-year), and then group by transaction-year to get the number of users who made a transaction each year

#--------------------------------------------------
# Group By + Update

# Insert a column in transactions indicating the number of transactions per user

transactions['NUserTransactions'] = transactions.groupby('UserID').UserID.transform(lambda x: len(x))
# Insert columns in transactions indicating the first transaction date and last transaction date per user

transactions['first_date'] = transactions.groupby('UserID').TransactionDate.transform(lambda x: x.min())
transactions['last_date'] = transactions.groupby('UserID').TransactionDate.transform(lambda x: x.max())
# For each transaction, get the date of the previous transaction made by the same user

transactions['prev_trans'] = transactions.groupby('UserID').TransactionDate.transform(lambda x: x.shift(1))
#======================================================================================================
# Joining DataFrames

Expand All @@ -231,14 +233,16 @@
users['Registered'] = pd.to_datetime(users.Registered)
users['Cancelled'] = pd.to_datetime(users.Cancelled)
transactions['TransactionDate'] = pd.to_datetime(transactions.TransactionDate)

sessions['SessionDate'] = pd.to_datetime(sessions.SessionDate)
#--------------------------------------------------
# Basic Joins

# Join users to transactions, keeping all rows from transactions and only matching rows from users (left join)
transactions.merge(users, how='left', on='UserID')

# Which transactions aren't tied to a user in users? (anti join)
unique_users = users.UserID.unique()
transactions[~transactions.UserID.isin(unique_users)]

# Join users to transactions, keeping only rows from transactions and users that match via UserID (inner join)
transactions.merge(users, how='inner', on='UserID')
Expand All @@ -247,11 +251,13 @@
transactions.merge(users, how='outer', on='UserID')

# Determine which sessions occured on the same day each user registered

users.merge(sessions, left_on=['UserID', 'Registered'], right_on=['UserID', 'SessionDate'])
# Build a dataset with every possible (UserID, ProductID) pair (cross join)

# Determine how much quantity of each product was purchased by each user

summary = users.merge(transactions, on='UserID', how='left')
summary.ProductID = summary.ProductID.astype('category')
summary.pivot_table(index='ProductID', columns='UserID', values='Quantity', aggfunc='sum', fill_value=0)
# For each user, get each possible pair of pair transactions (TransactionID1, TransactionID2)

# Join each user to his/her first occuring transaction in the transactions table
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