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import pandas as pd
# load a csv file
e_commerce_data_path_csv = "./data/data.csv"
e_commerce_csv_df = pd.read_csv(
e_commerce_data_path_csv, encoding='unicode_escape', nrows=1000)
# show columns
e_commerce_csv_df.columns
# > Index(['InvoiceNo', 'StockCode', 'Description', 'Quantity', 'InvoiceDate',
# > 'UnitPrice', 'CustomerID', 'Country'],
# > dtype='object')
# show types
e_commerce_csv_df.dtypes
# > InvoiceNo object
# > StockCode object
# > Description object
# > Quantity int64
# > InvoiceDate object
# > UnitPrice float64
# > CustomerID float64
# > Country object
# > dtype: object
# change types
e_commerce_csv_df = e_commerce_csv_df.convert_dtypes()
# New dtypes
e_commerce_csv_df.dtypes
# > InvoiceNo string
# > StockCode string
# > Description string
# > Quantity Int64
# > InvoiceDate string
# > UnitPrice Float64
# > CustomerID Int64
# > Country string
# > dtype: object
# Cast a pandas object to a specified dtype dtype via dictionary, quantity from int64 to float64, and customerID from int64 to flat64. This
# is just a dummy example, and I am not telling you that converting customerid to float is a smart move:)
temp_dtype_change_df = e_commerce_csv_df.astype(
{'Quantity': 'float64',
'CustomerID': 'float64'
}
)
temp_dtype_change_df.dtypes
# > InvoiceNo string
# > StockCode string
# > Description string
# > Quantity float64
# > InvoiceDate string
# > UnitPrice Float64
# > CustomerID float64
# > Country string
# > dtype: object
# load json
e_commerce_data_path_json = "./data/data_subset.json"
e_commerce_json_df = pd.read_json(
e_commerce_data_path_json, encoding='unicode_escape')
# join the csv and the json to a new dataframe
len(e_commerce_csv_df) + len(e_commerce_json_df)
# > 1004
e_commerce_appended_df = e_commerce_csv_df.append(e_commerce_json_df)
e_commerce_appended_df
# > InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice CustomerID Country
# > 0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 2010-12-01 08:26:00 2.55 17850 United Kingdom
# > 1 536365 71053 WHITE METAL LANTERN 6 2010-12-01 08:26:00 3.39 17850 United Kingdom
# > 2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 2010-12-01 08:26:00 2.75 17850 United Kingdom
# > 3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 2010-12-01 08:26:00 3.39 17850 United Kingdom
# > 4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 2010-12-01 08:26:00 3.39 17850 United Kingdom
# > .. ... ... ... ... ... ... ... ...
# > 999 536520 21358 TOAST ITS - HAPPY BIRTHDAY 2 2010-12-01 12:43:00 1.25 14729 United Kingdom
# > 0 536370 22492 MINI PAINT SET VINTAGE 36 2010-12-01 08:45:00 0.65 12583 France
# > 1 536372 22632 HAND WARMER RED POLKA DOT 6 2010-12-01 09:01:00 1.85 17850 United Kingdom
# > 2 536389 22727 ALARM CLOCK BAKELIKE RED 4 2010-12-01 10:03:00 3.75 12431 Australia
# > 3 562106 22993 SET OF 4 PANTRY JELLY MOULDS 1 2011-08-02 15:19:00 1.25 14076 United Kingdom
# >
# > [1004 rows x 8 columns]
len(e_commerce_appended_df)
# > 1004
# print out first few rows of the dataframe
e_commerce_appended_df.head(10)
# > InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice CustomerID Country
# > 0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 12/1/2010 8:26 2.55 17850 United Kingdom
# > 1 536365 71053 WHITE METAL LANTERN 6 12/1/2010 8:26 3.39 17850 United Kingdom
# > 2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 12/1/2010 8:26 2.75 17850 United Kingdom
# > 3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 12/1/2010 8:26 3.39 17850 United Kingdom
# > 4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 12/1/2010 8:26 3.39 17850 United Kingdom
# > 5 536365 22752 SET 7 BABUSHKA NESTING BOXES 2 12/1/2010 8:26 7.65 17850 United Kingdom
# > 6 536365 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6 12/1/2010 8:26 4.25 17850 United Kingdom
# > 7 536366 22633 HAND WARMER UNION JACK 6 12/1/2010 8:28 1.85 17850 United Kingdom
# > 8 536366 22632 HAND WARMER RED POLKA DOT 6 12/1/2010 8:28 1.85 17850 United Kingdom
# > 9 536367 84879 ASSORTED COLOUR BIRD ORNAMENT 32 12/1/2010 8:34 1.69 13047 United Kingdom
# do a lambda to change of the timestamp from / to epoch
# before
e_commerce_appended_df.dtypes
# > InvoiceNo object
# > StockCode object
# > Description object
# > Quantity Int64
# > InvoiceDate object
# > UnitPrice object
# > CustomerID Int64
# > Country object
# > dtype: object
e_commerce_appended_df['InvoiceDate'] = pd.to_datetime(
e_commerce_appended_df['InvoiceDate'])
# after
e_commerce_appended_df.dtypes
# > InvoiceNo object
# > StockCode object
# > Description object
# > Quantity Int64
# > InvoiceDate datetime64[ns]
# > UnitPrice object
# > CustomerID Int64
# > Country object
# > dtype: object
# Filter out two columns "Country" and "Quantity"
e_commerce_appended_df.columns
# > Index(['InvoiceNo', 'StockCode', 'Description', 'Quantity', 'InvoiceDate',
# > 'UnitPrice', 'CustomerID', 'Country'],
# > dtype='object')
e_commerce_appended_df = e_commerce_appended_df.drop(
["Country", "Quantity"], axis="columns")
e_commerce_appended_df.columns
# > Index(['InvoiceNo', 'StockCode', 'Description', 'InvoiceDate', 'UnitPrice',
# > 'CustomerID'],
# > dtype='object')
# normalize the dataframe
# normalize a Pandas Column with Maximum Absolute Scaling using Pandas
e_commerce_csv_df.head(5)
# > InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice CustomerID Country
# > 0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 12/1/2010 8:26 2.55 17850 United Kingdom
# > 1 536365 71053 WHITE METAL LANTERN 6 12/1/2010 8:26 3.39 17850 United Kingdom
# > 2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 12/1/2010 8:26 2.75 17850 United Kingdom
# > 3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 12/1/2010 8:26 3.39 17850 United Kingdom
# > 4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 12/1/2010 8:26 3.39 17850 United Kingdom
cols_to_normalize = ["Quantity", "UnitPrice"]
def absolute_maximum_scale(series):
return series / series.abs().max()
for column in cols_to_normalize:
e_commerce_csv_df[column] = absolute_maximum_scale(
e_commerce_csv_df[column])
e_commerce_csv_df.head(5)
# > InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice CustomerID Country
# > 0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 0.01 12/1/2010 8:26 0.015455 17850 United Kingdom
# > 1 536365 71053 WHITE METAL LANTERN 0.01 12/1/2010 8:26 0.020545 17850 United Kingdom
# > 2 536365 84406B CREAM CUPID HEARTS COAT HANGER 0.013333 12/1/2010 8:26 0.016667 17850 United Kingdom
# > 3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 0.01 12/1/2010 8:26 0.020545 17850 United Kingdom
# > 4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 0.01 12/1/2010 8:26 0.020545 17850 United Kingdom
# pivot the normalized dataframe
e_commerce_csv_df["Country"].unique()
# > <StringArray>
# > ['United Kingdom', 'France', 'Australia', 'Netherlands']
# > Length: 4, dtype: string
e_commerce_csv_df["unique_id"] = e_commerce_csv_df["InvoiceNo"] + \
e_commerce_csv_df["StockCode"] + \
e_commerce_csv_df["CustomerID"].astype("str")
e_commerce_pivoted = (e_commerce_csv_df
.filter(items=["unique_id", "UnitPrice", "Country"])
.pivot_table(
index="unique_id",
columns="Country", # Column(s) we want to pivot.
# Column with values that we want to have in our new pivoted columns.
values="UnitPrice",
# Even if there is not aggregation we need to provide aggregation funciton.
aggfunc="mean"
)
.reset_index()
)
e_commerce_pivoted
# > Country unique_id Australia France Netherlands United Kingdom
# > 0 5363652173017850 <NA> <NA> <NA> 0.025758
# > 1 5363652275217850 <NA> <NA> <NA> 0.046364
# > 2 5363657105317850 <NA> <NA> <NA> 0.020545
# > 3 53636584029E17850 <NA> <NA> <NA> 0.020545
# > 4 53636584029G17850 <NA> <NA> <NA> 0.020545
# > .. ... ... ... ... ...
# > 940 C5363912198417548 <NA> <NA> <NA> 0.001758
# > 941 C5363912255317548 <NA> <NA> <NA> 0.01
# > 942 C5363912255617548 <NA> <NA> <NA> 0.01
# > 943 C5363912255717548 <NA> <NA> <NA> 0.01
# > 944 C5365062296017897 <NA> <NA> <NA> 0.025758
# >
# > [945 rows x 5 columns]
# store dataframe as parquet file
e_commerce_pivoted.to_parquet('./data/e_commerce_pivoted.parquet.gzip',
compression='gzip')
# > None
# read parquet file
pd.read_parquet(
'./data/e_commerce_pivoted.parquet.gzip')
# > Country unique_id Australia France Netherlands United Kingdom
# > 0 5363652173017850 <NA> <NA> <NA> 0.025758
# > 1 5363652275217850 <NA> <NA> <NA> 0.046364
# > 2 5363657105317850 <NA> <NA> <NA> 0.020545
# > 3 53636584029E17850 <NA> <NA> <NA> 0.020545
# > 4 53636584029G17850 <NA> <NA> <NA> 0.020545
# > .. ... ... ... ... ...
# > 940 C5363912198417548 <NA> <NA> <NA> 0.001758
# > 941 C5363912255317548 <NA> <NA> <NA> 0.01
# > 942 C5363912255617548 <NA> <NA> <NA> 0.01
# > 943 C5363912255717548 <NA> <NA> <NA> 0.01
# > 944 C5365062296017897 <NA> <NA> <NA> 0.025758
# >
# > [945 rows x 5 columns]