Commentary :

This code performs the following operations:

It imports the SimpleImputer class from the sklearn.impute module. The SimpleImputer class is used for imputing missing values in a dataset.

It creates an instance of SimpleImputer called imp_mean. This imputer will replace missing values (which are represented as np.nan in this case) with the mean value of the corresponding column. The strategy for imputation is set to 'mean'.

It applies the SimpleImputer object to the unit_price and unit_cost columns of the df_product DataFrame using the fit_transform() method, which returns the transformed DataFrame with missing values imputed.

It creates a copy of the original df_product DataFrame called df_product_2 using the copy() method.

It replaces the unit_price and unit_cost columns of df_product_2 with the imputed values by assigning imp_values.round() to those columns. The round() method is used to round the imputed values to the nearest integer.

It checks for any remaining missing values in the DataFrame using the isnull() method, and sums up the number of missing values in each column using the sum() method. The resulting output is the number of missing values in each column of the updated DataFrame df_product_2.

The final output of this code will show the number of missing values in the updated df_product_2 DataFrame after imputing missing values with the mean of each column. This approach can be useful for handling missing data in a dataset, especially if there are only a small number of missing values.

Commentary :

This code performs imputation of missing values in the "unit_price" and "unit_cost" columns of the DataFrame "df_product" using scikit-learn's SimpleImputer.

The first line of the code initializes an instance of SimpleImputer named "imp_mean". This class provides a way to impute missing values with a given strategy, which in this case is the median of the non-missing values. The missing_values argument specifies the value to be considered as missing, which is NaN in this case.

The second line of the code uses the fit_transform method of the "imp_mean" object to impute missing values in the "unit_price" and "unit_cost" columns of the "df_product" DataFrame. The method returns a NumPy array named "imp_values" that contains the imputed values.

The third line of the code creates a copy of the "df_product" DataFrame named "df_product_3". This is done to avoid modifying the original DataFrame.

The fourth line of the code assigns the imputed values to the "unit_price" and "unit_cost" columns of the "df_product_3" DataFrame. The round() method is called on the "imp_values" array to round off the values to the nearest integer.

The last line of the code checks if there are still any missing values in the "df_product_3" DataFrame by calling the isnull() method and then the sum() method. If the output shows that there are no missing values in either column, it confirms that the imputation operation was successful.

Commentary :

This code performs imputation of missing values in the "unit_price" and "unit_cost" columns of the DataFrame "df_product" using the median value of each category. The code achieves this in the following steps:

The first line creates a copy of the original DataFrame "df_product" and names it "df_product_4".

The second line initiates a for loop to iterate over the columns "unit_price" and "unit_cost".

The third line fills the missing values in each column. It uses the fillna() method to replace the missing values in each column with the median value of the respective category. This is done by grouping "df_product_4" by the "category_small_cd" column and computing the median value of the respective column using the transform() method. The round() method is used to round the median value to the nearest integer.

The fourth line checks if there are still any missing values in the "df_product_4" DataFrame by calling the isnull() method and then the sum() method. If the output shows that there are no missing values in either column, it confirms that the imputation operation was successful.

Thus, the code imputes missing values in the "unit_price" and "unit_cost" columns of the DataFrame "df_product" using the median value of each category, and creates a new DataFrame "df_product_4" with the imputed values. The imputation is done by copying the original DataFrame, filling missing values in each column using the median value of the respective category, and checking if there are any missing values remaining.

Commentary :

This code performs data processing on a Pandas DataFrame called df_receipt to calculate the sales rate of customers in the year 2019 compared to their total sales. Here's a step-by-step explanation of what the code does:

The first line creates a new DataFrame called df_receipt_2019 that filters the rows of df_receipt to only include sales from the year 2019 (20190101 <= sales_ymd <= 20191231). It then groups the rows by customer_id and calculates the sum of the amount column for each group. The resulting DataFrame has two columns: customer_id and amount_2019.

The second line creates a new DataFrame called df_receipt_all that groups the rows of df_receipt by customer_id and calculates the sum of the amount column for each group. The resulting DataFrame has two columns: customer_id and amount_all.

The third line creates a new DataFrame called df_sales_rate that merges the customer_id column of df_customer with the amount_2019 and amount_all columns of df_receipt_2019 and df_receipt_all, respectively. The merge is a left join, meaning that all rows of df_customer are included in the resulting DataFrame, but only the matching rows of df_receipt_2019 and df_receipt_all are included. The resulting DataFrame has three columns: customer_id, amount_2019, and amount_all.

The fourth and fifth lines fill any missing values in the amount_2019 and amount_all columns with 0.

The sixth line creates a new column called amount_rate in df_sales_rate by applying a lambda function to the amount_2019 and amount_all columns. The lambda function returns 0 if amount_2019 is 0, and the ratio of amount_2019 to amount_all otherwise. The resulting amount_rate column represents the sales rate of each customer in the year 2019 compared to their total sales.

The seventh line fills any missing values in the amount_rate column with 0.

The eighth line filters the rows of df_sales_rate to only include customers with a positive amount_rate, sorts the DataFrame by amount_rate in descending order, and returns the first 10 rows. These are the top 10 customers with the highest sales rate in the year 2019 compared to their total sales.

Commentary :

This code performs data processing on two Pandas DataFrames called df_geocode and df_customer. Here's a step-by-step explanation of what the code does:

The first line creates a new DataFrame called df_geocode_1 by grouping the rows of df_geocode by postal_cd (postal code) and calculating the mean longitude and latitude for each group. The resulting DataFrame has three columns: postal_cd, m_longitude, and m_latitude.

The second line creates a new DataFrame called df_customer_1 by merging df_customer and df_geocode_1 on the postal_cd column. The merge is an inner join, meaning that only the rows that have matching values in both DataFrames are included in the resulting DataFrame. The resulting DataFrame has all the columns of df_customer plus the m_longitude and m_latitude columns from df_geocode_1.

The third line displays the first 10 rows of the resulting DataFrame (df_customer_1) using the .head(10) method. This shows a sample of the merged data that includes customer information with their corresponding postal code's mean longitude and latitude values.

Commentary :

This code performs some geospatial calculations and data processing on three Pandas DataFrames called df_customer_1, df_store, and df_tmp. Here's a step-by-step explanation of what the code does:

The first line defines a Python function called calc_distance_numpy that takes four arguments: x1 and y1 (latitude and longitude of point 1) and x2 and y2 (latitude and longitude of point 2). The function uses the Haversine formula to calculate the distance between the two points in kilometers, assuming a spherical earth with radius of 6371 km. This implementation uses NumPy functions to perform the calculations more efficiently than the previous implementation.

The second line creates a new DataFrame called df_tmp by merging df_customer_1 and df_store on the application_store_cd and store_cd columns, respectively. The merge is an inner join, meaning that only the rows that have matching values in both DataFrames are included in the resulting DataFrame. The resulting DataFrame has all the columns of both DataFrames, plus some renamed columns: address_x is renamed to customer_address, and address_y is renamed to store_address.

The third line creates a new column in df_tmp called distance by calling the calc_distance_numpy function on four columns of df_tmp using NumPy array operations. Instead of using the .apply() method, this implementation takes advantage of NumPy's ability to perform element-wise computations on arrays. The resulting distance value is stored in the distance column.

The fourth line selects a subset of columns from df_tmp (customer_id, customer_address, store_address, and distance) using double square brackets and displays the first 10 rows of the resulting DataFrame using the .head(10) method. This shows a sample of the merged data that includes customer and store information with the corresponding distances between them.

Commentary :

This code performs the following tasks:

Group the df_receipt dataframe by customer_id and calculate the sum of amount for each customer. The resulting dataframe is stored in df_receipt_tmp.

Merge the df_customer dataframe with the df_receipt_tmp dataframe based on the customer_id column. This is a left join, so all customers in df_customer will be included in the resulting dataframe df_customer_u.

Replace any missing values in the sum_amount column with 0.

Sort the rows in df_customer_u by sum_amount in descending order, and then by customer_id in ascending order.

Remove any duplicates in df_customer_u based on the customer_name and postal_cd columns, keeping only the first occurrence of each duplicate. The resulting dataframe is stored back in df_customer_u.

Print out the number of rows in the original df_customer dataframe (df_customer_cnt), the number of rows in the updated df_customer_u dataframe (df_customer_u_cnt), and the difference between the two (diff).

Overall, this code is performing data cleaning and manipulation to create a new dataframe df_customer_u that includes information about each customer's total purchase amount (sum_amount), sorted by this amount in descending order, with any duplicates removed based on the customer_name and postal_cd columns.

Commentary :

This code is merging two dataframes, df_customer and df_customer_u, to identify and match unique customers based on their name and postal code.

The resulting merged dataframe is stored in a new dataframe called df_customer_n. The merge function is used to join the two dataframes, and the argument on=['customer_name', 'postal_cd'] specifies that the merge should be performed based on these two columns.

The resulting merged dataframe contains two customer ID columns, customer_id_x and customer_id_y. To avoid confusion, the rename function is used to rename these columns to customer_id and integration_id, respectively.

The last line of code compares the number of unique customer IDs in the customer_id column with the number of unique customer IDs in the integration_id column. If the difference is zero, it means that there are no duplicate customers in the merged dataframe. If the difference is non-zero, it means that there are still some customers that were not successfully matched based on their name and postal code, and their IDs are still duplicated.

Commentary :

This code performs a train-test split of the data based on the sales information of each customer.

First, it aggregates the sales information of each customer from the df_receipt dataframe by grouping the dataframe by customer_id and summing the amount column. The resulting dataframe is stored in df_sales_customer.

Next, it filters out the customers who did not make any purchases (i.e., customers with amount equal to 0) using the query method, and updates df_sales_customer to only include these customers.

Then, it performs an inner join of df_customer with the customer_id column of df_sales_customer, to only keep customers who made purchases. The resulting dataframe is stored in df_tmp.

Finally, it splits df_tmp into training and test sets using train_test_split method from scikit-learn, with a test size of 0.2 and a random state of 71. It then prints the percentage of data that is in the training and test sets.

Commentary :

This code is performing time series cross-validation on a pandas DataFrame df_ts_amount. It uses the TimeSeriesSplit function from the sklearn.model_selection module to generate three train-test splits.

TimeSeriesSplit is a cross-validator that provides train/test indices to split time series data samples. It takes four parameters:

n_splits: the number of splits
max_train_size: maximum size of training data
test_size: size of the test data
gap: the number of samples to skip before starting a new split

The code sets n_splits=3, max_train_size=12, test_size=6, and gap=0 to create three splits with a training period of 12 months and a testing period of 6 months.

The code then filters the data to only include sales data up to June 2019 (df_ts_amount.query('sales_ym <= "201906"')) and creates a list series_list to store the train and test sets generated by TimeSeriesSplit. The for loop iterates over the splits generated by TimeSeriesSplit and appends each split to series_list. Finally, the code assigns the first train-test split to df_train_1 and df_test_1, the second split to df_train_2 and df_test_2, and the third split to df_train_3 and df_test_3.

Commentary :

This code performs random under-sampling to balance the binary target variable is_buy_flag.

First, the total purchase amount for each customer is computed using groupby and is stored in df_tmp. Then, df_customer and df_tmp are merged using a left join to include all customers, even if they have not made any purchases. The is_buy_flag column is created by using np.where to assign 1 to customers who made purchases and 0 to those who did not.

Next, the RandomUnderSampler function from the imblearn library is used to balance the target variable. The fit_resample method is applied to df_tmp with the is_buy_flag column as the target variable. This creates a new DataFrame, df_down_sampling, with an equal number of cases for both target variable values.

Finally, the number of cases with is_buy_flag values of 0 and 1 are printed to confirm that the random under-sampling was successful.

Commentary :

In this code, df_gender_std is created by selecting the 'gender_cd' and 'gender' columns from the df_customer dataframe, and then removing duplicate rows using the drop_duplicates() method. This creates a new dataframe with unique combinations of 'gender_cd' and 'gender' values.

df_customer_std is created by dropping the 'gender' column from the df_customer dataframe using the drop() method with the columns parameter set to 'gender'. This creates a new dataframe with all the columns from df_customer except the 'gender' column.

The purpose of this code is to create two separate dataframes, one with unique combinations of 'gender_cd' and 'gender', and another with all columns from df_customer except 'gender'. These dataframes can then be used for further analysis or modeling.

Commentary :

This code performs an inner join between two dataframes df_product and df_category on the column category_small_cd. The resulting dataframe df_product_full contains all the columns from df_product and df_category, and the join is based on the matching values of category_small_cd.

The new dataframe df_product_full also contains additional columns from df_category, specifically category_major_name, category_medium_name, and category_small_name. These columns contain categorical information about the product category, and they are included in the merged dataframe using the how='inner' option, which keeps only the rows that have matching values in both dataframes. The .head(3) method is used to display the first 3 rows of the resulting merged dataframe.

Commentary : 

This line of code exports the df_product_full DataFrame to a CSV file with a filename P_df_product_full_UTF-8BOM_header.csv.

The to_csv() method of a pandas DataFrame is used to write the contents of the DataFrame to a CSV file. It has several parameters, including the filename and the encoding to use.

In this case, the filename parameter is set to '../data/P_df_product_full_UTF-8BOM_header.csv', which specifies the directory and filename of the CSV file. The '../data/' prefix specifies the relative directory in which the file should be saved.

The encoding parameter is set to 'utf_8_sig', which specifies the encoding format to use for the CSV file. 'utf_8_sig' is a variant of UTF-8 encoding that includes a byte order mark (BOM) at the beginning of the file. The BOM is a special marker that indicates the byte order of the file, and can help programs determine the encoding format when opening the file.

The index parameter is set to False, which specifies that the DataFrame index should not be included in the CSV file.

Commentary :

This line of code exports a pandas DataFrame df_product_full to a CSV file named "P_df_product_full_CP932_header.csv" in the "../data/" directory.

The exported CSV file uses the "CP932" character encoding, which is a Japanese character encoding also known as "Windows-31J". This encoding is commonly used in Japan for text data and is compatible with Windows operating systems.

The parameter encoding='CP932' specifies the character encoding to use when exporting the CSV file.

The parameter index=False specifies that the index column should not be included in the exported CSV file.

Commentary :

This code saves the df_product_full DataFrame to a CSV file called "P_df_product_full_UTF-8_noh.csv" in the "../data/" directory.

The first argument specifies the filename and path where the CSV file will be saved. The header argument is set to False, which means that the header row of the DataFrame will not be included in the output file. The encoding argument is set to 'UTF-8', which specifies the character encoding to use when writing the file. Finally, index is set to False, which means that the index of the DataFrame will not be included in the output file.

Overall, this code exports the df_product_full DataFrame to a CSV file without the header row and with UTF-8 encoding.

Commentary :

The code above reads a CSV file P_df_product_full_UTF-8_noh.csv with UTF-8 encoding and no header row, and assigns column names to the dataframe.

pd.read_csv('../data/P_df_product_full_UTF-8_noh.csv', dtype={1:str, 2:str, 3:str}, encoding='UTF-8', header=None): This reads the CSV file without a header row, and sets the second, third and fourth columns to string data type. The resulting dataframe has columns named 0, 1, 2, 3, 4, 5, 6, 7 and 8.

df_product_full.columns = ['product_cd','category_major_cd', 'category_medium_cd', 'category_small_cd', 'unit_price','unit_cost','category_major_name', 'category_medium_name', 'category_small_name']: This assigns new column names to the dataframe columns. The resulting dataframe has columns named product_cd, category_major_cd, category_medium_cd, category_small_cd, unit_price, unit_cost, category_major_name, category_medium_name, and category_small_name.

df_product_full.head(3): This displays the first 3 rows of the dataframe df_product_full.

Commentary :

This code reads a CSV file into a Pandas DataFrame with customized column names using the read_csv function.

Here is an explanation of the code:

c_names: A list of strings that define the customized column names.
pd.read_csv(): A Pandas function to read a CSV file and return a DataFrame.
'../data/P_df_product_full_UTF-8_noh.csv': The file path of the CSV file to be read.

names=c_names: Specifies the customized column names using the c_names list.
dtype={'category_major_cd':str, 'category_medium_cd':str, 'category_small_cd':str}: Specifies that the data type of the columns category_major_cd, category_medium_cd, and category_small_cd should be str.

encoding='UTF-8': Specifies that the file encoding should be 'UTF-8'.
header=None: Specifies that there is no header row in the CSV file.

Finally, the resulting DataFrame is displayed using the head() method to show the first three rows.

Commentary :

The code is writing the contents of a pandas DataFrame called df_product_full to a TSV (Tab-Separated Values) file located at '../d/P_df_product_full_UTF-8_header.tsv'.

The .to_csv() method is called on the df_product_full DataFrame, which is a method provided by pandas for exporting data to a CSV (Comma-Separated Values) or TSV file.

The first argument to the method, '../d/P_df_product_full_UTF-8_header.tsv', is the file path and name where the DataFrame should be saved. The file will be saved in the ../d/ directory with the name P_df_product_full_UTF-8_header.tsv.

The second argument, sep='\t', specifies that the values in the file should be separated by tabs instead of commas.

The third argument, encoding='UTF-8', specifies that the encoding of the file should be UTF-8, which is a standard encoding for Unicode characters.

The fourth argument, index=False, specifies that the DataFrame's index should not be included in the output file.

Commentary :

The code reads data from a TSV (Tab-Separated Values) file located at '../data/P_df_product_full_UTF-8_header.tsv' into a pandas DataFrame called df_product_full.

The pd.read_csv() function is called to read the data from the file. The first argument to the function, '../data/P_df_product_full_UTF-8_header.tsv', specifies the path and name of the file to read.

The second argument to the function, dtype={'category_major_cd':str, 'category_medium_cd':str, 'category_small_cd':str}, specifies the data types for certain columns in the DataFrame. Specifically, the columns category_major_cd, category_medium_cd, and category_small_cd are being set to be of type string (str).

The third argument to the function, sep='\t', specifies that the values in the file are separated by tabs instead of commas.

The fourth argument to the function, encoding='UTF-8', specifies that the encoding of the file is UTF-8.

The resulting DataFrame, df_product_full, is then displayed with the head(3) method, which shows the first three rows of the DataFrame.