EcommerceTools is a data science toolkit for those working in technical ecommerce, marketing science, and technical seo and includes a wide range of features to aid analysis and model building. The package is written in Python and is designed to be used with Pandas and works within a Jupyter notebook environment or in standalone Python projects.

You can install EcommerceTools and its dependencies via PyPi by entering pip3 install ecommercetools in your terminal, or !pip3 install ecommercetools within a Jupyter notebook cell.

• Transactions
• Products
• Customers
• Advertising
• Operations
• Marketing
• NLP
• SEO
• Reports

1. Load sample transaction items data

If you want to get started with the transactions, products, and customers features, you can use the load_sample_data() function to load a set of real world data. This imports the transaction items from widely-used Online Retail dataset and reformats it ready for use by EcommerceTools.

from ecommercetools import utilities

transaction_items = utilities.load_sample_data()
transaction_items.head()

2. Create a transaction items dataframe

The utilities module includes a range of tools that allow you to format data, so it can be used within other EcommerceTools functions. The load_transaction_items() function is used to create a Pandas dataframe of formatted transactional item data. When loading your transaction items data, all you need to do is define the column mappings, and the function will reformat the dataframe accordingly.

import pandas as pd
from ecommercetools import utilities

transaction_items = utilities.load_transaction_items('transaction_items_non_standard_names.csv',
                                 date_column='InvoiceDate',
                                 order_id_column='InvoiceNo',
                                 customer_id_column='CustomerID',
                                 sku_column='StockCode',
                                 quantity_column='Quantity',
                                 unit_price_column='UnitPrice'
                                 )
transaction_items.to_csv('transaction_items.csv', index=False)
print(transaction_items.head())

3. Create a transactions dataframe

The get_transactions() function takes the formatted Pandas dataframe of transaction items and returns a Pandas dataframe of aggregated transaction data, which includes features identifying the order number.

import pandas as pd
from ecommercetools import customers

transaction_items = pd.read_csv('transaction_items.csv')
transactions = transactions.get_transactions(transaction_items)
transactions.to_csv('transactions.csv', index=False)
print(transactions.head())

products_df = products.get_products(transaction_items)
products_df.head()

repurchase_rates = products.get_repurchase_rates(transaction_items)
repurchase_rates.head(3).T

from ecommercetools import customers

customers_df = customers.get_customers(transaction_items)
customers_df.head()

from ecommercetools import customers

cohorts_df = customers.get_cohorts(transaction_items, period='M')
cohorts_df.head()

from ecommercetools import customers

cohort_matrix_df = customers.get_cohort_matrix(transaction_items, period='M', percentage=True)
cohort_matrix_df.head()

from ecommercetools import customers

cohort_matrix_df = customers.get_cohort_matrix(transaction_items, period='M', percentage=False)
cohort_matrix_df.head()

from ecommercetools import customers

retention_df = customers.get_retention(transactions_df)
retention_df.head()

This is an extension of the regular Recency, Frequency, Monetary value (RFM) model that includes an additional parameter "H" for heterogeneity. This shows the number of unique SKUs purchased by each customer. While typically unassociated with targeting, this value can be very useful in identifying which customers should probably be buying a broader mix of products than they currently are, as well as spotting those who may have stopped buying certain items.

from ecommercetools import customers

rfm_df = customers.get_rfm_segments(customers_df)
rfm_df.head()

from ecommercetools import customers 

latency_df = customers.get_latency(transactions_df)
latency_df.head()

from ecommercetools import customers

abc_df = customers.get_abc_segments(customers_df, months=12, abc_class_name='abc_class_12m', abc_rank_name='abc_rank_12m')
abc_df.head()

EcommerceTools allows you to predict the AOV, Customer Lifetime Value (CLV) and expected number of orders via the Gamma-Gamma and BG/NBD models from the excellent Lifetimes package. By passing the dataframe of transactions from get_transactions() to the get_customer_predictions() function, EcommerceTools will fit the BG/NBD and Gamma-Gamma models and predict the AOV, order quantity, and CLV for each customer in the defined number of future days after the end of the observation period.

customer_predictions = customers.get_customer_predictions(transactions_df, 
                                                          observation_period_end='2011-12-09', 
                                                          days=90)
customer_predictions.head(10)

from ecommercetools import advertising

product_names = ['fly rods', 'fly reels']
keywords_prepend = ['buy', 'best', 'cheap', 'reduced']
keywords_append = ['for sale', 'price', 'promotion', 'promo', 'coupon', 'voucher', 'shop', 'suppliers']
campaign_name = 'fly_fishing'

keywords = advertising.generate_ad_keywords(product_names, keywords_prepend, keywords_append, campaign_name)
keywords.head()

from ecommercetools import advertising

text = "Fly Reels from {Orvis|Loop|Sage|Airflo|Nautilus} for {trout|salmon|grayling|pike}"
spin = advertising.generate_spintax(text, single=False)

spin

['Fly Reels from Orvis for trout',
 'Fly Reels from Orvis for salmon',
 'Fly Reels from Orvis for grayling',
 'Fly Reels from Orvis for pike',
 'Fly Reels from Loop for trout',
 'Fly Reels from Loop for salmon',
 'Fly Reels from Loop for grayling',
 'Fly Reels from Loop for pike',
 'Fly Reels from Sage for trout',
 'Fly Reels from Sage for salmon',
 'Fly Reels from Sage for grayling',
 'Fly Reels from Sage for pike',
 'Fly Reels from Airflo for trout',
 'Fly Reels from Airflo for salmon',
 'Fly Reels from Airflo for grayling',
 'Fly Reels from Airflo for pike',
 'Fly Reels from Nautilus for trout',
 'Fly Reels from Nautilus for salmon',
 'Fly Reels from Nautilus for grayling',
 'Fly Reels from Nautilus for pike']

inventory_classification = operations.get_inventory_classification(transaction_items)
inventory_classification.head()

from ecommercetools import marketing

trading_calendar_df = marketing.get_trading_calendar('2021-01-01', days=365)
trading_calendar_df.head()

from ecommercetools import marketing

trading_events_df = marketing.get_trading_events('2021-01-01', days=365)
trading_events_df.head()

The get_summaries() function of the nlp module takes a Pandas dataframe containing text and returns a machine-generated summary of the content using a Huggingface Transformers pipeline via PyTorch. To use this feature, first load your Pandas dataframe and import the nlp module from ecommercetools.

import pandas as pd
from ecommercetools import nlp 

pd.set_option('max_colwidth', 1000)
df = pd.read_csv('text.csv')
df.head()

Specify the name of the Pandas dataframe, the column containing the text you wish to summarise (i.e. product_description), and specify a column name in which to store the machine-generated summary. The min_length and max_length arguments control the number of words generated, while the do_sample argument controls whether the generated text is completely unique (do_sample=False) or extracted from the text (do_sample=True).

df = nlp.get_summaries(df, 'product_description', 'sampled_summary', min_length=50, max_length=100, do_sample=True)
df = nlp.get_summaries(df, 'product_description', 'unsampled_summary', min_length=50, max_length=100, do_sample=False)
df = nlp.get_summaries(df, 'product_description', 'unsampled_summary_20_to_30', min_length=20, max_length=30, do_sample=False)

Since the model used for text summarisation is very large (1.2 GB plus), this function will take some time to complete. Once loaded, summaries are generated within a second or two per piece of text, so it is advisable to try smaller volumes of data initially.

The get_sitemaps() function takes the location of a robots.txt file (always stored at the root of a domain), and returns the URLs of any XML sitemaps listed within.

from ecommercetools import seo

sitemaps = seo.get_sitemaps("https://www.flyandlure.org/robots.txt")
print(sitemaps)

The get_dataframe() function allows you to download the URLs in an XML sitemap to a Pandas dataframe. If the sitemap contains child sitemaps, each of these will be retrieved. You can save the Pandas dataframe to CSV in the usual way.

from ecommercetools import seo

df = seo.get_sitemap("https://flyandlure.org/sitemap.xml")
print(df.head())

The get_core_web_vitals() function retrieves the Core Web Vitals metrics for a list of sites from the Google PageSpeed Insights API and returns results in a Pandas dataframe. The function requires a a Google PageSpeed Insights API key.

from ecommercetools import seo

pagespeed_insights_key = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
urls = ['https://www.bbc.co.uk', 'https://www.bbc.co.uk/iplayer']
df = seo.get_core_web_vitals(pagespeed_insights_key, urls)
print(df.head())

The get_knowledge_graph() function returns the Google Knowledge Graph data for a given search term. This requires the use of a Google Knowledge Graph API key. By default, the function returns output in a Pandas dataframe, but you can pass the output="json" argument if you wish to receive the JSON data back.

from ecommercetools import seo

knowledge_graph_key = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
knowledge_graph = seo.get_knowledge_graph(knowledge_graph_key, "tesla", output="dataframe")
print(knowledge_graph)

The query_google_search_console() function runs a search query on the Google Search Console API and returns data in a Pandas dataframe. This function requires a JSON client secrets key with access to the Google Search Console API.

from ecommercetools import seo

key = "google-search-console.json"
site_url = "https://flyandlure.org"
payload = {
    'startDate': "2019-01-01",
    'endDate': "2019-12-31",
    'dimensions': ["page", "device", "query"],
    'rowLimit': 100,
    'startRow': 0
}

df = seo.query_google_search_console(key, site_url, payload)
print(df.head())

To fetch all results, set fetch_all to True. This will automatically paginate through your Google Search Console data and return all results. Be aware that if you do this you may hit Google's quota limit if you run a query over an extended period, or have a busy site with lots of page or query dimensions.

from ecommercetools import seo

key = "google-search-console.json"
site_url = "https://flyandlure.org"
payload = {
    'startDate': "2019-01-01",
    'endDate': "2019-12-31",
    'dimensions': ["page", "device", "query"],
    'rowLimit': 25000,
    'startRow': 0
}

df = seo.query_google_search_console(key, site_url, payload, fetch_all=True)
print(df.head())

payload_before = {
    'startDate': "2021-08-11",
    'endDate': "2021-08-31",
    'dimensions': ["page","query"],    
}

payload_after = {
    'startDate': "2021-07-21",
    'endDate': "2021-08-10",
    'dimensions': ["page","query"],    
}

df = seo.query_google_search_console_compare(key, site_url, payload_before, payload_after, fetch_all=False)
df.sort_values(by='clicks_change', ascending=False).head()

The get_indexed_pages() function uses the "site:" prefix to search Google for the number of pages "indexed". This is very approximate and may not be a perfect representation, but it's usually a good guide of site "size" in the absence of other data.

from ecommercetools import seo

urls = ['https://www.bbc.co.uk', 'https://www.bbc.co.uk/iplayer', 'https://flyandlure.org']
df = seo.get_indexed_pages(urls)
print(df.head())

The google_autocomplete() function returns a set of keyword suggestions from Google Autocomplete. The include_expanded=True argument allows you to expand the number of suggestions shown by appending prefixes and suffixes to the search terms.

from ecommercetools import seo

suggestions = seo.google_autocomplete("data science", include_expanded=False)
print(suggestions)

suggestions = seo.google_autocomplete("data science", include_expanded=True)
print(suggestions)

The get_robots() function returns the contents of a robots.txt file in a Pandas dataframe so it can be parsed and analysed.

from ecommercetools import seo

robots = seo.get_robots("https://www.flyandlure.org/robots.txt")
print(robots)

The get_serps() function returns a Pandas dataframe containing the Google search engine results for a given search term. Note that this function is not suitable for large-scale scraping and currently includes no features to prevent it from being blocked.

from ecommercetools import seo

serps = seo.get_serps("data science blog")
print(serps)

The classify_pages() function returns an ABCD classification of Google Search Console data. This calculates the cumulative sum of clicks and then categorises pages using the ABC algorithm (the first 80% are classed A, the next 10% are classed B, and the final 10% are classed C, with the zero click pages classed D).

from ecommercetools import seo

key = "client_secrets.json"
site_url = "example-domain.co.uk"
start_date = '2022-10-01'
end_date = '2022-10-31'

df_classes = seo.classify_pages(key, site_url, start_date, end_date, output='classes')
print(df_classes.head())

df_summary = seo.classify_pages(key, site_url, start_date, end_date, output='summary')
print(df_summary)

                                                page  clicks  impressions    ctr  position  clicks_cumsum  clicks_running_pc  pc_share class  class_rank
0  https://practicaldatascience.co.uk/machine-lea...    3890        36577  10.64     12.64           3890           8.382898  8.382898     A           1
1  https://practicaldatascience.co.uk/data-scienc...    2414        16618  14.53     14.30           6304          13.585036  5.202138     A           2
2  https://practicaldatascience.co.uk/data-scienc...    2378        71496   3.33     16.39           8682          18.709594  5.124558     A           3
3  https://practicaldatascience.co.uk/data-scienc...    1942        14274  13.61     15.02          10624          22.894578  4.184984     A           4
4  https://practicaldatascience.co.uk/data-scienc...    1738        23979   7.25     11.80          12362          26.639945  3.745367     A           5
  

class  pages  impressions  clicks   avg_ctr  avg_position  share_of_clicks  share_of_impressions
0     A     63       747643   36980  5.126349     22.706825             79.7                  43.7
1     B     46       639329    4726  3.228043     31.897826             10.2                  37.4
2     C    190       323385    4698  2.393632     38.259368             10.1                  18.9
3     D     36         1327       0  0.000000     25.804722              0.0                   0.1

The Reports module creates weekly, monthly, quarterly, or yearly reports for customers and orders and calculates a range of common ecommerce metrics to show business performance.

The customers_report() function takes a formatted dataframe of transaction items (see above) and a desired frequency (D for daily, W for weekly, M for monthly, Q for quarterly) and calculates aggregate metrics for each period.

The function returns the number of orders, the number of customers, the number of new customers, the number of returning customers, and the acquisition rate (or proportion of new customers). For monthly reporting, I would recommend a 13-month period so you can compare the last month with the same month the previous year.

from ecommercetools import reports

df_customers_report = reports.customers_report(transaction_items, frequency='M')
print(df_customers_report.head(13))

The transactions_report() function takes a formatted dataframe of transaction items (see above) and a desired frequency (D for daily, W for weekly, M for monthly, Q for quarterly) and calculates aggregate metrics for each period.

The metrics returned are: customers, orders, revenue, SKUs, units, average order value, average SKUs per order, average units per order, and average revenue per customer.

from ecommercetools import reports

df_orders_report = reports.transactions_report(transaction_items, frequency='M')
print(df_orders_report.head(13))