code: def make_ecommerce_dataframes(with_integer_time_index=False):
      region_df = pd.DataFrame(
          {"id": ["United States", "Mexico"], "language": ["en", "sp"]},
      )
  
      store_df = pd.DataFrame(
          {
              "id": range(6),
              "région_id": ["United States"] * 3
              + ["Mexico"] * 2
              + [np.nan],
              "num_square_feet": list(range(30000, 60000, 6000))
              + [np.nan],
          },
      )
  
      product_df = pd.DataFrame(
          {
              "id": [
                  "Haribo sugar-free gummy bears",
                  "car",
                  "toothpaste",
                  "brown bag",
                  "coke zero",
                  "taco clock",
              ],
              "department": [
                  "food",
                  "electronics",
                  "health",
                  "food",
                  "food",
                  "electronics",
              ],
              "rating": [3.5, 4.0, 4.5, 1.5, 5.0, 5.0],
              "url": [
                  "google.com",
                  "https://www.featuretools.com/",
                  "amazon.com",
                  "www.featuretools.com",
                  "bit.ly",
                  "featuretools.com/demos/",
              ],
          },
      )
      customer_times = {
          "signup_date": [
              datetime(2011, 4, 8),
              datetime(2011, 4, 9),
              datetime(2011, 4, 6),
          ],
          # some point after signup date
          "upgrade_date": [
              datetime(2011, 4, 10),
              datetime(2011, 4, 11),
              datetime(2011, 4, 7),
          ],
          "cancel_date": [
              datetime(2011, 6, 8),
              datetime(2011, 10, 9),
              datetime(2012, 1, 6),
          ],
          "birthday": [
              datetime(1993, 3, 8),
              datetime(1926, 8, 2),
              datetime(1993, 4, 20),
          ],
      }
      if with_integer_time_index:
          customer_times["signup_date"] = [6, 7, 4]
          customer_times["upgrade_date"] = [18, 26, 5]
          customer_times["cancel_date"] = [27, 28, 29]
          customer_times["birthday"] = [2, 1, 3]
  
      customer_df = pd.DataFrame(
          {
              "id": pd.Categorical([0, 1, 2]),
              "age": [33, 25, 56],
              "région_id": ["United States"] * 3,
              "cohort": [0, 1, 0],
              "cohort_name": [
                  "Early Adopters",
                  "Late Adopters",
                  "Early Adopters",
              ],
              "loves_ice_cream": [True, False, True],
              "favorite_quote": [
                  "The proletariat have nothing to lose but their chains",
                  "Capitalism deprives us all of self-determination",
                  "All members of the working classes must seize the "
                  "means of production.",
              ],
              "signup_date": customer_times["signup_date"],
              # some point after signup date
              "upgrade_date": customer_times["upgrade_date"],
              "cancel_date": customer_times["cancel_date"],
              "cancel_reason": ["reason_1", "reason_2", "reason_1"],
              "engagement_level": [1, 3, 2],
              "full_name": [
                  "Mr. John Doe",
                  "Doe, Mrs. Jane",
                  "James Brown",
              ],
              "email": [
                  "john.smith@example.com",
                  np.nan,
                  "team@featuretools.com",
              ],
              "phone_number": [
                  "555-555-5555",
                  "555-555-5555",
                  "1-(555)-555-5555",
              ],
              "birthday": customer_times["birthday"],
          },
      )
  
      ips = [
          "192.168.0.1",
          "2001:4860:4860::8888",
          "0.0.0.0",
          "192.168.1.1:2869",
          np.nan,
          np.nan,
      ]
      filepaths = [
          "/home/user/docs/Letter.txt",
          "./inthisdir",
          "C:\\user\\docs\\Letter.txt",
          "~/.rcinfo",
          "../../greatgrandparent",
          "data.json",
      ]
  
      session_df = pd.DataFrame(
          {
              "id": [0, 1, 2, 3, 4, 5],
              "customer_id": pd.Categorical([0, 0, 0, 1, 1, 2]),
              "device_type": [0, 1, 1, 0, 0, 1],
              "device_name": [
                  "PC",
                  "Mobile",
                  "Mobile",
                  "PC",
                  "PC",
                  "Mobile",
              ],
              "ip": ips,
              "filepath": filepaths,
          },
      )
  
      times = list(
          [datetime(2011, 4, 9, 10, 30, i * 6) for i in range(5)]
          + [datetime(2011, 4, 9, 10, 31, i * 9) for i in range(4)]
          + [datetime(2011, 4, 9, 10, 40, 0)]
          + [datetime(2011, 4, 10, 10, 40, i) for i in range(2)]
          + [datetime(2011, 4, 10, 10, 41, i * 3) for i in range(3)]
          + [datetime(2011, 4, 10, 11, 10, i * 3) for i in range(2)],
      )
      if with_integer_time_index:
          times = list(range(8, 18)) + list(range(19, 26))
  
      values = list(
          [i * 5 for i in range(5)]
          + [i * 1 for i in range(4)]
          + [0]
          + [i * 5 for i in range(2)]
          + [i * 7 for i in range(3)]
          + [np.nan] * 2,
      )
  
      values_2 = list(
          [i * 2 for i in range(5)]
          + [i * 1 for i in range(4)]
          + [0]
          + [i * 2 for i in range(2)]
          + [i * 3 for i in range(3)]
          + [np.nan] * 2,
      )
  
      values_many_nans = list(
          [np.nan] * 5
          + [i * 1 for i in range(4)]
          + [0]
          + [np.nan] * 2
          + [i * 3 for i in range(3)]
          + [np.nan] * 2,
      )
  
      latlong = list(
          [(values[i], values_2[i]) for i, _ in enumerate(values)]
      )
      latlong2 = list(
          [(values_2[i], -values[i]) for i, _ in enumerate(values)]
      )
      zipcodes = list(
          ["02116"] * 5
          + ["02116-3899"] * 4
          + ["0"]
          + ["1234567890"] * 2
          + ["12345-6789"] * 2
          + [np.nan] * 3,
      )
      countrycodes = list(
          ["US"] * 5 + ["AL"] * 4 + [np.nan] * 5 + ["ALB"] * 2 + ["USA"]
      )
      subregioncodes = list(
          ["US-AZ"] * 5
          + ["US-MT"] * 4
          + [np.nan] * 3
          + ["UG-219"] * 2
          + ["ZM-06"] * 3,
      )
      log_df = pd.DataFrame(
          {
              "id": range(17),
              "session_id": [0] * 5
              + [1] * 4
              + [2] * 1
              + [3] * 2
              + [4] * 3
              + [5] * 2,
              "product_id": ["coke zero"] * 3
              + ["car"] * 2
              + ["toothpaste"] * 3
              + ["brown bag"] * 2
              + ["Haribo sugar-free gummy bears"]
              + ["coke zero"] * 4
              + ["taco clock"] * 2,
              "datetime": times,
              "value": values,
              "value_2": values_2,
              "latlong": latlong,
              "latlong2": latlong2,
              "zipcode": zipcodes,
              "countrycode": countrycodes,
              "subregioncode": subregioncodes,
              "value_many_nans": values_many_nans,
              "priority_level": [0] * 2
              + [1] * 5
              + [0] * 6
              + [2] * 2
              + [1] * 2,
              "purchased": [True] * 11 + [False] * 4 + [True, False],
              "url": ["https://www.featuretools.com/"] * 2
              + ["amazon.com"] * 2
              + [
                  "www.featuretools.com",
                  "bit.ly",
                  "featuretools.com/demos/",
                  "www.google.co.in/" "http://lplay.google.co.in",
                  " ",
                  "invalid_url",
                  "an",
                  "microsoft.com/search/",
              ]
              + [np.nan] * 5,
              "email_address": [
                  "john.smith@example.com",
                  np.nan,
                  "team@featuretools.com",
              ]
              * 5
              + [" prefix@space.com", "suffix@space.com "],
              "comments": [coke_zero_review()]
              + ["I loved it"] * 2
              + car_reviews()
              + toothpaste_reviews()
              + brown_bag_reviews()
              + [gummy_review()]
              + ["I loved it"] * 4
              + taco_clock_reviews(),
          },
      )
  
      return {
          "régions": region_df,
          "stores": store_df,
          "products": product_df,
          "customers": customer_df,
          "sessions": session_df,
          "log": log_df,
      }
  
file_path: featuretools/featuretools/tests/testing_utils/mock_ds.py
imports: from datetime import datetime
  import numpy
  import pandas
  from woodwork.logical_types import URL, Boolean, Categorical, CountryCode, Datetime, Double, EmailAddress, Filepath, Integer, IPAddress, LatLong, NaturalLanguage, Ordinal, PersonFullName, PhoneNumber, PostalCode, SubRegionCode
  from featuretools.entityset import EntitySet
label_desc: Use image node_id 2 for calling a global function with example usage: make_ecommerce_dataframes(with_integer_time_index) and returns: dict