doc string tweaks, cleanup code

museum_analytics/constants.py

@@ -1,7 +1,6 @@
-
-
 DEFAULT_MUSEUM_DATA_SOURCE_URL = "https://en.wikipedia.org/wiki/List_of_most_visited_museums"
+WIKIPEDIA_REST_API_URL_PREFIX = "https://en.wikipedia.org/api/rest_v1/page/html"
 
-# csv file from https://simplemaps.com/data/world-cities
+# csv file downloaded from https://simplemaps.com/data/world-cities
 RAW_POPULATION_DATA_FILE = "data/worldcities.csv"
 MUSEUM_DATA_FILE = "data/updated_museum_data.csv"

museum_analytics/data_setup.py

@@ -1,7 +1,5 @@
 import os
 import requests
-import wikipediaapi
-from bs4 import BeautifulSoup
 from dotenv import load_dotenv
 from museum_analytics import constants
 from io import StringIO
@@ -10,17 +8,23 @@ import pandas as pd
 load_dotenv()
 
 WIKIPEDIA_USER_AGENT = os.getenv("WIKIPEDIA_USER_AGENT")
-SOUP_PER_SECTION: dict[str, BeautifulSoup] = {}
+
+
+class TableNotFound(Exception):
+    pass
 
 
 def get_first_data_table_from_wikipedia(page_title: str) -> pd.DataFrame:
+    """Get data from a wikipedia page matching the provided title.
+
+    Args:
+        page_title: the title of the wikipedia page.
+
+    Returns:
+        A pandas data frame of the found table.
     """
 
-    :param page_title:
+    page_url = f"{constants.WIKIPEDIA_REST_API_URL_PREFIX}/{page_title}"
-    :return:
-    """
-
-    page_url = f"https://en.wikipedia.org/api/rest_v1/page/html/{page_title}"
     print(f"Fetching data from Wikipedia: {page_url}")
     with requests.Session() as session:
         session.headers["User-Agent"] = WIKIPEDIA_USER_AGENT
@@ -28,18 +32,23 @@ def get_first_data_table_from_wikipedia(page_title: str) -> pd.DataFrame:
 
     tables = pd.read_html(StringIO(html), attrs={"class": "wikitable"})
     # TODO: add a way to specifically fetch a table
-    df = tables[0]
+    if not tables:
+        raise TableNotFound(f"No tables found in {page_url}")
 
+    else:
+        df = tables[0]
+        # removing last row with "nan" values
         if df.iloc[-1].isna().all():
             df = df.iloc[:-1]
         return df
 
 
-def _get_city_population_data_frame_from_raw_data(museum_cities_df: pd.DataFrame) -> pd.DataFrame:
+def get_city_population_data_frame_from_raw_data() -> pd.DataFrame:
-    """
+    """Get the population data from a static csv file downloaded from
+    https://simplemaps.com/data/world-cities website.
 
-    :param museum_cities_df:
+    Returns:
-    :return:
+        A data frame of the cities population data.
     """
     raw_df = pd.read_csv(constants.RAW_POPULATION_DATA_FILE)
     raw_df = raw_df.drop(["city_ascii", "lat", "lng", "iso2", "iso3", "capital", "id"], axis=1)
@@ -54,25 +63,39 @@ def _get_city_population_data_frame_from_raw_data(museum_cities_df: pd.DataFrame
     return cities_population
 
 
-def _update_city_population(museum_df) -> pd.DataFrame:
+def update_city_population(museum_dataframe: pd.DataFrame) -> pd.DataFrame:
-    city_pop = _get_city_population_data_frame_from_raw_data(museum_df)
+    """Add a population column corresponding to city name to the provided museum data frame from
-    museum_df = pd.merge(
+    a population dataframe.
-        museum_df,
+
+    Args:
+        museum_dataframe (): The museum data frame.
+
+    Returns:
+        The updated museum data frame with population column.
+    """
+    city_pop = get_city_population_data_frame_from_raw_data(museum_dataframe)
+    museum_dataframe = pd.merge(
+        museum_dataframe,
         city_pop,
         left_on=["City_Clean", "Country"],
         right_on=["city", "country"],
         how="left"
     )
-    museum_df.drop(columns=["city", "country"], inplace=True)
+    museum_dataframe.drop(columns=["city", "country"], inplace=True)
 
-    return museum_df
+    return museum_dataframe
 
 
 def get_museum_data() -> pd.DataFrame:
-    url = constants.DEFAULT_MUSEUM_DATA_SOURCE_URL
+    """Fetch the museum data from the Wikipedia page, Convert and transform to appropriate format and
-    page = url.rsplit("/", 1)[-1]
+    data types. Fixes ambiguity where city name may have a detailed syntax like Washington, D.C. keeping
-    museum_df = get_first_data_table_from_wikipedia(page_title=page)
+    only the name Washington.
-    # print(museum_df.to_string())
+
+    Returns:
+        A pandas data frame of museum data.
+    """
+    page_title = constants.DEFAULT_MUSEUM_DATA_SOURCE_URL.rsplit("/", 1)[-1]
+    museum_df = get_first_data_table_from_wikipedia(page_title=page_title)
 
     city_corrections = {
         'Washington, D.C.': 'Washington',
@@ -86,7 +109,7 @@ def get_museum_data() -> pd.DataFrame:
     museum_df["Visitors_clean"] = museum_df["Visitors_clean"].astype(int)
     museum_df["Visitors"] = museum_df["Visitors_clean"]
     museum_df = museum_df.drop(columns=["Visitors_clean"])
-    museum_df = _update_city_population(museum_df)
+    museum_df = update_city_population(museum_df)
     return museum_df