def test_part_list() -> None:
# Have to initial new to avoid time out error
trends = tdc.TrendsCollector()
builder = tdc.DesignerTrendsCollector(["test", "wordcloud"])
trends.builder = builder
trends.find_related_queries()
assert builder.trends.list_product_parts == "Product parts: related_queries"
def test_wordcloud() -> None:
# Have to initial new to avoid time out error
trends = tdc.TrendsCollector()
builder = tdc.DesignerTrendsCollector(["test", "wordcloud"])
trends.builder = builder
trends.make_wordcloud()
assert type(builder.trends.return_product) == type(dict())
def four_step_trendsearch(
keyword_list: list,
timeframe: str = "today 5-y",
language: str = "en-US",
category: int = 0,
timezone: int = 360,
country: str = "",
property_filter="",
resolution: str = "COUNTRY",
**kwargs,
) -> dict:
"""four step search
A four step search includes:
1. Overtime
2. By regions
3. By related topics
4. By related queries
Args:
keyword_list (list): Keyword-list with the items to search for.
timeframe (str, optional): Time frame, respectively, period to search for.
Defaults to "today 5-y".
language (str, optional): Search language. Defaults to "en-US".
category (int, optional): Define a specific [search category](https://github.com/pat310/google-trends-api/wiki/Google-Trends-Categories). Defaults to 0.
timezone (int, optional): [Search timezone](https://developers.google.com/maps/documentation/timezone/overview). Defaults to 360.
country (str, optional): The country, where to search for. Defaults to "".
property_filter (str, optional): Property filer of the search; only in news, images, YouTube, shopping. Defaults to "".
resolution (str, optional): The resolution / zoom-factor, where to search for. Defaults to "COUNTRY".
Returns:
dict: Dictionary with the keys: 'interest_over_time', 'interest_by_region', 'related_topics', 'related_queries' and its dataframes.
"""
trends = trends_collector.TrendsCollector()
builder = trends_collector.DesignerTrendsCollector(
keyword_list=keyword_list,
timeframe=timeframe,
language=language,
category=category,
timezone=timezone,
country=country,
property_filter=property_filter,
**kwargs,
)
trends.builder = builder
trends.find_interest_over_time()
trends.find_interest_by_region(resolution=resolution)
trends.find_related_topics()
trends.find_related_queries()
return builder.trends.return_product
""" """ ### Loading the model `trends_collector` """ from ai2business.kpi_collector import trends_collector """ Setup the search with the keyword list and write it into the `builder` """ keyword_list: list = ["Corona", "S&P 500", "Hope"] trends = trends_collector.TrendsCollector() builder = trends_collector.DesignerTrendsCollector(keyword_list=keyword_list) trends.builder = builder """ ### Add the `trend-search` functions, in this particular case: 1. Searching over time 2. Searching in a special area 3. Searching for related queries 4. Searching for realted topics All the information will be generated and stored in the `builder`. """ trends.find_interest_over_time()
"""Test-Enviroment for trends_collector.py"""
import pandas as pd
from ai2business.kpi_collector import trends_collector as tdc
trends = tdc.TrendsCollector()
builder = tdc.DesignerTrendsCollector(["AI", "Business", "AI2Business"])
trends.builder = builder
def test_interest_over_time() -> None:
trends.find_interest_over_time()
assert type(builder.trends.return_product["interest_over_time"]) == type(
pd.DataFrame())
def test_interest_by_region() -> None:
trends.find_interest_by_region()
assert type(builder.trends.return_product["interest_by_region"]) == type(
pd.DataFrame())
def test_trending_searches() -> None:
trends.find_trending_searches("japan")
assert type(builder.trends.return_product["trending_searches"]) == type(
pd.DataFrame())
def test_today_searches() -> None:
trends.find_today_searches()
assert type(builder.trends.return_product["today_searches"]) == type(