Python all() function
Much of my coding efforts are directed towards analyzing financial data specifically targeting potential cash secured puts or covered calls. Recently, I had to refine some market overview displays. I needed a listing of the current market “gainers” and market “losers”. There is one site which I scrape using panda read_html function to grab a hadful of tables. My problem arises from two factors. One is there are no labels or titles to the tables and they can vary. The a table can arise from special (temporart) events and the weekends or holidays might call for the tables to re-arrange their order. This requires that I look directly at the panda table and derive which table I might be examining. One table I look for lists all the “gainers” currenly in the market. What I do know is that the third column of that table will always contain a plus sign (the % change of all the gainers). I had nested loops initially to do this but I refactored the code to use the “all()” function to discover which table has all “+” signs in the column 3. My standard caveat applies here - there is likely a better way to do this - so let me know.
‘‘‘python import pandas as pd import requests
def get_html_tables(url, timeout=4): content = requests.get(url).content tables = pd.read_html(content) return tables
tables = get_html_tables("/my/url") for table in tables: for x in range(6): val = table.iloc[x, 3] vals.append(val) all_gainers_b = all(["+" in str(v) for v in vals]) if all_gainers_b: gainers_table = table
’’’
