Perhaps commands that don't require immediate confirmation, like "Hey Siri, send my next meeting's location to my Volvo" or "Hey Siri, send to my Volvo" With the latency it exhibits, I just can't imagine it being that satisfying to overlay Siri as an interface for it. However, occasionally VOC is slow to complete request. Quick commands like lock, unlock, check status, etc would be great. I had and I still couldn't complete requests with it. # Define the start and end dates (full year of 2022) start_date = datetime(2022, 1, 1) end_date = datetime(2022, 12, 31) # Download the dividends for each symbol and concatenate the results dfs = for symbol in symbols: stock = yf.Ticker(symbol) dividends = stock.history(start=start_date, end=end_date).to_frame(name=symbol) dfs.append(dividends) #drop columns with NaN and/or zero values df = pd.concat(dfs, axis=1).dropna(axis=1, how='all') df = df.loc df.index.name = "Date" #add 'Annual Dividend' as the last row df.loc = df.sum(axis=0) #create a new frame with with 'Annual Dividend' row only annual_div = df.loc.I was wondering the same thing the other day and looked to make sure that I had granted VOC access to Siri. The dividend payments for each stock will then be added up so that a new dataframe with only the annual dividend totals can be created. Once concatenated, we will drop any symbols that do not pay any dividends - where entire columns have NaN or zero values. We will then concatenate the results into a single dataframe. For each symbol, we will take yf.Ticker() to download the stock data and. To download dividend information for multiple stocks using the yfinance library, we will use a for loop to iterate over a list of symbols. Here is the first 5 tickers from the symbols list: companies=pd.read_html('') table = companies #the below code removes tickers that have no data (from previous experience) df = ("BRK.B|BF.B") = False] symbols = df.to_list() To retrieve the symbols from the appropriate table on the webpage, we will utilize the pd.read html() function. We will get the list of stocks by scraping the Wikipedia page. As previously said, we will examine which S&P500 stocks pay dividends. The end result will be a a single dataframe indexed with the desired stock symbols and the column with the total paid dividends. We can easily and download dividend information for multiple stocks using the yfinance library. import yfinance as yf import pandas as pd from datetime import datetime import matplotlib.pyplot as plt Let’s first import all libraries that we will need throughout this post. It’s important to consider the overall financial health of the company, the sustainability of the dividend, and the company’s growth prospects before making an investment decision based solely on the dividend yield. While a high dividend yield can be attractive to investors seeking regular income, it does not necessarily mean that it is the best investment. This is post is only educational and it is not an investment advice. We will check which stocks that are part of S&P500 index paid dividends to their investors in 2022. In this quick blog post, we will show how to automate the process of finding, analyzing and comparing dividend data using the yfinance library in Python. Finding and comparing dividend stocks may be a daunting process. Dividends are a major source of passive income for many investors.
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