Update yf_docs.py

yf_docs.py

@@ -269,87 +269,4 @@ Shape: (47, 4)
 - `1wk`: 1 week
 - `1mo`: 1 month
 - `3mo`: 3 months
-
-
-####
-In dealing with financial data from multiple tickers, specifically using yfinance and pandas, the process can be broken down into a few key steps: downloading the data, organizing it in a structured format, and accessing it in a way that aligns with the user's needs. Below, the answer is organized into clear, actionable segments.
-
-Downloading Data for Multiple Tickers
-Direct Download and DataFrame Creation
-Single Ticker, Single DataFrame Approach:
-
-For individual tickers, the DataFrame downloaded directly from yfinance comes with single-level column names but lacks a ticker column. By iterating over each ticker, adding a ticker column, and then combining these into a single DataFrame, a clear structure for each ticker's data is maintained.
-import yfinance as yf
-import pandas as pd
-
-tickerStrings = ['AAPL', 'MSFT']
-df_list = []
-for ticker in tickerStrings:
-    data = yf.download(ticker, group_by="Ticker", period='2d')
-    data['ticker'] = ticker  # Add ticker column
-    df_list.append(data)
-
-# Combine all dataframes into a single dataframe
-df = pd.concat(df_list)
-df.to_csv('ticker.csv')
-Condensed Single DataFrame Approach:
-
-Achieve the same result as above with a one-liner using list comprehension, streamlining the process of fetching and combining data.
-# Download 2 days of data for each ticker in tickerStrings, add a 'ticker' column for identification, and concatenate into a single DataFrame with continuous indexing.
-df = pd.concat([yf.download(ticker, group_by="Ticker", period='2d').assign(ticker=ticker) for ticker in tickerStrings], ignore_index=True)
-Multi-Ticker, Structured DataFrame Approach
-When downloading data for multiple tickers simultaneously, yfinance groups data by ticker, resulting in a DataFrame with multi-level column headers. This structure can be reorganized for easier access.
-Unstacking Column Levels:
-# Define a list of ticker symbols to download
-tickerStrings = ['AAPL', 'MSFT']
-
-# Download 2 days of data for each ticker, grouping by 'Ticker' to structure the DataFrame with multi-level columns
-df = yf.download(tickerStrings, group_by='Ticker', period='2d')
-
-# Transform the DataFrame: stack the ticker symbols to create a multi-index (Date, Ticker), then reset the 'Ticker' level to turn it into a column
-df = df.stack(level=0).rename_axis(['Date', 'Ticker']).reset_index(level=1)
-Handling CSV Files with Multi-Level Column Names
-To read a CSV file that has been saved with yfinance data (which often includes multi-level column headers), adjustments are necessary to ensure the DataFrame is accessible in the desired format.
-
-Reading and Adjusting Multi-Level Columns:
-# Read the CSV file. The file has multi-level headers, hence header=[0, 1].
-df = pd.read_csv('test.csv', header=[0, 1])
-
-# Drop the first row as it contains only the Date information in one column, which is redundant after setting the index.
-df.drop(index=0, inplace=True)
-
-# Convert the 'Unnamed: 0_level_0', 'Unnamed: 0_level_1' column (which represents dates) to datetime format.
-# This assumes the dates are in the 'YYYY-MM-DD' format.
-df[('Unnamed: 0_level_0', 'Unnamed: 0_level_1')] = pd.to_datetime(df[('Unnamed: 0_level_0', 'Unnamed: 0_level_1')])
-
-# Set the datetime column as the index of the DataFrame. This makes time series analysis more straightforward.
-df.set_index(('Unnamed: 0_level_0', 'Unnamed: 0_level_1'), inplace=True)
-
-# Clear the name of the index to avoid confusion, as it previously referred to the multi-level column names.
-df.index.name = None
-Flattening Multi-Level Columns for Easier Access
-Depending on the initial structure of the DataFrame, multi-level columns many need to be flattened to a single level, adding clarity and simplicity to the dataset.
-
-Flattening and Reorganizing Based on Ticker Level:
-For DataFrames where the ticker symbol is at the top level of the column headers:
-df.stack(level=0).rename_axis(['Date', 'Ticker']).reset_index(level=1)
-If the ticker symbol is at the bottom level:
-df.stack(level=1).rename_axis(['Date', 'Ticker']).reset_index(level=1)
-Individual Ticker File Management
-For those preferring to manage each ticker's data separately, downloading and saving each ticker's data to individual files can be a straightforward approach.
-
-Downloading and Saving Individual Ticker Data:
-for ticker in tickerStrings:
-    # Downloads historical market data from Yahoo Finance for the specified ticker.
-    # The period ('prd') and interval ('intv') for the data are specified as string variables.
-    data = yf.download(ticker, group_by="Ticker", period='prd', interval='intv')
-
-    # Adds a new column named 'ticker' to the DataFrame. This column is filled with the ticker symbol.
-    # This step is helpful for identifying the source ticker when multiple DataFrames are combined or analyzed separately.
-    data['ticker'] = ticker
-
-    # Saves the DataFrame to a CSV file. The file name is dynamically generated using the ticker symbol,
-    # allowing each ticker's data to be saved in a separate file for easy access and identification.
-    # For example, if the ticker symbol is 'AAPL', the file will be named 'ticker_AAPL.csv'.
-    data.tocsv(f'ticker{ticker}.csv')
 """