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‎Python_1_Dowload__YahooData.py‎

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# -*- coding: utf-8 -*-
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"""
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Created on Fri Nov 27 08:09:11 2020
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@author: Tin
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"""
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# Download stock data from Yahoo Finance
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import yfinance as yf
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yf.pdr_override()
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# input
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symbol = 'AAPL'
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start = '2014-01-01'
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end = '2018-01-01'
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# dataframe
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df = yf.download(symbol,start,end)
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# View the first 5 rows
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print('First 5 Rows')
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print(df.head())
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print('-'*80)
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# View the last 5 rows
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print('Last 5 Rows')
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print(df.tail())

‎Python_2_Modify_Date.py‎

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# -*- coding: utf-8 -*-
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"""
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Created on Fri Nov 27 08:09:11 2020
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@author: Tin
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"""
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# Modify Yahoo Dataframe Date
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import pandas as pd # Dataframe Library
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pd.set_option('max_columns', None) # To show all columns
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import yfinance as yf
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yf.pdr_override()
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# input
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symbol = 'AAPL'
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start = '2014-01-01'
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end = '2018-01-01'
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# dataframe
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data = yf.download(symbol,start,end)
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# View the first 5 rows
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print('First 5 Rows')
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print(data.head())
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print('-'*80)
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# Date becomes a columns
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df = data.copy() # Copy the original data
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dfn = df.reset_index()
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print(dfn.head())
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print('-'*80)
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# Add Year, Month, Day
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df['Year'] = df.index.year
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df['Month'] = df.index.month
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df['Day'] = df.index.day
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print('Year, Month, & Day')
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print(df.head())
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print('-'*80)
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# Convert Daily to Weekly
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weekly = data.copy()
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weekly = weekly.resample('W').last()
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print('Weekly Data')
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print(weekly.head())
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print('-'*80)
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# Convert Daily to Monthly
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monthly = data.copy()
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monthly = monthly.resample('1M').mean()
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print('Monthly Data')
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print(monthly.head())
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print('-'*80)
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# Choose Particular Year to analyze
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monthly = data.copy()
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monthly = monthly.reset_index()
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y2017 = monthly[monthly['Date'].dt.year==2017]
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print("Analyze Particular Year in Historical Data")
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print(y2017)
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print('-'*80)
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month_name = data.copy()
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# Convert Daily to Monthly
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# 'BMS', which stands for "business month start frequency"
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# 'BM', which stands for "business month end frequency"
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month_name = month_name.asfreq('BM')
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print('Number of the Month')
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print(month_name.head())
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print('-'*80)
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import calendar
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month_name['Month_Number'] = month_name.index.month
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month_name['Month_ABBR'] = month_name['Month_Number'].apply(lambda x: calendar.month_abbr[x])
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print('Abbreviation for Months')
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print(month_name.head())
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print('-'*80)
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print('Month Name')
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month_name['Month_Name'] = month_name['Month_Number'].apply(lambda x: calendar.month_name[x])
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print(month_name.head())
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print('-'*80)
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# Pivot Table Date
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df_months = pd.pivot_table(df, index=df.index.month, columns=df.index.year, values = 'Adj Close') # each months
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print('Year by Year')
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print(df_months)
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print('-'*80)
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df_days = pd.pivot_table(df, index=df.index.day, columns=df.index.year, values = 'Adj Close') # daily for one whole months
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print('Year by Year in daily rows')
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print(df_days)
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print('-'*80)
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df_all_columns = pd.pivot_table(df, index=df.index.month, columns=df.index.year)
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print('All columns in yearly')
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print(df_all_columns)
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print('-'*80)
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stock_data = df.copy()
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stock_data['Year'] = df.index.year
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stock_data['Month'] = df.index.month
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stock_data['Day'] = df.index.day
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stock_data['Week_Day'] = df.index.dayofweek
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stock_data['Week_Day_Name'] = df.index.strftime('%A')
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print('Number of day with M-F')
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print(stock_data.tail(10))
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print('-'*80)
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approach1 = stock_data.groupby(['Year', 'Month']).first()['Adj Close']
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print('# of Month')
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print(approach1.tail(12))
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print('-'*80)
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approach2 = stock_data.groupby(['Year', 'Day']).first()['Adj Close']
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print('# of Day')
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print(approach2.tail(12))
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print('-'*80)
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print('Convert Date to String')
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string_date = data.copy()
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string_date['Date'] = string_date.index
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print(string_date.head())
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print('-'*80)
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string_date['Date'] = string_date['Date'].dt.strftime("%Y%m%d").astype(int)
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print('Convert Date to Numbers')
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print(string_date.head())
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