Is there a more elegant way to concatenate data frames?

[db042190]

Hi, I'm picturing a script that will include in a loop everything after the setting of "today" and before the "print" prototyped in the code below. The loop will read symbols from a notepad file line by line. I would substitute the hardcoded Ticker settings you see below with the symbol just read from notepad. If the record read is the first, the download call where AAPL is now a placeholder would be called. Otherwise the placeholder for the WMT call would be executed.

The download method allows passing multiple tickers in a call but long term I think this will be more flexible. From what I can tell, if you don't pass multiple symbols in a call, you don't get a column for Ticker.

Can I make this more elegant? It seems awkward.

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import yfinance as yf import pandas as pd from datetime import date   today = date.today()   Ticker="AAPL" data1 = yf.download(Ticker, start="2023-05-01", end=today).round(2) data1["Ticker"]=Ticker   Ticker="WMT" data2 = yf.download(Ticker, start="2023-05-01", end=today).round(2) data2["Ticker"]=Ticker   data1=[data1,data2] data1 = pd.concat(data1)   print(data1)

[deanhystad]

Looks fine to me.

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import yfinance as yf import pandas as pd from datetime import date   tickers = ("AAPL", "WMT")  # Or read from file today = date.today() start = today - timedelta(days=7) data = None for ticker in tickers:     x = yf.download(ticker, start=start, end=today, progress=False).round(2)     x.insert(0, "Ticker", ticker)     if data is None:         data = x     else:         data = pd.concat((data, x)) data = data.sort_index() print(data)

Output:
           Ticker    Open    High     Low   Close  Adj Close    Volume
Date
2023-06-06   AAPL  179.97  180.12  177.43  179.21     179.21  64848400
2023-06-06    WMT  149.70  150.19  148.51  149.78     149.78   5005200
2023-06-07   AAPL  178.44  181.21  177.32  177.82     177.82  61944600
2023-06-07    WMT  149.25  150.36  149.04  150.00     150.00   8085500
2023-06-08   AAPL  177.90  180.84  177.46  180.57     180.57  50214900
2023-06-08    WMT  150.39  152.43  149.79  152.17     152.17   6291000
2023-06-09   AAPL  181.50  182.23  180.63  180.96     180.96  48870700
2023-06-09    WMT  152.16  153.72  151.60  153.09     153.09   5201300
2023-06-12   AAPL  181.27  183.89  180.97  183.79     183.79  54274900
2023-06-12    WMT  153.43  154.30  153.17  154.10     154.10   4904500

I moved the ticker column. I think it makes more sense to place it ahead of the financial information. Also sorted the resulting table by the date index and changed the starting data to a calculation instead of a string. Just for fun.

[db042190]

much more elegant. thank you.

[snippsat]

Some tips about dates in Pandas and if look Date so is lower in header column and need a fix.
So here have i remove datatime import an used Pandas own date functionality
Can fine use both,but when first has import Pandas don't need a addition import of datetime.

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import yfinance as yf import pandas as pd   tickers = ("AAPL", "WMT")  # Or read from file today = pd.to_datetime("today") start = today - pd.Timedelta(days=7) data = None for ticker in tickers:     x = yf.download(ticker, start=start, end=today, progress=False).round(2)     x.insert(0, "Ticker", ticker)     if data is None:         data = x     else:         data = pd.concat((data, x))     data = data.sort_index() print(data)

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>>> data            Ticker    Open    High     Low   Close  Adj Close    Volume Date                                                                  2023-06-06   AAPL  179.97  180.12  177.43  179.21     179.21  64848400 2023-06-06    WMT  149.70  150.19  148.51  149.78     149.78   5005200 2023-06-07   AAPL  178.44  181.21  177.32  177.82     177.82  61944600 2023-06-07    WMT  149.25  150.36  149.04  150.00     150.00   8085500 2023-06-08   AAPL  177.90  180.84  177.46  180.57     180.57  50214900 2023-06-08    WMT  150.39  152.43  149.79  152.17     152.17   6291000 2023-06-09   AAPL  181.50  182.23  180.63  180.96     180.96  48870700 2023-06-09    WMT  152.16  153.72  151.60  153.09     153.09   5201300 2023-06-12   AAPL  181.27  183.89  180.97  183.79     183.79  54274900 2023-06-12    WMT  153.43  154.30  153.17  154.10     154.10   4904500 2023-06-13   AAPL  182.80  184.15  182.47  183.13     183.13  27582874 2023-06-13    WMT  154.52  155.49  154.07  155.40     155.40   1844848   >>> data.info() <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 12 entries, 2023-06-06 to 2023-06-13 Data columns (total 7 columns):  #   Column     Non-Null Count  Dtype  ---  ------     --------------  -----   0   Ticker     12 non-null     object  1   Open       12 non-null     float64  2   High       12 non-null     float64  3   Low        12 non-null     float64  4   Close      12 non-null     float64  5   Adj Close  12 non-null     float64  6   Volume     12 non-null     int64  dtypes: float64(5), int64(1), object(1) memory usage: 768.0+ bytes

So in info we see no Date info,to fix this.

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>>> data = data.reset_index() >>> data.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 12 entries, 0 to 11 Data columns (total 8 columns):  #   Column     Non-Null Count  Dtype         ---  ------     --------------  -----          0   Date       12 non-null     datetime64[ns]  1   Ticker     12 non-null     object         2   Open       12 non-null     float64        3   High       12 non-null     float64        4   Low        12 non-null     float64        5   Close      12 non-null     float64        6   Adj Close  12 non-null     float64        7   Volume     12 non-null     int64         dtypes: datetime64[ns](1), float64(5), int64(1), object(1) memory usage: 900.0+ bytes

So now have a working DataFrame,Date see datetime64[ns]
Then can eg do a plot with Date and low last 90 days,high using eg seaborn

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import yfinance as yf import pandas as pd import matplotlib.pyplot as plt import seaborn as sns   tickers = ("AAPL", "WMT")  # Or read from file today = pd.to_datetime("today") start = today - pd.Timedelta(days=90) data = None for ticker in tickers:     x = yf.download(ticker, start=start, end=today, progress=False).round(2)     x.insert(0, "Ticker", ticker)     if data is None:         data = x     else:         data = pd.concat((data, x))     data = data.sort_index() #print(data) data = data.reset_index() # Plot plt.figure(figsize=(15, 6)) sns.set_style("darkgrid") sns.lineplot(data=data, x='Date', y='High', label='High') sns.lineplot(data=data, x='Date', y='Low', label='Low') plt.xlabel('Date') plt.ylabel('Price') plt.title('High and Low Stock Prices') plt.legend() plt.show()