Thank you!

That works.

I was not familiar with the use of the shebang in that way, with the
dependencies in the script.  That eliminates the need to set up venv
ahead of time, or having to activate and deactivate the environment. 
This is virtual Environment made easy!

Marc

On 6/9/25 9:35 PM, Hector Gonzalez Jaime via Dng wrote:
>
> On 6/9/25 20:15, Marc Shapiro via Dng wrote:
>> A while back, I posted about issues with using yfinance and python's
>> virtual environments.  I was having problems with getting the venv to
>> work, even though it seemed fairly straightforward.  I ended up
>> rewriting the python scrips to avoid using yfinance to get around the
>> problem.
>>
>> I decided, however, that I really should get venvs working for the
>> next time that I write something in python.  So I created a venv and
>> installed yfinance in it.  As expected, it pulled in a slew of other
>> packages.  I copied the old version of my program (that requires
>> yfinance) into the directory containing the venv and tried to run
>> it.  The results are as follows:
>>
>> ---------- First, it says that it can't find curl_cffi ----------
>>
>> ModuleNotFoundError: No module named 'curl_cffi'
>>
>> What am I doing wrong?  It seems like everything should be working,
>> now, but it isn't.
>>
>> Marc
>
> I don´t see anything wrong with what you did, I just did the same in
> my system (daedalus) and a simple yfinance program runs.
>
> Have you tried with uv?  https://docs.astral.sh/uv/
>
> A program to get a ticker with uv and yfinance:
>
> ----snip----
> #!/usr/bin/env -S uv run --script
> # /// script
> # requires-python = "~=3.13"
> # dependencies = [
> #     "yfinance",
> # ]
> # ///
>
> import yfinance as yf
> ticker = 'AAPL'
> data = yf.download(ticker, start='2024-01-01', end='2025-01-01')
> print(data.info())
> print(data.head())
>
> ----snip----
>
> I put that in a file, and made it executable, then ran it.
>
> It returns this:
>
> FutureWarning: YF.download() has changed argument auto_adjust default
> to True
>   data = yf.download(ticker, start='2024-01-01', end='2025-01-01')
> [*********************100%***********************]  1 of 1 completed
> <class 'pandas.core.frame.DataFrame'>
> DatetimeIndex: 252 entries, 2024-01-02 to 2024-12-31
> Data columns (total 5 columns):
>  #   Column          Non-Null Count  Dtype
> ---  ------          --------------  -----
>  0   (Close, AAPL)   252 non-null    float64
>  1   (High, AAPL)    252 non-null    float64
>  2   (Low, AAPL)     252 non-null    float64
>  3   (Open, AAPL)    252 non-null    float64
>  4   (Volume, AAPL)  252 non-null    int64
> dtypes: float64(4), int64(1)
> memory usage: 11.8 KB
> None
> Price            Close        High         Low        Open Volume
> Ticker            AAPL        AAPL        AAPL        AAPL AAPL
> Date
> 2024-01-02  184.290405  187.070052  182.553128  185.789422 82488700
> 2024-01-03  182.910507  184.528662  182.096461  182.880727 58414500
> 2024-01-04  180.587540  181.758954  179.565029  180.825785 71983600
> 2024-01-05  179.862823  181.431339  178.860172  180.666948 62303300
> 2024-01-08  184.210983  184.250701  180.180502  180.766209 59144500
>
>>
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