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| author | eeshabarua <61480751+eeshabarua@users.noreply.github.com> | 2025-03-12 22:47:09 -0400 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2025-03-12 21:47:09 -0500 |
| commit | f612a123f135f93da662ca0543998dc90a0c50d6 (patch) | |
| tree | ccd8fa57d72e983c02a50caabe6121eb6c2e373c /scripts-2023/README.md | |
| parent | 87472cbe8f20612b21f26f8b1a8e8f3c2d55163d (diff) | |
* Add 2023 Scripts and Data
* Update README.md
Diffstat (limited to 'scripts-2023/README.md')
| -rw-r--r-- | scripts-2023/README.md | 10 |
1 files changed, 10 insertions, 0 deletions
diff --git a/scripts-2023/README.md b/scripts-2023/README.md new file mode 100644 index 0000000..0a29c18 --- /dev/null +++ b/scripts-2023/README.md @@ -0,0 +1,10 @@ +# Populating 2023 Data into a CSV + +This is the (crude) first draft of documentation for the 2023 data pipeline! + +How this works: + +- All the input/output dataframes that the scripts operate with are located in the directory data-2023/in/ and data-2023/out/. +- To follow the pipeline, drag/drop the new year's investment portfolio .CSV file into data-2023/in/. +- Follow 01-clean.ipynb and 02-populate-tickers.ipynb respectively. +- The final dataframe, will be populated as 'data-2023/out/df*with_ticker*...csv'. Please replicate this process and compare your results with 'data-2023/out/master_df_with_ticker.csv', which is the final dataframe that I uploaded to MongoDB. |