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fix(time-series/ex3): update subejct and audit examples to be coherent 

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nprimo 2023-11-27 10:55:12 +00:00 committed by Niccolò Primo
parent 8aa262a4d8
commit 463aaa7c54
2 changed files with 12 additions and 8 deletions
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18
subjects/ai/time-series/README.md
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@ -118,15 +118,19 @@ market_data = pd.DataFrame(index=index,
1. **Without using a for loop**, compute the daily returns (return(d) = (price(d)-price(d-1))/price(d-1)) for all the companies and returns a DataFrame as:
| Date | ('Price', 'AAPL') | ('Price', 'AMZN') | ('Price', 'DAI') | ('Price', 'FB') | ('Price', 'GE') |
| :------------------ | ----------------: | ----------------: | ---------------: | --------------: | --------------: |
| 2021-01-01 00:00:00 | nan | nan | nan | nan | nan |
| 2021-01-04 00:00:00 | 1.01793 | 0.0512955 | 3.84709 | -0.503488 | 0.33529 |
| 2021-01-05 00:00:00 | -0.222884 | -1.64623 | -0.71817 | -5.5036 | -4.15882 |
```console
Ticker AAPL AMZN DAI FB GE
Date
2021-01-01 NaN NaN NaN NaN NaN
2021-01-04 -2.668008 -4.716002 -1.885721 0.496173 1.862998
2021-01-05 -2.194111 -2.747143 -0.165338 0.318410 0.085519
2021-01-06 -1.164307 -1.194895 -2.595224 -0.219974 -0.805512
2021-01-07 3.428472 3.778445 -0.956788 -1.538637 0.108276
```
Note: The data is generated randomly, the values you may have a different results. But, this shows the expected DataFrame structure.
Note: The data is generated randomly, the values you may have lead to a different result. The above example shows the expected DataFrame structure.
`Hint use groupby`
`Hint use pivot_table`
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2
subjects/ai/time-series/audit/README.md
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@ -114,7 +114,7 @@ The first way to do it is to compute the return without for loop is to use `pct_
###### Is the outputted DataFrame's shape `(261, 5)` without having used a for loop and the is the output the same as the one returned with this line of code? The DataFrame contains random data. Make sure the output and the one returned by this code is based on the same DataFrame.
```python
market_data.loc[market_data.index.get_level_values('Ticker')=='AAPL'].sort_index().pct_change()
market_data.pivot_table(values="Price", index="Date", columns="Ticker").pct_change()
```
---