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

with task

subjects/ai/time-series/README.md

@@ -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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subjects/ai/time-series/audit/README.md

@@ -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()
 ```
 
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