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docs: Update .Dataframe in README.md #73

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16 changes: 8 additions & 8 deletions README.md
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Expand Up @@ -162,10 +162,10 @@ In order to explore and understand the capabilities of MAM, a "Random DataFrame
attributions = MAM(random_df=True)
```

After the Object MAM is created, we can check out our database now with the addition of our **journey_id** and with sessions grouped in **journeys** using the **attriute *".DataFrame"***.
After the Object MAM is created, we can check out our database now with the addition of our **journey_id** and with sessions grouped in **journeys** using the **method *".as_pd_dataframe()"***.

```python
attributions.DataFrame
attributions.as_pd_dataframe()
```
<table>
<thead>
Expand Down Expand Up @@ -273,11 +273,11 @@ attributions.DataFrame

This attribute is **updated** for **every attribution model** generated. Only in the case of heuristic models, a new column is appended containing the attribution value given by said model.

>**Note:** The attribute *.DataFrame* does not interfere with any model calculations. Should it be altered by usage, the following results aren't affected.
>**Note:** The method *.as_pd_dataframe()* does not interfere with any model calculations. Should it be altered by usage, the following results aren't affected.

```python
attributions.attribution_last_click()
attributions.DataFrame
attributions.as_pd_dataframe()
```

<table>
Expand Down Expand Up @@ -436,7 +436,7 @@ attributions.group_by_channels_models
</table>


As with the *.DataFrame* attribute, *group_by_channels_models* is also updated for every model used **without the limitation** of not displaying algorithmic results.
As with the *.as_pd_dataframe()* method, *group_by_channels_models* is also updated for every model used **without the limitation** of not displaying algorithmic results.

```python
attributions.attribution_shapley()
Expand Down Expand Up @@ -507,13 +507,13 @@ attributions.group_by_channels_models

### About the Models

All heuristic models behave the same when using the attributes *.DataFrame* and *.group_by_channels_models*, as explained before, and the **output** of all heuristic **model's methods** return a **tuple** containing two **pandas Series**.
All heuristic models behave the same when using the method *.as_pd_dataframe()* and the attribute *.group_by_channels_models*, as explained before, and the **output** of all heuristic **model's methods** return a **tuple** containing two **pandas Series**.

```python
attribution_first_click = attributions.attribution_first_click()
```

The **first** Series of the tuple are the results in a **journey granularity**, similar to the observed in the *.DataFrame* attribute
The **first** Series of the tuple are the results in a **journey granularity**, similar to the observed in the *.as_pd_dataframe()* method.


```python
Expand Down Expand Up @@ -796,7 +796,7 @@ attributions.attribution_time_decay(

##### Markov Chains

Uppon being called, this model returns a tuple with **four** components. The first two (indexed 0 and 1) are just like with the heuristic models, with the representation of the *.DataFrame* and *.group_by_channels_models* respectively. As for the third and fourth components (indexed 2 and 3) the results are the **transition matrix** and the **removal effect table**.
Uppon being called, this model returns a tuple with **four** components. The first two (indexed 0 and 1) are just like with the heuristic models, with the representation of the *.as_pd_dataframe()* and *.group_by_channels_models* respectively. As for the third and fourth components (indexed 2 and 3) the results are the **transition matrix** and the **removal effect table**.

To start off, it is possible to indicate if same **state transitions** are considered or not (*e.g.* Direct to Direct).

Expand Down