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vertica · Oct 24, 2024 · add5b4f · add5b4f
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diff --git a/docs/source/contribution_guidelines_code_auto_doc_example.rst b/docs/source/contribution_guidelines_code_auto_doc_example.rst
@@ -315,13 +315,13 @@ And to reference a module named vDataFrame:
 
     .. seealso:: 
 
-        :py:mod:`vDataFrame`
+        :py:func:`~verticapy.vDataFrame`
 
 **Output:**
 
 .. seealso:: 
 
-   :py:mod:`vDataFrame`
+   :py:func:`~verticapy.vDataFrame`
 
 Now you can go through the below examples to understand the usage in detail. From the examples you will note a few things:
 

diff --git a/docs/source/examples_business_booking.rst b/docs/source/examples_business_booking.rst
@@ -279,7 +279,7 @@ It looks like there are two main predictors: 'mode_hotel_cluster_count' and 'tri
 - look for a shorter trip duration.
 - not click as much (spend more time at the same web page).
 
-Let's add our prediction to the :py:mod:`vDataFrame`.
+Let's add our prediction to the :py:func:`~verticapy.vDataFrame`.
 
 .. code-block:: python
 

diff --git a/docs/source/examples_business_churn.rst b/docs/source/examples_business_churn.rst
@@ -203,7 +203,7 @@ ________
 Machine Learning
 -----------------
 
-:py:func:`~verticapy.machine_learning.vertica.LogisticRegression` is a very powerful algorithm and we can use it to detect churns. Let's split our :py:mod:`vDataFrame` into training and testing set to evaluate our model.
+:py:func:`~verticapy.machine_learning.vertica.LogisticRegression` is a very powerful algorithm and we can use it to detect churns. Let's split our :py:func:`~verticapy.vDataFrame` into training and testing set to evaluate our model.
 
 .. ipython:: python
 

diff --git a/docs/source/examples_business_football.rst b/docs/source/examples_business_football.rst
@@ -979,7 +979,7 @@ To compute a ``k-means`` model, we need to find a value for 'k'. Let's draw an :
     model_kmeans.fit("football_clustering", predictors)
     model_kmeans.clusters_
 
-Let's add the prediction to the :py:mod:`vDataFrame`.
+Let's add the prediction to the :py:func:`~verticapy.vDataFrame`.
 
 .. code-block:: python
 
@@ -1983,7 +1983,7 @@ Looking at the importance of each feature, it seems like direct confrontations a
 .. raw:: html
     :file: /project/data/VerticaPy/docs/figures/examples_football_features_importance.html
 
-Let's add the predictions to the :py:mod:`vDataFrame`.
+Let's add the predictions to the :py:func:`~verticapy.vDataFrame`.
 
 Draws are pretty rare, so we'll only consider them if a tie was very likely to occur.
 

diff --git a/docs/source/examples_business_insurance.rst b/docs/source/examples_business_insurance.rst
@@ -38,7 +38,7 @@ You can skip the below cell if you already have an established connection.
     
     vp.connect("VerticaDSN")
 
-Let's create a new schema and assign the data to a :py:mod:`vDataFrame` object.
+Let's create a new schema and assign the data to a :py:func:`~verticapy.vDataFrame` object.
 
 .. code-block:: ipython
 

diff --git a/docs/source/examples_business_movies.rst b/docs/source/examples_business_movies.rst
@@ -43,7 +43,7 @@ You can skip the below cell if you already have an established connection.
     
     vp.connect("VerticaDSN")
 
-Let's  create a new schema and assign the data to a :py:mod:`vDataFrame` object.
+Let's  create a new schema and assign the data to a :py:func:`~verticapy.vDataFrame` object.
 
 .. code-block:: ipython
 
@@ -349,7 +349,7 @@ Let's join our notoriety metrics for actors and directors with the main dataset.
         ],
     )
 
-As we did many operation, it can be nice to save the :py:mod:`vDataFrame` as a table in the Vertica database.
+As we did many operation, it can be nice to save the :py:func:`~verticapy.vDataFrame` as a table in the Vertica database.
 
 .. code-block:: python
 
@@ -754,7 +754,7 @@ Let's create a model to evaluate an unbiased score for each different movie.
 .. raw:: html
     :file: /project/data/VerticaPy/docs/figures/examples_movies_filmtv_complete_model_report.html
 
-The model is good. Let's add it in our :py:mod:`vDataFrame`.
+The model is good. Let's add it in our :py:func:`~verticapy.vDataFrame`.
 
 .. code-block:: python
 
@@ -926,7 +926,7 @@ By looking at the elbow curve, we can choose 15 clusters. Let's create a ``k-mea
     model_kmeans.fit(filmtv_movies_complete, predictors)
     model_kmeans.clusters_
 
-Let's add the clusters in the :py:mod:`vDataFrame`.
+Let's add the clusters in the :py:func:`~verticapy.vDataFrame`.
 
 
 .. code-block:: python

diff --git a/docs/source/examples_business_smart_meters.rst b/docs/source/examples_business_smart_meters.rst
@@ -44,7 +44,7 @@ You can skip the below cell if you already have an established connection.
     
     vp.connect("VerticaDSN")
 
-Create the :py:mod:`vDataFrame` of the datasets:
+Create the :py:func:`~verticapy.vDataFrame` of the datasets:
 
 .. code-block:: python
 

diff --git a/docs/source/examples_business_spam.rst b/docs/source/examples_business_spam.rst
@@ -138,7 +138,7 @@ Let's compute some statistics using the length of the message.
 .. raw:: html
     :file: /project/data/VerticaPy/docs/figures/examples_spam_table_clean_2.html
 
-Let's add the most occurent words in our :py:mod:`vDataFrame` and compute the correlation vector.
+Let's add the most occurent words in our :py:func:`~verticapy.vDataFrame` and compute the correlation vector.
 
 .. code-block:: python
 

diff --git a/docs/source/examples_business_spotify.rst b/docs/source/examples_business_spotify.rst
@@ -88,7 +88,7 @@ Create a new schema, "spotify".
 Data Loading
 -------------
 
-Load the datasets into the :py:mod:`vDataFrame` with :py:func:`~verticapy.read_csv` and then view them with :py:func:`~verticapy.vDataFrame.head`.
+Load the datasets into the :py:func:`~verticapy.vDataFrame` with :py:func:`~verticapy.read_csv` and then view them with :py:func:`~verticapy.vDataFrame.head`.
 
 .. code-block::
 

diff --git a/docs/source/examples_learn_iris.rst b/docs/source/examples_learn_iris.rst
@@ -221,7 +221,7 @@ Let's plot the model to see the perfect separation.
 .. raw:: html
     :file: /project/data/VerticaPy/docs/figures/examples_model_plot.html
 
-We can add this probability to the :py:mod:`vDataFrame`.
+We can add this probability to the :py:func:`~verticapy.vDataFrame`.
 
 .. code-block:: python
 
@@ -275,7 +275,7 @@ Let's create a model to classify the Iris virginica.
 .. raw:: html
     :file: /project/data/VerticaPy/docs/figures/examples_iris_table_ml_cv_2.html
 
-We have another excellent model. Let's add it to the :py:mod:`vDataFrame`.
+We have another excellent model. Let's add it to the :py:func:`~verticapy.vDataFrame`.
 
 .. code-block:: python
 

diff --git a/docs/source/examples_learn_pokemon.rst b/docs/source/examples_learn_pokemon.rst
@@ -250,7 +250,7 @@ In terms of missing values, our only concern is the Pokemon's second type (Type_
 .. raw:: html
     :file: /project/data/VerticaPy/docs/figures/examples_pokemon_table_clean_2.html
 
-Let's use the current_relation method to see how our data preparation so far on the :py:mod:`vDataFrame` generates SQL code.
+Let's use the current_relation method to see how our data preparation so far on the :py:func:`~verticapy.vDataFrame` generates SQL code.
 
 .. ipython:: python
 

diff --git a/docs/source/examples_learn_titanic.rst b/docs/source/examples_learn_titanic.rst
@@ -302,7 +302,7 @@ Survival correlates strongly with whether or not a passenger has a lifeboat (the
 - Passengers with a lifeboat
 - Passengers without a lifeboat
 
-Before we move on: we did a lot of work to clean up this data, but we haven't saved anything to our Vertica database! Let's look at the modifications we've made to the :py:mod:`vDataFrame`.
+Before we move on: we did a lot of work to clean up this data, but we haven't saved anything to our Vertica database! Let's look at the modifications we've made to the :py:func:`~verticapy.vDataFrame`.
 
 .. ipython:: python
 
@@ -322,7 +322,7 @@ VerticaPy dynamically generates SQL code whenever you make modifications to your
     vp.set_option("sql_on", False)
     print(titanic.info())
 
-Let's move on to modeling our data. Save the :py:mod:`vDataFrame` to your Vertica database.
+Let's move on to modeling our data. Save the :py:func:`~verticapy.vDataFrame` to your Vertica database.
 
 .. ipython:: python
     :okwarning:

diff --git a/docs/source/examples_understand_africa_education.rst b/docs/source/examples_understand_africa_education.rst
@@ -260,7 +260,7 @@ Eight seems to be a suitable number of clusters. Let's compute a ``k-means`` mod
     model = KMeans(n_cluster = 8)
     model.fit(africa, X = ["lon", "lat"])
 
-We can add the prediction to the :py:mod:`vDataFrame` and draw the scatter map.
+We can add the prediction to the :py:func:`~verticapy.vDataFrame` and draw the scatter map.
 
 .. code-block:: python
 
@@ -500,7 +500,7 @@ Let's look at the feature importance for each model.
 
 Feature importance between between math score and the reading score are almost identical.
 
-We can add these predictions to the main :py:mod:`vDataFrame`.
+We can add these predictions to the main :py:func:`~verticapy.vDataFrame`.
 
 .. code-block:: python
 

diff --git a/docs/source/examples_understand_covid19.rst b/docs/source/examples_understand_covid19.rst
@@ -283,14 +283,14 @@ Because of the upward monotonic trend, we can also look at the correlation betwe
 
     covid19["elapsed_days"] = covid19["date"] - fun.min(covid19["date"])._over(by = [covid19["state"]])
 
-We can generate the SQL code of the :py:mod:`vDataFrame` 
-to see what happens behind the scenes when we modify our data from within the :py:mod:`vDataFrame`.
+We can generate the SQL code of the :py:func:`~verticapy.vDataFrame` 
+to see what happens behind the scenes when we modify our data from within the :py:func:`~verticapy.vDataFrame`.
 
 .. ipython:: python
 
     print(covid19.current_relation())
 
-The :py:mod:`vDataFrame` memorizes all of our operations on the data to dynamically generate the correct SQL statement and passes computation and aggregation to Vertica.
+The :py:func:`~verticapy.vDataFrame` memorizes all of our operations on the data to dynamically generate the correct SQL statement and passes computation and aggregation to Vertica.
 
 Let's see the correlation between the number of deaths and the other variables.
 

diff --git a/docs/source/user_guide_data_ingestion.rst b/docs/source/user_guide_data_ingestion.rst
@@ -148,7 +148,7 @@ In the following example, we will use :py:func:`~verticapy.read_csv` to ingest a
 
     titanic = load_titanic()
 
-To convert a subset of the dataset to a CSV file, select the desired rows in the dataset and use the :py:func:`~verticapy.to_csv` :py:mod:`vDataFrame` method:
+To convert a subset of the dataset to a CSV file, select the desired rows in the dataset and use the :py:func:`~verticapy.to_csv` :py:func:`~verticapy.vDataFrame` method:
 
 .. ipython:: python
 

diff --git a/docs/source/user_guide_data_preparation_decomposition.rst b/docs/source/user_guide_data_preparation_decomposition.rst
@@ -89,7 +89,7 @@ Notice that the predictors are now independant and combined together and they ha
 
     model.explained_variance_
 
-Most of the information is in the first two components with more than 97.7% of explained variance. We can export this result to a :py:mod:`vDataFrame`.
+Most of the information is in the first two components with more than 97.7% of explained variance. We can export this result to a :py:func:`~verticapy.vDataFrame`.
 
 .. code-block::
 

diff --git a/docs/source/user_guide_data_preparation_features_engineering.rst b/docs/source/user_guide_data_preparation_features_engineering.rst
@@ -10,7 +10,7 @@ Features engineering makes use of many techniques - too many to go over in this
 Customized Features Engineering
 --------------------------------
 
-To build a customized feature, you can use the :py:func:`~verticapy.vDataFrame.eval` method of the :py:mod:`vDataFrame`. Let's look at an example with the well-known 'Titanic' dataset.
+To build a customized feature, you can use the :py:func:`~verticapy.vDataFrame.eval` method of the :py:func:`~verticapy.vDataFrame`. Let's look at an example with the well-known 'Titanic' dataset.
 
 .. code-block:: python