first correction

Author: oualib

docs/source/user_guide_data_exploration_charts.rst

@@ -28,13 +28,16 @@ Let's start with pies and histograms. Drawing the pie or histogram of a categori
 
 .. code-block::
 
+  # Setting the plotting lib
   vp.set_option("plotting_lib", "highcharts")
+  
   titanic = load_titanic()
   titanic["pclass"].bar()
 
 .. ipython:: python
   :suppress:
 
+  # Setting the plotting lib
   vp.set_option("plotting_lib", "highcharts")
   titanic = load_titanic()
   fig = titanic["pclass"].bar()
@@ -141,28 +144,28 @@ You can also change the occurences by another aggregation with the `method` and
 .. raw:: html
   :file: /project/data/VerticaPy/docs/figures/user_guides_data_exploration_titanic_age_hist_avs.html
 
-
 VerticaPy uses the same process for other graphics, like 2-dimensional histograms and bar charts.
 
 Let us showcase another plotting library for these plots.
 
-
 .. code-block::
-
+  
+  # Setting the plotting lib
   vp.set_option("plotting_lib", "plotly")
+
   titanic.bar(["pclass", "survived"])
 
 .. ipython:: python
   :suppress:
 
+  # Setting the plotting lib
   vp.set_option("plotting_lib", "plotly")
   fig = titanic.bar(["pclass", "survived"])
   fig.write_html("/project/data/VerticaPy/docs/figures/user_guides_data_exploration_titanic_bar_pclass_surv.html")
 
 .. raw:: html
   :file: /project/data/VerticaPy/docs/figures/user_guides_data_exploration_titanic_bar_pclass_surv.html
 
-
 .. note:: VerticaPy has three main plotting libraries. Look at :ref:`chart_gallery` section for all the different plots.
 
 .. code-block::
@@ -242,7 +245,7 @@ Box plots are useful for understanding statistical dispersion.
 .. raw:: html
   :file: /project/data/VerticaPy/docs/figures/user_guides_data_exploration_titanic_boxplot_one.html
 
-Scatter and bubble plots are also useful for identifying patterns in your data. Note, however, that these methods don't use aggregations; VerticaPy downsamples the data before plotting. You can use the 'max_nb_points' to limit the number of points and avoid unnecessary memory usage.
+Scatter and bubble plots are also useful for identifying patterns in your data. Note, however, that these methods don't use aggregations; VerticaPy downsamples the data before plotting. You can use the `max_nb_points` to limit the number of points and avoid unnecessary memory usage.
 
 .. code-block::
     
@@ -323,8 +326,10 @@ For more information on scatter look at :py:mod:`verticapy.vDataFrame.scatter`.
 Hexbin plots can be useful for generating heatmaps. These summarize data in a similar way to scatter plots, but compute aggregations to get the final results.
 
 .. ipython:: python
-
+  
+  # Setting the plotting lib
   vp.set_option("plotting_lib", "matplotlib")
+
   @savefig user_guides_data_exploration_iris_hexbin.png
   iris.hexbin(
       ["SepalLengthCm", "SepalWidthCm"], 
@@ -337,6 +342,7 @@ Hexbin, scatter, and bubble plots also allow you to provide a background image.
 .. code-block:: python
 
   africa = load_africa_education()
+
   # displaying avg students score in Africa
   africa.hexbin(
       ["lon", "lat"],
@@ -349,6 +355,7 @@ Hexbin, scatter, and bubble plots also allow you to provide a background image.
   :suppress:
 
   africa = load_africa_education()
+
   # displaying avg students score in Africa
   @savefig user_guides_data_exploration_africa_hexbin.png
   africa.hexbin(
@@ -360,17 +367,6 @@ Hexbin, scatter, and bubble plots also allow you to provide a background image.
 
 It is also possible to use SHP datasets to draw maps.
 
-.. code-block:: python
-
-  africa = load_africa_education()
-  # displaying avg students score in Africa
-  africa.hexbin(
-      ["lon", "lat"],
-      method = "avg",
-      of = "zralocp",
-      img = "img/africa.png",
-  )
-
 .. ipython:: python
 
   # Africa Dataset
@@ -412,7 +408,9 @@ Since time-series plots do not aggregate the data, it's important to choose the
   :suppress:
   :okwarning:
 
+  # Setting the plotting lib
   vp.set_option("plotting_lib", "plotly")
+
   fig = amazon["number"].plot(
       ts = "date", 
       by = "state", 

docs/source/user_guide_data_exploration_descriptive_statistics.rst

@@ -17,7 +17,7 @@ The :py:func:`~verticapy.vDataFrame.agg` method is the best way to compute multi
   help(vp.vDataFrame.agg)
 
 This is a tremendously useful function for understanding your data. 
-Let's use the `churn dataset <https://github.com/vertica/VerticaPy/tree/master/docs/source/notebooks/data_exploration/correlations/data>`_
+Let's use the `churn dataset <https://github.com/vertica/VerticaPy/blob/master/examples/business/churn/customers.csv>`_
 
 .. code-block::
 
@@ -122,7 +122,9 @@ You can also use the 'groupby' method to compute customized aggregations.
           "gender",
           "Contract",
       ],
-      ["AVG(DECODE(Churn, 'Yes', 1, 0)) AS Churn"],
+      [
+          "AVG(DECODE(Churn, 'Yes', 1, 0)) AS Churn",
+      ],
   )
 
 .. ipython:: python
@@ -133,7 +135,9 @@ You can also use the 'groupby' method to compute customized aggregations.
           "gender",
           "Contract",
       ],
-      ["AVG(DECODE(Churn, 'Yes', 1, 0)) AS Churn"],
+      [
+          "AVG(DECODE(Churn, 'Yes', 1, 0)) AS Churn",
+      ],
   )
   html_file = open("/project/data/VerticaPy/docs/figures/user_guides_data_exploration_descriptive_stats_group_by.html", "w")
   html_file.write(res._repr_html_())
@@ -148,7 +152,10 @@ You can also use the 'groupby' method to compute customized aggregations.
   import verticapy.sql.functions as fun
 
   vdf.groupby(
-      ["gender", "Contract"],
+      [
+          "gender",
+          "Contract",
+      ],
       [
           fun.min(vdf["tenure"])._as("min_tenure"),
           fun.max(vdf["tenure"])._as("max_tenure"),
@@ -161,7 +168,10 @@ You can also use the 'groupby' method to compute customized aggregations.
   import verticapy.sql.functions as fun
 
   res = vdf.groupby(
-      ["gender", "Contract"],
+      [
+          "gender",
+          "Contract",
+      ],
       [
           fun.min(vdf["tenure"])._as("min_tenure"),
           fun.max(vdf["tenure"])._as("max_tenure"),

docs/source/user_guide_data_ingestion.rst

@@ -111,6 +111,7 @@ To ingest the file into Vertica, remove the `genSQL` parameter from the above co
     :file: /project/data/VerticaPy/docs/figures/user_guide_data_ingestion_iris.html
 
 When the file to ingest is not located on your local machine, and is on the server instead, then you must set the `ingest_local` parameter to False. 
+
 `ingest_local` is True by default.
 
 .. note:: In some cases where the CSV file has a very complex structure, local ingestion might fail. If this occurs, you will have to move the file into the database and then ingest the file from that location.
@@ -130,8 +131,7 @@ syntax in the path parameter (in this case for multiple CSV files): `path = "pat
 Ingest CSV files
 ----------------
 
-In addition to :py:func:`~verticapy.read_file`, you can also ingest CSV files with the :py:func:`~verticapy.read_csv` function, 
-which ingests the file using flex tables. This function provides options not available in :py:func:`~verticapy.read_file`, such as:
+In addition to :py:func:`~verticapy.read_file`, you can also ingest CSV files with the :py:func:`~verticapy.read_csv` function, which ingests the file using flex tables. This function provides options not available in :py:func:`~verticapy.read_file`, such as:
 
 - `sep`: specify the column separator.
 - `parse_nrows`: the function creates a file of nrows from the data file to identify 
@@ -140,18 +140,15 @@ the data types. This file is then dropped and the entire data file is ingested.
 
 For a full list of supported options, see :py:func:`~verticapy.read_csv` or use the :py:func:`~verticapy.help` function.
 
-In the following example, we will use :py:func:`~verticapy.read_csv` to ingest a 
-subset of the Titanic dataset. To begin, load the entire Titanic dataset using the 
-:py:func:`~verticapy.datasets.load_titanic` function:
+In the following example, we will use :py:func:`~verticapy.read_csv` to ingest a subset of the Titanic dataset. To begin, load the entire Titanic dataset using the :py:func:`~verticapy.datasets.load_titanic` function:
 
 .. ipython:: python
 
     from verticapy.datasets import load_titanic
 
     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` 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` ``vDataFrame`` method:
 
 .. ipython:: python
 
@@ -163,7 +160,8 @@ Before ingesting the above CSV file, we can check its columns and their data typ
 
 .. ipython:: python
 
-    vp.pcsv(path = "titanic_subset.csv",
+    vp.pcsv(
+        path = "titanic_subset.csv",
         sep = ",",
         na_rep = "",
     )
@@ -212,8 +210,7 @@ For a full list of supported options, see the :py:func:`~verticapy.read_json` or
 
 VerticaPy also provides a :py:func:`~verticapy.pjson` function to parse JSON files to identify columns and their respective data types.
 
-In the following example, we load the iris dataset using the :py:func:`~verticapy.datasets.load_iris` dataset, 
-convert the vDataFrame to JSON format with the :py:func:`~verticapy.to_json` method, then ingest the JSON file into Vetica:
+In the following example, we load the iris dataset using the :py:func:`~verticapy.datasets.load_iris` dataset, convert the vDataFrame to JSON format with the :py:func:`~verticapy.to_json` method, then ingest the JSON file into Vetica:
 
 .. code-block:: python