chore(readme): add package rename and migration guide

Author: portellaa

README.md

@@ -1,5 +1,7 @@
 # ydata-profiling
 
+> **`ydata-profiling` is now `data-profiling`.** This package has been renamed to `data-profiling`. Please follow the [Migration Guide](#migration-guide) as soon as possible — the old package will no longer receive updates or bug fixes.
+
 [![Build Status](https://github.com/ydataai/pandas-profiling/actions/workflows/tests.yml/badge.svg?branch=master)](https://github.com/ydataai/pandas-profiling/actions/workflows/tests.yml)
 [![PyPI download month](https://img.shields.io/pypi/dm/ydata-profiling.svg)](https://pypi.python.org/pypi/ydata-profiling/)
 [![](https://pepy.tech/badge/pandas-profiling)](https://pypi.org/project/ydata-profiling/)
@@ -33,6 +35,40 @@ The package outputs a simple and digested analysis of a dataset, including **tim
 > **Looking for a scalable solution that can fully integrate with your database systems?**<br>
 > Leverage YData Fabric Data Catalog to connect to different databases and storages (Oracle, snowflake, PostGreSQL, GCS, S3, etc.) and leverage an interactive and guided profiling experience in Fabric. Check out the [Community Version](http://ydata.ai/register?utm_source=ydata-profiling&utm_medium=documentation&utm_campaign=YData%20Fabric%20Community).
 
+## Migration Guide
+ 
+### 1. Uninstall the old package
+ 
+```bash
+pip uninstall ydata-profiling
+```
+ 
+### 2. Install the new package
+ 
+```bash
+pip install data-profiling
+```
+ 
+### 3. Update your imports
+ 
+Find and replace all occurrences of the old import in your codebase:
+ 
+```python
+# Before
+import ydata_profiling
+from ydata_profiling import ProfileReport
+
+# After
+import data_profiling
+from data_profiling import ProfileReport
+```
+ 
+You can use this one-liner to find all affected files:
+ 
+```bash
+grep -r "ydata_profiling" . --include="*.py"
+```
+
 ## ▶️ Quickstart
 
 ### Install

docs/advanced_settings/analytics.md

@@ -2,14 +2,14 @@
 
 ## Overview
 
-`ydata-profiling` is a powerful library designed to generate profile reports from pandas and Spark Dataframe objects. 
-As part of our ongoing efforts to improve user experience and functionality, `ydata-profiling` 
+`data-profiling` is a powerful library designed to generate profile reports from pandas and Spark Dataframe objects. 
+As part of our ongoing efforts to improve user experience and functionality, `data-profiling` 
 includes a telemetry feature. This feature collects anonymous usage data, helping us understand how the
 library is used and identify areas for improvement.
 
 The primary goal of collecting telemetry data is to:
 
-- Enhance the functionality and performance of the ydata-profiling library
+- Enhance the functionality and performance of the data-profiling library
 - Prioritize new features based on user engagement
 - Identify common issues and bugs to improve overall user experience
 
@@ -18,15 +18,15 @@ The primary goal of collecting telemetry data is to:
 The telemetry system collects non-personal, anonymous information such as:
 
 - Python version
-- `ydata-profiling` version
-- Frequency of use of `ydata-profiling` features
+- `data-profiling` version
+- Frequency of use of `data-profiling` features
 - Errors or exceptions thrown within the library
 
 ## Disabling usage analytics
 
 We respect your choice to not participate in our telemetry collection. If you prefer to disable telemetry, you can do so
 by setting an environment variable on your system. Disabling telemetry will not affect the functionality 
-of the ydata-profiling library, except for the ability to contribute to its usage analytics.
+of the data-profiling library, except for the ability to contribute to its usage analytics.
 
 
 ### Set an Environment Variable

docs/advanced_settings/available_settings.md

@@ -1,6 +1,6 @@
 # Available Settings
 
-A set of options is available in order to customize the behaviour of ``ydata-profiling`` and the appearance of the generated report. The depth of customization allows the creation of behaviours highly targeted at the specific dataset being analysed. The available settings are listed below. To learn how to change them, check :doc:`changing_settings`.
+A set of options is available in order to customize the behaviour of ``data-profiling`` and the appearance of the generated report. The depth of customization allows the creation of behaviours highly targeted at the specific dataset being analysed. The available settings are listed below. To learn how to change them, check :doc:`changing_settings`.
 
 ## General settings
 
@@ -45,8 +45,8 @@ Configure the schema type for a given dataset.
   import json
   import pandas as pd
 
-  from ydata_profiling import ProfileReport
-  from ydata_profiling.utils.cache import cache_file
+  from data_profiling import ProfileReport
+  from data_profiling.utils.cache import cache_file
 
   file_name = cache_file(
       "titanic.csv",

docs/advanced_settings/changing_settings.md

@@ -44,21 +44,21 @@ r = ProfileReport(
 
 ## Through a custom configuration file
 
-To control `ydata-profiling` through a custom file, you can start with
+To control `data-profiling` through a custom file, you can start with
 one of the sample configuration files below:
 
 -   [default configuration
-    file](https://github.com/ydataai/ydata-profiling/blob/master/src/ydata_profiling/config_default.yaml)
+    file](https://github.com/Data-Centric-AI-Community/data-profiling/blob/master/src/data_profiling/config_default.yaml)
     (default)
 -   [minimal configuration
-    file](https://github.com/ydataai/ydata-profiling/blob/master/src/ydata_profiling/config_minimal.yaml)
+    file](https://github.com/Data-Centric-AI-Community/data-profiling/blob/master/src/data_profiling/config_minimal.yaml)
     (minimal computation, optimized for performance)
 
 Change the configuration to your liking and point towards that
 configuration file when computing the report:
 
 ``` python linenums="1" title="Custom configuration file"
-from ydata_profiling import ProfileReport
+from data_profiling import ProfileReport
 
 profile = ProfileReport(df, config_file="your_config.yml")
 profile.to_file("report.html")
@@ -70,7 +70,7 @@ Any configuration setting can also be read from environment variables.
 For example:
 
 ```python linenums="1" title="Setting title for the report with parameters"
-from ydata_profiling import ProfileReport
+from data_profiling import ProfileReport
 
 profile = ProfileReport(df, title="My Custom Profiling Report")
 ```
@@ -79,7 +79,7 @@ is equivalent to setting the title as an environment variable
 
 ```python linenums="1" title="Set title through environment variables"
 import os
-from ydata_profiling import ProfileReport
+from data_profiling import ProfileReport
 
 os.environ("PROFILE_TITLE")='My Custom Profiling Report'
 

docs/advanced_settings/collaborative_data_profiling.md

@@ -4,7 +4,7 @@
 
 [YData Fabric](https://ydata.ai/products/fabric) is a Data-Centric AI
 development platform. YData Fabric provides all capabilities of
-ydata-profiling in a hosted environment combined with a guided UI
+data-profiling in a hosted environment combined with a guided UI
 experience.
 
 [Fabric\'s Data Catalog](https://ydata.ai/products/data_catalog)

docs/features/big_data.md

@@ -2,15 +2,15 @@
 
 <img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=baa0e45f-0c03-4190-9646-9d8ea2640ba2" />
 
-By default, `ydata-profiling` comprehensively summarizes the input
+By default, `data-profiling` comprehensively summarizes the input
 dataset in a way that gives the most insights for data analysis. For
 small datasets, these computations can be performed in *quasi*
 real-time. For larger datasets, deciding upfront which calculations to
 make might be required. Whether a computation scales to a large datasets
 not only depends on the exact size of the dataset, but also on its
 complexity and on whether fast computations are available. If the
 computation time of the profiling becomes a bottleneck,
-`ydata-profiling` offers several alternatives to overcome it.
+`data-profiling` offers several alternatives to overcome it.
 
 !!! info "Scale in a fully managed system"
 
@@ -27,9 +27,9 @@ computation time of the profiling becomes a bottleneck,
     This mode was introduced in version v4.0.0
 
 
-`ydata-profiling` now supports Spark Dataframes profiling. You can find
+`data-profiling` now supports Spark Dataframes profiling. You can find
 an example of the integration
-[here](https://github.com/ydataai/ydata-profiling/blob/master/examples/features/spark_example.py).
+[here](https://github.com/Data-Centric-AI-Community/data-profiling/blob/master/examples/features/spark_example.py).
 
 **Features supported:** - Univariate variables' analysis - Head and Tail
 dataset sample - Correlation matrices: Pearson and Spearman
@@ -38,7 +38,7 @@ dataset sample - Correlation matrices: Pearson and Spearman
 histogram computation
 
 Keep an eye on the
-[GitHub](https://github.com/ydataai/ydata-profiling/issues) page to
+[GitHub](https://github.com/Data-Centric-AI-Community/data-profiling/issues) page to
 follow the updates on the implementation of [Pyspark Dataframes
 support](https://github.com/orgs/ydataai/projects/16/views/2).
 
@@ -48,7 +48,7 @@ support](https://github.com/orgs/ydataai/projects/16/views/2).
 
     This mode was introduced in version v2.4.0
 
-`ydata-profiling` includes a minimal configuration file where the most
+`data-profiling` includes a minimal configuration file where the most
 expensive computations are turned off by default. This is the
 recommended starting point for larger datasets.
 
@@ -58,7 +58,7 @@ profile.to_file("output.html")
 ```
 
 This configuration file can be found here:
-[config_minimal.yaml](https://github.com/ydataai/ydata-profiling/blob/master/src/ydata_profiling/config_minimal.yaml).
+[config_minimal.yaml](https://github.com/Data-Centric-AI-Community/data-profiling/blob/master/src/data_profiling/config_minimal.yaml).
 More details on settings and configuration are available in
 `../advanced_usage/available_settings`{.interpreted-text role="doc"}.
 
@@ -103,7 +103,7 @@ that only the interactions with these variables in specific are
 computed.
 
 ``` python linenums="1" title="Disable expensive computations"
-from ydata_profiling import ProfileReport
+from data_profiling import ProfileReport
 import pandas as pd
 
 # Reading the data
@@ -127,14 +127,14 @@ role="doc"}.
 
 # Concurrency
 
-`ydata-profiling` is a project under active development. One of the
+`data-profiling` is a project under active development. One of the
 highly desired features is the addition of a scalable backend such as
 [Modin](https://github.com/modin-project/modin) or
 [Dask](https://dask.org/).
 
 Keep an eye on the
-[GitHub](https://github.com/ydataai/ydata-profiling/issues) page to
+[GitHub](https://github.com/Data-Centric-AI-Community/data-profiling/issues) page to
 follow the updates on the implementation of a concurrent and highly
 scalable backend. Specifically, development of a Spark backend is
 [currently
-underway](https://github.com/ydataai/ydata-profiling/projects/3).
+underway](https://github.com/Data-Centric-AI-Community/data-profiling/projects/3).

docs/features/collaborative_data_profiling.md

@@ -9,11 +9,11 @@ A collaborative experience to profile datasets & relational databases
 
 [YData Fabric](https://ydata.ai/products/fabric) is a Data-Centric AI
 development platform. YData Fabric provides all capabilities of
-ydata-profiling in a hosted environment combined with a guided UI
+data-profiling in a hosted environment combined with a guided UI
 experience.
 
 [Fabric's Data Catalog](https://ydata.ai/products/data_catalog), 
-a scalable and interactive version of ydata-profiling,
+a scalable and interactive version of data-profiling,
 provides a comprehensive and powerful tool designed to enable data
 professionals, including data scientists and data engineers, to manage
 and understand data within an organization. The Data Catalog act as a

docs/features/comparing_datasets.md

@@ -4,18 +4,18 @@
 !!! note "Dataframes compare support"
 
     Profiling compare is supported from
-    ydata-profiling version 3.5.0 onwards.
+    data-profiling version 3.5.0 onwards.
     Profiling compare is not *(yet!)* available for Spark Dataframes
 
-`ydata-profiling` can be used to compare multiple version of the same
+`data-profiling` can be used to compare multiple version of the same
 dataset. This is useful when comparing data from multiple time periods,
 such as two years. Another common scenario is to view the dataset
 profile for training, validation and test sets in machine learning.
 
 The following syntax can be used to compare two datasets:
 
 ``` python linenums="1" title="Comparing 2 datasets"
-from ydata_profiling import ProfileReport
+from data_profiling import ProfileReport
 
 train_df = pd.read_csv("train.csv")
 train_report = ProfileReport(train_df, title="Train")
@@ -37,7 +37,7 @@ In order to compare more than two reports, the following syntax can be
 used:
 
 ``` python linenums="1" title="Comparing more than 2 datasets"
-from ydata_profiling import ProfileReport, compare
+from data_profiling import ProfileReport, compare
 
 comparison_report = compare([train_report, validation_report, test_report])
 

docs/features/custom_reports.md

@@ -2,7 +2,7 @@
 
 In some situations, a user might want to customize the appearance
 of the report to match personal preferences or a corporate brand.
-``ydata-profiling`` offers two major customization dimensions: 
+``data-profiling`` offers two major customization dimensions: 
 the **styling of the HTML report** and the **styling of the visualizations
 and plots** contained within. 
 
@@ -64,7 +64,7 @@ values overview can also be customized via the ``plot`` argument. To customize
 the palette used by the correlation matrix, use the ``correlation`` key:
 
 ``` python linenums="1" title="Changing visualizations color palettes"
-  from ydata_profiling import ProfileReport
+  from data_profiling import ProfileReport
 
   profile = ProfileReport(
       df,
@@ -77,7 +77,7 @@ Similarly, the palette for *Missing values* can be changed using ``missing`` arg
 
 ``` python linenums="1" python
 
-  from ydata_profiling import ProfileReport
+  from data_profiling import ProfileReport
 
   profile = ProfileReport(
       df,
@@ -87,7 +87,7 @@ Similarly, the palette for *Missing values* can be changed using ``missing`` arg
   )
 ```
 
-``ydata-profiling`` accepts all ``cmap`` values (colormaps) accepted by ``matplotlib``.
+``data-profiling`` accepts all ``cmap`` values (colormaps) accepted by ``matplotlib``.
 The list of available colour maps can [be accessed here](https://matplotlib.org/stable/tutorials/colors/colormaps.html>).
 Alternatively, it is possible to create [custom palettes](https://matplotlib.org/stable/gallery/color/custom_cmap.html>).
 

docs/features/metadata.md

@@ -4,7 +4,7 @@
 
 When sharing reports with coworkers or publishing online, it might be
 important to include metadata of the dataset, such as author, copyright
-holder or descriptions. `ydata-profiling` allows complementing a report
+holder or descriptions. `data-profiling` allows complementing a report
 with that information. Inspired by [schema.org\'s
 Dataset](https://schema.org/Dataset), the currently supported properties
 are *description*, *creator*, *author*, *url*, *copyright_year* and
@@ -33,7 +33,7 @@ report.to_file(Path("stata_auto_report.html"))
 
 In addition to providing dataset details, often users want to include
 column-specific descriptions when sharing reports with team members and
-stakeholders. `ydata-profiling` supports creating these descriptions, so
+stakeholders. `data-profiling` supports creating these descriptions, so
 that the report includes a built-in data dictionary. By default, the
 descriptions are presented in the *Overview* section of the report, next
 to each variable.
@@ -64,7 +64,7 @@ Alternatively, column descriptions can be loaded from a JSON file:
 ``` python linenums="1" title="Generate a report with descriptions per variable from a JSON definitions file"
 import json
 import pandas as pd
-import ydata_profiling
+import data_profiling
 
 definition_file = dataset_column_definition.json
 
@@ -87,17 +87,17 @@ report.to_file("report.html")
 
 In addition to providing dataset details, users often want to include
 set type schemas. This is particularly important when integrating
-`ydata-profiling` generation with the information already in a data
-catalog. When using `ydata-profiling` ProfileReport, users can set the
+`data-profiling` generation with the information already in a data
+catalog. When using `data-profiling` ProfileReport, users can set the
 type_schema property to control the generated profiling data types. By
 default, the `type_schema` is automatically inferred with [visions](https://github.com/dylan-profiler/visions).
 
 ``` python linenums="1" title="Set the variable type schema to Generate the profile report"
 import json
 import pandas as pd
 
-from ydata_profiling import ProfileReport
-from ydata_profiling.utils.cache import cache_file
+from data_profiling import ProfileReport
+from data_profiling.utils.cache import cache_file
 
 file_name = cache_file(
     "titanic.csv",