Version1: README.md

README.md

@@ -0,0 +1,104 @@
+# CleanData
+-----------------
+
+# pandas: powerful Python data analysis toolkit
+Insert clickable quick links here 
+
+
+## What is it?
+
+Cleandata is an easy to use Python library for data cleansing operations with the main focus on reducing the steps required on each sub-process included in the data pre-processing step with scientific approach:
+1. Reducing memory consumption for dataset with decreased byte sizes.
+2. Considering MAR, MCAR, and MNAR cases to treat case-specific NA values.
+3. Treating and dropping duplicated values.
+4. Using scientific methods to treat anomalies.
+5. Treating text typos.
+6. Using AI state-of-the-art models for Table Question Answering purposes.
+
+
+
+## Table of Contents
+
+- [Main Features](#main-features)
+- [Where to get it](#where-to-get-it)
+- [Dependencies](#dependencies)
+- [Installation from sources](#installation-from-sources)
+- [License](#license)
+- [Documentation](#documentation)
+
+
+## Main Features
+Here are just a few of the things that pandas does well:
+
+  - Easy handling of [**missing data**][missing-data] (represented as
+    `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
+  - Size mutability: columns can be [**inserted and
+    deleted**][insertion-deletion] from DataFrame and higher dimensional
+    objects
+  - Automatic and explicit [**data alignment**][alignment]: objects can
+    be explicitly aligned to a set of labels, or the user can simply
+    ignore the labels and let `Series`, `DataFrame`, etc. automatically
+    align the data for you in computations
+  - Powerful, flexible [**group by**][groupby] functionality to perform
+    split-apply-combine operations on data sets, for both aggregating
+    and transforming data
+  - Make it [**easy to convert**][conversion] ragged,
+    differently-indexed data in other Python and NumPy data structures
+    into DataFrame objects
+  - Intelligent label-based [**slicing**][slicing], [**fancy
+    indexing**][fancy-indexing], and [**subsetting**][subsetting] of
+    large data sets
+  - Intuitive [**merging**][merging] and [**joining**][joining] data
+    sets
+  - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
+    data sets
+  - [**Hierarchical**][mi] labeling of axes (possible to have multiple
+    labels per tick)
+  - Robust IO tools for loading data from [**flat files**][flat-files]
+    (CSV and delimited), [**Excel files**][excel], [**databases**][db],
+    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
+  - [**Time series**][timeseries]-specific functionality: date range
+    generation and frequency conversion, moving window statistics,
+    date shifting and lagging
+
+
+## Where to get it
+The source code is currently hosted on GitHub at:
+https://github.com/Kokolipa/CleanData-package
+
+Binary installers for the latest released version are available at the [Python
+Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/).
+TODO: Fix the links after deployment
+
+```sh
+# conda
+conda install -c conda-forge CleanData
+```
+
+```sh
+# or PyPI
+pip install CleanData
+```
+
+
+## Dependencies
+
+| Package           | Version   |
+|-------------------|-----------|
+| numpy             | >=1.23.5  |
+| scikit-learn      | ==1.2.2   |
+| pyod              | ==1.1.2   |
+| pyspellchecker    | >=0.8.1   |
+| transformers      | >=4.38.2  |
+
+
+## License
+[MIT](https://github.com/Kokolipa/CleanData-package/blob/CleanData_main/LICENSE)
+
+## Documentation
+TODO: Include the link to the documentation using GitHub Pages.
+<!-- The official documentation is hosted on [GitHub Pages](). -->
+
+
+
+