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+pandas: powerful Python data analysis toolkit
+What is it?
+
+pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal.
+Table of Contents
+
+    Main Features
+    Where to get it
+    Dependencies
+    Installation from sources
+    License
+    Documentation
+    Background
+    Getting Help
+    Discussion and Development
+    Contributing to pandas
+
+Main Features
+
+Here are just a few of the things that pandas does well:
+
+    Easy handling of 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 from DataFrame and higher dimensional objects
+    Automatic and explicit data 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 functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
+    Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
+    Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
+    Intuitive merging and joining data sets
+    Flexible reshaping and pivoting of data sets
+    Hierarchical labeling of axes (possible to have multiple labels per tick)
+    Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format
+    Time series-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/pandas-dev/pandas
+
+Binary installers for the latest released version are available at the Python Package Index (PyPI) and on Conda.
+
+# conda
+conda install -c conda-forge pandas
+
+# or PyPI
+pip install pandas
+
+The list of changes to pandas between each release can be found here. For full details, see the commit logs at https://github.com/pandas-dev/pandas.
+Dependencies
+
+    NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays
+    python-dateutil - Provides powerful extensions to the standard datetime module
+    pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations
+
+See the full installation instructions for minimum supported versions of required, recommended and optional dependencies.
+Installation from sources
+
+To install pandas from source you need Cython in addition to the normal dependencies above. Cython can be installed from PyPI:
+
+pip install cython
+
+In the pandas directory (same one where you found this file after cloning the git repo), execute:
+
+pip install .
+
+or for installing in development mode:
+
+python -m pip install -ve . --no-build-isolation --config-settings=editable-verbose=true
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+# Pandas
+
+Pandas is a Python library used for data manipulation and analysis.
+
+## Installation
+
+```bash
+pip install pandas