reddyprasade / pandas-practice

Pandas

What is Pandas?

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pandas is a Python package providing 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.

Main Features

Here are just a few of the things that pandas does well:

• Easy handling of [missing data][missing-data] (represented as NaN) 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.

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# conda
conda install pandas

# or PyPI
pip install pandas

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Dependencies

• NumPy
• python-dateutil
• pytz