pandas is the utility belt for data analysts using python. The package centers around the pandas
DataFrame, a two-dimensional data structure with indexable rows and columns. It has effectively taken the best parts of Base R, R packages like plyr and reshape2 and consolidated them into a single library. It has lots of features (see library highlights). pandas gets its name from panel data, an econometrics term for multidimensional structured datasets (McKinney 5., 2013)2. http://pandas.pydata.org/pandas-docs/stable/index.html
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 toward this goal.
pandas is well suited for many different kinds of data:
- Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
- Ordered and unordered (not necessarily fixed-frequency) time series data.
- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
- Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure
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