pandas consists of the following elements
|1||Series||1D labeled homogeneously-typed array|
|2||DataFrame||General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column|
The best way to think about the pandas data structures is as flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert and remove objects from these containers in a dictionary-like fashion.
Also, we would like sensible default behaviors for the common API functions which take into account the typical orientation of time series and cross-sectional data sets. When using ndarrays to store 2- and 3-dimensional data, a burden is placed on the user to consider the orientation of the data set when writing functions; axes are considered more or less equivalent (except when C- or Fortran-contiguousness matters for performance). In pandas, the axes are intended to lend more semantic meaning to the data; i.e., for a particular data set there is likely to be a “right” way to orient the data. The goal, then, is to reduce the amount of mental effort required to code up data transformations in downstream functions.
For example, with tabular data (DataFrame) it is more semantically helpful to think of the index (the rows) and the columns rather than axis 0 and axis 1. And iterating through the columns of the DataFrame thus results in more readable code:
for col in df.columns: series = df[col] # do something with series
All pandas data structures are value-mutable (the values they contain can be altered) but not always size-mutable. The length of a Series cannot be changed, but, for example, columns can be inserted into a DataFrame. However, the vast majority of methods produce new objects and leave the input data untouched. In general, though, we like to favor immutability where sensible.
pandas is actively supported today by a community of like-minded individuals around the world who contribute their valuable time and energy to help make open source pandas possible. Thanks to all of our contributors.
If you’re interested in contributing, please visit Contributing to pandas webpage.
The governance process that pandas project has used informally since its inception in 2008 is formalized in Project Governance documents . The documents clarify how decisions are made and how the various elements of our community interact, including the relationship between open source collaborative development and work that may be funded by for-profit or non-profit entities.
Wes McKinney is the Benevolent Dictator for Life (BDFL).
The list of the Core Team members and more detailed information can be found on the people’s page of the governance repo.
The information about current institutional partners can be found on pandas website page
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