Pandas
Template:Short description Template:Primary sources Template:Lowercase title {{#invoke:Infobox|infobox}}
In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.[1] The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals.[2]
- Your First Machine Learning Project in Python Step-By-Step
https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
- Machine Learning Mastery With Python
https://machinelearningmastery.com/machine-learning-with-python/
Library features
- DataFrame object for data manipulation with integrated indexing.
- Tools for reading and writing data between in-memory data structures and different file formats.
- Data alignment and integrated handling of missing data.
- Reshaping and pivoting of data sets.
- Label-based slicing, fancy indexing, and subsetting of large data sets.
- Data structure column insertion and deletion.
- Group by engine allowing split-apply-combine operations on data sets.
- Data set merging and joining.
- Hierarchical axis indexing to work with high-dimensional data in a lower-dimensional data structure.
- Time series-functionality: Date range generation[3] and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging.
- Provides data filtration.
The library is highly optimized for performance, with critical code paths written in Cython or C.[4]
Dataframes
Pandas is mainly used for Machine Learning in form of dataframes. Pandas allow importing data of various file formats such as csv, excel etc. Pandas allows various data manipulation operations such as groupby, join, merge, melt, concatenation as well as data cleaning features such as filling, replacing or imputing null values.
History
Developer Wes McKinney started working on pandas in 2008 while at AQR Capital Management out of the need for a high performance, flexible tool to perform quantitative analysis on financial data. Before leaving AQR he was able to convince management to allow him to open source the library.
Another AQR employee, Chang She, joined the effort in 2012 as the second major contributor to the library.
In 2015, pandas signed on as a fiscally sponsored project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States.[5]
See also
- matplotlib
- NumPy
- SciPy
- R (programming language)
- Scikit-learn (sklearn)
- statsmodels
- List of numerical analysis software
References
| references-column-width
| references-column-count references-column-count-{{#if:1|{{{1}}}}} }}
| {{#if:
| references-column-width }} }}" style="{{#if:
| {{#iferror: {{#ifexpr: 1 > 1 }}
| Template:Column-width
| Template:Column-count }}
| {{#if:
| Template:Column-width }} }} list-style-type: {{#switch:
| upper-alpha
| upper-roman
| lower-alpha
| lower-greek
| lower-roman = {{{group}}}
| #default = decimal}};">