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https://github.com/reddyprasade/pandas-practice
Pandas
https://github.com/reddyprasade/pandas-practice
daat data-analysis data-science flexible labeling missing-data missing-values pandas pandas-profiling
Last synced: 11 days ago
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Pandas
- Host: GitHub
- URL: https://github.com/reddyprasade/pandas-practice
- Owner: reddyprasade
- License: gpl-3.0
- Created: 2020-02-16T05:51:56.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-25T13:45:50.000Z (over 4 years ago)
- Last Synced: 2025-01-11T20:08:49.294Z (18 days ago)
- Topics: daat, data-analysis, data-science, flexible, labeling, missing-data, missing-values, pandas, pandas-profiling
- Language: Jupyter Notebook
- Size: 4.99 MB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# What is Pandas?
***
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.
***
```sh
# conda
conda install pandas
```
```sh
# or PyPI
pip install pandas
```
***
## Dependencies
- [NumPy](https://www.numpy.org)
- [python-dateutil](https://labix.org/python-dateutil)
- [pytz](https://pythonhosted.org/pytz)