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pandas 2.2.3

0

Powerful data structures for data analysis, time series, and statistics

Powerful data structures for data analysis, time series, and statistics

Stars: 43501, Watchers: 43501, Forks: 17876, Open Issues: 3607

The pandas-dev/pandas repo was created 14 years ago and the last code push was Yesterday.
The project is extremely popular with a mindblowing 43501 github stars!

How to Install pandas

You can install pandas using pip

pip install pandas

or add it to a project with poetry

poetry add pandas

Package Details

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BSD 3-Clause License Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team All rights reserved. Copyright (c) 2011-2023, Open source contributors. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. 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HISTORY OF THE SOFTWARE ========================== Python was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI, see https://www.cwi.nl) in the Netherlands as a successor of a language called ABC. Guido remains Python's principal author, although it includes many contributions from others. In 1995, Guido continued his work on Python at the Corporation for National Research Initiatives (CNRI, see https://www.cnri.reston.va.us) in Reston, Virginia where he released several versions of the software. In May 2000, Guido and the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. In October of the same year, the PythonLabs team moved to Digital Creations, which became Zope Corporation. In 2001, the Python Software Foundation (PSF, see https://www.python.org/psf/) was formed, a non-profit organization created specifically to own Python-related Intellectual Property. Zope Corporation was a sponsoring member of the PSF. 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Homepage
https://pandas.pydata.org
PyPi:
https://pypi.org/project/pandas/
Documentation:
https://pandas.pydata.org/docs/
GitHub Repo:
https://github.com/pandas-dev/pandas

Classifiers

  • Scientific/Engineering
No  pandas  pypi packages just yet.

Errors

A list of common pandas errors.

Code Examples

Here are some pandas code examples and snippets.

  • Plot 3 different Pandas Dataframes in the same chart

    import pandas as pd
    import numpy as np
    from matplotlib import pyplot as plt
    
    #using numpy's randint to generate some data
    df1 = pd.DataFrame(np.random.randint(0,100,size=(10, 2)), columns=list('XY'))
    df2 = pd.DataFrame(np.random.randint(0,100,size=(10, 2)), columns=list('XY'))
    df3 = pd.DataFrame(np.random.randint(0,100,size=(10, 2)), columns=list('XY'))
    
    df1.head(), df2.head(), df3.head()
    
    (    X   Y
     0  82  32
     1  79  13
     2  87  19
     3   6  73
     4   1  38,
         X   Y
     0  47  62
     1  41   0
     2  98  78
     3  63  83
     4  31  59,
         X   Y
     0  57  25
     1  49  27
     2   9  29
     3  93  75
     4  23  80)
    
    # Get handle of first figure to pass to other plot() calls as ax
    ax =  df1.plot(color="red", figsize=(10,5))
    df2.plot(ax=ax, color="orange", figsize=(10,5))
    df3.plot(ax=ax, color="green", figsize=(10,5))
    
    # Note: subsequent plots need to happen in the same notebook cell.
    
    <AxesSubplot:>
    

    /code/multiple-dataframes-plot-same-chart/pandas.plot()-multiple-dataframes.png

  • Use pandas and matplotlib to produce a chart of natural gas prices

    In this example Python code, we use requests to fetch data from the EIA.gov website and json from the Python standard library to parse the json data. Next, we load the daily prices into a Pandas DataFrame and format the date column and set it as the index. Finally, we use Pandas .plot() to create a chart saving it as a png image.

GitHub Issues

The pandas package has 3607 open issues on GitHub

  • BUG: groupby transform doesn't respect Series index anymore
  • BUG: Incorrect behavior of window aggregation functions on disjoint windows skipping overflowing elements
  • Revert "PERF: nancorr pearson (#42761)"
  • BUG: frame.shift(axis=1) with ArrayManager
  • REGR: Series.fillna(downcast=False)
  • Switch deps to Conda
  • BUG: REGRESSION: DataFrame.corr() floating point inaccuracy
  • BUG: ArrayManager indexing mismatched behavior
  • BUG: add_categories raises if the new category is included in the old
  • BUG: concat with empty dataframe with columns passed and nonempty dataframe coerces dtype to object
  • added staircase entry in Domain Specific and Accessors section of pan…
  • DOC: add staircase to pandas ecosystem page in docs
  • BUG: bootstrap_plot samples without replacement
  • TST: Added test for setitem loc using datetime-like str
  • BUG: read_csv with memory_map=True on BytesIO object fails

See more issues on GitHub

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