10BC0 CLN: removed pandas.sandbox · pandas-dev/pandas@361a2b4 · GitHub
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CLN: removed pandas.sandbox
xref #9615 Author: gfyoung <gfyoung17@gmail.com> Closes #13670 from gfyoung/sandbox-removal and squashes the following commits: 2a014aa [gfyoung] CLN: removed pandas.sandbox
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  • setup.py

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    ‎doc/source/ecosystem.rst‎

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    `Plotly’s <https://plot.ly/>`__ `Python API <https://plot.ly/python/>`__ enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL and `D3.js <http://d3js.org/>`__. The library supports plotting directly from a pandas DataFrame and cloud-based collaboration. Users of `matplotlib, ggplot for Python, and Seaborn <https://plot.ly/python/matplotlib-to-plotly-tutorial/>`__ can convert figures into interactive web-based plots. Plots can be drawn in `IPython Notebooks <https://plot.ly/ipython-notebooks/>`__ , edited with R or MATLAB, modified in a GUI, or embedded in apps and dashboards. Plotly is free for unlimited sharing, and has `cloud <https://plot.ly/product/plans/>`__, `offline <https://plot.ly/python/offline/>`__, or `on-premise <https://plot.ly/product/enterprise/>`__ accounts for private use.
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    `Pandas-Qt <https://github.com/datalyze-solutions/pandas-qt>`__
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    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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    Spun off from the main pandas library, the `Pandas-Qt <https://github.com/datalyze-solutions/pandas-qt>`__
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    library enables DataFrame visualization and manipulation in PyQt4 and PySide applications.
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    .. _ecosystem.ide:
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    IDE

    ‎doc/source/faq.rst‎

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    Visualizing Data in Qt applications
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    -----------------------------------
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    .. warning::
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    The ``qt`` support is **deprecated and will be removed in a future version**.
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    We refer users to the external package `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_.
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    There is experimental support for visualizing DataFrames in PyQt4 and PySide
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    applications. At the moment you can display and edit the values of the cells
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    in the DataFrame. Qt will take care of displaying just the portion of the
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    DataFrame that is currently visible and the edits will be immediately saved to
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    the underlying DataFrame
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    To demonstrate this we will create a simple PySide application that will switch
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    between two editable DataFrames. For this will use the ``DataFrameModel`` class
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    that handles the access to the DataFrame, and the ``DataFrameWidget``, which is
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    just a thin layer around the ``QTableView``.
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    .. code-block:: python
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    import numpy as np
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    import pandas as pd
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    from pandas.sandbox.qtpandas import DataFrameModel, DataFrameWidget
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    from PySide import QtGui, QtCore
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    # Or if you use PyQt4:
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    # from PyQt4 import QtGui, QtCore
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    class MainWidget(QtGui.QWidget):
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    def __init__(self, parent=None):
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    super(MainWidget, self).__init__(parent)
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    # Create two DataFrames
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    self.df1 = pd.DataFrame(np.arange(9).reshape(3, 3),
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    columns=['foo', 'bar', 'baz'])
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    self.df2 = pd.DataFrame({
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    'int': [1, 2, 3],
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    'float': [1.5, 2.5, 3.5],
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    'string': ['a', 'b', 'c'],
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    'nan': [np.nan, np.nan, np.nan]
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    }, index=['AAA', 'BBB', 'CCC'],
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    columns=['int', 'float', 'string', 'nan'])
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    # Create the widget and set the first DataFrame
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    self.widget = DataFrameWidget(self.df1)
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    # Create the buttons for changing DataFrames
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    self.button_first = QtGui.QPushButton('First')
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    self.button_first.clicked.connect(self.on_first_click)
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    self.button_second = QtGui.QPushButton('Second')
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    self.button_second.clicked.connect(self.on_second_click)
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    # Set the layout
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    vbox = QtGui.QVBoxLayout()
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    vbox.addWidget(self.widget)
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    hbox = QtGui.QHBoxLayout()
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    hbox.addWidget(self.button_first)
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    hbox.addWidget(self.button_second)
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    vbox.addLayout(hbox)
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    self.setLayout(vbox)
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    def on_first_click(self):
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    '''Sets the first DataFrame'''
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    self.widget.setDataFrame(self.df1)
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    def on_second_click(self):
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    '''Sets the second DataFrame'''
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    self.widget.setDataFrame(self.df2)
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    if __name__ == '__main__':
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    import sys
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    # Initialize the application
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    app = QtGui.QApplication(sys.argv)
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    mw = MainWidget()
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    mw.show()
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    app.exec_()
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    There is no support for such visualization in pandas. However, the external
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    package `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_ does
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    provide this functionality.

    ‎doc/source/whatsnew/v0.19.0.txt‎

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    Removal of prior version deprecations/changes
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    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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    - The ``pd.sandbox`` module has been removed in favor of the external library ``pandas-qt`` (:issue:`13670`)
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    - ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`)
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    - ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`)
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    - ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`)

    ‎pandas/api/tests/test_api.py‎

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    # these are optionally imported based on testing
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    # & need to be ignored
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    ignored = ['tests', 'rpy', 'sandbox', 'locale']
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    ignored = ['tests', 'rpy', 'locale']
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    # top-level sub-packages
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    lib = ['api', 'compat', 'computation', 'core',

    ‎pandas/sandbox/__init__.py‎

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    ‎pandas/sandbox/qtpandas.py‎

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    ‎setup.py‎

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    'pandas.io.sas',
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    'pandas.formats',
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    'pandas.rpy',
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    'pandas.sandbox',
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    'pandas.sparse',
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    'pandas.sparse.tests',
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    'pandas.stats',

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