faq.rst

.. currentmodule:: pandas

Frequently Asked Questions (FAQ)

.. ipython:: python
   :suppress:

   import numpy as np
   np.random.seed(123456)
   np.set_printoptions(precision=4, suppress=True)
   import pandas as pd
   pd.options.display.max_rows = 15
   import matplotlib
   try:
      matplotlib.style.use('ggplot')
   except AttributeError:
      pd.options.display.mpl_style = 'default'
   import matplotlib.pyplot as plt
   plt.close('all')

DataFrame memory usage

As of pandas version 0.15.0, the memory usage of a dataframe (including the index) is shown when accessing the info method of a dataframe. A configuration option, display.memory_usage (see :ref:`options`), specifies if the dataframe's memory usage will be displayed when invoking the df.info() method.

For example, the memory usage of the dataframe below is shown when calling df.info():

.. ipython:: python

    dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
              'complex128', 'object', 'bool']
    n = 5000
    data = dict([ (t, np.random.randint(100, size=n).astype(t))
                    for t in dtypes])
    df = pd.DataFrame(data)
    df['categorical'] = df['object'].astype('category')

    df.info()

The + symbol indicates that the true memory usage could be higher, because pandas does not count the memory used by values in columns with dtype=object.

.. versionadded:: 0.17.1

Passing memory_usage='deep' will enable a more accurate memory usage report, that accounts for the full usage of the contained objects. This is optional as it can be expensive to do this deeper introspection.

.. ipython:: python

   df.info(memory_usage='deep')

By default the display option is set to True but can be explicitly overridden by passing the memory_usage argument when invoking df.info().

The memory usage of each column can be found by calling the memory_usage method. This returns a Series with an index represented by column names and memory usage of each column shown in bytes. For the dataframe above, the memory usage of each column and the total memory usage of the dataframe can be found with the memory_usage method:

.. ipython:: python

    df.memory_usage()

    # total memory usage of dataframe
    df.memory_usage().sum()

By default the memory usage of the dataframe's index is not shown in the returned Series, the memory usage of the index can be shown by passing the index=True argument:

.. ipython:: python

    df.memory_usage(index=True)

The memory usage displayed by the info method utilizes the memory_usage method to determine the memory usage of a dataframe while also formatting the output in human-readable units (base-2 representation; i.e., 1KB = 1024 bytes).

See also :ref:`Categorical Memory Usage <categorical.memory>`.

Byte-Ordering Issues

Occasionally you may have to deal with data that were created on a machine with a different byte order than the one on which you are running Python. To deal with this issue you should convert the underlying NumPy array to the native system byte order before passing it to Series/DataFrame/Panel constructors using something similar to the following:

.. ipython:: python

   x = np.array(list(range(10)), '>i4') # big endian
   newx = x.byteswap().newbyteorder() # force native byteorder
   s = pd.Series(newx)

See the NumPy documentation on byte order for more details.

Visualizing Data in Qt applications

Warning

The qt support is deprecated and will be removed in a future version. We refer users to the external package pandas-qt.

There is experimental support for visualizing DataFrames in PyQt4 and PySide applications. At the moment you can display and edit the values of the cells in the DataFrame. Qt will take care of displaying just the portion of the DataFrame that is currently visible and the edits will be immediately saved to the underlying DataFrame

To demonstrate this we will create a simple PySide application that will switch between two editable DataFrames. For this will use the DataFrameModel class that handles the access to the DataFrame, and the DataFrameWidget, which is just a thin layer around the QTableView.

import numpy as np
import pandas as pd
from pandas.sandbox.qtpandas import DataFrameModel, DataFrameWidget
from PySide import QtGui, QtCore

# Or if you use PyQt4:
# from PyQt4 import QtGui, QtCore

class MainWidget(QtGui.QWidget):
    def __init__(self, parent=None):
        super(MainWidget, self).__init__(parent)

        # Create two DataFrames
        self.df1 = pd.DataFrame(np.arange(9).reshape(3, 3),
                                columns=['foo', 'bar', 'baz'])
        self.df2 = pd.DataFrame({
                'int': [1, 2, 3],
                'float': [1.5, 2.5, 3.5],
                'string': ['a', 'b', 'c'],
                'nan': [np.nan, np.nan, np.nan]
            }, index=['AAA', 'BBB', 'CCC'],
            columns=['int', 'float', 'string', 'nan'])

        # Create the widget and set the first DataFrame
        self.widget = DataFrameWidget(self.df1)

        # Create the buttons for changing DataFrames
        self.button_first = QtGui.QPushButton('First')
        self.button_first.clicked.connect(self.on_first_click)
        self.button_second = QtGui.QPushButton('Second')
        self.button_second.clicked.connect(self.on_second_click)

        # Set the layout
        vbox = QtGui.QVBoxLayout()
        vbox.addWidget(self.widget)
        hbox = QtGui.QHBoxLayout()
        hbox.addWidget(self.button_first)
        hbox.addWidget(self.button_second)
        vbox.addLayout(hbox)
        self.setLayout(vbox)

    def on_first_click(self):
        '''Sets the first DataFrame'''
        self.widget.setDataFrame(self.df1)

    def on_second_click(self):
        '''Sets the second DataFrame'''
        self.widget.setDataFrame(self.df2)

if __name__ == '__main__':
    import sys

    # Initialize the application
    app = QtGui.QApplication(sys.argv)
    mw = MainWidget()
    mw.show()
    app.exec_()