# Frequently Asked Questions (FAQ)

## 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 Options and Settings), 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()`:

    In [1]: dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
       ...:           'complex128', 'object', 'bool']
       ...:

    In [2]: n = 5000

    In [3]: data = dict([ (t, np.random.randint(100, size=n).astype(t))
       ...:                 for t in dtypes])
       ...:

    In [4]: df = pd.DataFrame(data)

    In [5]: df['categorical'] = df['object'].astype('category')

    In [6]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 5000 entries, 0 to 4999
    Data columns (total 8 columns):
    bool               5000 non-null bool
    complex128         5000 non-null complex128
    datetime64[ns]     5000 non-null datetime64[ns]
    float64            5000 non-null float64
    int64              5000 non-null int64
    object             5000 non-null object
    timedelta64[ns]    5000 non-null timedelta64[ns]
    categorical        5000 non-null category
    dtypes: bool(1), category(1), complex128(1), datetime64[ns](1), float64(1), int64(1), object(1), timedelta64[ns](1)
    memory usage: 323.0+ KB

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`.

New in version 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.

    In [7]: df.info(memory_usage='deep')
    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 5000 entries, 0 to 4999
    Data columns (total 8 columns):
    bool               5000 non-null bool
    complex128         5000 non-null complex128
    datetime64[ns]     5000 non-null datetime64[ns]
    float64            5000 non-null float64
    int64              5000 non-null int64
    object             5000 non-null object
    timedelta64[ns]    5000 non-null timedelta64[ns]
    categorical        5000 non-null category
    dtypes: bool(1), category(1), complex128(1), datetime64[ns](1), float64(1), int64(1), object(1), timedelta64[ns](1)
    memory usage: 440.2 KB

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:

    In [8]: df.memory_usage()
    Out[8]:
    bool                5000
    complex128         80000
    datetime64[ns]     40000
    float64            40000
    int64              40000
    object             40000
    timedelta64[ns]    40000
    categorical         5800
    dtype: int64

    # total memory usage of dataframe
    In [9]: df.memory_usage().sum()
    Out[9]: 290800

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:

    In [10]: df.memory_usage(index=True)
    Out[10]:
    Index              40000
    bool                5000
    complex128         80000
    datetime64[ns]     40000
    float64            40000
    int64              40000
    object             40000
    timedelta64[ns]    40000
    categorical         5800
    dtype: int64

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 Categorical Memory Usage.

## 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:

    In [11]: x = np.array(list(range(10)), '>i4') # big endian

    In [12]: newx = x.byteswap().newbyteorder() # force native byteorder

    In [13]: 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_()