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# Frequently Asked Questions (FAQ)

## Adding Features to your pandas Installation

pandas is a powerful tool and already has a plethora of data manipulation operations implemented, most of them are very fast as well. It’s very possible however that certain functionality that would make your life easier is missing. In that case you have several options:

1. Open an issue on Github , explain your need and the sort of functionality you would like to see implemented.

2. Fork the repo, Implement the functionality yourself and open a PR on Github.

3. Write a method that performs the operation you are interested in and Monkey-patch the pandas class as part of your IPython profile startup or PYTHONSTARTUP file.

   For example, here is an example of adding an `just_foo_cols()` method to the dataframe class:

    import pandas as pd
    def just_foo_cols(self):
        """Get a list of column names containing the string 'foo'

        """
        return [x for x in self.columns if 'foo' in x]

    pd.DataFrame.just_foo_cols = just_foo_cols # monkey-patch the DataFrame class
    df = pd.DataFrame([list(range(4))], columns=["A","foo","foozball","bar"])
    df.just_foo_cols()
    del pd.DataFrame.just_foo_cols # you can also remove the new method

Monkey-patching is usually frowned upon because it makes your code less portable and can cause subtle bugs in some circumstances. Monkey-patching existing methods is usually a bad idea in that respect. When used with proper care, however, it’s a very useful tool to have.

## Migrating from scikits.timeseries to pandas >= 0.8.0

Starting with pandas 0.8.0, users of scikits.timeseries should have all of the features that they need to migrate their code to use pandas. Portions of the scikits.timeseries codebase for implementing calendar logic and timespan frequency conversions (but not resampling, that has all been implemented from scratch from the ground up) have been ported to the pandas codebase.

The scikits.timeseries notions of `Date` and `DateArray` are responsible for implementing calendar logic:

    In [16]: dt = ts.Date('Q', '1984Q3')

    # sic
    In [17]: dt
    Out[17]: <Q-DEC : 1984Q1>

    In [18]: dt.asfreq('D', 'start')
    Out[18]: <D : 01-Jan-1984>

    In [19]: dt.asfreq('D', 'end')
    Out[19]: <D : 31-Mar-1984>

    In [20]: dt + 3
    Out[20]: <Q-DEC : 1984Q4>

`Date` and `DateArray` from scikits.timeseries have been reincarnated in pandas `Period` and `PeriodIndex`:

    In [1]: pnow('D')  # scikits.timeseries.now()
    Out[1]: Period('2014-07-11', 'D')

    In [2]: Period(year=2007, month=3, day=15, freq='D')
    Out[2]: Period('2007-03-15', 'D')

    In [3]: p = Period('1984Q3')

    In [4]: p
    Out[4]: Period('1984Q3', 'Q-DEC')

    In [5]: p.asfreq('D', 'start')
    Out[5]: Period('1984-07-01', 'D')

    In [6]: p.asfreq('D', 'end')
    Out[6]: Period('1984-09-30', 'D')

    In [7]: (p + 3).asfreq('T') + 6 * 60 + 30
    Out[7]: Period('1985-07-01 06:29', 'T')

    In [8]: rng = period_range('1990', '2010', freq='A')

    In [9]: rng
    Out[9]:
    <class 'pandas.tseries.period.PeriodIndex'>
    [1990, ..., 2010]
    Length: 21, Freq: A-DEC

    In [10]: rng.asfreq('B', 'end') - 3
    Out[10]:
    <class 'pandas.tseries.period.PeriodIndex'>
    [1990-12-26, ..., 2010-12-28]
    Length: 21, Freq: B

scikits.timeseries | pandas | Notes
--- | --- | ---
Date | Period | A span of time, from yearly through to secondly
DateArray | PeriodIndex | An array of timespans
convert | resample | Frequency conversion in scikits.timeseries
convert_to_annual | pivot_annual | currently supports up to daily frequency, see GH736

### PeriodIndex / DateArray properties and functions

The scikits.timeseries `DateArray` had a number of information properties. Here are the pandas equivalents:

scikits.timeseries | pandas | Notes
--- | --- | ---
get_steps | `np.diff(idx.values)` |
has_missing_dates | `not idx.is_full` |
is_full | `idx.is_full` |
is_valid | `idx.is_monotonic and idx.is_unique` |
is_chronological | `is_monotonic` |
`arr.sort_chronologically()` | `idx.order()` |

### Frequency conversion

Frequency conversion is implemented using the `resample` method on TimeSeries and DataFrame objects (multiple time series). `resample` also works on panels (3D). Here is some code that resamples daily data to montly:

    In [11]: rng = period_range('Jan-2000', periods=50, freq='M')

    In [12]: data = Series(np.random.randn(50), index=rng)

    In [13]: data
    Out[13]:
    2000-01    0.469112
    2000-02   -0.282863
    2000-03   -1.509059
    2000-04   -1.135632
    2000-05    1.212112
    ...
    2003-09   -0.013960
    2003-10   -0.362543
    2003-11   -0.006154
    2003-12   -0.923061
    2004-01    0.895717
    2004-02    0.805244
    Freq: M, Length: 50

    In [14]: data.resample('A', how=np.mean)
    Out[14]:
    2000   -0.394510
    2001   -0.244628
    2002   -0.221633
    2003   -0.453773
    2004    0.850481
    Freq: A-DEC, dtype: float64

### Plotting

Much of the plotting functionality of scikits.timeseries has been ported and adopted to pandas’s data structures. For example:

    In [15]: rng = period_range('1987Q2', periods=10, freq='Q-DEC')

    In [16]: data = Series(np.random.randn(10), index=rng)

    In [17]: plt.figure(); data.plot()
    Out[17]: <matplotlib.axes.AxesSubplot at 0xaa39722c>

### Converting to and from period format

Use the `to_timestamp` and `to_period` instance methods.

### Treatment of missing data

Unlike scikits.timeseries, pandas data structures are not based on NumPy’s `MaskedArray` object. Missing data is represented as `NaN` in numerical arrays and either as `None` or `NaN` in non-numerical arrays. Implementing a version of pandas’s data structures that use MaskedArray is possible but would require the involvement of a dedicated maintainer. Active pandas developers are not interested in this.

### Resampling with timestamps and periods

`resample` has a `kind` argument which allows you to resample time series with a DatetimeIndex to PeriodIndex:

    In [18]: rng = date_range('1/1/2000', periods=200, freq='D')

    In [19]: data = Series(np.random.randn(200), index=rng)

    In [20]: data[:10]
    Out[20]:
    2000-01-01   -0.076467
    2000-01-02   -1.187678
    2000-01-03    1.130127
    2000-01-04   -1.436737
    2000-01-05   -1.413681
    2000-01-06    1.607920
    2000-01-07    1.024180
    2000-01-08    0.569605
    2000-01-09    0.875906
    2000-01-10   -2.211372
    Freq: D, dtype: float64

    In [21]: data.index
    Out[21]:
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2000-01-01, ..., 2000-07-18]
    Length: 200, Freq: D, Timezone: None

    In [22]: data.resample('M', kind='period')
    Out[22]:
    2000-01   -0.175775
    2000-02    0.094874
    2000-03    0.124949
    2000-04    0.066215
    2000-05   -0.040364
    2000-06    0.116263
    2000-07   -0.263235
    Freq: M, dtype: float64

Similarly, resampling from periods to timestamps is possible with an optional interval (`'start'` or `'end'`) convention:

    In [23]: rng = period_range('Jan-2000', periods=50, freq='M')

    In [24]: data = Series(np.random.randn(50), index=rng)

    In [25]: resampled = data.resample('A', kind='timestamp', convention='end')

    In [26]: resampled.index
    Out[26]:
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2000-12-31, ..., 2004-12-31]
    Length: 5, Freq: A-DEC, Timezone: None

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

    In [28]: newx = x.byteswap().newbyteorder() # force native byteorder

    In [29]: s = Series(newx)

See the NumPy documentation on byte order for more details.

## Visualizing Data in Qt applications

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_()

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