CategoricalIndex.get_duplicates() [source]

For potential users coming from SAS this page is meant to demonstrate how different SAS operations would be performed in pandas. If you?re new to pandas, you might want to first read through 10 Minutes to pandas to familiarize yourself with the library. As is customary, we import pandas and numpy as follows: In [1]: import pandas as pd In [2]: import numpy as np Note Throughout this tutorial, the pandas DataFrame will be displayed by calling df.head(), which displays the first N (default 5)

MultiIndex.reorder_levels(order) [source] Rearrange levels using input order. May not drop or duplicate levels

Series.put(*args, **kwargs) [source] Applies the put method to its values attribute if it has one. See also numpy.ndarray.put

DatetimeIndex.values return the underlying data as an ndarray

This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the Cookbook Customarily, we import as follows: In [1]: import pandas as pd In [2]: import numpy as np In [3]: import matplotlib.pyplot as plt Object Creation See the Data Structure Intro section Creating a Series by passing a list of values, letting pandas create a default integer index: In [4]: s = pd.Series([1,3,5,np.nan,6,8]) In [5]: s Out[5]: 0 1.0 1 3.0 2 5.0 3 NaN 4

Panel4D.ffill(axis=None, inplace=False, limit=None, downcast=None) [source] Synonym for NDFrame.fillna(method=?ffill?)

Here we discuss a lot of the essential functionality common to the pandas data structures. Here?s how to create some of the objects used in the examples from the previous section: In [1]: index = pd.date_range('1/1/2000', periods=8) In [2]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e']) In [3]: df = pd.DataFrame(np.random.randn(8, 3), index=index, ...: columns=['A', 'B', 'C']) ...: In [4]: wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', '

Panel4D.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last') [source]

Resampler.ffill(limit=None) [source] Forward fill the values Parameters: limit : integer, optional limit of how many values to fill See also Series.fillna, DataFrame.fillna