Python Data Analysis Cookbook

pandas

The following creates a fixed frequency datetime index:

pandas.date_range(start=None, end=None, periods=None, freq='D', tz=None, normalize=False, name=None, closed=None)

The following argument generate various summary statistics, ignoring NaN values:

pandas.DataFrame.describe(self, percentile_width=None, percentiles=None, include=None, exclude=None)

The following creates a DataFrame object from a dictionary of array-like objects or dictionaries:

pandas.DataFrame. from_dict(data, orient='columns', dtype=None)

The following argument finds NaN and None values:

pandas.isnull(obj)

The following argument merges DataFrame objects with a database-like join on columns or indices:

pandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, ...