computing ecosystem in Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much blocks) (GH #158) 1.5 v.0.6.0 (November 25, 2011) 1.5.1 New Features • Added melt function to pandas.core.reshape • Added level parameter to group by level in Series and DataFrame descriptive statistics Development Team is a collection of developers focused on the improvement of Python’s data libraries. The core team that coordinates development can be found on Github. If you’re interested in contributing, please

computing ecosystem in Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much blocks) (GH #158) 1.4 v.0.6.0 (November 25, 2011) 1.4.1 New Features • Added melt function to pandas.core.reshape • Added level parameter to group by level in Series and DataFrame descriptive statistics Development Team is a collection of developers focused on the improvement of Python’s data libraries. The core team that coordinates development can be found on Github. If you’re interested in contributing, please

computing ecosystem in Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much major_axis=date_range(’20010102’,periods=4), ....: minor_axis=[’A’,’B’,’C’,’D’]) ....: In [60]: p core.panel.Panel’> Dimensions: 3 (items) x 4 (major_axis) x 4 (minor_axis) Items axis: ItemA to ItemC Major_axis print option: In [37]: pd.set_option(’expand_frame_repr’, False) In [38]: wide_frame core.frame.DataFrame’> Int64Index: 5 entries, 0 to 4 Data columns (total 16 columns): 0 5 non-null values

computing ecosystem in Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much blocks) (GH #158) 1.6 v.0.6.0 (November 25, 2011) 1.6.1 New Features • Added melt function to pandas.core.reshape • Added level parameter to group by level in Series and DataFrame descriptive statistics Development Team is a collection of developers focused on the improvement of Python’s data libraries. The core team that coordinates development can be found on Github. If you’re interested in contributing, please

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 4.3 Visualizing Data in Qt applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 5 Package overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 899 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 899 computing ecosystem in Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 3.5 Visualizing Data in Qt applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 4 Package overview computing ecosystem in Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much list of tuple (GH4370) • all offset operations now return Timestamp types (rather than datetime), Business/Week frequencies were incorrect (GH4069) • to_excel now converts np.inf into a string representation

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 3.5 Visualizing Data in Qt applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4 Package overview computing ecosystem in Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.13.1 core.frame.DataFrame’> Int64Index: 10 entries, 0 to 9 Data columns (total 3 columns): A float64 B float64

features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Custom Business Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 .groupby(. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 4.3 Visualizing Data in Qt applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 5 Package overview . . . . . . . . . . . . . 745 20.7.3 Custom Business Days (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746 20.7.4 Business Hour . . . . . . . . . . . . . . . . . . . . .

features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Custom Business Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 .groupby(. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 4.3 Visualizing Data in Qt applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 5 Package overview . . . . . . . . . . . . . 743 20.7.3 Custom Business Days (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744 20.7.4 Business Hour . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 1.9.1.1 Custom Business Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 1.9.1.2 .groupby(..) syntax . . . . . . . . . . 862 xvii 19.8.3 Custom Business Days . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 864 19.8.4 Business Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 865 19.8.5 Custom Business Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867 19.8.6 Offset Aliases . . . . . . . . . . .