lxml fails to parse. a list of parsers to try until success is also valid • The internal pandas class hierarchy has changed (slightly). The previous PandasObject now is called PandasContainer and a new BeautifulSoup==4.2.0 is detected (GH4214) 1.1.4 Experimental Features • Added experimental CustomBusinessDay class to support DateOffsets with custom holiday calendars and custom weekmasks. (GH2301) Note: This uses major_axis=date_range(’20010102’,periods=4), ....: minor_axis=[’A’,’B’,’C’,’D’]) ....: In [60]: p <class ’pandas.core.panel.Panel’> Dimensions: 3 (items) x 4 (major_axis) x 4 (minor_axis) Items axis: ItemA

@Completion, @Parsing, @Reference. It allows you to completely define the command in the command class directly. To simplify the generation of the code and OSGi headers, Karaf 4.x provides the karaf-services- Enables/disables dynamic-import for a given bundle. bundle:find-class Locates a specified class in any deployed bundle bundle:headers Displays OSGi headers bundle karaf@root(bundle)> karaf@root(bundle)> capabilities classes diag dynamic-import find-class headers info install list refresh requirements resolve restart services start start-level stop uninstall

API) with relu activation (using Activation module) function. Sequential model exposes Model class to create customized models as well. We can use sub-classing concept to create our own complex model create our own customized layers. Customized layer can be created by sub-classing the Keras.Layer class and it is similar to sub-classing Keras models. Core Modules Keras also provides a lot of built-in HDF5Matrix data = HDF5Matrix('data.hdf5', 'data') to_categorical It is used to convert class vector into binary class matrix. >>> from keras.utils import to_categorical >>> labels = [0, 1, 2, 3, 4,

2 Defining custom windows for rolling operations We’ve added a pandas.api.indexers.BaseIndexer() class that allows users to define how window bounds are created during rolling operations. Users can define ... "text_col": ["a", "b", "c"], ... "float_col": [0.0, 0.1, 0.2]}) >>> df.info(verbose=True) <class 'pandas.core.frame.DataFrame'> RangeIndex: 3 entries, 0 to 2 Data columns (total 3 columns): int_col "text_col": ["a", "b", "c"], ....: "float_col": [0.0, 0.1, 0.2]}) ....: In [35]: df.info(verbose=True) <class 'pandas.core.frame.DataFrame'> RangeIndex: 3 entries, 0 to 2 Data columns (total 3 columns): # Column

id="traceHandler" class="org.apache.karaf.decanter.collector.camel.DecanterTraceEventHandler"> class="org.apache.camel timeout.ms=30000 # Serializer class for key that implements the Serializer interface #key.serializer=org.apache.kafka.common.serialization.StringSerializer # Serializer class for value that implements the memory=33554432 # Serializer class for key that implements the Serializer interface # key.serializer=org.apache.kafka.common.serialization.StringSerializer # Serializer class for value that implements the

NumFOCUS sponsored project. This will help ensure the success of the development of pandas as a world-class open-source project and makes it possible to donate to the project. Project governance The governance have value 0 and 1. 0 for not survived and 1 for survived. • Pclass: There are 3 classes: Class 1, Class 2 and Class 3. • Name: Name of passenger. • Sex: Gender of passenger. • Age: Age of passenger. rows x 12 columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns):

NumFOCUS sponsored project. This will help ensure the success of the development of pandas as a world-class open-source project and makes it possible to donate to the project. Project governance The governance have value 0 and 1. 0 for not survived and 1 for survived. • Pclass: There are 3 classes: Class 1, Class 2 and Class 3. • Name: Name of passenger. • Sex: Gender of passenger. • Age: Age of passenger. 373450 8.0500 NaN S I’m interested in a technical summary of a DataFrame In [9]: titanic.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns):

NumFOCUS sponsored project. This will help ensure the success of the development of pandas as a world-class open-source project and makes it possible to donate to the project. Project governance The governance have value 0 and 1. 0 for not survived and 1 for survived. • Pclass: There are 3 classes: Class 1, Class 2 and Class 3. • Name: Name of passenger. • Sex: Gender of passenger. • Age: Age of passenger. rows x 12 columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns):

in Series.interpolate() if argument order is required, but omit- ted (GH10633, GH24014). • Fixed class type displayed in exception message in DataFrame.dropna() if invalid axis parameter passed (GH25555) • Allow Index and RangeIndex to be passed to numpy min and max functions (GH26125) • Use actual class name in repr of empty objects of a Series subclass (GH27001). • Bug in DataFrame where passing an NumFOCUS sponsored project. This will help ensure the success of development of pandas as a world- class open-source project, and makes it possible to donate to the project. 3.1.5 Project governance The

in Series.interpolate() if argument order is required, but omit- ted (GH10633, GH24014). • Fixed class type displayed in exception message in DataFrame.dropna() if invalid axis parameter passed (GH25555) • Allow Index and RangeIndex to be passed to numpy min and max functions (GH26125) • Use actual class name in repr of empty objects of a Series subclass (GH27001). • Bug in DataFrame where passing an NumFOCUS sponsored project. This will help ensure the success of development of pandas as a world- class open-source project, and makes it possible to donate to the project. 3.1.5 Project governance The