pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: month/quarter/year defined by the frequency of the DateTimeIndex / Timestamp (GH4565, GH6998) • Local variable usage has changed in pandas.eval()/DataFrame.eval()/DataFrame.query() (GH5987). For the DataFrame locals – Local variables must be referred to explicitly. This means that even if you have a local variable that is not a column you must still refer to it with the ’@’ prefix. – You can have an expression

. . . . . . . . . . . . . . . . . . . . . 396 12.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 397 12.4 Frequently Used Options . . . . . . pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: 7 4 NaN 5 11 6 13 dtype: float64 • Added a DataFrame.round method to round the values to a variable number of decimal places (GH10568). In [49]: df = pd.DataFrame(np.random.random([3, 3]), columns=['A'

. . . . . . . . . . . . . . . . . . . . . 326 11.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 327 11.4 Frequently Used Options . . . . . . pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: subplots=True may draw unnecessary minor xticks and yticks (GH7801) • Bug in StataReader which did not read variable labels in 117 files due to difference between Stata docu- mentation and implementation (GH7816)

. . . . . . . . . . . . . . . . . . . . . 318 11.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 319 11.4 Frequently Used Options . . . . . . pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: subplots=True may draw unnecessary minor xticks and yticks (GH7801) • Bug in StataReader which did not read variable labels in 117 files due to difference between Stata docu- mentation and implementation (GH7816)

pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: all of the open handles are 0. Essentially you have a local instance of HDFStore referenced by a variable. Once you close it, it will report closed. Other references (to the same file) will continue to accessed (GH3982, GH3985, GH4028, GH4054) • Series.hist will now take the figure from the current environment if one is not passed • Fixed bug where a 1xN DataFrame would barf on a 1xN mask (GH4071) • Fixed

performance level. Load balancing It becomes necessary for Kyuubi in a real-world production environment to ensure high availability because of multi-tenant access. It effectively prevents single point tar zxf spark-3.3.2-bin-hadoop3.tgz Configuration The kyuubi-env.sh file is used to set system environment variables to the kyuubi server process and engine processes it creates. The kyuubi-defaults.conf several ways to configure the system and corresponding engines. Environments You can configure the environment variables in $KYUUBI_HOME/conf/kyuubi-env.sh, e.g, JAVA_HOME, then this java runtime will be used

deployment, then only you need to be sure is that the below environments are correct: Java runtime environment SPARK_HOME for the Spark engine FLINK_HOME and kyuubi.engine.type in $KYUUBI_HOME/conf/kyuubi- system-widely, e.g. in the .bashrc file. java -version java version "1.8.0_251" Java(TM) SE Runtime Environment (build 1.8.0_251-b08) Java HotSpot(TM) 64-Bit Server VM (build 25.251-b08, mixed mode) Or, export 11.0.5.jdk/Contents/Home java -version java version "11.0.5" 2019-10-15 LTS Java(TM) SE Runtime Environment 18.9 (build 11.0.5+10-LTS) Java HotSpot(TM) 64-Bit Server VM 18.9 (build 11.0.5+10-LTS, mixed

deployment, then only you need to be sure is that the below environments are correct: Java runtime environment SPARK_HOME for the Spark engine FLINK_HOME and kyuubi.engine.type in $KYUUBI_HOME/conf/kyuubi- system-widely, e.g. in the .bashrc file. java -version java version "1.8.0_251" Java(TM) SE Runtime Environment (build 1.8.0_251-b08) Java HotSpot(TM) 64-Bit Server VM (build 25.251-b08, mixed mode) Or, export 11.0.5.jdk/Contents/Home java -version java version "11.0.5" 2019-10-15 LTS Java(TM) SE Runtime Environment 18.9 (build 11.0.5+10-LTS) Java HotSpot(TM) 64-Bit Server VM 18.9 (build 11.0.5+10-LTS, mixed

. 331 3.3.5 Creating a development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 3.3.6 Creating a Windows development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512 12.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 513 12.4 Frequently Used Options . . . . . . value_labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121 pandas.io.stata.StataReader.variable_labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121 pandas.io.stata.StataWriter.write_file

. 329 3.3.5 Creating a development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 3.3.6 Creating a Windows development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510 12.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 511 12.4 Frequently Used Options . . . . . . value_labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1118 pandas.io.stata.StataReader.variable_labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 1118 pandas.io.stata.StataWriter.write_file