VBS treats COM safearrays, which are used to keep lists in the Main API. VBS expects every array element to be a VARIANT, which is too strict a limitation for any high performance API. We may lift this (psa); 23 2 Environment-specific notes This covers the necessary functionality for all array element types except interface references. These need special helpers to manage the reference counting correctly passes the first IMachine reference to another API function (assuming that there is at least one element in the array, to simplify the example): SAFEARRAY psa = g_pVBoxFuncs->pfnSafeArrayOutParamAlloc();

VBS treats COM safearrays, which are used to keep lists in the Main API. VBS expects every array element to be a VARIANT, which is too strict a limitation for any high performance API. We may lift this psa); g_pVBoxFuncs->pfnSafeArrayDestroy(psa); This covers the necessary functionality for all array element types except interface references. These need special helpers to manage the reference counting correctly passes the first IMachine reference to another API function (assuming that there is at least one element in the array, to simplify the example): SAFEARRAY psa = g_pVBoxFuncs->pfnSafeArrayOutParamAlloc();

VBS treats COM safearrays, which are used to keep lists in the Main API. VBS expects every array element to be a VARIANT, which is too strict a limitation for any high performance API. We may lift this psa); g_pVBoxFuncs->pfnSafeArrayDestroy(psa); This covers the necessary functionality for all array element types except interface references. These need special helpers to manage the reference counting correctly passes the first IMachine reference to another API function (assuming that there is at least one element in the array, to simplify the example): SAFEARRAY psa = g_pVBoxFuncs->pfnSafeArrayOutParamAlloc();

VBS treats COM safearrays, which are used to keep lists in the Main API. VBS expects every array element to be a VARIANT, which is too strict a limitation for any high performance API. We may lift this psa); g_pVBoxFuncs->pfnSafeArrayDestroy(psa); This covers the necessary functionality for all array element types except interface references. These need special helpers to manage the reference counting correctly passes the first IMachine reference to another API function (assuming that there is at least one element in the array, to simplify the example): SAFEARRAY psa = g_pVBoxFuncs->pfnSafeArrayOutParamAlloc();

VBS treats COM safearrays, which are used to keep lists in the Main API. VBS expects every array element to be a VARIANT, which is too strict a limitation for any high performance API. We may lift this psa); g_pVBoxFuncs->pfnSafeArrayDestroy(psa); This covers the necessary functionality for all array element types except interface references. These need special helpers to manage the reference counting correctly passes the first IMachine reference to another API function (assuming that there is at least one element in the array, to simplify the example): SAFEARRAY psa = g_pVBoxFuncs->pfnSafeArrayOutParamAlloc();

__unicode__ method, gets python 2 and python 3 compatible string methods (__str__, __bytes__, and __repr__). Plus string safety throughout. Now employed in many places throughout the pandas library. (GH4090, GH4092) object In [53]: s.dropna().values.item() == ’w’ True The last element yielded by the iterator will be a Series containing the last element of the longest string in the Series with all other elements being decorator to allow explicitly checking a website as a proxy for seeing if there is network connectivity. Plus, new optional_args decorator factory for decorators. (GH3910, GH3914) • Fixed testing issue where

245373 [5 rows x 4 columns] Specifying an apply that operates on a Series (to return a single element) In [43]: panel.apply(lambda x: x.dtype, axis=’items’) Out[43]: A B C D 2000-01-03 float64 float64 allowing 2/3 compatibility. It contains both list and itera- tor versions of range, filter, map and zip, plus other necessary elements for Python 3 compatibility. lmap, lzip, lrange and lfilter all produce lists and df2 s1 and s2 Added the .bool() method to NDFrame objects to facilitate evaluating of single-element boolean Series: In [1]: Series([True]).bool() Out[1]: True In [2]: Series([False]).bool() Out[2]:

result, but retains in result’s Index. (GH10150) • Bug in pd.eval using numexpr engine coerces 1 element numpy array to scalar (GH10546) • Bug in pd.concat with axis=0 when column is of dtype category length_of_indexer returns wrong results (GH9995) • Bug in csv parser causing lines with initial whitespace plus one non-space character to be skipped. (GH9710) • Bug in C csv parser causing spurious NaNs when utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect as it didn’t

import udf # create a general Python UDF @udf(result_type=DataTypes.BIGINT()) def plus_one(i): return i + 1 table.select(plus_one(col('id'))).to_pandas() 1.1. Getting Started 17 pyflink-docs, Release release-1 @udf(result_type=DataTypes.BIGINT(), func_type='pandas') def pandas_plus_one(series): return series + 1 table.select(pandas_plus_one(col('id'))).to_pandas() /Users/duanchen/sourcecode/flink/flink-python/dev/ use the Python function in SQL API table_env.create_temporary_function("plus_one", plus_one) table_env.sql_query("SELECT plus_one(id) FROM {}".format(table)).to_pandas() Another example is UDFs used

import udf # create a general Python UDF @udf(result_type=DataTypes.BIGINT()) def plus_one(i): return i + 1 table.select(plus_one(col('id'))).to_pandas() 1.1. Getting Started 17 pyflink-docs, Release release-1 @udf(result_type=DataTypes.BIGINT(), func_type='pandas') def pandas_plus_one(series): return series + 1 table.select(pandas_plus_one(col('id'))).to_pandas() /Users/duanchen/sourcecode/flink/flink-python/dev/ use the Python function in SQL API table_env.create_temporary_function("plus_one", plus_one) table_env.sql_query("SELECT plus_one(id) FROM {}".format(table)).to_pandas() Another example is UDFs used