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When using the AEL engine for processing data Row by row or All rows, you can only have one input step in your transformation. Now the data scientist can operate with an entire set in the training data frame. The training dataset can contain multiple types of data which allows for a broader scope, without the need to join data ahead of time. Because data frames combine the behavior of lists and matrices, it is well-suited for the analytical needs of statistical data. For example, data scientists may want to bring in a training dataset before an actual dataset. The All rows option is commonly used for data frames. A data frame is used for storing data tables and is composed of a list of vectors of equal length. Decide if you want to process your data Row by row (standard PDI behavior) or All rows at once. Use this tab to make selections for moving data from PDI fields to Python variables.
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If you install Python using the Anaconda distribution, all the required libraries will be installed. Matplotlib (1.5.3 or later) Matplotlib is a plotting library for Python and NumPy. It also allows Java programs to access Python objects. Py4J (0.10.2 or later) is a bridge between Python and Java, permitting Python programs running with a Python interpreter to dynamically access Java objects in a JVM. NumPy arrays can be fast, easy to work with, providing users opportunities to perform calculations across entire arrays. A NumPy array is a table of values, all of the same type, which is indexed by a tuple of positive integers. NumPy (1.14.0 or later) is a library for the Python programming language which adds both robust support for multi-dimensional arrays and matrices, and a large collection of high-level mathematical functions to operate on these arrays. Ultimately, you want to be able to insert and remove objects from these containers in a dictionary-like fashion.
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For example, DataFrame is a container for Series, and Series is a container for scalars. The pandas DataFrame along with the Series are the two parts of the pandas data structure, a flexible container for lower dimensional data.
When you send all rows, the data sent is considered a dataset. When you deliver the input row-by-row, the field values of each incoming row are mapped to separate variables containing built-in types, such as numerics, strings, and Booleans. You set the names of these field values which are then available within the Python script.
You can opt to send all rows to Python at once, or send rows one-by-one. This step offers several options for execution. You can choose to map upstream data from a PDI input step to generate data or have the Python script generate its own data.
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This step helps developers and data scientists take advantage of the strengths of the Python's versatile programming language to develop predictive solutions using existing PDI steps. Instead of writing code to connect to relational databases and Hadoop file systems, and to join and filter data, PDI allows the developer to focus their coding efforts on the data science-driven algorithms.
The Python Executor step leverages the Python programming language as part of the data integration pipeline from within PDI.