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Before you begin, ensure the Python extensions are installed in your Azure Data Lake Analytics account.
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* Navigate to you Data Lake Analytics Account in the Azure portal
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* In the left menu, under **GETTING STARTED**click on**Sample Scripts**
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*Click**Install U-SQL Extensions** then **OK**
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* Navigate to your Data Lake Analytics Account in the Azure portal
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* In the left menu, under **GETTING STARTED**select**Sample Scripts**
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*Select**Install U-SQL Extensions** then **OK**
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## Overview
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### Schemas
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* Index vectors in Pandas are not supported in U-SQL. All input data frames in the Python function always have a 64-bit numerical index from 0 through the number of rows minus 1.
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* U-SQL datasets cannot have duplicate column names
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* U-SQL datasets column names that are not strings.
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* Index vectors in Pandas aren't supported in U-SQL. All input data frames in the Python function always have a 64-bit numerical index from 0 through the number of rows minus 1.
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* U-SQL datasets can't have duplicate column names
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* U-SQL datasets column names that aren't strings.
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### Python Versions
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All the standard Python modules are included.
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### Additional Python modules
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### More Python modules
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Besides the standard Python libraries, several commonly used Python libraries are included:
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### Input and Output size limitations
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Every vertex has a limited amount of memory assigned to it. Currently, that limit is 6 GB for an AU. Because the input and output DataFrames must exist in memory in the Python code, the total size for the input and output cannot exceed 6 GB.
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Every vertex has a limited amount of memory assigned to it. Currently, that limit is 6 GB for an AU. Because the input and output DataFrames must exist in memory in the Python code, the total size for the input and output can't exceed 6 GB.
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