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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Requires spark >= 2.3\n", | |
"# Requires PyArrow (pip install pyarrow)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from pyspark import SparkContext, SQLContext\n", | |
"\n", | |
"import pyspark.sql.types as T\n", | |
"import pyspark.sql.functions as F\n", | |
"\n", | |
"from pyspark.sql.functions import pandas_udf\n", | |
"from pyspark.sql.functions import PandasUDFType" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"sc = SparkContext()\n", | |
"sqlc = SQLContext(sc)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<style scoped>\n", | |
" .dataframe tbody tr th:only-of-type {\n", | |
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" .dataframe thead th {\n", | |
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"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>group_col</th>\n", | |
" <th>column1</th>\n", | |
" <th>column2</th>\n", | |
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" <th>2</th>\n", | |
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" <td>5</td>\n", | |
" <td>6</td>\n", | |
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" <th>3</th>\n", | |
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" </tr>\n", | |
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"</table>\n", | |
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], | |
"text/plain": [ | |
" group_col column1 column2\n", | |
"0 a 1 1\n", | |
"1 c 3 4\n", | |
"2 e 5 6\n", | |
"3 a 2 7\n", | |
"4 a 3 2\n", | |
"5 e 3 6" | |
] | |
}, | |
"execution_count": 4, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"schema = T.StructType(\n", | |
" [\n", | |
" T.StructField('group_col', T.StringType()),\n", | |
" T.StructField('column1', T.IntegerType()),\n", | |
" T.StructField('column2', T.IntegerType())\n", | |
" ])\n", | |
"\n", | |
"df = sqlc.createDataFrame(\n", | |
" [\n", | |
" ['a', 1, 1], \n", | |
" ['c', 3, 4], \n", | |
" ['e', 5, 6],\n", | |
" ['a', 2, 7],\n", | |
" ['a', 3, 2],\n", | |
" ['e', 3, 6]\n", | |
" ],\n", | |
" schema=schema)\n", | |
"\n", | |
"df.toPandas()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# make schema for the return dataframe of the udf\n", | |
"udf_schema = T.StructType(df.schema.fields + [T.StructField('size', T.IntegerType())])\n", | |
"\n", | |
"# define a function that inputs a dataframe (corresponding to grouped data)\n", | |
"# and outputs a dataframe with the specified data\n", | |
"# In this case, the function appends the size of the grouped dataframe as a column.\n", | |
"\n", | |
"@pandas_udf(udf_schema, PandasUDFType.GROUPED_MAP)\n", | |
"def add_count(grouped_df):\n", | |
" return grouped_df.assign(size=len(grouped_df))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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"</style>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
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" <th>group_col</th>\n", | |
" <th>column1</th>\n", | |
" <th>column2</th>\n", | |
" <th>size</th>\n", | |
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" <th>3</th>\n", | |
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" <td>3</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>a</td>\n", | |
" <td>2</td>\n", | |
" <td>7</td>\n", | |
" <td>3</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>5</th>\n", | |
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" <td>3</td>\n", | |
" </tr>\n", | |
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"</div>" | |
], | |
"text/plain": [ | |
" group_col column1 column2 size\n", | |
"0 e 5 6 2\n", | |
"1 e 3 6 2\n", | |
"2 c 3 4 1\n", | |
"3 a 1 1 3\n", | |
"4 a 2 7 3\n", | |
"5 a 3 2 3" | |
] | |
}, | |
"execution_count": 7, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df.groupBy('group_col').apply(add_count).toPandas()" | |
] | |
}, | |
{ | |
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"execution_count": null, | |
"metadata": {}, | |
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"source": [] | |
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{ | |
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