{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quickstart: Pandas API on Spark\n", "\n", "This is a short introduction to pandas API on Spark, geared mainly for new users. This notebook shows you some key differences between pandas and pandas API on Spark. You can run this examples by yourself in 'Live Notebook: pandas API on Spark' at [the quickstart page](https://spark.apache.org/docs/latest/api/python/getting_started/index.html).\n", "\n", "Customarily, we import pandas API on Spark as follows:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import pyspark.pandas as ps\n", "from pyspark.sql import SparkSession" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Object Creation\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a pandas-on-Spark Series by passing a list of values, letting pandas API on Spark create a default integer index:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "s = ps.Series([1, 3, 5, np.nan, 6, 8])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1.0\n", "1 3.0\n", "2 5.0\n", "3 NaN\n", "4 6.0\n", "5 8.0\n", "dtype: float64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a pandas-on-Spark DataFrame by passing a dict of objects that can be converted to series-like." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "psdf = ps.DataFrame(\n", " {'a': [1, 2, 3, 4, 5, 6],\n", " 'b': [100, 200, 300, 400, 500, 600],\n", " 'c': [\"one\", \"two\", \"three\", \"four\", \"five\", \"six\"]},\n", " index=[10, 20, 30, 40, 50, 60])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " a b c\n", "10 1 100 one\n", "20 2 200 two\n", "30 3 300 three\n", "40 4 400 four\n", "50 5 500 five\n", "60 6 600 six" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a pandas DataFrame by passing a numpy array, with a datetime index and labeled columns:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "dates = pd.date_range('20130101', periods=6)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n", " '2013-01-05', '2013-01-06'],\n", " dtype='datetime64[ns]', freq='D')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "pdf = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-010.912558-0.795645-0.2891150.187606
2013-01-02-0.059703-1.2338970.316625-1.226828
2013-01-030.332871-1.262010-0.434844-0.579920
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2013-01-061.485582-0.709306-0.202637-0.248766
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" ], "text/plain": [ " A B C D\n", "2013-01-01 0.912558 -0.795645 -0.289115 0.187606\n", "2013-01-02 -0.059703 -1.233897 0.316625 -1.226828\n", "2013-01-03 0.332871 -1.262010 -0.434844 -0.579920\n", "2013-01-04 0.924016 -1.022019 -0.405249 -1.036021\n", "2013-01-05 -0.772209 -1.228099 0.068901 0.896679\n", "2013-01-06 1.485582 -0.709306 -0.202637 -0.248766" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, this pandas DataFrame can be converted to a pandas-on-Spark DataFrame" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "psdf = ps.from_pandas(pdf)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pyspark.pandas.frame.DataFrame" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(psdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks and behaves the same as a pandas DataFrame." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-010.912558-0.795645-0.2891150.187606
2013-01-02-0.059703-1.2338970.316625-1.226828
2013-01-030.332871-1.262010-0.434844-0.579920
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" ], "text/plain": [ " A B C D\n", "2013-01-01 0.912558 -0.795645 -0.289115 0.187606\n", "2013-01-02 -0.059703 -1.233897 0.316625 -1.226828\n", "2013-01-03 0.332871 -1.262010 -0.434844 -0.579920\n", "2013-01-04 0.924016 -1.022019 -0.405249 -1.036021\n", "2013-01-05 -0.772209 -1.228099 0.068901 0.896679\n", "2013-01-06 1.485582 -0.709306 -0.202637 -0.248766" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also, it is possible to create a pandas-on-Spark DataFrame from Spark DataFrame easily.\n", "\n", "Creating a Spark DataFrame from pandas DataFrame" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "spark = SparkSession.builder.getOrCreate()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "sdf = spark.createDataFrame(pdf)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-------------------+--------------------+--------------------+\n", "| A| B| C| D|\n", "+--------------------+-------------------+--------------------+--------------------+\n", "| 0.91255803205208|-0.7956452608556638|-0.28911463069772175| 0.18760566615081622|\n", "|-0.05970271470242...| -1.233896949308984| 0.3166246451758431| -1.2268284000402265|\n", "| 0.33287106947536615|-1.2620100816441786| -0.4348444277082644| -0.5799199651437185|\n", "| 0.9240158461589916|-1.0220190956326003| -0.4052488880650239| -1.0360212104348547|\n", "| -0.7722090016558953|-1.2280986385313222| 0.0689011451939635| 0.8966790729426755|\n", "| 1.4855822995785612|-0.7093056426018517| -0.2026366848847041|-0.24876619876451092|\n", "+--------------------+-------------------+--------------------+--------------------+\n", "\n" ] } ], "source": [ "sdf.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating pandas-on-Spark DataFrame from Spark DataFrame." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "psdf = sdf.pandas_api()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
00.912558-0.795645-0.2891150.187606
1-0.059703-1.2338970.316625-1.226828
20.332871-1.262010-0.434844-0.579920
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" ], "text/plain": [ " A B C D\n", "0 0.912558 -0.795645 -0.289115 0.187606\n", "1 -0.059703 -1.233897 0.316625 -1.226828\n", "2 0.332871 -1.262010 -0.434844 -0.579920\n", "3 0.924016 -1.022019 -0.405249 -1.036021\n", "4 -0.772209 -1.228099 0.068901 0.896679\n", "5 1.485582 -0.709306 -0.202637 -0.248766" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having specific [dtypes](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-dtypes) . Types that are common to both Spark and pandas are currently supported." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "A float64\n", "B float64\n", "C float64\n", "D float64\n", "dtype: object" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is how to show top rows from the frame below.\n", "\n", "Note that the data in a Spark dataframe does not preserve the natural order by default. The natural order can be preserved by setting `compute.ordered_head` option but it causes a performance overhead with sorting internally." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
00.912558-0.795645-0.2891150.187606
1-0.059703-1.2338970.316625-1.226828
20.332871-1.262010-0.434844-0.579920
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ABCD
count6.0000006.0000006.0000006.000000
mean0.470519-1.041829-0.157720-0.334542
std0.8094280.2415110.2945200.793014
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75%0.924016-0.7956450.0689010.187606
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ABCD
51.485582-0.709306-0.202637-0.248766
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30.924016-1.022019-0.405249-1.036021
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ABCD
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1-0.059703-1.2338970.316625-1.226828
4-0.772209-1.2280990.0689010.896679
30.924016-1.022019-0.405249-1.036021
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51.485582-0.709306-0.202637-0.248766
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" ], "text/plain": [ " A B C D\n", "2 0.332871 -1.262010 -0.434844 -0.579920\n", "1 -0.059703 -1.233897 0.316625 -1.226828\n", "4 -0.772209 -1.228099 0.068901 0.896679\n", "3 0.924016 -1.022019 -0.405249 -1.036021\n", "0 0.912558 -0.795645 -0.289115 0.187606\n", "5 1.485582 -0.709306 -0.202637 -0.248766" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf.sort_values(by='B')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Missing Data\n", "Pandas API on Spark primarily uses the value `np.nan` to represent missing data. It is by default not included in computations. \n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "pdf1 = pdf.reindex(index=dates[0:4], columns=list(pdf.columns) + ['E'])" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "pdf1.loc[dates[0]:dates[1], 'E'] = 1" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "psdf1 = ps.from_pandas(pdf1)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCDE
2013-01-010.912558-0.795645-0.2891150.1876061.0
2013-01-02-0.059703-1.2338970.316625-1.2268281.0
2013-01-030.332871-1.262010-0.434844-0.579920NaN
2013-01-040.924016-1.022019-0.405249-1.036021NaN
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" ], "text/plain": [ " A B C D E\n", "2013-01-01 0.912558 -0.795645 -0.289115 0.187606 1.0\n", "2013-01-02 -0.059703 -1.233897 0.316625 -1.226828 1.0\n", "2013-01-03 0.332871 -1.262010 -0.434844 -0.579920 NaN\n", "2013-01-04 0.924016 -1.022019 -0.405249 -1.036021 NaN" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To drop any rows that have missing data." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCDE
2013-01-010.912558-0.795645-0.2891150.1876061.0
2013-01-02-0.059703-1.2338970.316625-1.2268281.0
\n", "
" ], "text/plain": [ " A B C D E\n", "2013-01-01 0.912558 -0.795645 -0.289115 0.187606 1.0\n", "2013-01-02 -0.059703 -1.233897 0.316625 -1.226828 1.0" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf1.dropna(how='any')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Filling missing data." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCDE
2013-01-010.912558-0.795645-0.2891150.1876061.0
2013-01-02-0.059703-1.2338970.316625-1.2268281.0
2013-01-030.332871-1.262010-0.434844-0.5799205.0
2013-01-040.924016-1.022019-0.405249-1.0360215.0
\n", "
" ], "text/plain": [ " A B C D E\n", "2013-01-01 0.912558 -0.795645 -0.289115 0.187606 1.0\n", "2013-01-02 -0.059703 -1.233897 0.316625 -1.226828 1.0\n", "2013-01-03 0.332871 -1.262010 -0.434844 -0.579920 5.0\n", "2013-01-04 0.924016 -1.022019 -0.405249 -1.036021 5.0" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf1.fillna(value=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Operations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stats\n", "Performing a descriptive statistic:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "A 0.470519\n", "B -1.041829\n", "C -0.157720\n", "D -0.334542\n", "dtype: float64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spark Configurations\n", "\n", "Various configurations in PySpark could be applied internally in pandas API on Spark.\n", "For example, you can enable Arrow optimization to hugely speed up internal pandas conversion. See also PySpark Usage Guide for Pandas with Apache Arrow in PySpark documentation." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "prev = spark.conf.get(\"spark.sql.execution.arrow.pyspark.enabled\") # Keep its default value.\n", "ps.set_option(\"compute.default_index_type\", \"distributed\") # Use default index prevent overhead.\n", "import warnings\n", "warnings.filterwarnings(\"ignore\") # Ignore warnings coming from Arrow optimizations." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "900 ms ± 186 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "spark.conf.set(\"spark.sql.execution.arrow.pyspark.enabled\", True)\n", "%timeit ps.range(300000).to_pandas()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.08 s ± 227 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "spark.conf.set(\"spark.sql.execution.arrow.pyspark.enabled\", False)\n", "%timeit ps.range(300000).to_pandas()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "ps.reset_option(\"compute.default_index_type\")\n", "spark.conf.set(\"spark.sql.execution.arrow.pyspark.enabled\", prev) # Set its default value back." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grouping\n", "By “group by” we are referring to a process involving one or more of the following steps:\n", "\n", "- Splitting the data into groups based on some criteria\n", "- Applying a function to each group independently\n", "- Combining the results into a data structure" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "psdf = ps.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',\n", " 'foo', 'bar', 'foo', 'foo'],\n", " 'B': ['one', 'one', 'two', 'three',\n", " 'two', 'two', 'one', 'three'],\n", " 'C': np.random.randn(8),\n", " 'D': np.random.randn(8)})" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
0fooone1.039632-0.571950
1barone0.9720891.085353
2footwo-1.931621-2.579164
3barthree-0.654371-0.340704
4footwo-0.1570800.893736
5bartwo0.8827950.024978
6fooone-0.1493840.201667
7foothree-1.3551360.693883
\n", "
" ], "text/plain": [ " A B C D\n", "0 foo one 1.039632 -0.571950\n", "1 bar one 0.972089 1.085353\n", "2 foo two -1.931621 -2.579164\n", "3 bar three -0.654371 -0.340704\n", "4 foo two -0.157080 0.893736\n", "5 bar two 0.882795 0.024978\n", "6 foo one -0.149384 0.201667\n", "7 foo three -1.355136 0.693883" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grouping and then applying the [sum()](https://spark.apache.org/docs/latest/api/python/reference/pyspark.pandas/api/pyspark.pandas.groupby.GroupBy.sum.html) function to the resulting groups." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BCD
A
baronethreetwo1.2005130.769627
fooonetwotwoonethree-2.553589-1.361828
\n", "
" ], "text/plain": [ " C D\n", "A \n", "bar 1.200513 0.769627\n", "foo -2.553589 -1.361828" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf.groupby('A').sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grouping by multiple columns forms a hierarchical index, and again we can apply the sum function." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CD
AB
fooone0.890248-0.370283
two-2.088701-1.685428
barthree-0.654371-0.340704
foothree-1.3551360.693883
bartwo0.8827950.024978
one0.9720891.085353
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" ], "text/plain": [ " C D\n", "A B \n", "foo one 0.890248 -0.370283\n", " two -2.088701 -1.685428\n", "bar three -0.654371 -0.340704\n", "foo three -1.355136 0.693883\n", "bar two 0.882795 0.024978\n", " one 0.972089 1.085353" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psdf.groupby(['A', 'B']).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "pser = pd.Series(np.random.randn(1000),\n", " index=pd.date_range('1/1/2000', periods=1000))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "psser = ps.Series(pser)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "psser = psser.cummax()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "variable=0
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "psdf.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more details, [Plotting](https://spark.apache.org/docs/latest/api/python/reference/pyspark.pandas/frame.html#plotting) documentation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting data in/out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CSV\n", "\n", "CSV is straightforward and easy to use. See [here](https://spark.apache.org/docs/latest/api/python/reference/pyspark.pandas/api/pyspark.pandas.DataFrame.to_csv.html) to write a CSV file and [here](https://spark.apache.org/docs/latest/api/python/reference/pyspark.pandas/api/pyspark.pandas.read_csv.html) to read a CSV file." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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