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[SPARK-35587][PYTHON][DOCS] Initial porting of Koalas documentation ### What changes were proposed in this pull request? This PR proposes to port Koalas documentation to PySpark documentation as its initial step. It ports almost as is except these differences: - Renamed import from `databricks.koalas` to `pyspark.pandas`. - Renamed `to_koalas` -> `to_pandas_on_spark` - Renamed `(Series|DataFrame).koalas` -> `(Series|DataFrame).pandas_on_spark` - Added a `ps_` prefix in the RST file names of Koalas documentation Other then that, - Excluded `python/docs/build/html` in linter - Fixed GA dependency installataion ### Why are the changes needed? To document pandas APIs on Spark. ### Does this PR introduce _any_ user-facing change? Yes, it adds new documentations. ### How was this patch tested? Manually built the docs and checked the output. Closes #32726 from HyukjinKwon/SPARK-35587. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-06-03 22:11:09 -04:00
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"source": [
"# 10 minutes to Koalas\n",
"\n",
"This is a short introduction to Koalas, geared mainly for new users. This notebook shows you some key differences between pandas and Koalas. You can run this examples by yourself on a live notebook [here](https://mybinder.org/v2/gh/pyspark.pandas/master?filepath=docs%2Fsource%2Fgetting_started%2F10min.ipynb). For Databricks Runtime, you can import and run [the current .ipynb file](https://raw.githubusercontent.com/databricks/koalas/master/docs/source/getting_started/10min.ipynb) out of the box. Try it on [Databricks Community Edition](https://community.cloud.databricks.com/) for free.\n",
"\n",
"Customarily, we import Koalas 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 ks\n",
"from pyspark.sql import SparkSession"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Object Creation\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating a Koalas Series by passing a list of values, letting Koalas create a default integer index:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"s = ks.Series([1, 3, 5, np.nan, 6, 8])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
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"output_type": "execute_result"
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating a Koalas DataFrame by passing a dict of objects that can be converted to series-like."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"kdf = ks.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",
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"output_type": "execute_result"
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"kdf"
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{
"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)"
]
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"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')"
]
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"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates"
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{
"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": {},
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" <th>2013-01-06</th>\n",
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" A B C D\n",
"2013-01-01 -0.621429 1.515041 -1.735483 -1.235009\n",
"2013-01-02 0.844961 -0.999771 0.108356 0.109456\n",
"2013-01-03 1.343862 -1.257980 0.099766 -0.137677\n",
"2013-01-04 3.001767 -0.208167 -1.059449 0.312599\n",
"2013-01-05 -0.035864 0.312126 0.252281 0.627551\n",
"2013-01-06 -1.200404 0.276134 -0.344308 -0.367934"
]
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"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
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"source": [
"pdf"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, this pandas DataFrame can be converted to a Koalas DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"kdf = ks.from_pandas(pdf)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyspark.pandas.frame.DataFrame"
]
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"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
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"source": [
"type(kdf)"
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks and behaves the same as a pandas DataFrame though"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
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" A B C D\n",
"2013-01-01 -0.621429 1.515041 -1.735483 -1.235009\n",
"2013-01-02 0.844961 -0.999771 0.108356 0.109456\n",
"2013-01-03 1.343862 -1.257980 0.099766 -0.137677\n",
"2013-01-04 3.001767 -0.208167 -1.059449 0.312599\n",
"2013-01-05 -0.035864 0.312126 0.252281 0.627551\n",
"2013-01-06 -1.200404 0.276134 -0.344308 -0.367934"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also, it is possible to create a Koalas DataFrame from Spark DataFrame. \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",
"| -0.6214290839748133| 1.5150410562536945| -1.7354827055737831| -1.2350091172431052|\n",
"| 0.8449607212376394| -0.9997705636655247| 0.10835607649858589| 0.1094555359929294|\n",
"| 1.3438622379103737| -1.2579798113362755| 0.0997664833965215|-0.13767658889070905|\n",
"| 3.001767403315059|-0.20816676142436616| -1.0594485090898984| 0.31259853367492724|\n",
"|-0.03586387305407219| 0.3121259401964947| 0.2522808041799677| 0.6275512901423211|\n",
"| -1.2004042904971255| 0.27613400857508563|-0.34430818441482375|-0.36793440398703187|\n",
"+--------------------+--------------------+--------------------+--------------------+\n",
"\n"
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}
],
"source": [
"sdf.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating Koalas DataFrame from Spark DataFrame.\n",
"`to_koalas()` is automatically attached to Spark DataFrame and available as an API when Koalas is imported."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"kdf = sdf.to_koalas()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
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"text/plain": [
" A B C D\n",
"0 -0.621429 1.515041 -1.735483 -1.235009\n",
"1 0.844961 -0.999771 0.108356 0.109456\n",
"2 1.343862 -1.257980 0.099766 -0.137677\n",
"3 3.001767 -0.208167 -1.059449 0.312599\n",
"4 -0.035864 0.312126 0.252281 0.627551\n",
"5 -1.200404 0.276134 -0.344308 -0.367934"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf"
]
},
{
"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": [
"kdf.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Viewing Data\n",
"\n",
"See the [API Reference](https://koalas.readthedocs.io/en/latest/reference/index.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See the top rows of the frame. The results may not be the same as pandas though: unlike pandas, the data in a Spark dataframe is not _ordered_, it has no intrinsic notion of index. When asked for the head of a dataframe, Spark will just take the requested number of rows from a partition. Do not rely on it to return specific rows, use `.loc` or `iloc` instead."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
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"text/plain": [
" A B C D\n",
"0 -0.621429 1.515041 -1.735483 -1.235009\n",
"1 0.844961 -0.999771 0.108356 0.109456\n",
"2 1.343862 -1.257980 0.099766 -0.137677\n",
"3 3.001767 -0.208167 -1.059449 0.312599\n",
"4 -0.035864 0.312126 0.252281 0.627551"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display the index, columns, and the underlying numpy data.\n",
"\n",
"You can also retrieve the index; the index column can be ascribed to a DataFrame, see later"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([0, 1, 2, 3, 4, 5], dtype='int64')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.index"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['A', 'B', 'C', 'D'], dtype='object')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.columns"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.62142908, 1.51504106, -1.73548271, -1.23500912],\n",
" [ 0.84496072, -0.99977056, 0.10835608, 0.10945554],\n",
" [ 1.34386224, -1.25797981, 0.09976648, -0.13767659],\n",
" [ 3.0017674 , -0.20816676, -1.05944851, 0.31259853],\n",
" [-0.03586387, 0.31212594, 0.2522808 , 0.62755129],\n",
" [-1.20040429, 0.27613401, -0.34430818, -0.3679344 ]])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.to_numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Describe shows a quick statistic summary of your data"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>6.000000</td>\n",
" <td>6.000000</td>\n",
" <td>6.000000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.555482</td>\n",
" <td>-0.060436</td>\n",
" <td>-0.446473</td>\n",
" <td>-0.115169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.517076</td>\n",
" <td>1.007223</td>\n",
" <td>0.792741</td>\n",
" <td>0.648616</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-1.200404</td>\n",
" <td>-1.257980</td>\n",
" <td>-1.735483</td>\n",
" <td>-1.235009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.621429</td>\n",
" <td>-0.999771</td>\n",
" <td>-1.059449</td>\n",
" <td>-0.367934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-0.035864</td>\n",
" <td>-0.208167</td>\n",
" <td>-0.344308</td>\n",
" <td>-0.137677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.343862</td>\n",
" <td>0.312126</td>\n",
" <td>0.108356</td>\n",
" <td>0.312599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>3.001767</td>\n",
" <td>1.515041</td>\n",
" <td>0.252281</td>\n",
" <td>0.627551</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"count 6.000000 6.000000 6.000000 6.000000\n",
"mean 0.555482 -0.060436 -0.446473 -0.115169\n",
"std 1.517076 1.007223 0.792741 0.648616\n",
"min -1.200404 -1.257980 -1.735483 -1.235009\n",
"25% -0.621429 -0.999771 -1.059449 -0.367934\n",
"50% -0.035864 -0.208167 -0.344308 -0.137677\n",
"75% 1.343862 0.312126 0.108356 0.312599\n",
"max 3.001767 1.515041 0.252281 0.627551"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transposing your data"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A</th>\n",
" <td>-0.621429</td>\n",
" <td>0.844961</td>\n",
" <td>1.343862</td>\n",
" <td>3.001767</td>\n",
" <td>-0.035864</td>\n",
" <td>-1.200404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>1.515041</td>\n",
" <td>-0.999771</td>\n",
" <td>-1.257980</td>\n",
" <td>-0.208167</td>\n",
" <td>0.312126</td>\n",
" <td>0.276134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>-1.735483</td>\n",
" <td>0.108356</td>\n",
" <td>0.099766</td>\n",
" <td>-1.059449</td>\n",
" <td>0.252281</td>\n",
" <td>-0.344308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>-1.235009</td>\n",
" <td>0.109456</td>\n",
" <td>-0.137677</td>\n",
" <td>0.312599</td>\n",
" <td>0.627551</td>\n",
" <td>-0.367934</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5\n",
"A -0.621429 0.844961 1.343862 3.001767 -0.035864 -1.200404\n",
"B 1.515041 -0.999771 -1.257980 -0.208167 0.312126 0.276134\n",
"C -1.735483 0.108356 0.099766 -1.059449 0.252281 -0.344308\n",
"D -1.235009 0.109456 -0.137677 0.312599 0.627551 -0.367934"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sorting by its index"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
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" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-1.200404</td>\n",
" <td>0.276134</td>\n",
" <td>-0.344308</td>\n",
" <td>-0.367934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.035864</td>\n",
" <td>0.312126</td>\n",
" <td>0.252281</td>\n",
" <td>0.627551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3.001767</td>\n",
" <td>-0.208167</td>\n",
" <td>-1.059449</td>\n",
" <td>0.312599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.343862</td>\n",
" <td>-1.257980</td>\n",
" <td>0.099766</td>\n",
" <td>-0.137677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.844961</td>\n",
" <td>-0.999771</td>\n",
" <td>0.108356</td>\n",
" <td>0.109456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.621429</td>\n",
" <td>1.515041</td>\n",
" <td>-1.735483</td>\n",
" <td>-1.235009</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"5 -1.200404 0.276134 -0.344308 -0.367934\n",
"4 -0.035864 0.312126 0.252281 0.627551\n",
"3 3.001767 -0.208167 -1.059449 0.312599\n",
"2 1.343862 -1.257980 0.099766 -0.137677\n",
"1 0.844961 -0.999771 0.108356 0.109456\n",
"0 -0.621429 1.515041 -1.735483 -1.235009"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.sort_index(ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sorting by value"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
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" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.343862</td>\n",
" <td>-1.257980</td>\n",
" <td>0.099766</td>\n",
" <td>-0.137677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.844961</td>\n",
" <td>-0.999771</td>\n",
" <td>0.108356</td>\n",
" <td>0.109456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3.001767</td>\n",
" <td>-0.208167</td>\n",
" <td>-1.059449</td>\n",
" <td>0.312599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-1.200404</td>\n",
" <td>0.276134</td>\n",
" <td>-0.344308</td>\n",
" <td>-0.367934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.035864</td>\n",
" <td>0.312126</td>\n",
" <td>0.252281</td>\n",
" <td>0.627551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.621429</td>\n",
" <td>1.515041</td>\n",
" <td>-1.735483</td>\n",
" <td>-1.235009</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2 1.343862 -1.257980 0.099766 -0.137677\n",
"1 0.844961 -0.999771 0.108356 0.109456\n",
"3 3.001767 -0.208167 -1.059449 0.312599\n",
"5 -1.200404 0.276134 -0.344308 -0.367934\n",
"4 -0.035864 0.312126 0.252281 0.627551\n",
"0 -0.621429 1.515041 -1.735483 -1.235009"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.sort_values(by='B')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Missing Data\n",
"Koalas 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": [
"kdf1 = ks.from_pandas(pdf1)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
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" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>-0.621429</td>\n",
" <td>1.515041</td>\n",
" <td>-1.735483</td>\n",
" <td>-1.235009</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02</th>\n",
" <td>0.844961</td>\n",
" <td>-0.999771</td>\n",
" <td>0.108356</td>\n",
" <td>0.109456</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-03</th>\n",
" <td>1.343862</td>\n",
" <td>-1.257980</td>\n",
" <td>0.099766</td>\n",
" <td>-0.137677</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" <td>3.001767</td>\n",
" <td>-0.208167</td>\n",
" <td>-1.059449</td>\n",
" <td>0.312599</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"text/plain": [
" A B C D E\n",
"2013-01-01 -0.621429 1.515041 -1.735483 -1.235009 1.0\n",
"2013-01-02 0.844961 -0.999771 0.108356 0.109456 1.0\n",
"2013-01-03 1.343862 -1.257980 0.099766 -0.137677 NaN\n",
"2013-01-04 3.001767 -0.208167 -1.059449 0.312599 NaN"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To drop any rows that have missing data."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>-0.621429</td>\n",
" <td>1.515041</td>\n",
" <td>-1.735483</td>\n",
" <td>-1.235009</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02</th>\n",
" <td>0.844961</td>\n",
" <td>-0.999771</td>\n",
" <td>0.108356</td>\n",
" <td>0.109456</td>\n",
" <td>1.0</td>\n",
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"</table>\n",
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],
"text/plain": [
" A B C D E\n",
"2013-01-01 -0.621429 1.515041 -1.735483 -1.235009 1.0\n",
"2013-01-02 0.844961 -0.999771 0.108356 0.109456 1.0"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf1.dropna(how='any')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Filling missing data."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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" <th>2013-01-01</th>\n",
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" <tr>\n",
" <th>2013-01-02</th>\n",
" <td>0.844961</td>\n",
" <td>-0.999771</td>\n",
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" <td>0.109456</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-03</th>\n",
" <td>1.343862</td>\n",
" <td>-1.257980</td>\n",
" <td>0.099766</td>\n",
" <td>-0.137677</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" <td>3.001767</td>\n",
" <td>-0.208167</td>\n",
" <td>-1.059449</td>\n",
" <td>0.312599</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"2013-01-01 -0.621429 1.515041 -1.735483 -1.235009 1.0\n",
"2013-01-02 0.844961 -0.999771 0.108356 0.109456 1.0\n",
"2013-01-03 1.343862 -1.257980 0.099766 -0.137677 5.0\n",
"2013-01-04 3.001767 -0.208167 -1.059449 0.312599 5.0"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf1.fillna(value=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Stats\n",
"Operations in general exclude missing data.\n",
"\n",
"Performing a descriptive statistic:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A 0.555482\n",
"B -0.060436\n",
"C -0.446473\n",
"D -0.115169\n",
"dtype: float64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Spark Configurations\n",
"\n",
"Various configurations in PySpark could be applied internally in Koalas.\n",
"For example, you can enable Arrow optimization to hugely speed up internal pandas conversion. See <a href=\"https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html\">PySpark Usage Guide for Pandas with Apache Arrow</a>."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"prev = spark.conf.get(\"spark.sql.execution.arrow.enabled\") # Keep its default value.\n",
"ks.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": [
"311 ms ± 30.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"spark.conf.set(\"spark.sql.execution.arrow.enabled\", True)\n",
"%timeit ks.range(300000).to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.25 s ± 29.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"spark.conf.set(\"spark.sql.execution.arrow.enabled\", False)\n",
"%timeit ks.range(300000).to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"ks.reset_option(\"compute.default_index_type\")\n",
"spark.conf.set(\"spark.sql.execution.arrow.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": [
"kdf = ks.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": [
"<div>\n",
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"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
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" <th>C</th>\n",
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" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <td>0.392094</td>\n",
" <td>-0.197885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>bar</td>\n",
" <td>one</td>\n",
" <td>0.397240</td>\n",
" <td>0.768301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>foo</td>\n",
" <td>two</td>\n",
" <td>-1.683135</td>\n",
" <td>-0.210606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>bar</td>\n",
" <td>three</td>\n",
" <td>-1.776986</td>\n",
" <td>-0.092022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>foo</td>\n",
" <td>two</td>\n",
" <td>-0.499332</td>\n",
" <td>0.463287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>bar</td>\n",
" <td>two</td>\n",
" <td>0.386921</td>\n",
" <td>1.995358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>foo</td>\n",
" <td>one</td>\n",
" <td>-0.514731</td>\n",
" <td>1.042816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>foo</td>\n",
" <td>three</td>\n",
" <td>0.194186</td>\n",
" <td>1.745033</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"0 foo one 0.392094 -0.197885\n",
"1 bar one 0.397240 0.768301\n",
"2 foo two -1.683135 -0.210606\n",
"3 bar three -1.776986 -0.092022\n",
"4 foo two -0.499332 0.463287\n",
"5 bar two 0.386921 1.995358\n",
"6 foo one -0.514731 1.042816\n",
"7 foo three 0.194186 1.745033"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Grouping and then applying the [sum()](https://koalas.readthedocs.io/en/latest/reference/api/pyspark.pandas.groupby.GroupBy.sum.html#databricks.koalas.groupby.GroupBy.sum) function to the resulting groups."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th></th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" <tr>\n",
" <th>A</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>bar</th>\n",
" <td>-0.992825</td>\n",
" <td>2.671637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>foo</th>\n",
" <td>-2.110918</td>\n",
" <td>2.842644</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" C D\n",
"A \n",
"bar -0.992825 2.671637\n",
"foo -2.110918 2.842644"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.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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"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th></th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" <tr>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">foo</th>\n",
" <th>one</th>\n",
" <td>-0.122637</td>\n",
" <td>0.844931</td>\n",
" </tr>\n",
" <tr>\n",
" <th>two</th>\n",
" <td>-2.182467</td>\n",
" <td>0.252681</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bar</th>\n",
" <th>three</th>\n",
" <td>-1.776986</td>\n",
" <td>-0.092022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>foo</th>\n",
" <th>three</th>\n",
" <td>0.194186</td>\n",
" <td>1.745033</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">bar</th>\n",
" <th>two</th>\n",
" <td>0.386921</td>\n",
" <td>1.995358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>one</th>\n",
" <td>0.397240</td>\n",
" <td>0.768301</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" C D\n",
"A B \n",
"foo one -0.122637 0.844931\n",
" two -2.182467 0.252681\n",
"bar three -1.776986 -0.092022\n",
"foo three 0.194186 1.745033\n",
"bar two 0.386921 1.995358\n",
" one 0.397240 0.768301"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.groupby(['A', 'B']).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting\n",
"See the <a href=\"https://koalas.readthedocs.io/en/latest/reference/frame.html#plotting\">Plotting</a> docs."
]
},
{
"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": [
"kser = ks.Series(pser)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"kser = kser.cummax()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
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" define('plotly', function(require, exports, module) {\n",
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" * The buffer module from node.js, for the browser.\n",
" *\n",
" * @author Feross Aboukhadijeh <https://feross.org>\n",
" * @license MIT\n",
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"/*!\n",
" * @overview es6-promise - a tiny implementation of Promises/A+.\n",
" * @copyright Copyright (c) 2014 Yehuda Katz, Tom Dale, Stefan Penner and contributors (Conversion to ES6 API by Jake Archibald)\n",
" * @license Licensed under MIT license\n",
" * See https://raw.githubusercontent.com/stefanpenner/es6-promise/master/LICENSE\n",
" * @version v4.2.8+1e68dce6\n",
" */\n",
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"/*!\n",
" * Determine if an object is a Buffer\n",
" *\n",
" * @author Feross Aboukhadijeh <https://feross.org>\n",
" * @license MIT\n",
" */\n",
"e.exports=function(t){return null!=t&&(n(t)||function(t){return\"function\"==typeof t.readFloatLE&&\"function\"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],466:[function(t,e,r){\"use strict\";e.exports=\"undefined\"!=typeof navigator&&(/MSIE/.test(navigator.userAgent)||/Trident\\//.test(navigator.appVersion))},{}],467:[function(t,e,r){\"use strict\";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||\"undefined\"==typeof navigator||(e=navigator.userAgent),e&&e.headers&&\"string\"==typeof e.headers[\"user-agent\"]&&(e=e.headers[\"user-agent\"]),\"string\"!=typeof e)return!1;var r=t.tablet?i.test(e):n.test(e);return!r&&t.tablet&&t.featureDetect&&navigator&&navigator.maxTouchPoints>1&&-1!==e.indexOf(\"Macintosh\")&&-1!==e.indexOf(\"Safari\")&&(r=!0),r}},{}],468:[function(t,e,r){\"use strict\";e.exports=function(t){var e=typeof t;return null!==t&&(\"object\"===e||\"function\"===e)}},{}],469:[function(t,e,r){\"use strict\";var n=Object.prototype.toString;e.exports=function(t){var e;return\"[object Object]\"===n.call(t)&&(null===(e=Object.getPrototypeOf(t))||e===Object.getPrototypeOf({}))}},{}],470:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e,r=t.length,n=0;n<r;n++)if(((e=t.charCodeAt(n))<9||e>13)&&32!==e&&133!==e&&160!==e&&5760!==e&&6158!==e&&(e<8192||e>8205)&&8232!==e&&8233!==e&&8239!==e&&8287!==e&&8288!==e&&12288!==e&&65279!==e)return!1;return!0}},{}],471:[function(t,e,r){\"use strict\";e.exports=function(t){return\"string\"==typeof t&&(t=t.trim(),!!(/^[mzlhvcsqta]\\s*[-+.0-9][^mlhvzcsqta]+/i.test(t)&&/[\\dz]$/i.test(t)&&t.length>4))}},{}],472:[function(t,e,r){e.exports=function(t,e,r){return t*(1-r)+e*r}},{}],473:[function(t,e,r){!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof e?e.exports=n():(t=t||self).mapboxgl=n()}(this,(function(){\"use strict\";var t,e,r;function n(n,i){if(t)if(e){var a=\"var sharedChunk = {}; (\"+t+\")(sharedChunk); (\"+e+\")(sharedChunk);\",o={};t(o),(r=i(o)).workerUrl=window.URL.createObjectURL(new Blob([a],{type:\"text/javascript\"}))}else e=i;else t=i}return n(0,(function(t){function e(t,e){return t(e={exports:{}},e.exports),e.exports}var r=n;function n(t,e,r,n){this.cx=3*t,this.bx=3*(r-t)-this.cx,this.ax=1-this.cx-this.bx,this.cy=3*e,this.by=3*(n-e)-this.cy,this.ay=1-this.cy-this.by,this.p1x=t,this.p1y=n,this.p2x=r,this.p2y=n}n.prototype.sampleCurveX=function(t){return((this.ax*t+this.bx)*t+this.cx)*t},n.prototype.sampleCurveY=function(t){return((this.ay*t+this.by)*t+this.cy)*t},n.prototype.sampleCurveDerivativeX=function(t){return(3*this.ax*t+2*this.bx)*t+this.cx},n.prototype.solveCurveX=function(t,e){var r,n,i,a,o;for(void 0===e&&(e=1e-6),i=t,o=0;o<8;o++){if(a=this.sampleCurveX(i)-t,Math.abs(a)<e)return i;var s=this.sampleCurveDerivativeX(i);if(Math.abs(s)<1e-6)break;i-=a/s}if((i=t)<(r=0))return r;if(i>(n=1))return n;for(;r<n;){if(a=this.sampleCurveX(i),Math.abs(a-t)<e)return i;t>a?r=i:n=i,i=.5*(n-r)+r}return i},n.prototype.solve=function(t,e){return this.sampleCurveY(this.solveCurveX(t,e))};var i=a;function a(t,e){this.x=t,this.y=e}function o(t,e,n,i){var a=new r(t,e,n,i);return function(t){return a.solve(t)}}a.prototype={clone:function(){return new a(this.x,this.y)},add:function(t){return this.clone()._add(t)},sub:function(t){return this.clone()._sub(t)},multByPoint:function(t){return this.clone()._multByPoint(t)},divByPoi
"/*\n",
"object-assign\n",
"(c) Sindre Sorhus\n",
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" *\n",
" * Copyright (c) 2014-2015, Jon Schlinkert.\n",
" * Licensed under the MIT license.\n",
" */\n",
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"/*\n",
" * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
" * @license MIT\n",
" * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
" */\n",
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"/*!\n",
" * repeat-string <https://github.com/jonschlinkert/repeat-string>\n",
" *\n",
" * Copyright (c) 2014-2015, Jon Schlinkert.\n",
" * Licensed under the MIT License.\n",
" */\n",
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting data in/out\n",
"See the <a href=\"https://koalas.readthedocs.io/en/latest/reference/io.html\">Input/Output\n",
"</a> docs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### CSV\n",
"\n",
"CSV is straightforward and easy to use. See <a href=\"https://koalas.readthedocs.io/en/latest/reference/api/pyspark.pandas.DataFrame.to_csv.html#databricks.koalas.DataFrame.to_csv\">here</a> to write a CSV file and <a href=\"https://koalas.readthedocs.io/en/latest/reference/api/databricks.koalas.read_csv.html#databricks.koalas.read_csv\">here</a> to read a CSV file."
]
},
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"cell_type": "code",
"execution_count": 50,
"metadata": {},
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" <td>0.904636</td>\n",
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" </tr>\n",
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" <th>9</th>\n",
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" <td>0.904636</td>\n",
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"execution_count": 50,
"metadata": {},
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],
"source": [
"kdf.to_csv('foo.csv')\n",
"ks.read_csv('foo.csv').head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Parquet\n",
"\n",
"Parquet is an efficient and compact file format to read and write faster. See <a href=\"https://koalas.readthedocs.io/en/latest/reference/api/pyspark.pandas.DataFrame.to_parquet.html#databricks.koalas.DataFrame.to_parquet\">here</a> to write a Parquet file and <a href=\"https://koalas.readthedocs.io/en/latest/reference/api/databricks.koalas.read_parquet.html#databricks.koalas.read_parquet\">here</a> to read a Parquet file."
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{
"cell_type": "code",
"execution_count": 51,
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" A B C D\n",
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"metadata": {},
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}
],
"source": [
"kdf.to_parquet('bar.parquet')\n",
"ks.read_parquet('bar.parquet').head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Spark IO\n",
"\n",
"In addition, Koalas fully support Spark's various datasources such as ORC and an external datasource. See <a href=\"https://koalas.readthedocs.io/en/latest/reference/api/pyspark.pandas.DataFrame.to_spark_io.html#databricks.koalas.DataFrame.to_spark_io\">here</a> to write it to the specified datasource and <a href=\"https://koalas.readthedocs.io/en/latest/reference/api/databricks.koalas.read_spark_io.html#databricks.koalas.read_spark_io\">here</a> to read it from the datasource."
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"6 1.498758 1.534086 0.904636 0.726606\n",
"7 1.498758 1.534086 0.904636 0.856176\n",
"8 1.498758 1.534086 0.904636 0.856176\n",
"9 1.498758 1.534086 0.904636 1.532448"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kdf.to_spark_io('zoo.orc', format=\"orc\")\n",
"ks.read_spark_io('zoo.orc', format=\"orc\").head(10)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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