{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "\"Dask\n", "\n", "\n", "# Dask DataFrame - parallelized pandas\n", "\n", "Looks and feels like the pandas API, but for parallel and distributed workflows. \n", "\n", "At its core, the `dask.dataframe` module implements a \"blocked parallel\" `DataFrame` object that looks and feels like the pandas API, but for parallel and distributed workflows. One Dask `DataFrame` is comprised of many in-memory pandas `DataFrame`s separated along the index. One operation on a Dask `DataFrame` triggers many pandas operations on the constituent pandas `DataFrame`s in a way that is mindful of potential parallelism and memory constraints.\n" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "\"Dask\n", "\n", "**Related Documentation**\n", "\n", "* [DataFrame documentation](https://docs.dask.org/en/latest/dataframe.html)\n", "* [DataFrame screencast](https://youtu.be/AT2XtFehFSQ)\n", "* [DataFrame API](https://docs.dask.org/en/latest/dataframe-api.html)\n", "* [DataFrame examples](https://examples.dask.org/dataframe.html)\n", "* [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/)\n", "\n", "## When to use `dask.dataframe`\n", "\n", "pandas is great for tabular datasets that fit in memory. A general rule of thumb for pandas is:\n", "\n", "> \"Have 5 to 10 times as much RAM as the size of your dataset\"\n", ">\n", "> ~ Wes McKinney (2017) in [10 things I hate about pandas](https://wesmckinney.com/blog/apache-arrow-pandas-internals/)\n", "\n", "Here \"size of dataset\" means dataset size on _the disk_.\n", "\n", "Dask becomes useful when the datasets exceed the above rule.\n", "\n", "In this notebook, you will be working with the New York City Airline data. This dataset is only ~200MB, so that you can download it in a reasonable time, but `dask.dataframe` will scale to datasets **much** larger than memory.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the datasets you will be using in this notebook:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:30.171841Z", "iopub.status.busy": "2025-11-04T15:43:30.171342Z", "iopub.status.idle": "2025-11-04T15:43:30.627380Z", "shell.execute_reply": "2025-11-04T15:43:30.626778Z" } }, "outputs": [], "source": [ "%run prep.py -d flights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up your local cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a local Dask cluster and connect it to the client. Don't worry about this bit of code for now, you will learn more in the Distributed notebook." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:30.630019Z", "iopub.status.busy": "2025-11-04T15:43:30.629741Z", "iopub.status.idle": "2025-11-04T15:43:32.101031Z", "shell.execute_reply": "2025-11-04T15:43:32.100397Z" } }, "outputs": [ { "data": { "text/html": [ "
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Client

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Client-03752752-b995-11f0-8f11-7c1e521a36f9

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Connection method: Cluster objectCluster type: distributed.LocalCluster
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Cluster Info

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LocalCluster

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8e16eabd

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\n", " Dashboard: http://127.0.0.1:8787/status\n", " \n", " Workers: 4\n", "
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Status: runningUsing processes: True
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Scheduler Info

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Scheduler

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Scheduler-d81c5769-c7fa-4278-b247-2e1d804ebb2e

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\n", " Comm: tcp://127.0.0.1:43335\n", " \n", " Workers: 4\n", "
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Workers

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Worker: 0

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\n", " Comm: tcp://127.0.0.1:45437\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:33991\n", "
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Worker: 1

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Worker: 2

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\n", " Nanny: tcp://127.0.0.1:40581\n", "
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Worker: 3

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\n", " Nanny: tcp://127.0.0.1:34335\n", "
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" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client\n", "\n", "client = Client(n_workers=4)\n", "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dask Diagnostic Dashboard\n", "\n", "Dask Distributed provides a useful Dashboard to visualize the state of your cluster and computations.\n", "\n", "If you're on **JupyterLab or Binder**, you can use the [Dask JupyterLab extension](https://github.com/dask/dask-labextension) (which should be already installed in your environment) to open the dashboard plots:\n", "* Click on the Dask logo in the left sidebar\n", "* Click on the magnifying glass icon, which will automatically connect to the active dashboard (if that doesn't work, you can type/paste the dashboard link http://127.0.0.1:8787 in the field)\n", "* Click on **\"Task Stream\"**, **\"Progress Bar\"**, and **\"Worker Memory\"**, which will open these plots in new tabs\n", "* Re-organize the tabs to suit your workflow!\n", "\n", "Alternatively, click on the dashboard link displayed in the Client details above: http://127.0.0.1:8787/status. It will open a new browser tab with the Dashboard." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Reading and working with datasets\n", "\n", "Let's read an extract of flights in the USA across several years. This data is specific to flights out of the three airports in the New York City area." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:32.103744Z", "iopub.status.busy": "2025-11-04T15:43:32.103271Z", "iopub.status.idle": "2025-11-04T15:43:32.106098Z", "shell.execute_reply": "2025-11-04T15:43:32.105552Z" } }, "outputs": [], "source": [ "import os\n", "import dask" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By convention, we import the module `dask.dataframe` as `dd`, and call the corresponding `DataFrame` object `ddf`.\n", "\n", "**Note**: The term \"Dask DataFrame\" is slightly overloaded. Depending on the context, it can refer to the module or the DataFrame object. To avoid confusion, throughout this notebook:\n", "- `dask.dataframe` (note the all lowercase) refers to the API, and\n", "- `DataFrame` (note the CamelCase) refers to the object.\n", "\n", "The following filename includes a glob pattern `*`, so all files in the path matching that pattern will be read into the same `DataFrame`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:32.108481Z", "iopub.status.busy": "2025-11-04T15:43:32.108189Z", "iopub.status.idle": "2025-11-04T15:43:32.220762Z", "shell.execute_reply": "2025-11-04T15:43:32.220123Z" } }, "outputs": [ { "data": { "text/html": [ "
Dask DataFrame Structure:
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DateDayOfWeekDepTimeCRSDepTimeArrTimeCRSArrTimeUniqueCarrierFlightNumTailNumActualElapsedTimeCRSElapsedTimeAirTimeArrDelayDepDelayOriginDestDistanceTaxiInTaxiOutCancelledDiverted
npartitions=10
datetime64[ns]int64float64int64float64int64objectint64float64float64int64float64float64float64objectobjectfloat64float64float64int64int64
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Dask Name: read-csv, 1 graph layer
" ], "text/plain": [ "Dask DataFrame Structure:\n", " Date DayOfWeek DepTime CRSDepTime ArrTime CRSArrTime UniqueCarrier FlightNum TailNum ActualElapsedTime CRSElapsedTime AirTime ArrDelay DepDelay Origin Dest Distance TaxiIn TaxiOut Cancelled Diverted\n", "npartitions=10 \n", " datetime64[ns] int64 float64 int64 float64 int64 object int64 float64 float64 int64 float64 float64 float64 object object float64 float64 float64 int64 int64\n", " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "Dask Name: read-csv, 1 graph layer" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import dask.dataframe as dd\n", "\n", "ddf = dd.read_csv(\n", " os.path.join(\"data\", \"nycflights\", \"*.csv\"), parse_dates={\"Date\": [0, 1, 2]}\n", ")\n", "ddf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dask has not loaded the data yet, it has:\n", "- investigated the input path and found that there are ten matching files\n", "- intelligently created a set of jobs for each chunk -- one per original CSV file in this case" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the representation of the `DataFrame` object contains no data - Dask has just done enough to read the start of the first file, and infer the column names and dtypes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lazy Evaluation\n", "\n", "Most Dask Collections, including Dask `DataFrame` are evaluated lazily, which means Dask constructs the logic (called task graph) of your computation immediately but \"evaluates\" them only when necessary. You can view this task graph using `.visualize()`.\n", "\n", "You will learn more about this in the Delayed notebook, but for now, note that we need to call `.compute()` to trigger actual computations." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:32.223367Z", "iopub.status.busy": "2025-11-04T15:43:32.223158Z", "iopub.status.idle": "2025-11-04T15:43:33.581930Z", "shell.execute_reply": "2025-11-04T15:43:33.581321Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf.visualize()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some functions like `len` and `head` also trigger a computation. Specifically, calling `len` will:\n", "- load actual data, (that is, load each file into a pandas DataFrame)\n", "- then apply the corresponding functions to each pandas DataFrame (also known as a partition)\n", "- combine the subtotals to give you the final grand total" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:33.584496Z", "iopub.status.busy": "2025-11-04T15:43:33.584120Z", "iopub.status.idle": "2025-11-04T15:43:34.095395Z", "shell.execute_reply": "2025-11-04T15:43:34.094833Z" } }, "outputs": [ { "data": { "text/plain": [ "9990" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load and count number of rows\n", "len(ddf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can view the start and end of the data as you would in pandas:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.097862Z", "iopub.status.busy": "2025-11-04T15:43:34.097497Z", "iopub.status.idle": "2025-11-04T15:43:34.141281Z", "shell.execute_reply": "2025-11-04T15:43:34.140646Z" } }, "outputs": [ { "data": { "text/html": [ "
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DateDayOfWeekDepTimeCRSDepTimeArrTimeCRSArrTimeUniqueCarrierFlightNumTailNumActualElapsedTime...AirTimeArrDelayDepDelayOriginDestDistanceTaxiInTaxiOutCancelledDiverted
01990-01-0111621.015401747.01701US33NaN86.0...NaN46.041.0EWRPIT319.0NaNNaN00
11990-01-0221547.015401700.01701US33NaN73.0...NaN-1.07.0EWRPIT319.0NaNNaN00
21990-01-0331546.015401710.01701US33NaN84.0...NaN9.06.0EWRPIT319.0NaNNaN00
31990-01-0441542.015401710.01701US33NaN88.0...NaN9.02.0EWRPIT319.0NaNNaN00
41990-01-0551549.015401706.01701US33NaN77.0...NaN5.09.0EWRPIT319.0NaNNaN00
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" ], "text/plain": [ " Date DayOfWeek DepTime CRSDepTime ArrTime CRSArrTime \\\n", "0 1990-01-01 1 1621.0 1540 1747.0 1701 \n", "1 1990-01-02 2 1547.0 1540 1700.0 1701 \n", "2 1990-01-03 3 1546.0 1540 1710.0 1701 \n", "3 1990-01-04 4 1542.0 1540 1710.0 1701 \n", "4 1990-01-05 5 1549.0 1540 1706.0 1701 \n", "\n", " UniqueCarrier FlightNum TailNum ActualElapsedTime ... AirTime \\\n", "0 US 33 NaN 86.0 ... NaN \n", "1 US 33 NaN 73.0 ... NaN \n", "2 US 33 NaN 84.0 ... NaN \n", "3 US 33 NaN 88.0 ... NaN \n", "4 US 33 NaN 77.0 ... NaN \n", "\n", " ArrDelay DepDelay Origin Dest Distance TaxiIn TaxiOut Cancelled \\\n", "0 46.0 41.0 EWR PIT 319.0 NaN NaN 0 \n", "1 -1.0 7.0 EWR PIT 319.0 NaN NaN 0 \n", "2 9.0 6.0 EWR PIT 319.0 NaN NaN 0 \n", "3 9.0 2.0 EWR PIT 319.0 NaN NaN 0 \n", "4 5.0 9.0 EWR PIT 319.0 NaN NaN 0 \n", "\n", " Diverted \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf.head()" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "raises-exception" ] }, "source": [ "```python\n", "ddf.tail()\n", "\n", "# ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n", "\n", "# +----------------+---------+----------+\n", "# | Column | Found | Expected |\n", "# +----------------+---------+----------+\n", "# | CRSElapsedTime | float64 | int64 |\n", "# | TailNum | object | float64 |\n", "# +----------------+---------+----------+\n", "\n", "# The following columns also raised exceptions on conversion:\n", "\n", "# - TailNum\n", "# ValueError(\"could not convert string to float: 'N54711'\")\n", "\n", "# Usually this is due to dask's dtype inference failing, and\n", "# *may* be fixed by specifying dtypes manually by adding:\n", "\n", "# dtype={'CRSElapsedTime': 'float64',\n", "# 'TailNum': 'object'}\n", "\n", "# to the call to `read_csv`/`read_table`.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike `pandas.read_csv` which reads in the entire file before inferring datatypes, `dask.dataframe.read_csv` only reads in a sample from the beginning of the file (or first file if using a glob). These inferred datatypes are then enforced when reading all partitions.\n", "\n", "In this case, the datatypes inferred in the sample are incorrect. The first `n` rows have no value for `CRSElapsedTime` (which pandas infers as a `float`), and later on turn out to be strings (`object` dtype). Note that Dask gives an informative error message about the mismatch. When this happens you have a few options:\n", "\n", "- Specify dtypes directly using the `dtype` keyword. This is the recommended solution, as it's the least error prone (better to be explicit than implicit) and also the most performant.\n", "- Increase the size of the `sample` keyword (in bytes)\n", "- Use `assume_missing` to make `dask` assume that columns inferred to be `int` (which don't allow missing values) are actually `floats` (which do allow missing values). In our particular case this doesn't apply.\n", "\n", "In our case we'll use the first option and directly specify the `dtypes` of the offending columns. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.143852Z", "iopub.status.busy": "2025-11-04T15:43:34.143467Z", "iopub.status.idle": "2025-11-04T15:43:34.153082Z", "shell.execute_reply": "2025-11-04T15:43:34.152351Z" } }, "outputs": [], "source": [ "ddf = dd.read_csv(\n", " os.path.join(\"data\", \"nycflights\", \"*.csv\"),\n", " parse_dates={\"Date\": [0, 1, 2]},\n", " dtype={\"TailNum\": str, \"CRSElapsedTime\": float, \"Cancelled\": bool},\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.155469Z", "iopub.status.busy": "2025-11-04T15:43:34.155257Z", "iopub.status.idle": "2025-11-04T15:43:34.198802Z", "shell.execute_reply": "2025-11-04T15:43:34.198198Z" } }, "outputs": [ { "data": { "text/html": [ "
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DateDayOfWeekDepTimeCRSDepTimeArrTimeCRSArrTimeUniqueCarrierFlightNumTailNumActualElapsedTime...AirTimeArrDelayDepDelayOriginDestDistanceTaxiInTaxiOutCancelledDiverted
9941999-01-251632.0635803.0817CO437N2721391.0...68.0-14.0-3.0EWRRDU416.04.019.0False0
9951999-01-262632.0635751.0817CO437N1621779.0...62.0-26.0-3.0EWRRDU416.03.014.0False0
9961999-01-273631.0635756.0817CO437N1221685.0...66.0-21.0-4.0EWRRDU416.04.015.0False0
9971999-01-284629.0635803.0817CO437N2621094.0...69.0-14.0-6.0EWRRDU416.05.020.0False0
9981999-01-295632.0635802.0817CO437N1222590.0...67.0-15.0-3.0EWRRDU416.05.018.0False0
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" ], "text/plain": [ " Date DayOfWeek DepTime CRSDepTime ArrTime CRSArrTime \\\n", "994 1999-01-25 1 632.0 635 803.0 817 \n", "995 1999-01-26 2 632.0 635 751.0 817 \n", "996 1999-01-27 3 631.0 635 756.0 817 \n", "997 1999-01-28 4 629.0 635 803.0 817 \n", "998 1999-01-29 5 632.0 635 802.0 817 \n", "\n", " UniqueCarrier FlightNum TailNum ActualElapsedTime ... AirTime \\\n", "994 CO 437 N27213 91.0 ... 68.0 \n", "995 CO 437 N16217 79.0 ... 62.0 \n", "996 CO 437 N12216 85.0 ... 66.0 \n", "997 CO 437 N26210 94.0 ... 69.0 \n", "998 CO 437 N12225 90.0 ... 67.0 \n", "\n", " ArrDelay DepDelay Origin Dest Distance TaxiIn TaxiOut Cancelled \\\n", "994 -14.0 -3.0 EWR RDU 416.0 4.0 19.0 False \n", "995 -26.0 -3.0 EWR RDU 416.0 3.0 14.0 False \n", "996 -21.0 -4.0 EWR RDU 416.0 4.0 15.0 False \n", "997 -14.0 -6.0 EWR RDU 416.0 5.0 20.0 False \n", "998 -15.0 -3.0 EWR RDU 416.0 5.0 18.0 False \n", "\n", " Diverted \n", "994 0 \n", "995 0 \n", "996 0 \n", "997 0 \n", "998 0 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf.tail() # now works" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading from remote storage\n", "\n", "If you're thinking about distributed computing, your data is probably stored remotely on services (like Amazon's S3 or Google's cloud storage) and is in a friendlier format (like Parquet). Dask can read data in various formats directly from these remote locations **lazily** and **in parallel**.\n", "\n", "Here's how you can read the NYC taxi cab data from Amazon S3:\n", "\n", "```python\n", "ddf = dd.read_parquet(\n", " \"s3://nyc-tlc/trip data/yellow_tripdata_2012-*.parquet\",\n", ")\n", "```\n", "\n", "You can also leverage Parquet-specific optimizations like column selection and metadata handling, learn more in [the Dask documentation on working with Parquet files](https://docs.dask.org/en/stable/dataframe-parquet.html)." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Computations with `dask.dataframe`\n", "\n", "Let's compute the maximum of the flight delay.\n", "\n", "With just pandas, we would loop over each file to find the individual maximums, then find the final maximum over all the individual maximums.\n", "\n", "```python\n", "import pandas as pd\n", "\n", "files = os.listdir(os.path.join('data', 'nycflights'))\n", "\n", "maxes = []\n", "\n", "for file in files:\n", " df = pd.read_csv(os.path.join('data', 'nycflights', file))\n", " maxes.append(df.DepDelay.max())\n", " \n", "final_max = max(maxes)\n", "```\n", "\n", "`dask.dataframe` lets us write pandas-like code, that operates on larger-than-memory datasets in parallel." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.201290Z", "iopub.status.busy": "2025-11-04T15:43:34.200949Z", "iopub.status.idle": "2025-11-04T15:43:34.260804Z", "shell.execute_reply": "2025-11-04T15:43:34.260269Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 18.3 ms, sys: 3.4 ms, total: 21.7 ms\n", "Wall time: 53.1 ms\n" ] }, { "data": { "text/plain": [ "409.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "result = ddf.DepDelay.max()\n", "result.compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This creates the lazy computation for us and then runs it. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** Dask will delete intermediate results (like the full pandas DataFrame for each file) as soon as possible. This means you can handle datasets that are larger than memory but, repeated computations will have to load all of the data in each time. (Run the code above again, is it faster or slower than you would expect?)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can view the underlying task graph using `.visualize()`:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.263768Z", "iopub.status.busy": "2025-11-04T15:43:34.263541Z", "iopub.status.idle": "2025-11-04T15:43:34.487556Z", "shell.execute_reply": "2025-11-04T15:43:34.486950Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# notice the parallelism\n", "result.visualize()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "In this section you will do a few `dask.dataframe` computations. If you are comfortable with pandas then these should be familiar. You will have to think about when to call `.compute()`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. How many rows are in our dataset?\n", "\n", "_Hint_: how would you check how many items are in a list?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.491040Z", "iopub.status.busy": "2025-11-04T15:43:34.490641Z", "iopub.status.idle": "2025-11-04T15:43:34.493382Z", "shell.execute_reply": "2025-11-04T15:43:34.492833Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.495384Z", "iopub.status.busy": "2025-11-04T15:43:34.495209Z", "iopub.status.idle": "2025-11-04T15:43:34.552602Z", "shell.execute_reply": "2025-11-04T15:43:34.551979Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "9990" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ddf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. In total, how many non-canceled flights were taken?\n", "\n", "_Hint_: use [boolean indexing](https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.555084Z", "iopub.status.busy": "2025-11-04T15:43:34.554855Z", "iopub.status.idle": "2025-11-04T15:43:34.557492Z", "shell.execute_reply": "2025-11-04T15:43:34.556954Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.559547Z", "iopub.status.busy": "2025-11-04T15:43:34.559361Z", "iopub.status.idle": "2025-11-04T15:43:34.623629Z", "shell.execute_reply": "2025-11-04T15:43:34.622830Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "9383" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ddf[~ddf.Cancelled])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. In total, how many non-canceled flights were taken from each airport?\n", "\n", "*Hint*: use [groupby](https://pandas.pydata.org/pandas-docs/stable/groupby.html)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.626048Z", "iopub.status.busy": "2025-11-04T15:43:34.625664Z", "iopub.status.idle": "2025-11-04T15:43:34.628511Z", "shell.execute_reply": "2025-11-04T15:43:34.628012Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.630433Z", "iopub.status.busy": "2025-11-04T15:43:34.630251Z", "iopub.status.idle": "2025-11-04T15:43:34.709622Z", "shell.execute_reply": "2025-11-04T15:43:34.708902Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Origin\n", "EWR 4132\n", "JFK 1085\n", "LGA 4166\n", "Name: Origin, dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf[~ddf.Cancelled].groupby(\"Origin\").Origin.count().compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. What was the average departure delay from each airport?" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.712667Z", "iopub.status.busy": "2025-11-04T15:43:34.712438Z", "iopub.status.idle": "2025-11-04T15:43:34.715772Z", "shell.execute_reply": "2025-11-04T15:43:34.715184Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.718098Z", "iopub.status.busy": "2025-11-04T15:43:34.717733Z", "iopub.status.idle": "2025-11-04T15:43:34.831884Z", "shell.execute_reply": "2025-11-04T15:43:34.831100Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Origin\n", "EWR 12.500968\n", "JFK 17.053456\n", "LGA 10.169227\n", "Name: DepDelay, dtype: float64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf.groupby(\"Origin\").DepDelay.mean().compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. What day of the week has the worst average departure delay?" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.834263Z", "iopub.status.busy": "2025-11-04T15:43:34.834048Z", "iopub.status.idle": "2025-11-04T15:43:34.836878Z", "shell.execute_reply": "2025-11-04T15:43:34.836385Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.838975Z", "iopub.status.busy": "2025-11-04T15:43:34.838791Z", "iopub.status.idle": "2025-11-04T15:43:34.955493Z", "shell.execute_reply": "2025-11-04T15:43:34.954855Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf.groupby(\"DayOfWeek\").DepDelay.mean().idxmax().compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Let's say the distance column is erroneous and you need to add 1 to all values, how would you do this?" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.958312Z", "iopub.status.busy": "2025-11-04T15:43:34.957901Z", "iopub.status.idle": "2025-11-04T15:43:34.960560Z", "shell.execute_reply": "2025-11-04T15:43:34.960059Z" } }, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:34.962577Z", "iopub.status.busy": "2025-11-04T15:43:34.962396Z", "iopub.status.idle": "2025-11-04T15:43:35.079213Z", "shell.execute_reply": "2025-11-04T15:43:35.077306Z" }, "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/share/miniconda/envs/dask-tutorial/lib/python3.10/site-packages/dask/dataframe/core.py:4139: UserWarning: \n", "You did not provide metadata, so Dask is running your function on a small dataset to guess output types. It is possible that Dask will guess incorrectly.\n", "To provide an explicit output types or to silence this message, please provide the `meta=` keyword, as described in the map or apply function that you are using.\n", " Before: .apply(func)\n", " After: .apply(func, meta=('Distance', 'float64'))\n", "\n", " warnings.warn(meta_warning(meta))\n" ] }, { "data": { "text/plain": [ "0 320.0\n", "1 320.0\n", "2 320.0\n", "3 320.0\n", "4 320.0\n", " ... \n", "994 417.0\n", "995 417.0\n", "996 417.0\n", "997 417.0\n", "998 417.0\n", "Name: Distance, Length: 9990, dtype: float64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf[\"Distance\"].apply(\n", " lambda x: x + 1\n", ").compute() # don't worry about the warning, we'll discuss in the next sections\n", "\n", "# OR\n", "\n", "(ddf[\"Distance\"] + 1).compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sharing Intermediate Results\n", "\n", "When computing all of the above, we sometimes did the same operation more than once. For most operations, `dask.dataframe` stores the arguments, allowing duplicate computations to be shared and only computed once.\n", "\n", "For example, let's compute the mean and standard deviation for departure delay of all non-canceled flights. Since Dask operations are lazy, those values aren't the final results yet. They're just the steps required to get the result.\n", "\n", "If you compute them with two calls to compute, there is no sharing of intermediate computations." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:35.083220Z", "iopub.status.busy": "2025-11-04T15:43:35.082982Z", "iopub.status.idle": "2025-11-04T15:43:35.092837Z", "shell.execute_reply": "2025-11-04T15:43:35.092075Z" } }, "outputs": [], "source": [ "non_canceled = ddf[~ddf.Cancelled]\n", "mean_delay = non_canceled.DepDelay.mean()\n", "std_delay = non_canceled.DepDelay.std()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:35.095158Z", "iopub.status.busy": "2025-11-04T15:43:35.094794Z", "iopub.status.idle": "2025-11-04T15:43:35.256863Z", "shell.execute_reply": "2025-11-04T15:43:35.256332Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 60.6 ms, sys: 9.38 ms, total: 70 ms\n", "Wall time: 158 ms\n" ] } ], "source": [ "%%time\n", "\n", "mean_delay_res = mean_delay.compute()\n", "std_delay_res = std_delay.compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `dask.compute`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But let's try by passing both to a single `compute` call." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:35.259135Z", "iopub.status.busy": "2025-11-04T15:43:35.258920Z", "iopub.status.idle": "2025-11-04T15:43:35.363710Z", "shell.execute_reply": "2025-11-04T15:43:35.362688Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 49.8 ms, sys: 791 μs, total: 50.6 ms\n", "Wall time: 98.8 ms\n" ] } ], "source": [ "%%time\n", "\n", "mean_delay_res, std_delay_res = dask.compute(mean_delay, std_delay)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `dask.compute` takes roughly 1/2 the time. This is because the task graphs for both results are merged when calling `dask.compute`, allowing shared operations to only be done once instead of twice. In particular, using `dask.compute` only does the following once:\n", "\n", "- the calls to `read_csv`\n", "- the filter (`df[~df.Cancelled]`)\n", "- some of the necessary reductions (`sum`, `count`)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see what the merged task graphs between multiple results look like (and what's shared), you can use the `dask.visualize` function (you might want to use `filename='graph.pdf'` to save the graph to disk so that you can zoom in more easily):" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:35.366075Z", "iopub.status.busy": "2025-11-04T15:43:35.365860Z", "iopub.status.idle": "2025-11-04T15:43:35.763284Z", "shell.execute_reply": "2025-11-04T15:43:35.756156Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2c60efb7a6714bcba25266cd1faf2be2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "CytoscapeWidget(cytoscape_layout={'name': 'dagre', 'rankDir': 'BT', 'nodeSep': 10, 'edgeSep': 10, 'spacingFact…" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dask.visualize(mean_delay, std_delay, engine=\"cytoscape\")" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### `.persist()`\n", "\n", "While using a distributed scheduler (you will learn more about schedulers in the upcoming notebooks), you can keep some _data that you want to use often_ in the _distributed memory_. \n", "\n", "`persist` generates \"Futures\" (more on this later as well) and stores them in the same structure as your output. You can use `persist` with any data or computation that fits in memory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to analyze data only for non-canceled flights departing from JFK airport, you can either have two compute calls like in the previous section:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:36.128015Z", "iopub.status.busy": "2025-11-04T15:43:36.127661Z", "iopub.status.idle": "2025-11-04T15:43:36.133955Z", "shell.execute_reply": "2025-11-04T15:43:36.133464Z" } }, "outputs": [], "source": [ "non_cancelled = ddf[~ddf.Cancelled]\n", "ddf_jfk = non_cancelled[non_cancelled.Origin == \"JFK\"]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:36.136009Z", "iopub.status.busy": "2025-11-04T15:43:36.135803Z", "iopub.status.idle": "2025-11-04T15:43:36.310647Z", "shell.execute_reply": "2025-11-04T15:43:36.310112Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 70.5 ms, sys: 3.35 ms, total: 73.9 ms\n", "Wall time: 169 ms\n" ] }, { "data": { "text/plain": [ "18503.0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "ddf_jfk.DepDelay.mean().compute()\n", "ddf_jfk.DepDelay.sum().compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, consider persisting that subset of data in memory.\n", "\n", "See the \"Graph\" dashboard plot, the red squares indicate persisted data stored as Futures in memory. You will also notice an increase in Worker Memory (another dashboard plot) consumption." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:36.313092Z", "iopub.status.busy": "2025-11-04T15:43:36.312880Z", "iopub.status.idle": "2025-11-04T15:43:36.329130Z", "shell.execute_reply": "2025-11-04T15:43:36.328329Z" } }, "outputs": [], "source": [ "ddf_jfk = ddf_jfk.persist() # returns back control immediately" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:36.337224Z", "iopub.status.busy": "2025-11-04T15:43:36.333609Z", "iopub.status.idle": "2025-11-04T15:43:36.471015Z", "shell.execute_reply": "2025-11-04T15:43:36.470339Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 71.8 ms, sys: 5.22 ms, total: 77 ms\n", "Wall time: 127 ms\n" ] }, { "data": { "text/plain": [ "36.85799641792652" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "ddf_jfk.DepDelay.mean().compute()\n", "ddf_jfk.DepDelay.std().compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analyses on this persisted data is faster because we are not repeating the loading and selecting (non-canceled, JFK departure) operations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Custom code with Dask DataFrame\n", "\n", "`dask.dataframe` only covers a small but well-used portion of the pandas API.\n", "\n", "This limitation is for two reasons:\n", "\n", "1. The Pandas API is *huge*\n", "2. Some operations are genuinely hard to do in parallel, e.g, sorting.\n", "\n", "Additionally, some important operations like `set_index` work, but are slower than in pandas because they include substantial shuffling of data, and may write out to disk.\n", "\n", "**What if you want to use some custom functions that aren't (or can't be) implemented for Dask DataFrame yet?**\n", "\n", "You can open an issue on the [Dask issue tracker](https://github.com/dask/dask/issues) to check how feasible the function could be to implement, and you can consider contributing this function to Dask.\n", "\n", "In case it's a custom function or tricky to implement, `dask.dataframe` provides a few methods to make applying custom functions to Dask DataFrames easier:\n", "\n", "- [`map_partitions`](https://docs.dask.org/en/latest/generated/dask.dataframe.DataFrame.map_partitions.html): to run a function on each partition (each pandas DataFrame) of the Dask DataFrame\n", "- [`map_overlap`](https://docs.dask.org/en/latest/generated/dask.dataframe.rolling.map_overlap.html): to run a function on each partition (each pandas DataFrame) of the Dask DataFrame, with some rows shared between neighboring partitions\n", "- [`reduction`](https://docs.dask.org/en/latest/generated/dask.dataframe.Series.reduction.html): for custom row-wise reduction operations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a quick look at the `map_partitions()` function:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:36.473398Z", "iopub.status.busy": "2025-11-04T15:43:36.473182Z", "iopub.status.idle": "2025-11-04T15:43:36.477012Z", "shell.execute_reply": "2025-11-04T15:43:36.476430Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method map_partitions in module dask.dataframe.core:\n", "\n", "map_partitions(func, *args, **kwargs) method of dask.dataframe.core.DataFrame instance\n", " Apply Python function on each DataFrame partition.\n", " \n", " Note that the index and divisions are assumed to remain unchanged.\n", " \n", " Parameters\n", " ----------\n", " func : function\n", " The function applied to each partition. If this function accepts\n", " the special ``partition_info`` keyword argument, it will receive\n", " information on the partition's relative location within the\n", " dataframe.\n", " args, kwargs :\n", " Positional and keyword arguments to pass to the function.\n", " Positional arguments are computed on a per-partition basis, while\n", " keyword arguments are shared across all partitions. The partition\n", " itself will be the first positional argument, with all other\n", " arguments passed *after*. Arguments can be ``Scalar``, ``Delayed``,\n", " or regular Python objects. DataFrame-like args (both dask and\n", " pandas) will be repartitioned to align (if necessary) before\n", " applying the function; see ``align_dataframes`` to control this\n", " behavior.\n", " enforce_metadata : bool, default True\n", " Whether to enforce at runtime that the structure of the DataFrame\n", " produced by ``func`` actually matches the structure of ``meta``.\n", " This will rename and reorder columns for each partition,\n", " and will raise an error if this doesn't work,\n", " but it won't raise if dtypes don't match.\n", " transform_divisions : bool, default True\n", " Whether to apply the function onto the divisions and apply those\n", " transformed divisions to the output.\n", " align_dataframes : bool, default True\n", " Whether to repartition DataFrame- or Series-like args\n", " (both dask and pandas) so their divisions align before applying\n", " the function. This requires all inputs to have known divisions.\n", " Single-partition inputs will be split into multiple partitions.\n", " \n", " If False, all inputs must have either the same number of partitions\n", " or a single partition. Single-partition inputs will be broadcast to\n", " every partition of multi-partition inputs.\n", " meta : pd.DataFrame, pd.Series, dict, iterable, tuple, optional\n", " An empty ``pd.DataFrame`` or ``pd.Series`` that matches the dtypes\n", " and column names of the output. This metadata is necessary for\n", " many algorithms in dask dataframe to work. For ease of use, some\n", " alternative inputs are also available. Instead of a ``DataFrame``,\n", " a ``dict`` of ``{name: dtype}`` or iterable of ``(name, dtype)``\n", " can be provided (note that the order of the names should match the\n", " order of the columns). Instead of a series, a tuple of ``(name,\n", " dtype)`` can be used. If not provided, dask will try to infer the\n", " metadata. This may lead to unexpected results, so providing\n", " ``meta`` is recommended. For more information, see\n", " ``dask.dataframe.utils.make_meta``.\n", " \n", " Examples\n", " --------\n", " Given a DataFrame, Series, or Index, such as:\n", " \n", " >>> import pandas as pd\n", " >>> import dask.dataframe as dd\n", " >>> df = pd.DataFrame({'x': [1, 2, 3, 4, 5],\n", " ... 'y': [1., 2., 3., 4., 5.]})\n", " >>> ddf = dd.from_pandas(df, npartitions=2)\n", " \n", " One can use ``map_partitions`` to apply a function on each partition.\n", " Extra arguments and keywords can optionally be provided, and will be\n", " passed to the function after the partition.\n", " \n", " Here we apply a function with arguments and keywords to a DataFrame,\n", " resulting in a Series:\n", " \n", " >>> def myadd(df, a, b=1):\n", " ... return df.x + df.y + a + b\n", " >>> res = ddf.map_partitions(myadd, 1, b=2)\n", " >>> res.dtype\n", " dtype('float64')\n", " \n", " Here we apply a function to a Series resulting in a Series:\n", " \n", " >>> res = ddf.x.map_partitions(lambda x: len(x)) # ddf.x is a Dask Series Structure\n", " >>> res.dtype\n", " dtype('int64')\n", " \n", " By default, dask tries to infer the output metadata by running your\n", " provided function on some fake data. This works well in many cases, but\n", " can sometimes be expensive, or even fail. To avoid this, you can\n", " manually specify the output metadata with the ``meta`` keyword. This\n", " can be specified in many forms, for more information see\n", " ``dask.dataframe.utils.make_meta``.\n", " \n", " Here we specify the output is a Series with no name, and dtype\n", " ``float64``:\n", " \n", " >>> res = ddf.map_partitions(myadd, 1, b=2, meta=(None, 'f8'))\n", " \n", " Here we map a function that takes in a DataFrame, and returns a\n", " DataFrame with a new column:\n", " \n", " >>> res = ddf.map_partitions(lambda df: df.assign(z=df.x * df.y))\n", " >>> res.dtypes\n", " x int64\n", " y float64\n", " z float64\n", " dtype: object\n", " \n", " As before, the output metadata can also be specified manually. This\n", " time we pass in a ``dict``, as the output is a DataFrame:\n", " \n", " >>> res = ddf.map_partitions(lambda df: df.assign(z=df.x * df.y),\n", " ... meta={'x': 'i8', 'y': 'f8', 'z': 'f8'})\n", " \n", " In the case where the metadata doesn't change, you can also pass in\n", " the object itself directly:\n", " \n", " >>> res = ddf.map_partitions(lambda df: df.head(), meta=ddf)\n", " \n", " Also note that the index and divisions are assumed to remain unchanged.\n", " If the function you're mapping changes the index/divisions, you'll need\n", " to clear them afterwards:\n", " \n", " >>> ddf.map_partitions(func).clear_divisions() # doctest: +SKIP\n", " \n", " Your map function gets information about where it is in the dataframe by\n", " accepting a special ``partition_info`` keyword argument.\n", " \n", " >>> def func(partition, partition_info=None):\n", " ... pass\n", " \n", " This will receive the following information:\n", " \n", " >>> partition_info # doctest: +SKIP\n", " {'number': 1, 'division': 3}\n", " \n", " For each argument and keyword arguments that are dask dataframes you will\n", " receive the number (n) which represents the nth partition of the dataframe\n", " and the division (the first index value in the partition). If divisions\n", " are not known (for instance if the index is not sorted) then you will get\n", " None as the division.\n", "\n" ] } ], "source": [ "help(ddf.map_partitions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"Distance\" column in `ddf` is currently in miles. Let's say we want to convert the units to kilometers and we have a general helper function as shown below. In this case, we can use `map_partitions` to apply this function across each of the internal pandas `DataFrame`s in parallel. " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:36.479217Z", "iopub.status.busy": "2025-11-04T15:43:36.479031Z", "iopub.status.idle": "2025-11-04T15:43:36.484066Z", "shell.execute_reply": "2025-11-04T15:43:36.483538Z" } }, "outputs": [], "source": [ "def my_custom_converter(df, multiplier=1):\n", " return df * multiplier\n", "\n", "\n", "meta = pd.Series(name=\"Distance\", dtype=\"float64\")\n", "\n", "distance_km = ddf.Distance.map_partitions(\n", " my_custom_converter, multiplier=0.6, meta=meta\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:36.486022Z", "iopub.status.busy": "2025-11-04T15:43:36.485826Z", "iopub.status.idle": "2025-11-04T15:43:36.676406Z", "shell.execute_reply": "2025-11-04T15:43:36.675794Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distance_km.visualize()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:36.679025Z", "iopub.status.busy": "2025-11-04T15:43:36.678802Z", "iopub.status.idle": "2025-11-04T15:43:36.706514Z", "shell.execute_reply": "2025-11-04T15:43:36.705941Z" } }, "outputs": [ { "data": { "text/plain": [ "0 191.4\n", "1 191.4\n", "2 191.4\n", "3 191.4\n", "4 191.4\n", "Name: Distance, dtype: float64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distance_km.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What is `meta`?\n", "\n", "Since Dask operates lazily, it doesn't always have enough information to infer the output structure (which includes datatypes) of certain operations.\n", "\n", "`meta` is a _suggestion_ to Dask about the output of your computation. Importantly, `meta` _never infers with the output structure_. Dask uses this `meta` until it can determine the actual output structure.\n", "\n", "Even though there are many ways to define `meta`, we suggest using a small pandas Series or DataFrame that matches the structure of your final output." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Close you local Dask Cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's good practice to always close any Dask cluster you create:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T15:43:36.709021Z", "iopub.status.busy": "2025-11-04T15:43:36.708635Z", "iopub.status.idle": "2025-11-04T15:43:37.039294Z", "shell.execute_reply": "2025-11-04T15:43:37.038679Z" } }, "outputs": [], "source": [ "client.shutdown()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { 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