zonca/01_environment.ipynb

## 01_environment.ipynb

      
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## 02_dataframe.ipynb

      
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## 03_array.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/dask/dask/main/docs/source/images/dask_horizontal.svg\"\n",
    "     width=\"60%\"\n",
    "     alt=\"Dask logo\\\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dask array provides a parallel, larger-than-memory implementation of NumPy. \n",
    "\n",
    "It will look and feel a lot like NumPy, but does not suffer from the same scalability limitations.\n",
    "\n",
    "\n",
    "<img src=\"https://docs.dask.org/en/latest/_images/dask-array.svg\" width=\"50%\" align=\"center\" alt=\"Dask array\">\n",
    "\n",
    "\n",
    "*  **Parallel**: Uses all of the cores on your computer\n",
    "*  **Larger-than-memory**:  Lets you work on datasets that are larger than your available memory by breaking up your array into many small pieces, operating on those pieces in an order that minimizes the memory footprint of your computation, and effectively streaming data from disk.\n",
    "*  **Blocked Algorithms**:  Perform large computations by performing many smaller computations\n",
    "\n",
    "In this notebook, we'll build some understanding by implementing some blocked algorithms from scratch. We'll then use Dask Array to analyze large datasets, in parallel, using a familiar NumPy-like API.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blocked Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A *blocked algorithm* executes on a large dataset by breaking it up into many small blocks.\n",
    "\n",
    "For example, consider taking the sum of a billion numbers.  We might instead break up the array into 1,000 chunks, each of size 1,000,000, take the sum of each chunk, and then take the sum of the intermediate sums.\n",
    "\n",
    "We achieve the intended result (one sum on one billion numbers) by performing many smaller results (one thousand sums on one million numbers each, followed by another sum of a thousand numbers.)\n",
    "\n",
    "We do exactly this with Python and NumPy in the following example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting holidays\n",
      "  Obtaining dependency information for holidays from https://files.pythonhosted.org/packages/2c/d8/0a6ae9402b5809135026ae6e8aa7802ad613429ffe13cc2727822f33a6b4/holidays-0.35-py3-none-any.whl.metadata\n",
      "  Downloading holidays-0.35-py3-none-any.whl.metadata (19 kB)\n",
      "Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.11/site-packages (from holidays) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.11/site-packages (from python-dateutil->holidays) (1.16.0)\n",
      "Downloading holidays-0.35-py3-none-any.whl (800 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m801.0/801.0 kB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: holidays\n",
      "Successfully installed holidays-0.35\n"
     ]
    }
   ],
   "source": [
    "!pip install holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created random data for array exercise in 0.24s\n"
     ]
    }
   ],
   "source": [
    "%run prep-alt.py -d random --small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data with h5py\n",
    "# this creates a pointer to the data, but does not actually load\n",
    "import os\n",
    "\n",
    "import h5py\n",
    "\n",
    "f = h5py.File(os.path.join(\"data\", \"random.hdf5\"), mode=\"r\")\n",
    "dset = f[\"/x\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<HDF5 dataset \"x\": shape (5000000,), type \"<f4\">"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Compute sum using blocked algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before using dask, let's consider the concept of blocked algorithms. We can compute the sum of a large number of elements by loading them chunk-by-chunk, and keeping a running total.\n",
    "\n",
    "Here we compute the sum of this large array on disk by \n",
    "\n",
    "1.  Computing the sum of each 1,000,000 sized chunk of the array\n",
    "2.  Computing the sum of the 1,000 intermediate sums\n",
    "\n",
    "Note that this is a sequential process in the notebook kernel, both the loading and summing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4999606.5\n",
      "CPU times: user 10.2 ms, sys: 3.64 ms, total: 13.9 ms\n",
      "Wall time: 13.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Compute sum of large array, one million numbers at a time\n",
    "sums = []\n",
    "for i in range(0, 1_000_000_000, 1_000_000):\n",
    "    chunk = dset[i : i + 1_000_000]  # pull out numpy array\n",
    "    sums.append(chunk.sum())\n",
    "\n",
    "total = sum(sums)\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`dask.array` contains these algorithms\n",
    "--------------------------------------------\n",
    "\n",
    "Dask.array is a NumPy-like library that does these kinds of tricks to operate on large datasets that don't fit into memory.  It extends beyond the linear problems discussed above to full N-Dimensional algorithms and a decent subset of the NumPy interface."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create `dask.array` object**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create a `dask.array` `Array` object with the `da.from_array` function.  This function accepts\n",
    "\n",
    "1.  `data`: Any object that supports NumPy slicing, like `dset`\n",
    "2.  `chunks`: A chunk size to tell us how to block up our array, like `(1_000_000,)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "                        <td> 19.07 MiB </td>\n",
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       "                        <td> (5000000,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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      ],
      "text/plain": [
       "dask.array<array, shape=(5000000,), dtype=float32, chunksize=(1000000,), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask.array as da\n",
    "\n",
    "x = da.from_array(dset, chunks=(1_000_000,))\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Manipulate `dask.array` object as you would a numpy array**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have an `Array` we perform standard numpy-style computations like arithmetic, mathematics, slicing, reductions, etc..\n",
    "\n",
    "The interface is familiar, but the actual work is different. `dask_array.sum()` builds an expression of the computation. It does not do the computation yet. `numpy_array.sum()` computes the sum immediately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                </thead>\n",
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       "                        <td> 4 B </td>\n",
       "                        <td> 4 B </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> () </td>\n",
       "                        <td> () </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 5 graph layers </td>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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      "text/plain": [
       "dask.array<sum-aggregate, shape=(), dtype=float32, chunksize=(), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = x.sum()\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Compute result**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dask.array objects are lazily evaluated.  Operations like `.sum` build up a graph of blocked tasks to execute.  \n",
    "\n",
    "We ask for the final result with a call to `.compute()`.  This triggers the actual computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4999606.5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example:  Compute the mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a small change to the example above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999213"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.mean().compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does this match your result from before?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example:  Compute the standard deviation\n",
    "\n",
    "Again, this follows regular NumPy syntax, except for the added `.compute()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99982464"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.std().compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise:  Meteorological data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created weather dataset in 0.14s\n"
     ]
    }
   ],
   "source": [
    "%run prep-alt.py -d weather --small"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is 2GB of weather data in HDF5 files in `data/weather-big/*.hdf5`.  We'll use the `h5py` library to interact with this data and `dask.array` to compute on it.\n",
    "\n",
    "Our goal is to visualize the average temperature on the surface of the Earth for this month.  This will require a mean over all of this data.  We'll do this in the following steps\n",
    "\n",
    "1.  Create `h5py.Dataset` objects for each of the days of data on disk (`dsets`)\n",
    "2.  Wrap these with `da.from_array` calls \n",
    "3.  Stack these datasets along time with a call to `da.stack`\n",
    "4.  Compute the mean along the newly stacked time axis with the `.mean()` method\n",
    "5.  Visualize the result with `matplotlib.pyplot.imshow`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<HDF5 dataset \"t2m\": shape (180, 360), type \"<f8\">"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from glob import glob\n",
    "\n",
    "import h5py\n",
    "\n",
    "filenames = sorted(glob(os.path.join(\"data\", \"weather-small\", \"*.hdf5\")))\n",
    "dsets = [h5py.File(filename, mode=\"r\")[\"/t2m\"] for filename in filenames]\n",
    "dsets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h5py._hl.dataset.Dataset"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dsets[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1: Integrate with `dask.array`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a list of `dask.array` objects out of your list of `h5py.Dataset` objects using the `da.from_array` function with a chunk size of `(500, 500)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncomment and run the cell below to see the solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>,\n",
       " dask.array<array, shape=(180, 360), dtype=float64, chunksize=(180, 360), chunktype=numpy.ndarray>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrays = [da.from_array(dset, chunks=(500, 500)) for dset in dsets]\n",
    "arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2: Stack list of `dask.array` objects into a single `dask.array` object with `da.stack`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack these along the first axis so that the shape of the resulting array is `(31, 5760, 11520)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 15.33 MiB </td>\n",
       "                        <td> 506.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
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       "                        <td> (1, 180, 360) </td>\n",
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       "                        <td colspan=\"2\"> 31 chunks in 63 graph layers </td>\n",
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      "text/plain": [
       "dask.array<stack, shape=(31, 180, 360), dtype=float64, chunksize=(1, 180, 360), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = da.stack(arrays, axis=0)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3: Plot the mean of this array along the time (`0th`) axis\n",
    "\n",
    "Complete the following:\n",
    "\n",
    "```python\n",
    "result = ...\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "plt.imshow(result, cmap='RdBu_r')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = x.mean(axis=0)\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "plt.imshow(result, cmap=\"RdBu_r\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performance comparison\n",
    "---------------------------\n",
    "\n",
    "The following experiment was performed on a personal laptop with 16GB of RAM and 8 CPU cores.  Your performance may vary. If you attempt the NumPy version then please ensure that you have more than 4GB of main memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NumPy: ~7s, Needs gigabytes of memory**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "import numpy as np\n",
    "\n",
    "%%time \n",
    "x = np.random.normal(10, 0.1, size=(20000, 20000)) \n",
    "y = x.mean(axis=0)[::100] \n",
    "y \n",
    "\n",
    "CPU times: user 6.73 s, sys: 331 ms, total: 7.16 s\n",
    "Wall time: 7.11 s\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dask Array: ~1.5s, Needs megabytes of memory**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "import dask.array as da\n",
    "\n",
    "%%time\n",
    "x = da.random.normal(10, 0.1, size=(20000, 20000), chunks=(1000, 1000))\n",
    "y = x.mean(axis=0)[::100] \n",
    "y.compute() \n",
    "\n",
    "CPU times: user 635 ms, sys: 119 ms, total: 754 ms\n",
    "Wall time: 1.69 s\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Discussion**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dask finished faster, but used more total CPU time because Dask was able to transparently parallelize the computation because of the chunk size."
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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## 04_delayed.ipynb

      
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## 05_dask_gateway.ipynb

      
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## 06_array_distributed.ipynb

      
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## 07_zarr_object_store.ipynb

      
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