07-cythonize-pandas-groupby.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "## Speed up Pandas' GroupBy with Cython\n",
    "\n",
    "Scenario: we have a large dataframe [comprising](https://en.wikipedia.org/wiki/User:Giraffedata/comprised_of) several smaller tables concatenated together.\n",
    "\n",
    "We've done all our data cleaning and munging on the columns of the large dataframe, and we need to break it up into small dataframes.  This scenario may, hypothetically, come up when doing sequence modeling with recurrent neural networks, and we need to prepare mini-batches of sequences for our model to train against.\n",
    "\n",
    "The obvious way to do it is `list(df.groupby(level=0, sort=False))`.  Unfortunately, Pandas can be really slow in this case -- making lots of little dataframes is an expensive operation.\n",
    "\n",
    "Since we can make certain guarantees of our data, can we speed this up?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from uuid import uuid4\n",
    "import toolz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 35241 entries, 4b8b61db1fc544e5b2f119e8ca15b5a6 to 0bbfa74e284c469db5b40c6dbc2f8cc2\n",
      "Data columns (total 3 columns):\n",
      "0    35241 non-null float64\n",
      "1    35241 non-null float64\n",
      "2    35241 non-null float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 1.1+ MB\n",
      "None\n"
     ]
    },
    {
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       "                                         0         1         2\n",
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      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nblocks = 10000\n",
    "rowsperblock = np.random.randint(1, 7, size=nblocks)\n",
    "ncols = 3\n",
    "\n",
    "idx = list(toolz.concat([[uuid4().hex] * rpb for rpb in rowsperblock]))\n",
    "df = pd.DataFrame(np.random.standard_normal(size=(rowsperblock.sum(), ncols)), index=idx)\n",
    "print(df.info())\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.is_monotonic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('4b8b61db1fc544e5b2f119e8ca15b5a6',\n",
       "                                           0         1         2\n",
       "  4b8b61db1fc544e5b2f119e8ca15b5a6  0.810006  1.992543  0.872308\n",
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       " ('3e9dea6b183f477f9d5886f0faf0fd9d',\n",
       "                                           0         1         2\n",
       "  3e9dea6b183f477f9d5886f0faf0fd9d  0.715063 -0.923540 -1.677322\n",
       "  3e9dea6b183f477f9d5886f0faf0fd9d -0.284031  0.377469 -0.811794\n",
       "  3e9dea6b183f477f9d5886f0faf0fd9d -0.750274  1.117333 -0.691580\n",
       "  3e9dea6b183f477f9d5886f0faf0fd9d  0.334399  0.510409 -1.145321\n",
       "  3e9dea6b183f477f9d5886f0faf0fd9d  0.315334 -0.695746 -0.185402)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.groupby(level=[0], sort=False))[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "954 ms ± 126 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit list(df.groupby(level=[0], sort=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    }
   ],
   "source": [
    "%prun list(df.groupby(level=[0], sort=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating lots of little dataframes is expensive\n",
    "* Pandas doesn't have a fast path for DataFrame creation with data that's already clean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Speedup version 1: use NumPy\n",
    "\n",
    "* For this problem, we're willing to adjust the output and generate a sequence of NumPy arrays rather than a sequence of dataframes.\n",
    "* With this adjustment, we'll see we can get a substantial speedup using NumPy operations.\n",
    "* Once it's rewritten to use NumPy, then we can get *further* speedups with Cython."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def splitby(df):\n",
    "    idx = df.index\n",
    "    # NumPy array of \"posts\" that delineate the row indices\n",
    "    # with which to split the dataframe.\n",
    "    posts = np.where(idx[1:] != idx[:-1])[0] + 1\n",
    "    split_labels = idx[np.concatenate([[0], posts, [-1]])]\n",
    "    split_data = np.split(df.values, posts, 0)\n",
    "    return list(zip(split_labels, split_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('4b8b61db1fc544e5b2f119e8ca15b5a6',\n",
       "  array([[ 0.81000581,  1.99254348,  0.87230783],\n",
       "         [-0.81787326, -0.52282892,  0.38347882],\n",
       "         [-1.21275775, -0.13009344,  0.02933438],\n",
       "         [ 0.13176327,  0.37590582,  1.68548682]])),\n",
       " ('3e9dea6b183f477f9d5886f0faf0fd9d',\n",
       "  array([[ 0.71506328, -0.9235403 , -1.67732214],\n",
       "         [-0.28403052,  0.37746918, -0.81179384],\n",
       "         [-0.75027367,  1.11733273, -0.69158037],\n",
       "         [ 0.33439915,  0.51040889, -1.14532109],\n",
       "         [ 0.31533402, -0.69574639, -0.18540204]]))]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitby(df)[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.5 ms ± 529 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit splitby(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions\n",
    "* Why is this faster?\n",
    "* What about `DataFrame.iloc`?  Can it give us fast slicing without the overhead?  Why or why not?\n",
    "* If we double the number of columns, how do you expect the two versions to scale?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Speedup version 2: Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
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   "source": [
    "%%cython -a\n",
    "\n",
    "cimport cython\n",
    "cimport numpy as cnp\n",
    "import numpy as np\n",
    "\n",
    "@cython.boundscheck(False)\n",
    "@cython.wraparound(False)\n",
    "def splitby_cython(df):\n",
    "    cdef:\n",
    "        cnp.ndarray[double, ndim=2, mode=\"c\"] cols = df.values\n",
    "        cnp.ndarray[object, mode=\"c\"] idx = df.index.values\n",
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    "        list result = []\n",
    "        int i, thispost = 0\n",
    "        \n",
    "    for i in range(1, n):\n",
    "        if idx[i] != idx[i-1]:\n",
    "            result.append((idx[i-1], cols[thispost:i]))\n",
    "            thispost = i\n",
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