Estudos em Python for Data Science - Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d2d09317",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb5b5d6",
   "metadata": {},
   "source": [
    "### Introducing Pandas Objects - `Series`, `DataFrame` and `Index`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b743e3fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Series\n",
    "data = pd.Series([0.25, 0.5, 0.75, 1.0])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4efeb31b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc217f18",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35cb8225",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c172be14",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32b2bf2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
    "                 index=['a', 'b', 'c', 'd'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23433915",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a06e1357",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
    "                 index=[2, 5, 3, 7])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73cd8c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "population_dict = {'California': 38332521,\n",
    "                   'Texas': 26448193,\n",
    "                   'New York': 19651127,\n",
    "                   'Florida': 19552860,\n",
    "                   'Illinois': 12882135}\n",
    "population = pd.Series(population_dict)\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50b377ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "population['Florida']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a2ba677",
   "metadata": {},
   "outputs": [],
   "source": [
    "population['California':'New York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1014e26f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series(5, index=[100, 200, 300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2504eba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series({2:'a', 1:'b', 3:'c'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2e89929",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series({2:'a', 1:'b', 3:'c'}, index=[3, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9841d3aa",
   "metadata": {},
   "source": [
    "### DataFrame as a generalized NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02812ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "area_dict = {'California': 423967,\n",
    "             'Texas': 695662,\n",
    "             'New York': 141297,\n",
    "             'Florida': 170312,\n",
    "             'Illinois': 149995}\n",
    "area = pd.Series(area_dict)\n",
    "area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fb00eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "states = pd.DataFrame({'population': population,\n",
    "                      'area': area})\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "527a9cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "states.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3825d697",
   "metadata": {},
   "outputs": [],
   "source": [
    "states.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0333023",
   "metadata": {},
   "outputs": [],
   "source": [
    "states['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54fab766",
   "metadata": {},
   "outputs": [],
   "source": [
    "states['population']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c4cbde",
   "metadata": {},
   "source": [
    "### Constructing DataFrame objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "509c16aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(population, columns=['population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97262f99",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [{'a': i, 'b': 2 * i}\n",
    "        for i in range(3)]\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "796bdaf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame([{'a': 1, 'b': 2}, {'b': 3, 'c': 4}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be37188a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(np.random.rand(3, 2),\n",
    "             columns=['foo', 'bar'],\n",
    "             index=['a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb3c5dd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.zeros(3, dtype=[('A', 'i8'), ('B', 'f8')])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34c512ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd752a46",
   "metadata": {},
   "source": [
    "### The Pandas Index Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44a580c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = pd.Index([2, 3, 5, 7, 11])\n",
    "ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1c9f453",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(ind.size, ind.shape, ind.ndim, ind.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfbd4f26",
   "metadata": {},
   "source": [
    "### Index as ordered set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ec154de",
   "metadata": {},
   "outputs": [],
   "source": [
    "indA = pd.Index([1, 3, 5, 7, 9])\n",
    "indB = pd.Index([2, 3, 5, 7, 11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d16ba2e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "indA.intersection(indB) # intersection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73fb7c74",
   "metadata": {},
   "outputs": [],
   "source": [
    "indA.union(indB)  # union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32ef6dda",
   "metadata": {},
   "outputs": [],
   "source": [
    "indA.symmetric_difference(indB)  # symmetric difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "418844bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
    "                 index=['a', 'b', 'c', 'd'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17ac1381",
   "metadata": {},
   "outputs": [],
   "source": [
    "'a' in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d103467f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00ff5660",
   "metadata": {},
   "outputs": [],
   "source": [
    "list(data.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cc97219",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['e'] = 1.25\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90249a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['a':'c']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "781764d4",
   "metadata": {},
   "source": [
    "### Indexers: loc, iloc, and ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b239902",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series(['a', 'b', 'c'], index=[1, 3, 5])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f464c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# explicit index when indexing\n",
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b948aaa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# implicit index when slicing\n",
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efaac96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d91961c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb45f17e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "042bbfd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1af8bad",
   "metadata": {},
   "source": [
    "### Data Selection in DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d78587ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "area = pd.Series({'California': 423967, 'Texas': 695662,\n",
    "                  'New York': 141297, 'Florida': 170312,\n",
    "                  'Illinois': 149995})\n",
    "pop = pd.Series({'California': 38332521, 'Texas': 26448193,\n",
    "                 'New York': 19651127, 'Florida': 19552860,\n",
    "                 'Illinois': 12882135})\n",
    "data = pd.DataFrame({'area':area, 'pop':pop})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "529e4e97",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e82c17fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1fe4909",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.area is data['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90c225b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.pop is data['pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffe49d94",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['density'] = data['pop'] / data['area']\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b78bf9",
   "metadata": {},
   "source": [
    "### DataFrame as two-dimensional array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53899252",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c33bc45f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fd5a76c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e57f1f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f67b3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.iloc[:3, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d04cfbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:'Illinois', :'pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44bb2a6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data.density > 100, ['pop', 'density']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfee1841",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.iloc[0, 2] = 90\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d98b8e22",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Florida':'Illinois']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cec3741",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba17d3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[data.density > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49cb582b",
   "metadata": {},
   "source": [
    "## Operating on Data in Pandas\n",
    "### Ufuncs: Index Preservation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0eeea15",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26249cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "ser = pd.Series(rng.randint(0, 10, 4))\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fef34a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(rng.randint(0, 10, (3, 4)),\n",
    "                  columns=['A', 'B', 'C', 'D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "303d219a",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.exp(ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8318726b",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sin(df * np.pi / 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943c58f1",
   "metadata": {},
   "source": [
    "## UFuncs: Index Alignment\n",
    "### Index alignment in Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18df8f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "area = pd.Series({'Alaska': 1723337,\n",
    "                  'Texas': 695662,\n",
    "                  'California': 423967}, name='area')\n",
    "population = pd.Series({'California': 38332521,\n",
    "                        'Texas': 26448193,\n",
    "                        'New York': 19651127}, name='population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01fed428",
   "metadata": {},
   "outputs": [],
   "source": [
    "population / area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "240f0183",
   "metadata": {},
   "outputs": [],
   "source": [
    "area.index | population.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eb7ac56",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.Series([2, 4, 6], index=[0, 1, 2])\n",
    "B = pd.Series([1, 3, 5], index=[1, 2, 3])\n",
    "A + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dbe0b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "A.add(B, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41ab9f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.DataFrame(rng.randint(0, 20, (2, 2)), columns=list('AB'))\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82bacff2",
   "metadata": {},
   "source": [
    "### Ufuncs: Operations Between DataFrame and Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b38c387a",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = rng.randint(10, size=(3, 4))\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7758691",
   "metadata": {},
   "outputs": [],
   "source": [
    "A - A[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "197efb50",
   "metadata": {},
   "source": [
    "### Handling Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f260250",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c96e2279",
   "metadata": {},
   "outputs": [],
   "source": [
    "vals1 = np.array([1, None, 3, 4])\n",
    "vals1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7750886",
   "metadata": {},
   "outputs": [],
   "source": [
    "for dtype in ['object', 'int']:\n",
    "    print(\"dtype =\", dtype)\n",
    "    %timeit np.arange(1E6, dtype=dtype).sum()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e689030",
   "metadata": {},
   "outputs": [],
   "source": [
    "#vals1.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27366169",
   "metadata": {},
   "outputs": [],
   "source": [
    "vals2 = np.array([1, np.nan, 3, 4]) \n",
    "vals2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15bca966",
   "metadata": {},
   "outputs": [],
   "source": [
    "1 + np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1def8687",
   "metadata": {},
   "outputs": [],
   "source": [
    "0 * np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7794c09e",
   "metadata": {},
   "outputs": [],
   "source": [
    "vals2.sum(), vals2.min(), vals2.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92ea3b50",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.nansum(vals2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a44fd9dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series([1, np.nan, 2, None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dd3daf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.Series(range(2), dtype=int)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "656eb228",
   "metadata": {},
   "outputs": [],
   "source": [
    "x[0] = None\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824ebed1",
   "metadata": {},
   "source": [
    "### Operating on Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0143d4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series([1, np.nan, 'hello', None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37694702",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16061210",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[data.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e87876b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b68fb113",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[1,      np.nan, 2],\n",
    "                   [2,      3,      5],\n",
    "                   [np.nan, 4,      6]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8300e623",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eca7f29a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a36e723",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[3] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60682ad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis='columns', how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9edea022",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis='rows', thresh=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d04ca84",
   "metadata": {},
   "source": [
    "### Filling null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "248a4267",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cacfaf6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7e1c7ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# forward-fill\n",
    "data.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31381d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# back-fill\n",
    "data.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b80618e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a54f45cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(method='ffill', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70d80a7a",
   "metadata": {},
   "source": [
    "## Hierarchical Indexing\n",
    "### A Multiply Indexed Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98ca8b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87544bd6",
   "metadata": {},
   "source": [
    "#### Bad way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05d65c32",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [('California', 2000), ('California', 2010),\n",
    "         ('New York', 2000), ('New York', 2010),\n",
    "         ('Texas', 2000), ('Texas', 2010)]\n",
    "populations = [33871648, 37253956,\n",
    "               18976457, 19378102,\n",
    "               20851820, 25145561]\n",
    "pop = pd.Series(populations, index=index)\n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6e7a08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop[('California', 2010):('Texas', 2000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44530e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop[[i for i in pop.index if i[1] == 2010]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963e6e54",
   "metadata": {},
   "source": [
    "#### The Better Way: Pandas MultiIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0768278d",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_tuples(index)\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df5c1b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = pop.reindex(index)\n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47e01270",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop[:, 2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b6ff3e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_df = pop.unstack()\n",
    "pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcf1524b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_df.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50c1858d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_df = pd.DataFrame({'total': pop,\n",
    "                       'under18': [9267089, 9284094,\n",
    "                                   4687374, 4318033,\n",
    "                                   5906301, 6879014]})\n",
    "pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c533e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "f_u18 = pop_df['under18'] / pop_df['total']\n",
    "f_u18.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "333ccdc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.rand(4, 2),\n",
    "                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],\n",
    "                  columns=['data1', 'data2'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62799bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {('California', 2000): 33871648,\n",
    "        ('California', 2010): 37253956,\n",
    "        ('Texas', 2000): 20851820,\n",
    "        ('Texas', 2010): 25145561,\n",
    "        ('New York', 2000): 18976457,\n",
    "        ('New York', 2010): 19378102}\n",
    "pd.Series(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d7e91f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc38c2df",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e4552a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.MultiIndex.from_product([['a', 'b'], [1, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dd50da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.MultiIndex(levels=[['a', 'b'], [1, 2]],\n",
    "              codes=[[0, 0, 1, 1], [0, 1, 0, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67527b4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop.index.names = ['state', 'year']\n",
    "pop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb5d053",
   "metadata": {},
   "source": [
    "### MultiIndex for columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1227d4d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hierarchical indices and columns\n",
    "index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]],\n",
    "                                   names=['year', 'visit'])\n",
    "columns = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']],\n",
    "                                     names=['subject', 'type'])\n",
    "\n",
    "# mock some data\n",
    "data = np.round(np.random.randn(4, 6), 1)\n",
    "data[:, ::2] *= 10\n",
    "data += 37\n",
    "\n",
    "# create the DataFrame\n",
    "health_data = pd.DataFrame(data, index=index, columns=columns)\n",
    "health_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "959c23fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "health_data['Guido']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b55cbf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "799a67e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop['California', 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7b94d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop['California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7daa12fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop.loc['California':'New York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "766f7442",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop[:, 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1764bde2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop[pop > 22000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a04fc62",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop[['California', 'Texas']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a244dfcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "health_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2898ac21",
   "metadata": {},
   "outputs": [],
   "source": [
    "health_data['Guido', 'HR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df06ae0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "health_data.iloc[:2, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6ba84b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "health_data.loc[:, ('Bob', 'HR')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d472efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = pd.IndexSlice\n",
    "health_data.loc[idx[:, 1], idx[:, 'HR']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9410d2b8",
   "metadata": {},
   "source": [
    "## Rearranging Multi-Indices\n",
    "### Sorted and unsorted indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72782a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_product([['a', 'c', 'b'], [1, 2]])\n",
    "data = pd.Series(np.random.rand(6), index=index)\n",
    "data.index.names = ['char', 'int']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9db671b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    data['a':'b']\n",
    "except KeyError as e:\n",
    "    print(type(e))\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23d8de7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.sort_index()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98672ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['a':'b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32d62415",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop.unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebc18fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop.unstack(level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddc2c6e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop.unstack().stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1d968bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_flat = pop.reset_index(name='population')\n",
    "pop_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d45bbc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_flat.set_index(['state', 'year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6568e6c",
   "metadata": {},
   "source": [
    "### Data Aggregations on Multi-Indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "952635a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "health_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d01a140b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mean = health_data.groupby(level='year').median()\n",
    "data_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cbeb473",
   "metadata": {},
   "outputs": [],
   "source": [
    "health_data.groupby(axis=1, level='type').median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d2ebd0",
   "metadata": {},
   "source": [
    "## Combining Datasets: Concat and Append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61f2aa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f0f0520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C\n",
       "0  A0  B0  C0\n",
       "1  A1  B1  C1\n",
       "2  A2  B2  C2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_df(cols, ind):\n",
    "    \"\"\"Quickly make a DataFrame\"\"\"\n",
    "    data = {c: [str(c) + str(i) for i in ind] for c in cols}\n",
    "    return pd.DataFrame(data, ind)\n",
    "make_df('ABC', range(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7144bfb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class display(object):\n",
    "    \"\"\"Display HTML representation of multiple objects\"\"\"\n",
    "    template = \"\"\"<div style=\"float: left; padding: 10px;\">\n",
    "    <p style='font-family:\"Courier New\", Courier, monospace'>{0}</p>{1}\n",
    "    </div>\"\"\"\n",
    "    def __init__(self, *args):\n",
    "        self.args = args\n",
    "        \n",
    "    def _repr_html_(self):\n",
    "        return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n",
    "                         for a in self.args)\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n",
    "                           for a in self.args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "324c9045",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1, 2, 3]\n",
    "y = [4, 5, 6]\n",
    "z = [7, 8, 9]\n",
    "np.concatenate([x, y, z])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78177226",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [[1, 2],\n",
    "     [3, 4]]\n",
    "np.concatenate([x, x], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf2a891",
   "metadata": {},
   "outputs": [],
   "source": [
    "ser1 = pd.Series(['A', 'B', 'C'], index=[1, 2, 3])\n",
    "ser2 = pd.Series(['D', 'E', 'F'], index=[4, 5, 6])\n",
    "pd.concat([ser1, ser2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fde46cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = make_df('AB', [1, 2])\n",
    "df2 = make_df('AB', [3, 4])\n",
    "display('df1', 'df2', 'pd.concat([df1, df2])')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4ffaf8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = make_df('AB', [0, 1])\n",
    "y = make_df('AB', [2, 3])\n",
    "y.index = x.index  # make duplicate indices!\n",
    "display('x', 'y', 'pd.concat([x, y])')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a832e0f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    pd.concat([x, y], verify_integrity=True)\n",
    "except ValueError as e:\n",
    "    print(\"ValueError:\", e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25d6534e",
   "metadata": {},
   "outputs": [],
   "source": [
    "display('x', 'y', 'pd.concat([x, y], ignore_index=True)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb573932",
   "metadata": {},
   "outputs": [],
   "source": [
    "display('x', 'y', \"pd.concat([x, y], keys=['x', 'y'])\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dcef015",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = make_df('ABC', [1, 2])\n",
    "df6 = make_df('BCD', [3, 4])\n",
    "display('df5', 'df6', 'pd.concat([df5, df6])')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f584a853",
   "metadata": {},
   "outputs": [],
   "source": [
    "display('df5', 'df6',\n",
    "        \"pd.concat([df5, df6], join='inner')\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be66fc46",
   "metadata": {},
   "outputs": [],
   "source": [
    "display('df1', 'df2', 'pd.concat([df1, df2])')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363a994b",
   "metadata": {},
   "source": [
    "## Combining Datasets: Merge and Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3e44ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "class display(object):\n",
    "    \"\"\"Display HTML representation of multiple objects\"\"\"\n",
    "    template = \"\"\"<div style=\"float: left; padding: 10px;\">\n",
    "    <p style='font-family:\"Courier New\", Courier, monospace'>{0}</p>{1}\n",
    "    </div>\"\"\"\n",
    "    def __init__(self, *args):\n",
    "        self.args = args\n",
    "        \n",
    "    def _repr_html_(self):\n",
    "        return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n",
    "                         for a in self.args)\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n",
    "                           for a in self.args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6640f67",
   "metadata": {},
   "source": [
    "### Relational Algebra"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d413999",
   "metadata": {},
   "source": [
    "#### One-to-one joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91a26bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                    'group': ['Accounting', 'Engineering', 'Engineering', 'HR']})\n",
    "df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
    "                    'hire_date': [2004, 2008, 2012, 2014]})\n",
    "display('df1', 'df2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eaa3894c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.merge(df1, df2)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0472c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = pd.DataFrame({'group': ['Accounting', 'Engineering', 'HR'],\n",
    "                    'supervisor': ['Carly', 'Guido', 'Steve']})\n",
    "display('df3', 'df4', 'pd.merge(df3, df4)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "826e8be5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = pd.DataFrame({'group': ['Accounting', 'Accounting',\n",
    "                              'Engineering', 'Engineering', 'HR', 'HR'],\n",
    "                    'skills': ['math', 'spreadsheets', 'coding', 'linux',\n",
    "                               'spreadsheets', 'organization']})\n",
    "display('df1', 'df5', \"pd.merge(df1, df5)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c22b4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                    'salary': [70000, 80000, 120000, 90000]})\n",
    "display('df1', 'df3', 'pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\")')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74efefe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\").drop('name', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d73bd612",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1a = df1.set_index('employee')\n",
    "df2a = df2.set_index('employee')\n",
    "display('df1a', 'df2a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d08dd525",
   "metadata": {},
   "outputs": [],
   "source": [
    "display('df1a', 'df2a',\n",
    "        \"pd.merge(df1a, df2a, left_index=True, right_index=True)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d62992c",
   "metadata": {},
   "outputs": [],
   "source": [
    "display('df1a', 'df2a', 'df1a.join(df2a)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fb42a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = pd.DataFrame({'name': ['Peter', 'Paul', 'Mary'],\n",
    "                    'food': ['fish', 'beans', 'bread']},\n",
    "                   columns=['name', 'food'])\n",
    "df7 = pd.DataFrame({'name': ['Mary', 'Joseph'],\n",
    "                    'drink': ['wine', 'beer']},\n",
    "                   columns=['name', 'drink'])\n",
    "display('df6', 'df7', 'pd.merge(df6, df7)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4aad9fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(df6, df7, how='inner')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d223be0f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "display('df6', 'df7', \"pd.merge(df6, df7, how='outer')\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd1d74e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "display('df6', 'df7', \"pd.merge(df6, df7, how='left')\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2392b223",
   "metadata": {},
   "outputs": [],
   "source": [
    "df8 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                    'rank': [1, 2, 3, 4]})\n",
    "df9 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                    'rank': [3, 1, 4, 2]})\n",
    "display('df8', 'df9', 'pd.merge(df8, df9, on=\"name\")')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "580e5849",
   "metadata": {},
   "outputs": [],
   "source": [
    "display('df8', 'df9', 'pd.merge(df8, df9, on=\"name\", suffixes=[\"_L\", \"_R\"])')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66bc98ef",
   "metadata": {},
   "source": [
    "## Example: US States Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8019e404",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = pd.read_csv('state-population.csv')\n",
    "areas = pd.read_csv('state-areas.csv')\n",
    "abbrevs = pd.read_csv('state-abbrevs.csv')\n",
    "\n",
    "display('pop.head()', 'areas.head()', 'abbrevs.head()')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9959c42c",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = pd.merge(pop, abbrevs, how='outer',\n",
    "                 left_on='state/region', right_on='abbreviation')\n",
    "merged = merged.drop('abbreviation', 1)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b6d3d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b11074d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged[merged['population'].isnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6410014b",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.loc[merged['state'].isnull(), 'state/region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e42b405",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.loc[merged['state/region'] == 'PR', 'state'] = 'Puerto Rico'\n",
    "merged.loc[merged['state/region'] == 'USA', 'state'] = 'United States'\n",
    "merged.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6cf21e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "final = pd.merge(merged, areas, on='state', how='left')\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2a722bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "final.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc802d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "final['state'][final['area (sq. mi)'].isnull()].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c1d8415",
   "metadata": {},
   "outputs": [],
   "source": [
    "final.dropna(inplace=True)\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e78cc80",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85538c33",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2010 = final.query(\"year == 2010 & ages == 'total'\")\n",
    "data2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e818966d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2010.set_index('state', inplace=True)\n",
    "density = data2010['population'] / data2010['area (sq. mi)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89165153",
   "metadata": {},
   "outputs": [],
   "source": [
    "density.sort_values(ascending=False, inplace=True)\n",
    "density.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e74f627e",
   "metadata": {},
   "outputs": [],
   "source": [
    "density.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcad90da",
   "metadata": {},
   "source": [
    "## Aggregation and Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "415d37d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "class display(object):\n",
    "    \"\"\"Display HTML representation of multiple objects\"\"\"\n",
    "    template = \"\"\"<div style=\"float: left; padding: 10px;\">\n",
    "    <p style='font-family:\"Courier New\", Courier, monospace'>{0}</p>{1}\n",
    "    </div>\"\"\"\n",
    "    def __init__(self, *args):\n",
    "        self.args = args\n",
    "        \n",
    "    def _repr_html_(self):\n",
    "        return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n",
    "                         for a in self.args)\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n",
    "                           for a in self.args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b72e564c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1035, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "planets = sns.load_dataset('planets')\n",
    "planets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "182ffc5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae29d79f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.374540\n",
       "1    0.950714\n",
       "2    0.731994\n",
       "3    0.598658\n",
       "4    0.156019\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "ser = pd.Series(rng.rand(5))\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68d46e8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.811925491708157"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f0a3484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5623850983416314"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bbaa6a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.155995</td>\n",
       "      <td>0.020584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.058084</td>\n",
       "      <td>0.969910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.866176</td>\n",
       "      <td>0.832443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.601115</td>\n",
       "      <td>0.212339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.708073</td>\n",
       "      <td>0.181825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B\n",
       "0  0.155995  0.020584\n",
       "1  0.058084  0.969910\n",
       "2  0.866176  0.832443\n",
       "3  0.601115  0.212339\n",
       "4  0.708073  0.181825"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': rng.rand(5),\n",
    "                  'B': rng.rand(5)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a261af35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.477888\n",
       "B    0.443420\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "34b9fe5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.088290\n",
       "1    0.513997\n",
       "2    0.849309\n",
       "3    0.406727\n",
       "4    0.444949\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "83dc376f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>498.00000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.73494</td>\n",
       "      <td>835.778671</td>\n",
       "      <td>2.509320</td>\n",
       "      <td>52.068213</td>\n",
       "      <td>2007.377510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.17572</td>\n",
       "      <td>1469.128259</td>\n",
       "      <td>3.636274</td>\n",
       "      <td>46.596041</td>\n",
       "      <td>4.167284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.328300</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>1989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>38.272250</td>\n",
       "      <td>0.212500</td>\n",
       "      <td>24.497500</td>\n",
       "      <td>2005.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>1.245000</td>\n",
       "      <td>39.940000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>999.600000</td>\n",
       "      <td>2.867500</td>\n",
       "      <td>59.332500</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.00000</td>\n",
       "      <td>17337.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          number  orbital_period        mass    distance         year\n",
       "count  498.00000      498.000000  498.000000  498.000000   498.000000\n",
       "mean     1.73494      835.778671    2.509320   52.068213  2007.377510\n",
       "std      1.17572     1469.128259    3.636274   46.596041     4.167284\n",
       "min      1.00000        1.328300    0.003600    1.350000  1989.000000\n",
       "25%      1.00000       38.272250    0.212500   24.497500  2005.000000\n",
       "50%      1.00000      357.000000    1.245000   39.940000  2009.000000\n",
       "75%      2.00000      999.600000    2.867500   59.332500  2011.000000\n",
       "max      6.00000    17337.500000   25.000000  354.000000  2014.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.dropna().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55862b99",
   "metadata": {},
   "source": [
    "### Split, apply, combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3e816312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data\n",
       "0   A     0\n",
       "1   B     1\n",
       "2   C     2\n",
       "3   A     3\n",
       "4   B     4\n",
       "5   C     5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "                   'data': range(6)}, columns=['key', 'data'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "243d7933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002AA65D5C650>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "00f7f76d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A       3\n",
       "B       5\n",
       "C       7"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4aec55a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002AA69589510>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5bd9378a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x000002AA686B6D10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['orbital_period']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6a5355b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method\n",
       "Astrometry                         631.180000\n",
       "Eclipse Timing Variations         4343.500000\n",
       "Imaging                          27500.000000\n",
       "Microlensing                      3300.000000\n",
       "Orbital Brightness Modulation        0.342887\n",
       "Pulsar Timing                       66.541900\n",
       "Pulsation Timing Variations       1170.000000\n",
       "Radial Velocity                    360.200000\n",
       "Transit                              5.714932\n",
       "Transit Timing Variations           57.011000\n",
       "Name: orbital_period, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['orbital_period'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "09ac54ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astrometry                     shape=(2, 6)\n",
      "Eclipse Timing Variations      shape=(9, 6)\n",
      "Imaging                        shape=(38, 6)\n",
      "Microlensing                   shape=(23, 6)\n",
      "Orbital Brightness Modulation  shape=(3, 6)\n",
      "Pulsar Timing                  shape=(5, 6)\n",
      "Pulsation Timing Variations    shape=(1, 6)\n",
      "Radial Velocity                shape=(553, 6)\n",
      "Transit                        shape=(397, 6)\n",
      "Transit Timing Variations      shape=(4, 6)\n"
     ]
    }
   ],
   "source": [
    "for (method, group) in planets.groupby('method'):\n",
    "    print(\"{0:30s} shape={1}\".format(method, group.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f2a49b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       method                       \n",
       "count  Astrometry                          2.0\n",
       "       Eclipse Timing Variations           9.0\n",
       "       Imaging                            38.0\n",
       "       Microlensing                       23.0\n",
       "       Orbital Brightness Modulation       3.0\n",
       "                                         ...  \n",
       "max    Pulsar Timing                    2011.0\n",
       "       Pulsation Timing Variations      2007.0\n",
       "       Radial Velocity                  2014.0\n",
       "       Transit                          2014.0\n",
       "       Transit Timing Variations        2014.0\n",
       "Length: 80, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['year'].describe().unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ab3fa5",
   "metadata": {},
   "source": [
    "### Aggregate, filter, transform, apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "faaf4eff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "                   'data1': range(6),\n",
    "                   'data2': rng.randint(0, 10, 6)},\n",
    "                   columns = ['key', 'data1', 'data2'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b7a3e1d3",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
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       "      <th>max</th>\n",
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       "      <th>median</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data1            data2           \n",
       "      min median max   min median max\n",
       "key                                  \n",
       "A       0    1.5   3     3    4.0   5\n",
       "B       1    2.5   4     0    3.5   7\n",
       "C       2    3.5   5     3    6.0   9"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').aggregate(['min', np.median, max])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "680e61db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        0      5\n",
       "B        1      7\n",
       "C        2      9"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').aggregate({'data1':'min',\n",
    "                            'data2': 'max'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aea89b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby('key').std()</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>4.949747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>4.242641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby('key').filter(filter_func)</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>"
      ],
      "text/plain": [
       "df\n",
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9\n",
       "\n",
       "df.groupby('key').std()\n",
       "       data1     data2\n",
       "key                   \n",
       "A    2.12132  1.414214\n",
       "B    2.12132  4.949747\n",
       "C    2.12132  4.242641\n",
       "\n",
       "df.groupby('key').filter(filter_func)\n",
       "  key  data1  data2\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "4   B      4      7\n",
       "5   C      5      9"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def filter_func(x):\n",
    "    return x['data2'].std() > 4\n",
    "\n",
    "display('df', \"df.groupby('key').std()\", \"df.groupby('key').filter(filter_func)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e4d0a377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1  data2\n",
       "0   -1.5    1.0\n",
       "1   -1.5   -3.5\n",
       "2   -1.5   -3.0\n",
       "3    1.5   -1.0\n",
       "4    1.5    3.5\n",
       "5    1.5    3.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').transform(lambda x: x - x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a56d69c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df</p><div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
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       "  <tbody>\n",
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       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
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       "      <th>5</th>\n",
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       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby('key', group_keys=False).apply(norm_by_data2)</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>"
      ],
      "text/plain": [
       "df\n",
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9\n",
       "\n",
       "df.groupby('key', group_keys=False).apply(norm_by_data2)\n",
       "  key     data1  data2\n",
       "0   A  0.000000      5\n",
       "1   B  0.142857      0\n",
       "2   C  0.166667      3\n",
       "3   A  0.375000      3\n",
       "4   B  0.571429      7\n",
       "5   C  0.416667      9"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def norm_by_data2(x):\n",
    "    # x is a DataFrame of group values\n",
    "    x['data1'] /= x['data2'].sum()\n",
    "    return x\n",
    "\n",
    "display('df', \"df.groupby('key', group_keys=False).apply(norm_by_data2)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fcd2b4d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "<string>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n"
     ]
    },
    {
     "data": {
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       "<div style=\"float: left; padding: 10px;\">\n",
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       "    .dataframe tbody tr th:only-of-type {\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>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby(L).sum()</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>"
      ],
      "text/plain": [
       "df\n",
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9\n",
       "\n",
       "df.groupby(L).sum()\n",
       "   data1  data2\n",
       "0      7     17\n",
       "1      4      3\n",
       "2      4      7"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = [0, 1, 0, 1, 2, 0]\n",
    "display('df', 'df.groupby(L).sum()')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f1af2e56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df.groupby(df['key']).sum()</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>"
      ],
      "text/plain": [
       "df\n",
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9\n",
       "\n",
       "df.groupby(df['key']).sum()\n",
       "     data1  data2\n",
       "key              \n",
       "A        3      8\n",
       "B        5      7\n",
       "C        7     12"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display('df', \"df.groupby(df['key']).sum()\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b8036f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2.groupby(mapping).sum()</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>consonant</th>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vowel</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>"
      ],
      "text/plain": [
       "df2\n",
       "     data1  data2\n",
       "key              \n",
       "A        0      5\n",
       "B        1      0\n",
       "C        2      3\n",
       "A        3      3\n",
       "B        4      7\n",
       "C        5      9\n",
       "\n",
       "df2.groupby(mapping).sum()\n",
       "           data1  data2\n",
       "key                    \n",
       "consonant     12     19\n",
       "vowel          3      8"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.set_index('key')\n",
    "mapping = {'A': 'vowel', 'B': 'consonant', 'C': 'consonant'}\n",
    "display('df2', 'df2.groupby(mapping).sum()')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6d6ae4a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>df2.groupby(str.lower).mean()</p><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>"
      ],
      "text/plain": [
       "df2\n",
       "     data1  data2\n",
       "key              \n",
       "A        0      5\n",
       "B        1      0\n",
       "C        2      3\n",
       "A        3      3\n",
       "B        4      7\n",
       "C        5      9\n",
       "\n",
       "df2.groupby(str.lower).mean()\n",
       "     data1  data2\n",
       "key              \n",
       "a      1.5    4.0\n",
       "b      2.5    3.5\n",
       "c      3.5    6.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display('df2', 'df2.groupby(str.lower).mean()')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6e0bf5c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th>vowel</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <th>consonant</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th>consonant</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               data1  data2\n",
       "key key                    \n",
       "a   vowel        1.5    4.0\n",
       "b   consonant    2.5    3.5\n",
       "c   consonant    3.5    6.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby([str.lower, mapping]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c9078f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>decade</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "      <th>2010s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Astrometry</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microlensing</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Velocity</th>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "decade                         1980s  1990s  2000s  2010s\n",
       "method                                                   \n",
       "Astrometry                       0.0    0.0    0.0    2.0\n",
       "Eclipse Timing Variations        0.0    0.0    5.0   10.0\n",
       "Imaging                          0.0    0.0   29.0   21.0\n",
       "Microlensing                     0.0    0.0   12.0   15.0\n",
       "Orbital Brightness Modulation    0.0    0.0    0.0    5.0\n",
       "Pulsar Timing                    0.0    9.0    1.0    1.0\n",
       "Pulsation Timing Variations      0.0    0.0    1.0    0.0\n",
       "Radial Velocity                  1.0   52.0  475.0  424.0\n",
       "Transit                          0.0    0.0   64.0  712.0\n",
       "Transit Timing Variations        0.0    0.0    0.0    9.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decade = 10 * (planets['year'] // 10)\n",
    "decade = decade.astype(str) + 's'\n",
    "decade.name = 'decade'\n",
    "planets.groupby(['method', decade])['number'].sum().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b217cd",
   "metadata": {},
   "source": [
    "## Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a04e92c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "titanic = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "84d35916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "209d8923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\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>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived\n",
       "sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('sex')[['survived']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "82cf4391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e7a3ed6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2b01b3a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.423729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.133663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class               First    Second     Third\n",
       "sex    age                                   \n",
       "female (0, 18]   0.909091  1.000000  0.511628\n",
       "       (18, 80]  0.972973  0.900000  0.423729\n",
       "male   (0, 18]   0.800000  0.600000  0.215686\n",
       "       (18, 80]  0.375000  0.071429  0.133663"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.cut(titanic['age'], [0, 18, 80])\n",
    "titanic.pivot_table('survived', ['sex', age], 'class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5649fb8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(-0.001, 14.454]</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(14.454, 512.329]</th>\n",
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       "      <th>Second</th>\n",
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       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.391304</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.260870</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.178571</td>\n",
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       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.098039</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.192308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fare            (-0.001, 14.454]                     (14.454, 512.329]  \\\n",
       "class                      First    Second     Third             First   \n",
       "sex    age                                                               \n",
       "female (0, 18]               NaN  1.000000  0.714286          0.909091   \n",
       "       (18, 80]              NaN  0.880000  0.444444          0.972973   \n",
       "male   (0, 18]               NaN  0.000000  0.260870          0.800000   \n",
       "       (18, 80]              0.0  0.098039  0.125000          0.391304   \n",
       "\n",
       "fare                                 \n",
       "class              Second     Third  \n",
       "sex    age                           \n",
       "female (0, 18]   1.000000  0.318182  \n",
       "       (18, 80]  0.914286  0.391304  \n",
       "male   (0, 18]   0.818182  0.178571  \n",
       "       (18, 80]  0.030303  0.192308  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare = pd.qcut(titanic['fare'], 2)\n",
    "titanic.pivot_table('survived', ['sex', age], [fare, 'class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fa61ff87",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>21.970121</td>\n",
       "      <td>16.118810</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>19.741782</td>\n",
       "      <td>12.661633</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare                       survived             \n",
       "class        First     Second      Third    First Second Third\n",
       "sex                                                           \n",
       "female  106.125798  21.970121  16.118810       91     70    72\n",
       "male     67.226127  19.741782  12.661633       45     17    47"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(index='sex', columns='class',\n",
    "                    aggfunc={'survived':sum, 'fare':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "55664a1f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third       All\n",
       "sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class', margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c3ef9bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "births = pd.read_csv('births-usa.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dfd8b50c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day gender  births\n",
       "0  1969      1  1.0      F    4046\n",
       "1  1969      1  1.0      M    4440\n",
       "2  1969      1  2.0      F    4454\n",
       "3  1969      1  2.0      M    4548\n",
       "4  1969      1  3.0      F    4548"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8851173e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>1753634</td>\n",
       "      <td>1846572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>16263075</td>\n",
       "      <td>17121550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>18310351</td>\n",
       "      <td>19243452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>19479454</td>\n",
       "      <td>20420553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>18229309</td>\n",
       "      <td>19106428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender         F         M\n",
       "decade                    \n",
       "1960     1753634   1846572\n",
       "1970    16263075  17121550\n",
       "1980    18310351  19243452\n",
       "1990    19479454  20420553\n",
       "2000    18229309  19106428"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births['decade'] = 10 * (births['year'] // 10)\n",
    "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "77d1ac2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()  # use Seaborn styles\n",
    "births.pivot_table('births', index='year', columns='gender', aggfunc='sum').plot()\n",
    "plt.ylabel('total births per year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ce8eea8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "quartiles = np.percentile(births['births'], [25, 50, 75])\n",
    "mu = quartiles[1]\n",
    "sig = 0.74 * (quartiles[2] - quartiles[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "21df8dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cf7b010e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set 'day' column to integer; it originally was a string due to nulls\n",
    "births['day'] = births['day'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d50fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a datetime index from the year, month, day\n",
    "births.index = pd.to_datetime(10000 * births.year +\n",
    "                              100 * births.month +\n",
    "                              births.day, format='%Y%m%d')\n",
    "\n",
    "births['dayofweek'] = births.index.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b4f2d3ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cassi\\AppData\\Local\\Temp\\ipykernel_10696\\832003187.py:6: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  plt.gca().set_xticklabels(['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "births.pivot_table('births', index='dayofweek',\n",
    "                    columns='decade', aggfunc='mean').plot()\n",
    "plt.gca().set_xticklabels(['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])\n",
    "plt.ylabel('mean births by day');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "cc9734e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>4009.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4247.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4500.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4571.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4603.625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       births\n",
       "1 1  4009.225\n",
       "  2  4247.400\n",
       "  3  4500.900\n",
       "  4  4571.350\n",
       "  5  4603.625"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births_by_date = births.pivot_table('births', \n",
    "                                    [births.index.month, births.index.day])\n",
    "births_by_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8f113f2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cassi\\AppData\\Local\\Temp\\ipykernel_10696\\4030178730.py:1: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
      "  births_by_date.index = [pd.datetime(2012, month, day)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>4009.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>4247.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>4500.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>4571.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>4603.625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              births\n",
       "2012-01-01  4009.225\n",
       "2012-01-02  4247.400\n",
       "2012-01-03  4500.900\n",
       "2012-01-04  4571.350\n",
       "2012-01-05  4603.625"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births_by_date.index = [pd.datetime(2012, month, day)\n",
    "                        for (month, day) in births_by_date.index]\n",
    "births_by_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9ad32bbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "births_by_date.plot(ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "588dfd6e",
   "metadata": {},
   "source": [
    "## Vectorized String Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "53c615eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  6, 10, 14, 22, 26])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([2, 3, 5, 7, 11, 13])\n",
    "x * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "53f95a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Peter', 'Paul', 'Mary', 'Guido']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = ['peter', 'Paul', 'MARY', 'gUIDO']\n",
    "[s.capitalize() for s in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5f91c86a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = ['peter', 'Paul', None, 'MARY', 'gUIDO']\n",
    "#[s.capitalize() for s in data]\n",
    "\n",
    "## Error because of the 'None' declaration\n",
    "# 'NoneType' object has no attribute 'capitalize'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "146ae5aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    peter\n",
       "1     Paul\n",
       "2     None\n",
       "3     MARY\n",
       "4    gUIDO\n",
       "dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names = pd.Series(data)\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "64f36584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Peter\n",
       "1     Paul\n",
       "2     None\n",
       "3     Mary\n",
       "4    Guido\n",
       "dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.str.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "afb5e97d",
   "metadata": {},
   "outputs": [],
   "source": [
    "monte = pd.Series(['Graham Chapman', 'John Cleese', 'Terry Gilliam',\n",
    "                   'Eric Idle', 'Terry Jones', 'Michael Palin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5870fe72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    graham chapman\n",
       "1       john cleese\n",
       "2     terry gilliam\n",
       "3         eric idle\n",
       "4       terry jones\n",
       "5     michael palin\n",
       "dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c382efcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14\n",
       "1    11\n",
       "2    13\n",
       "3     9\n",
       "4    11\n",
       "5    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d1c2aad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.startswith('T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2eff7ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [Graham, Chapman]\n",
       "1       [John, Cleese]\n",
       "2     [Terry, Gilliam]\n",
       "3         [Eric, Idle]\n",
       "4       [Terry, Jones]\n",
       "5     [Michael, Palin]\n",
       "dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f8a8502b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Graham\n",
       "1       John\n",
       "2      Terry\n",
       "3       Eric\n",
       "4      Terry\n",
       "5    Michael\n",
       "dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.extract('([A-Za-z]+)', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a0b3ef5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [Graham Chapman]\n",
       "1                  []\n",
       "2     [Terry Gilliam]\n",
       "3                  []\n",
       "4       [Terry Jones]\n",
       "5     [Michael Palin]\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.findall(r'^[^AEIOU].*[^aeiou]$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0b082b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Gra\n",
       "1    Joh\n",
       "2    Ter\n",
       "3    Eri\n",
       "4    Ter\n",
       "5    Mic\n",
       "dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6a9f8ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chapman\n",
       "1     Cleese\n",
       "2    Gilliam\n",
       "3       Idle\n",
       "4      Jones\n",
       "5      Palin\n",
       "dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.split().str.get(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "760d53d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Graham Chapman</td>\n",
       "      <td>B|C|D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John Cleese</td>\n",
       "      <td>B|D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Terry Gilliam</td>\n",
       "      <td>A|C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eric Idle</td>\n",
       "      <td>B|D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Terry Jones</td>\n",
       "      <td>B|C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael Palin</td>\n",
       "      <td>B|C|D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name   info\n",
       "0  Graham Chapman  B|C|D\n",
       "1     John Cleese    B|D\n",
       "2   Terry Gilliam    A|C\n",
       "3       Eric Idle    B|D\n",
       "4     Terry Jones    B|C\n",
       "5   Michael Palin  B|C|D"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_monte = pd.DataFrame({'name': monte,\n",
    "                           'info': ['B|C|D', 'B|D', 'A|C',\n",
    "                                    'B|D', 'B|C', 'B|C|D']})\n",
    "full_monte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a0ff24f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C  D\n",
       "0  0  1  1  1\n",
       "1  0  1  0  1\n",
       "2  1  0  1  0\n",
       "3  0  1  0  1\n",
       "4  0  1  1  0\n",
       "5  0  1  1  1"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_monte['info'].str.get_dummies('|')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f4edd28",
   "metadata": {},
   "source": [
    "## Working with Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aca69b76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 7, 4, 0, 0)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "datetime(year=2015,  month=7, day=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9776033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 7, 4, 0, 0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil import parser\n",
    "date = parser.parse(\"4th of July, 2015\")\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5813cc0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Saturday'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.strftime('%A')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0647b408",
   "metadata": {},
   "source": [
    "### Typed arrays of times: NumPy's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e0723747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('2015-07-04', dtype='datetime64[D]')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "date = np.array('2015-07-04', dtype=np.datetime64)\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2fa351b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2015-07-04')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2015-07-04')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d48254d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2015-07-04T12:00')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2015-07-04 12:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15760374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2015-07-04T12:59:59.500000000')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2015-07-04 12:59:59.50', 'ns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c4033a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-07-04 00:00:00')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "date = pd.to_datetime(\"4th of July, 2015\")\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "06d62636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Saturday'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.strftime('%A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e53f843f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
       "               '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
       "               '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date + pd.to_timedelta(np.arange(12), 'D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fce2938f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-04    0\n",
       "2014-08-04    1\n",
       "2015-07-04    2\n",
       "2015-08-04    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.DatetimeIndex(['2014-07-04', '2014-08-04',\n",
    "                          '2015-07-04', '2015-08-04'])\n",
    "data = pd.Series([0, 1, 2, 3], index=index)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "31e18a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-04    0\n",
       "2014-08-04    1\n",
       "2015-07-04    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['2014-07-04':'2015-07-04']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d3d83f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-07-04    2\n",
       "2015-08-04    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['2015']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0015beb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
       "               '2015-07-08'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.to_datetime([datetime(2015, 7, 3), '4th of July, 2015',\n",
    "                       '2015-Jul-6', '07-07-2015', '20150708'])\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3c561d8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['0 days', '1 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq=None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates - dates[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a2a69a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
       "               '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-07-03', '2015-07-10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e5b0d56f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
       "               '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-07-03', periods=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6cdd275d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03 00:00:00', '2015-07-03 01:00:00',\n",
       "               '2015-07-03 02:00:00', '2015-07-03 03:00:00',\n",
       "               '2015-07-03 04:00:00', '2015-07-03 05:00:00',\n",
       "               '2015-07-03 06:00:00', '2015-07-03 07:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-07-03', periods=8, freq='H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "41614eef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['0 days 00:00:00', '0 days 02:30:00', '0 days 05:00:00',\n",
       "                '0 days 07:30:00', '0 days 10:00:00', '0 days 12:30:00',\n",
       "                '0 days 15:00:00', '0 days 17:30:00', '0 days 20:00:00'],\n",
       "               dtype='timedelta64[ns]', freq='150T')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.timedelta_range(0, periods=9, freq=\"2H30T\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb4d30da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-01', '2015-07-02', '2015-07-03', '2015-07-06',\n",
       "               '2015-07-07'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import BDay\n",
    "pd.date_range('2015-07-01', periods=5, freq=BDay())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b938d61",
   "metadata": {},
   "source": [
    "## High-Performance Pandas: eval() and query()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4aba6664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.03 ms ± 82.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "rng = np.random.RandomState(42)\n",
    "x = rng.rand(1000000)\n",
    "y = rng.rand(1000000)\n",
    "%timeit x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44e2e1a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "260 ms ± 28.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.fromiter((xi + yi for xi, yi in zip(x, y)), dtype=x.dtype, count=len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb3eebbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (x > 0.5) & (y < 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9eafb64e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, ..., False, False, False])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp1 = (x > 0.5)\n",
    "tmp2 = (y < 0.5)\n",
    "mask = tmp1 & tmp2\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e86bd873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numexpr\n",
    "mask_numexpr = numexpr.evaluate('(x > 0.5) & (y < 0.5)')\n",
    "np.allclose(mask, mask_numexpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "602c9146",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "nrows, ncols = 100000, 100\n",
    "rng = np.random.RandomState(42)\n",
    "df1, df2, df3, df4 = (pd.DataFrame(rng.rand(nrows, ncols))\n",
    "                      for i in range(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b1707df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.8 ms ± 5.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df1 + df2 + df3 + df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2768fdc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.4 ms ± 384 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.eval('df1 + df2 + df3 + df4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8f0d494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(df1 + df2 + df3 + df4,\n",
    "            pd.eval('df1 + df2 + df3 + df4'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "642ed5d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1, df2, df3, df4, df5 = (pd.DataFrame(rng.randint(0, 1000, (100, 3)))\n",
    "                           for i in range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5993db3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1 = -df1 * df2 / (df3 + df4) - df5\n",
    "result2 = pd.eval('-df1 * df2 / (df3 + df4) - df5')\n",
    "np.allclose(result1, result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9885fda9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1 = (df1 < df2) & (df2 <= df3) & (df3 != df4)\n",
    "result2 = pd.eval('df1 < df2 <= df3 != df4')\n",
    "np.allclose(result1, result2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735165ed",
   "metadata": {},
   "source": [
    "### DataFrame.eval() for Column-Wise Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "81844e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.375506</td>\n",
       "      <td>0.406939</td>\n",
       "      <td>0.069938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.069087</td>\n",
       "      <td>0.235615</td>\n",
       "      <td>0.154374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.677945</td>\n",
       "      <td>0.433839</td>\n",
       "      <td>0.652324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.264038</td>\n",
       "      <td>0.808055</td>\n",
       "      <td>0.347197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.589161</td>\n",
       "      <td>0.252418</td>\n",
       "      <td>0.557789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  0.375506  0.406939  0.069938\n",
       "1  0.069087  0.235615  0.154374\n",
       "2  0.677945  0.433839  0.652324\n",
       "3  0.264038  0.808055  0.347197\n",
       "4  0.589161  0.252418  0.557789"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(rng.rand(1000, 3), columns=['A', 'B', 'C'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "50e59de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1 = (df['A'] + df['B']) / (df['C'] - 1)\n",
    "result2 = pd.eval(\"(df.A + df.B) / (df.C - 1)\")\n",
    "np.allclose(result1, result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4e9af899",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result3 = df.eval('(A + B) / (C - 1)')\n",
    "np.allclose(result1, result3)"
   ]
  }
 ],
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