b01-jupyter-lekcja.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Jupyter wprowadzenie\n",
    "\n",
    "Witaj świecie Jyputera!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Trochę o Markdown\n",
    "\n",
    "* pierwszy\n",
    "* drugi\n",
    "* trzeci \n",
    "\n",
    "\n",
    "- **pierwszy**\n",
    "\n",
    "    - sub pierwszy\n",
    "    - sub drugi\n",
    "\n",
    "- *drugi*\n",
    "- trzeci\n",
    "\n",
    "\n",
    "5. trzeci\n",
    "5. drugi\n",
    "5. pierwszy\n",
    "\n",
    "|imie|nazwisko    |wiek|\n",
    "|:---|-----------:|:--:|\n",
    "|Jan | Twardowski | 42 |\n",
    "|Mark|Watney|30|\n",
    "\n",
    "\n",
    "|imie|nazwisko|\n",
    "|-|-|\n",
    "|Jan|Twardowski|\n",
    "\n",
    "| Imię | Nazwisko   | Wiek |\n",
    "|------|------------|------|\n",
    "| Jan  | Twardowski | 42   |\n",
    "| Mark | Watney     | 30   |\n",
    "|      |            |      |\n",
    "\n",
    "Wykorzystaj polecenie `pip install jupyter`\n",
    "\n",
    "\n",
    "\n",
    "```python\n",
    "\n",
    "name = 'Jan'\n",
    "print(name)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan\n"
     ]
    }
   ],
   "source": [
    "name = 'Jan'\n",
    "print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "%magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "154 ns ± 4.22 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 10 -n 1_000_000\n",
    "\n",
    "first_name = 'Jan'\n",
    "last_name = 'Twardowski'\n",
    "\n",
    "full_name = first_name + ' ' + last_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "127 ns ± 3.49 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 10 -n 1_000_000\n",
    "\n",
    "first_name = 'Jan'\n",
    "last_name = 'Twardowski'\n",
    "\n",
    "full_name = f'{first_name} {last_name}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: jupyter in ./python38/lib/python3.8/site-packages (1.0.0)\n",
      "Requirement already satisfied: nbconvert in ./python38/lib/python3.8/site-packages (from jupyter) (5.6.1)\n",
      "Requirement already satisfied: ipywidgets in ./python38/lib/python3.8/site-packages (from jupyter) (7.5.1)\n",
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      "Requirement already satisfied: jupyter-core in ./python38/lib/python3.8/site-packages (from nbconvert->jupyter) (4.6.3)\n",
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      "Requirement already satisfied: decorator in ./python38/lib/python3.8/site-packages (from traitlets>=4.2->nbconvert->jupyter) (4.4.2)\n",
      "Requirement already satisfied: setuptools>=18.5 in ./python38/lib/python3.8/site-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets->jupyter) (41.2.0)\n",
      "Requirement already satisfied: backcall in ./python38/lib/python3.8/site-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets->jupyter) (0.2.0)\n",
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      "Requirement already satisfied: ptyprocess; os_name != \"nt\" in ./python38/lib/python3.8/site-packages (from terminado>=0.8.1->notebook->jupyter) (0.6.0)\n",
      "Requirement already satisfied: pyrsistent>=0.14.0 in ./python38/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert->jupyter) (0.16.0)\n",
      "Requirement already satisfied: attrs>=17.4.0 in ./python38/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert->jupyter) (19.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in ./python38/lib/python3.8/site-packages (from packaging->bleach->nbconvert->jupyter) (2.4.7)\n",
      "Requirement already satisfied: parso>=0.7.0 in ./python38/lib/python3.8/site-packages (from jedi>=0.10->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets->jupyter) (0.7.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Latex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\n",
       "$$\\int_{x=0}^{x=\\infty} x^\\pi dx$$\n"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "\n",
    "$$\\int_{x=0}^{x=\\infty} x^\\pi dx$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\n",
       "$$c = \\sqrt{a^2 + b^2}$$\n"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "\n",
    "$$c = \\sqrt{a^2 + b^2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\n",
       "\\begin{equation}\n",
       "H← ​​​60 ​+​ \\frac{​​30(B-R)​​}{Vmax-Vmin}  ​​, if V​max​​ = G\n",
       "\\end{equation}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "\n",
    "\\begin{equation}\n",
    "H← ​​​60 ​+​ \\frac{​​30(B-R)​​}{Vmax-Vmin}  ​​, if V​max​​ = G\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "$ F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\lim_{x \\to 0} (1+x)^{1/x} = e$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Latex\n",
    "\n",
    "Latex(r'$\\lim_{x \\to 0} (1+x)^{1/x} = e$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting numpy\n",
      "  Using cached numpy-1.18.5-cp38-cp38-macosx_10_9_x86_64.whl (15.1 MB)\n",
      "Installing collected packages: numpy\n",
      "Successfully installed numpy-1.18.5\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11c942c70>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 100)  # vector z 100 równo odległymi wartościami od -5 do 5\n",
    "y = np.sin(x)                # sinus wszystkich wartości x\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
      "  Using cached matplotlib-3.2.1-cp38-cp38-macosx_10_9_x86_64.whl (12.4 MB)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in ./python38/lib/python3.8/site-packages (from matplotlib) (2.4.7)\n",
      "Collecting kiwisolver>=1.0.1\n",
      "  Using cached kiwisolver-1.2.0-cp38-cp38-macosx_10_9_x86_64.whl (60 kB)\n",
      "Requirement already satisfied: numpy>=1.11 in ./python38/lib/python3.8/site-packages (from matplotlib) (1.18.5)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in ./python38/lib/python3.8/site-packages (from matplotlib) (2.8.1)\n",
      "Collecting cycler>=0.10\n",
      "  Using cached cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
      "Requirement already satisfied: six>=1.5 in ./python38/lib/python3.8/site-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n",
      "Installing collected packages: kiwisolver, cycler, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.2.0 matplotlib-3.2.1\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x118437e80>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "import random\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "x1 = [x*0.01 for x in range(0,628)]\n",
    "y1 = [math.sin(x*0.01)+random.gauss(0, 0.1) for x in range(0,628)]\n",
    "plt.plot(x1, y1)\n",
    "\n",
    "x2 = [x*0.5 for x in range(0,round(63/5))]\n",
    "y2 = [math.cos(x*0.5) for x in range(0,round(63/5))]\n",
    "plt.plot(x2, y2, 'o-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Using cached pandas-1.0.4-cp38-cp38-macosx_10_9_x86_64.whl (10.2 MB)\n",
      "Requirement already satisfied: numpy>=1.13.3 in ./python38/lib/python3.8/site-packages (from pandas) (1.18.5)\n",
      "Collecting pytz>=2017.2\n",
      "  Using cached pytz-2020.1-py2.py3-none-any.whl (510 kB)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in ./python38/lib/python3.8/site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: six>=1.5 in ./python38/lib/python3.8/site-packages (from python-dateutil>=2.6.1->pandas) (1.15.0)\n",
      "Installing collected packages: pytz, pandas\n",
      "Successfully installed pandas-1.0.4 pytz-2020.1\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "FILE = 'https://python.astrotech.io/_static/iris.csv'\n",
    "\n",
    "df = pd.read_csv(FILE, skiprows=1, names=[\n",
    "    'Sepal Length',\n",
    "    'Sepal Width',\n",
    "    'Petal Length',\n",
    "    'Petal Width',\n",
    "    'Species',\n",
    "])\n",
    "\n",
    "df['Species'].replace({\n",
    "    0: 'Setosa',\n",
    "    1: 'Versicolor',\n",
    "    2: 'Virginica',\n",
    "}, inplace=True)\n",
    "\n",
    "df = df.sample(frac=1.0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>Sepal Length</th>\n",
       "      <th>Sepal Width</th>\n",
       "      <th>Petal Length</th>\n",
       "      <th>Petal Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sepal Length  Sepal Width  Petal Length  Petal Width     Species\n",
       "0             6.6          3.0           4.4          1.4  Versicolor\n",
       "1             5.8          2.7           5.1          1.9   Virginica\n",
       "2             6.3          2.7           4.9          1.8   Virginica\n",
       "3             5.0          3.3           1.4          0.2      Setosa\n",
       "4             6.1          3.0           4.6          1.4  Versicolor\n",
       "..            ...          ...           ...          ...         ...\n",
       "145           6.3          2.8           5.1          1.5   Virginica\n",
       "146           6.4          2.8           5.6          2.2   Virginica\n",
       "147           5.6          2.9           3.6          1.3  Versicolor\n",
       "148           5.0          3.5           1.3          0.3      Setosa\n",
       "149           5.9          3.2           4.8          1.8  Versicolor\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c71b340>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sepal Length'].plot(kind='density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scipy\n",
      "  Using cached scipy-1.4.1-cp38-cp38-macosx_10_9_x86_64.whl (28.8 MB)\n",
      "Requirement already satisfied: numpy>=1.13.3 in ./python38/lib/python3.8/site-packages (from scipy) (1.18.5)\n",
      "Installing collected packages: scipy\n",
      "Successfully installed scipy-1.4.1\n"
     ]
    }
   ],
   "source": [
    "!pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12d09af70>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "df.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11cf77c10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11cf11df0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11cf38850>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11ce31b80>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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