slides_day2.ipynb

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   "source": [
    "# Introduction to Data Science using Python\n",
    "\n",
    "\n",
    "\n",
    "## Abhijit Dasgupta"
   ]
  },
  {
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   },
   "source": [
    "## Day Two\n",
    "\n",
    "| Day 1                        | Day 2                |\n",
    "| ---------------------------- | -------------------- |\n",
    "| Why Python for Data Science? | Data visualization   |\n",
    "| A Python Primer              | Statistical modeling |\n",
    "| Pandas for data munging      | Machine learning     |"
   ]
  },
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   "source": [
    "| Topic                                          | Notebook           |\n",
    "| ---------------------------------------------- | ------------------ |\n",
    "| Python primer                                  | 00_python_primer   |\n",
    "| Numpy and the data science stack (not covered) | 01_python_tools_ds |\n",
    "| Pandas for data munging                        | 02_python_pandas   |\n",
    "| Data visualization                             | 03_python_vis      |\n",
    "| Statistical modeling                           | 04_python_stat     |\n",
    "| Machine learning                               | 05_python_learning |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Data visualization"
   ]
  },
  {
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    }
   },
   "source": [
    "Data visualization is a basic task in data exploration and understanding. \n",
    "\n",
    "Data visualization provides an opportunity to enhance communication of the story within the data. \n",
    "\n",
    "A visual representation of the data and our innate ability at pattern recognition can help reveal the complexities in a cognitively accessible way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "### Florence Nightangle, Crimean War\n",
    "\n",
    "<center><img src=\"graphs/rose.jpg\"/></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split"
   },
   "source": [
    "<div style=\"margin-top:100px;\">\n",
    "\n",
    "+ Plotting war dead in Crimean war\n",
    "+ Developed \"rose plot\"\n",
    "+ Made point that most deaths were due to sepsis in hospital\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": []
  },
  {
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   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### John Snow, 1854\n",
    "\n",
    "<center><img src = \"graphs/snow_map.png\" align=\"center\" width=\"600\" height=\"600\"/></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split"
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "<div style=\"margin-top: 100px;\">\n",
    "\n",
    "+ Cholera outbreak in London\n",
    "+ Spatial mapping of households with cholera showed clustering\n",
    "+ Helped identify wells that were contaminated\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Why do we need visualization on top of analytics?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![](graphs/anscombe.png)\n",
    "\n",
    "In both sets of plots, all the variables have the same means and variances and the same pairwise correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "![](graphs/datasaurus.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\n",
    "|      Strip plot      |       Boxplot        |     Violin plot      |\n",
    "| :------------------: | :------------------: | :------------------: |\n",
    "| ![](graphs/box1.png) | ![](graphs/box2.png) | ![](graphs/box3.png) |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## General concepts"
   ]
  },
  {
   "cell_type": "markdown",
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     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Begin with the consumer in mind\n",
    "\n",
    "+ You have a deep understanding of the data you're presenting\n",
    "+ The person seeing the visualization **doesn't**\n",
    "+ Develop simpler visualizations first that are easier to explain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Tell a story\n",
    "\n",
    "+ Make sure the graphic is clear\n",
    "+ Make sure the main point you want to make \"pops\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
    }
   },
   "source": [
    "#### A matter of perception\n",
    "\n",
    "+ Color (including awareness of color deficiencies)\n",
    "+ Shape\n",
    "+ Fonts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Our starting point\n",
    "\n",
    "We will start our journey today from the `pandas` `DataFrame`\n",
    "\n",
    "+ Rectangular set of data\n",
    "+ Different columns can be of different types\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We will see different functions applied to `DataFrame` objects (on the right)\n",
    "\n",
    "We will see different functions from different packages applied on `DataFrame` objects (on the left)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "## Python setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "slide_type": ""
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_context('notebook')\n",
    "sns.set_style('white', {'font.family':'Futura', 'text.color':'1'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Python setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>make</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                make   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
       "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
       "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
       "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "   carb  \n",
       "0     4  \n",
       "1     4  \n",
       "2     1  \n",
       "3     1  \n",
       "4     2  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtcars = pd.read_csv('data/mtcars.csv')\n",
    "\n",
    "mtcars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12ea0ae20>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars.plot(x = 'hp', y = 'mpg', \n",
    "            kind = 'scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f753cd0>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=mtcars, \n",
    "                x = 'hp', y = 'mpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### A ggplot clone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (317677087)>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from plotnine import *\n",
    "\n",
    "(ggplot(mtcars) + \n",
    "    aes(x = 'hp', y = 'mpg')+\n",
    "    geom_point())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Some dynamic options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "hp=%{x}<br>mpg=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "showlegend": false,
         "type": "scatter",
         "x": [
          110,
          110,
          93,
          110,
          175,
          105,
          245,
          62,
          95,
          123,
          123,
          180,
          180,
          180,
          205,
          215,
          230,
          66,
          52,
          65,
          97,
          150,
          150,
          245,
          175,
          66,
          91,
          113,
          264,
          175,
          335,
          109
         ],
         "xaxis": "x",
         "y": [
          21,
          21,
          22.8,
          21.4,
          18.7,
          18.1,
          14.3,
          24.4,
          22.8,
          19.2,
          17.8,
          16.4,
          17.3,
          15.2,
          10.4,
          10.4,
          14.7,
          32.4,
          30.4,
          33.9,
          21.5,
          15.5,
          15.2,
          13.3,
          19.2,
          27.3,
          26,
          30.4,
          15.8,
          19.7,
          15,
          21.4
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"hp\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"mpg\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('b5c3a669-519b-4225-ab4e-bc389c07d1f0');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig = px.scatter(mtcars, x = 'hp', y = 'mpg')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1101\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1101\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\": \"JpP8FXbgAZLkfur7LiK3j9AGBhHNIvF742meBJrjO2ShJDhCG2I1uVvW+0DUtrmc\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\": \"xZlADit0Q04ISQEdKg2k3L4W9AwQBAuDs9nJL9fM/WwzL1tEU9VPNezOFX0nLEAz\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\": \"4BuPRZkdMKSnj3zoxiNrQ86XgNw0rYmBOxe7nshquXwwcauupgBF2DHLVG1WuZlV\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\": \"Dv1SQ87hmDqK6S5OhBf0bCuwAEvL5QYL0PuR/F1SPVhCS/r/abjkbpKDYL2zeM19\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1101\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1101' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1101\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1101\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\": \"JpP8FXbgAZLkfur7LiK3j9AGBhHNIvF742meBJrjO2ShJDhCG2I1uVvW+0DUtrmc\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\": \"xZlADit0Q04ISQEdKg2k3L4W9AwQBAuDs9nJL9fM/WwzL1tEU9VPNezOFX0nLEAz\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\": \"4BuPRZkdMKSnj3zoxiNrQ86XgNw0rYmBOxe7nshquXwwcauupgBF2DHLVG1WuZlV\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\": \"Dv1SQ87hmDqK6S5OhBf0bCuwAEvL5QYL0PuR/F1SPVhCS/r/abjkbpKDYL2zeM19\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1101\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1101' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1101\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"be3bd62b-263a-435f-95a0-a03a5f1b1b30\" data-root-id=\"1102\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"77a8323e-baa4-4726-850b-9bf574c93f8f\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1111\"}],\"center\":[{\"id\":\"1114\"},{\"id\":\"1118\"}],\"left\":[{\"id\":\"1115\"}],\"renderers\":[{\"id\":\"1136\"}],\"title\":{\"id\":\"1148\"},\"toolbar\":{\"id\":\"1126\"},\"x_range\":{\"id\":\"1103\"},\"x_scale\":{\"id\":\"1107\"},\"y_range\":{\"id\":\"1105\"},\"y_scale\":{\"id\":\"1109\"}},\"id\":\"1102\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1150\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1107\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1123\",\"type\":\"ResetTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1119\"},{\"id\":\"1120\"},{\"id\":\"1121\"},{\"id\":\"1122\"},{\"id\":\"1123\"},{\"id\":\"1124\"}]},\"id\":\"1126\",\"type\":\"Toolbar\"},{\"attributes\":{\"axis\":{\"id\":\"1115\"},\"dimension\":1,\"ticker\":null},\"id\":\"1118\",\"type\":\"Grid\"},{\"attributes\":{\"axis_label\":\"Horsepower\",\"formatter\":{\"id\":\"1152\"},\"ticker\":{\"id\":\"1112\"}},\"id\":\"1111\",\"type\":\"LinearAxis\"},{\"attributes\":{\"axis_label\":\"Miles per gallon\",\"formatter\":{\"id\":\"1150\"},\"ticker\":{\"id\":\"1116\"}},\"id\":\"1115\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1116\",\"type\":\"BasicTicker\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1125\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"overlay\":{\"id\":\"1125\"}},\"id\":\"1121\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1155\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1124\",\"type\":\"HelpTool\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1134\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1122\",\"type\":\"SaveTool\"},{\"attributes\":{\"data_source\":{\"id\":\"1133\"},\"glyph\":{\"id\":\"1134\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1135\"},\"selection_glyph\":null,\"view\":{\"id\":\"1137\"}},\"id\":\"1136\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"axis\":{\"id\":\"1111\"},\"ticker\":null},\"id\":\"1114\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1105\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1103\",\"type\":\"DataRange1d\"},{\"attributes\":{\"source\":{\"id\":\"1133\"}},\"id\":\"1137\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1120\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1152\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1154\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data\":{\"x\":[110,110,93,110,175,105,245,62,95,123,123,180,180,180,205,215,230,66,52,65,97,150,150,245,175,66,91,113,264,175,335,109],\"y\":{\"__ndarray__\":\"AAAAAAAANUAAAAAAAAA1QM3MzMzMzDZAZmZmZmZmNUAzMzMzM7MyQJqZmZmZGTJAmpmZmZmZLEBmZmZmZmY4QM3MzMzMzDZAMzMzMzMzM0DNzMzMzMwxQGZmZmZmZjBAzczMzMxMMUBmZmZmZmYuQM3MzMzMzCRAzczMzMzMJEBmZmZmZmYtQDMzMzMzM0BAZmZmZmZmPkAzMzMzM/NAQAAAAAAAgDVAAAAAAAAAL0BmZmZmZmYuQJqZmZmZmSpAMzMzMzMzM0DNzMzMzEw7QAAAAAAAADpAZmZmZmZmPkCamZmZmZkvQDMzMzMzszNAAAAAAAAALkBmZmZmZmY1QA==\",\"dtype\":\"float64\",\"shape\":[32]}},\"selected\":{\"id\":\"1155\"},\"selection_policy\":{\"id\":\"1154\"}},\"id\":\"1133\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1135\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1112\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1119\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1109\",\"type\":\"LinearScale\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1148\",\"type\":\"Title\"}],\"root_ids\":[\"1102\"]},\"title\":\"Bokeh Application\",\"version\":\"2.0.1\"}};\n",
       "  var render_items = [{\"docid\":\"77a8323e-baa4-4726-850b-9bf574c93f8f\",\"root_ids\":[\"1102\"],\"roots\":{\"1102\":\"be3bd62b-263a-435f-95a0-a03a5f1b1b30\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1102"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, output_file\n",
    "from bokeh.io import output_notebook, show\n",
    "output_notebook()\n",
    "p = figure()\n",
    "p.xaxis.axis_label = 'Horsepower'\n",
    "p.yaxis.axis_label = 'Miles per gallon'\n",
    "\n",
    "p.circle(mtcars['hp'], mtcars['mpg'], size=10);\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-c4924d3b3d1f4e8db8f47a72ee45eb8b\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-c4924d3b3d1f4e8db8f47a72ee45eb8b\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-c4924d3b3d1f4e8db8f47a72ee45eb8b\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-9986ebf302b1eaee6b9f0bfd42cb8b40\"}, \"mark\": \"point\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"hp\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"mpg\"}}, \"selection\": {\"selector002\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-9986ebf302b1eaee6b9f0bfd42cb8b40\": [{\"make\": \"Mazda RX4\", \"mpg\": 21.0, \"cyl\": 6, \"disp\": 160.0, \"hp\": 110, \"drat\": 3.9, \"wt\": 2.62, \"qsec\": 16.46, \"vs\": 0, \"am\": 1, \"gear\": 4, \"carb\": 4}, {\"make\": \"Mazda RX4 Wag\", \"mpg\": 21.0, \"cyl\": 6, \"disp\": 160.0, \"hp\": 110, \"drat\": 3.9, \"wt\": 2.875, \"qsec\": 17.02, \"vs\": 0, \"am\": 1, \"gear\": 4, \"carb\": 4}, {\"make\": \"Datsun 710\", \"mpg\": 22.8, \"cyl\": 4, \"disp\": 108.0, \"hp\": 93, \"drat\": 3.85, \"wt\": 2.32, \"qsec\": 18.61, \"vs\": 1, \"am\": 1, \"gear\": 4, \"carb\": 1}, {\"make\": \"Hornet 4 Drive\", \"mpg\": 21.4, \"cyl\": 6, \"disp\": 258.0, \"hp\": 110, \"drat\": 3.08, \"wt\": 3.215, \"qsec\": 19.44, \"vs\": 1, \"am\": 0, \"gear\": 3, \"carb\": 1}, {\"make\": \"Hornet Sportabout\", \"mpg\": 18.7, \"cyl\": 8, \"disp\": 360.0, \"hp\": 175, \"drat\": 3.15, \"wt\": 3.44, \"qsec\": 17.02, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 2}, {\"make\": \"Valiant\", \"mpg\": 18.1, \"cyl\": 6, \"disp\": 225.0, \"hp\": 105, \"drat\": 2.76, \"wt\": 3.46, \"qsec\": 20.22, \"vs\": 1, \"am\": 0, \"gear\": 3, \"carb\": 1}, {\"make\": \"Duster 360\", \"mpg\": 14.3, \"cyl\": 8, \"disp\": 360.0, \"hp\": 245, \"drat\": 3.21, \"wt\": 3.57, \"qsec\": 15.84, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 4}, {\"make\": \"Merc 240D\", \"mpg\": 24.4, \"cyl\": 4, \"disp\": 146.7, \"hp\": 62, \"drat\": 3.69, \"wt\": 3.19, \"qsec\": 20.0, \"vs\": 1, \"am\": 0, \"gear\": 4, \"carb\": 2}, {\"make\": \"Merc 230\", \"mpg\": 22.8, \"cyl\": 4, \"disp\": 140.8, \"hp\": 95, \"drat\": 3.92, \"wt\": 3.15, \"qsec\": 22.9, \"vs\": 1, \"am\": 0, \"gear\": 4, \"carb\": 2}, {\"make\": \"Merc 280\", \"mpg\": 19.2, \"cyl\": 6, \"disp\": 167.6, \"hp\": 123, \"drat\": 3.92, \"wt\": 3.44, \"qsec\": 18.3, \"vs\": 1, \"am\": 0, \"gear\": 4, \"carb\": 4}, {\"make\": \"Merc 280C\", \"mpg\": 17.8, \"cyl\": 6, \"disp\": 167.6, \"hp\": 123, \"drat\": 3.92, \"wt\": 3.44, \"qsec\": 18.9, \"vs\": 1, \"am\": 0, \"gear\": 4, \"carb\": 4}, {\"make\": \"Merc 450SE\", \"mpg\": 16.4, \"cyl\": 8, \"disp\": 275.8, \"hp\": 180, \"drat\": 3.07, \"wt\": 4.07, \"qsec\": 17.4, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 3}, {\"make\": \"Merc 450SL\", \"mpg\": 17.3, \"cyl\": 8, \"disp\": 275.8, \"hp\": 180, \"drat\": 3.07, \"wt\": 3.73, \"qsec\": 17.6, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 3}, {\"make\": \"Merc 450SLC\", \"mpg\": 15.2, \"cyl\": 8, \"disp\": 275.8, \"hp\": 180, \"drat\": 3.07, \"wt\": 3.78, \"qsec\": 18.0, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 3}, {\"make\": \"Cadillac Fleetwood\", \"mpg\": 10.4, \"cyl\": 8, \"disp\": 472.0, \"hp\": 205, \"drat\": 2.93, \"wt\": 5.25, \"qsec\": 17.98, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 4}, {\"make\": \"Lincoln Continental\", \"mpg\": 10.4, \"cyl\": 8, \"disp\": 460.0, \"hp\": 215, \"drat\": 3.0, \"wt\": 5.4239999999999995, \"qsec\": 17.82, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 4}, {\"make\": \"Chrysler Imperial\", \"mpg\": 14.7, \"cyl\": 8, \"disp\": 440.0, \"hp\": 230, \"drat\": 3.23, \"wt\": 5.345, \"qsec\": 17.42, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 4}, {\"make\": \"Fiat 128\", \"mpg\": 32.4, \"cyl\": 4, \"disp\": 78.7, \"hp\": 66, \"drat\": 4.08, \"wt\": 2.2, \"qsec\": 19.47, \"vs\": 1, \"am\": 1, \"gear\": 4, \"carb\": 1}, {\"make\": \"Honda Civic\", \"mpg\": 30.4, \"cyl\": 4, \"disp\": 75.7, \"hp\": 52, \"drat\": 4.93, \"wt\": 1.615, \"qsec\": 18.52, \"vs\": 1, \"am\": 1, \"gear\": 4, \"carb\": 2}, {\"make\": \"Toyota Corolla\", \"mpg\": 33.9, \"cyl\": 4, \"disp\": 71.1, \"hp\": 65, \"drat\": 4.22, \"wt\": 1.835, \"qsec\": 19.9, \"vs\": 1, \"am\": 1, \"gear\": 4, \"carb\": 1}, {\"make\": \"Toyota Corona\", \"mpg\": 21.5, \"cyl\": 4, \"disp\": 120.1, \"hp\": 97, \"drat\": 3.7, \"wt\": 2.465, \"qsec\": 20.01, \"vs\": 1, \"am\": 0, \"gear\": 3, \"carb\": 1}, {\"make\": \"Dodge Challenger\", \"mpg\": 15.5, \"cyl\": 8, \"disp\": 318.0, \"hp\": 150, \"drat\": 2.76, \"wt\": 3.52, \"qsec\": 16.87, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 2}, {\"make\": \"AMC Javelin\", \"mpg\": 15.2, \"cyl\": 8, \"disp\": 304.0, \"hp\": 150, \"drat\": 3.15, \"wt\": 3.435, \"qsec\": 17.3, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 2}, {\"make\": \"Camaro Z28\", \"mpg\": 13.3, \"cyl\": 8, \"disp\": 350.0, \"hp\": 245, \"drat\": 3.73, \"wt\": 3.84, \"qsec\": 15.41, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 4}, {\"make\": \"Pontiac Firebird\", \"mpg\": 19.2, \"cyl\": 8, \"disp\": 400.0, \"hp\": 175, \"drat\": 3.08, \"wt\": 3.845, \"qsec\": 17.05, \"vs\": 0, \"am\": 0, \"gear\": 3, \"carb\": 2}, {\"make\": \"Fiat X1-9\", \"mpg\": 27.3, \"cyl\": 4, \"disp\": 79.0, \"hp\": 66, \"drat\": 4.08, \"wt\": 1.935, \"qsec\": 18.9, \"vs\": 1, \"am\": 1, \"gear\": 4, \"carb\": 1}, {\"make\": \"Porsche 914-2\", \"mpg\": 26.0, \"cyl\": 4, \"disp\": 120.3, \"hp\": 91, \"drat\": 4.43, \"wt\": 2.14, \"qsec\": 16.7, \"vs\": 0, \"am\": 1, \"gear\": 5, \"carb\": 2}, {\"make\": \"Lotus Europa\", \"mpg\": 30.4, \"cyl\": 4, \"disp\": 95.1, \"hp\": 113, \"drat\": 3.77, \"wt\": 1.5130000000000001, \"qsec\": 16.9, \"vs\": 1, \"am\": 1, \"gear\": 5, \"carb\": 2}, {\"make\": \"Ford Pantera L\", \"mpg\": 15.8, \"cyl\": 8, \"disp\": 351.0, \"hp\": 264, \"drat\": 4.22, \"wt\": 3.17, \"qsec\": 14.5, \"vs\": 0, \"am\": 1, \"gear\": 5, \"carb\": 4}, {\"make\": \"Ferrari Dino\", \"mpg\": 19.7, \"cyl\": 6, \"disp\": 145.0, \"hp\": 175, \"drat\": 3.62, \"wt\": 2.77, \"qsec\": 15.5, \"vs\": 0, \"am\": 1, \"gear\": 5, \"carb\": 6}, {\"make\": \"Maserati Bora\", \"mpg\": 15.0, \"cyl\": 8, \"disp\": 301.0, \"hp\": 335, \"drat\": 3.54, \"wt\": 3.57, \"qsec\": 14.6, \"vs\": 0, \"am\": 1, \"gear\": 5, \"carb\": 8}, {\"make\": \"Volvo 142E\", \"mpg\": 21.4, \"cyl\": 4, \"disp\": 121.0, \"hp\": 109, \"drat\": 4.11, \"wt\": 2.78, \"qsec\": 18.6, \"vs\": 1, \"am\": 1, \"gear\": 4, \"carb\": 2}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "\n",
    "alt.Chart(mtcars).mark_point().encode(\n",
    "    x='hp',\n",
    "    y='mpg'\n",
    ").interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Static plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A pathway to learning ([Chris Moffit](https://pbpython.com/effective-matplotlib.html))\n",
    "\n",
    "1. Learn the basic matplotlib terminology, specifically what is a `Figure` and an `Axes` .\n",
    "2. Always use the object-oriented interface. Get in the habit of using it from the start of your analysis. (*not really getting into this, but basically don't use the Matlab form that was originally used*)\n",
    "3. Start your visualizations with basic pandas plotting.\n",
    "4. Use seaborn for the more complex statistical visualizations.\n",
    "5. Use matplotlib to customize the pandas or seaborn visualization.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": ""
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#mtcars.plot.hist(y = 'mpg');\n",
    "#mtcars['mpg'].plot(kind = 'hist')\n",
    "\n",
    "mtcars.plot(y = 'mpg', kind = 'hist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(mtcars.mpg, kde=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars['cyl'].value_counts().plot.bar();\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = mtcars, x = 'cyl');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds = pd.read_csv('data/diamonds.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diamonds.plot(x = 'carat', y = 'price', \n",
    "              kind = 'scatter');\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data = diamonds, \n",
    "            x = 'carat', y = 'price', \n",
    "            fit_reg=False);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diamonds.boxplot(column = 'price', by = 'color');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ordered_color = ['D','E','F','G','H','I','J']\n",
    "sns.catplot(data = diamonds, x = 'color', y = 'price', \n",
    "            order = ordered_color, color = 'blue', kind = 'box');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Barplot (categorical vs continuous)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ordered_colors = ['D','E','F','G','H','I','J']\n",
    "sns.barplot(data = diamonds, x = 'color', y = 'price', order = ordered_colors);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Joint plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data = diamonds, x = 'carat', y = 'price');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Facets and multiples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Line plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.read_csv('data/ts.csv')\n",
    "ts.dt = pd.to_datetime(ts.dt)\n",
    "sns.lineplot(data = ts, x = 'dt', y = 'value', hue = 'kind');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Facets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 875.85x648 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fmri = sns.load_dataset('fmri')\n",
    "sns.relplot(x=\"timepoint\", y=\"signal\", hue=\"event\", style=\"event\",\n",
    "            col=\"subject\", col_wrap=5,\n",
    "            height=3, aspect=.75, linewidth=2.5,\n",
    "            kind=\"line\", data=fmri.query(\"region == 'frontal'\"));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Pairs plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = pd.read_csv('data/iris.csv')\n",
    "sns.pairplot(data=iris);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Statistical modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Descriptive statistics\n",
    "\n",
    "+ Several descriptive statistics are available through **numpy**, like `mean`, `std` (standard deviation), `median`,`value_counts` (frequency distribution)\n",
    "+ We take advantage of split-apply-combine to investigate and describe data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "diamonds = pd.read_csv('data/diamonds.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>3169.954096</td>\n",
       "      <td>1838.0</td>\n",
       "      <td>3356.590935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>3076.752475</td>\n",
       "      <td>1739.0</td>\n",
       "      <td>3344.158685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>3724.886397</td>\n",
       "      <td>2343.5</td>\n",
       "      <td>3784.992007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>3999.135671</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>4051.102846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>4486.669196</td>\n",
       "      <td>3460.0</td>\n",
       "      <td>4215.944171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>5091.874954</td>\n",
       "      <td>3730.0</td>\n",
       "      <td>4722.387604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>5323.818020</td>\n",
       "      <td>4234.0</td>\n",
       "      <td>4438.187251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean  median          std\n",
       "color                                  \n",
       "D      3169.954096  1838.0  3356.590935\n",
       "E      3076.752475  1739.0  3344.158685\n",
       "F      3724.886397  2343.5  3784.992007\n",
       "G      3999.135671  2242.0  4051.102846\n",
       "H      4486.669196  3460.0  4215.944171\n",
       "I      5091.874954  3730.0  4722.387604\n",
       "J      5323.818020  4234.0  4438.187251"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(diamonds.groupby('color').price.\n",
    "     agg([np.mean, np.median, np.std]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(diamonds.groupby('color').price.\n",
    "     mean().plot());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Hypothesis tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "```\n",
    "import scipy.stats\n",
    "```\n",
    "\n",
    "| Function           | Test                                                    |\n",
    "| :----------------- | ------------------------------------------------------- |\n",
    "| `ttest_1samp`      | One-sample t-test                                       |\n",
    "| `ttest_ind`        | Two-sample t-test                                       |\n",
    "| `ttest_rel`        | Paired t-test                                           |\n",
    "| `wilcoxon`         | Wilcoxon signed-rank test (nonparametric paired t-test) |\n",
    "| `mannwhitneyu`         | Wilcoxon rank-sum test (nonparametric 2-sample t-test)  |\n",
    "| `chi2_contingency` | Chi-square test for independence                        |\n",
    "| `fisher_exact`     | Fisher's exact test on a 2x2 contingency table          |\n",
    "| `f_oneway`         | One-way ANOVA                                           |\n",
    "| `pearsonr`         | Testing for correlation                                 |\n",
    "|                    |                                                         |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "```\n",
    "import statsmodels.stats\n",
    "```\n",
    "\n",
    "| Functions           | Tests                                 |\n",
    "| :------------------ | ------------------------------------- |\n",
    "| `proportions_ztest` | Test for difference in proportions    |\n",
    "| `mcnemar`           | McNemar's test                        |\n",
    "| `sign_test`         | Sign test                             |\n",
    "| `multipletests`     | p-value correction for multiple tests |\n",
    "| `fdrcorrection`     | p-value correction by FDR             |\n",
    "|                     |                                       |\n",
    "\n"
   ]
  },
  {
   "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>Unnamed:_0</th>\n",
       "      <th>Complete_TCGA_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age_at_Initial_Pathologic_Diagnosis</th>\n",
       "      <th>ER_Status</th>\n",
       "      <th>PR_Status</th>\n",
       "      <th>HER2_Final_Status</th>\n",
       "      <th>Tumor</th>\n",
       "      <th>Tumor--T1_Coded</th>\n",
       "      <th>Node</th>\n",
       "      <th>...</th>\n",
       "      <th>NP_001193600</th>\n",
       "      <th>NP_061134</th>\n",
       "      <th>NP_932347</th>\n",
       "      <th>NP_003593</th>\n",
       "      <th>NP_997203</th>\n",
       "      <th>NP_001191293</th>\n",
       "      <th>NP_775791</th>\n",
       "      <th>NP_004065</th>\n",
       "      <th>NP_068752</th>\n",
       "      <th>NP_219494</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>TCGA-A2-A0CM</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>40</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>T2</td>\n",
       "      <td>T_Other</td>\n",
       "      <td>N0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.153614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>TCGA-BH-A18Q</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>56</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>T2</td>\n",
       "      <td>T_Other</td>\n",
       "      <td>N1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.881872</td>\n",
       "      <td>2.527072</td>\n",
       "      <td>-8.111243</td>\n",
       "      <td>-16.029761</td>\n",
       "      <td>-2.046065</td>\n",
       "      <td>-1.778435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.069752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>TCGA-A7-A0CE</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>57</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>T2</td>\n",
       "      <td>T_Other</td>\n",
       "      <td>N0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.644347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.625952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.306238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>TCGA-D8-A142</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>74</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>T3</td>\n",
       "      <td>T_Other</td>\n",
       "      <td>N0</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.107629</td>\n",
       "      <td>-0.97598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.508629</td>\n",
       "      <td>-12.337110</td>\n",
       "      <td>-9.546530</td>\n",
       "      <td>-4.066584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>TCGA-AO-A0J6</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>61</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Negative</td>\n",
       "      <td>T2</td>\n",
       "      <td>T_Other</td>\n",
       "      <td>N0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.043420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.231339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.753616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 12585 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed:_0 Complete_TCGA_ID  Gender  Age_at_Initial_Pathologic_Diagnosis  \\\n",
       "0           0     TCGA-A2-A0CM  FEMALE                                   40   \n",
       "1           1     TCGA-BH-A18Q  FEMALE                                   56   \n",
       "2           2     TCGA-A7-A0CE  FEMALE                                   57   \n",
       "3           3     TCGA-D8-A142  FEMALE                                   74   \n",
       "4           4     TCGA-AO-A0J6  FEMALE                                   61   \n",
       "\n",
       "  ER_Status PR_Status HER2_Final_Status Tumor Tumor--T1_Coded Node  ...  \\\n",
       "0  Negative  Negative          Negative    T2         T_Other   N0  ...   \n",
       "1  Negative  Negative          Negative    T2         T_Other   N1  ...   \n",
       "2  Negative  Negative          Negative    T2         T_Other   N0  ...   \n",
       "3  Negative  Negative          Negative    T3         T_Other   N0  ...   \n",
       "4  Negative  Negative          Negative    T2         T_Other   N0  ...   \n",
       "\n",
       "  NP_001193600 NP_061134 NP_932347 NP_003593  NP_997203 NP_001191293  \\\n",
       "0          NaN       NaN  1.153614       NaN        NaN          NaN   \n",
       "1     0.048144       NaN -0.881872  2.527072  -8.111243   -16.029761   \n",
       "2     0.644347       NaN  1.625952       NaN        NaN          NaN   \n",
       "3    -5.107629  -0.97598       NaN  2.508629 -12.337110    -9.546530   \n",
       "4    -1.043420       NaN       NaN       NaN  -3.231339          NaN   \n",
       "\n",
       "  NP_775791  NP_004065  NP_068752  NP_219494  \n",
       "0       NaN        NaN        NaN        NaN  \n",
       "1 -2.046065  -1.778435        NaN  -3.069752  \n",
       "2 -1.306238        NaN        NaN        NaN  \n",
       "3 -4.066584        NaN        NaN        NaN  \n",
       "4       NaN        NaN        NaN  -3.753616  \n",
       "\n",
       "[5 rows x 12585 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brca = pd.read_csv('data/brca.csv')\n",
    "brca.columns = brca.columns.str.replace(' ', '_')\n",
    "brca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.277"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy as sc\n",
    "import statsmodels as sm\n",
    "test_probe = 'NP_001193600'\n",
    "\n",
    "tst = sc.stats.ttest_ind(\n",
    "    brca[brca.ER_Status=='Positive'][test_probe],\n",
    "    brca[brca.ER_Status=='Negative'][test_probe],\n",
    "    nan_policy = 'omit')\n",
    "np.round(tst.pvalue, 3)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.996"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tst = sc.stats.mannwhitneyu(\n",
    "    brca[brca.ER_Status=='Positive'][test_probe],\n",
    "    brca[brca.ER_Status=='Negative'][test_probe],\n",
    "    alternative = 'two-sided')\n",
    "np.round(tst.pvalue, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Multiple tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expr_names = [u for u in list(brca.columns) if u.find('NP') > -1] \n",
    "            # Find all column names with NP\n",
    "\n",
    "exprs = brca[expr_names] # Extract the protein data\n",
    "\n",
    "groups = np.where(brca.ER_Status=='Positive', 1, 0)\n",
    "pvals_t = np.zeros(exprs.shape[1]) # initialize storage\n",
    "for i in range(exprs.shape[1]):\n",
    "    stat, pvals_t[i] = sc.stats.ttest_ind(exprs.iloc[groups==1, i],\n",
    "                              exprs.iloc[groups==0, i],\n",
    "                              nan_policy = 'omit')\n",
    "sns.distplot(pvals_t);\n",
    "plt.xlabel('P values');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regression analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf # Use the formula interface to statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.786</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.786</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   4598.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 04 Jun 2020</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:35:16</td>     <th>  Log-Likelihood:    </th> <td>-4.8222e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 53940</td>      <th>  AIC:               </th>  <td>9.645e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 53896</td>      <th>  BIC:               </th>  <td>9.649e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    43</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "               <td></td>                  <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                   <td> 2745.0643</td> <td>  415.804</td> <td>    6.602</td> <td> 0.000</td> <td> 1930.085</td> <td> 3560.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clarity[T.IF]</th>               <td> 4916.7221</td> <td>   83.694</td> <td>   58.746</td> <td> 0.000</td> <td> 4752.681</td> <td> 5080.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clarity[T.SI1]</th>              <td> 2686.1493</td> <td>   71.397</td> <td>   37.623</td> <td> 0.000</td> <td> 2546.210</td> <td> 2826.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clarity[T.SI2]</th>              <td> 2060.8180</td> <td>   71.809</td> <td>   28.699</td> <td> 0.000</td> <td> 1920.072</td> <td> 2201.564</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clarity[T.VS1]</th>              <td> 3710.1759</td> <td>   72.891</td> <td>   50.900</td> <td> 0.000</td> <td> 3567.309</td> <td> 3853.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clarity[T.VS2]</th>              <td> 3438.3999</td> <td>   71.792</td> <td>   47.894</td> <td> 0.000</td> <td> 3297.687</td> <td> 3579.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clarity[T.VVS1]</th>             <td> 4540.1420</td> <td>   77.314</td> <td>   58.724</td> <td> 0.000</td> <td> 4388.606</td> <td> 4691.678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clarity[T.VVS2]</th>             <td> 4343.0545</td> <td>   75.136</td> <td>   57.803</td> <td> 0.000</td> <td> 4195.788</td> <td> 4490.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Good]</th>                 <td>  708.5981</td> <td>  161.869</td> <td>    4.378</td> <td> 0.000</td> <td>  391.334</td> <td> 1025.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Ideal]</th>                <td> 1198.2067</td> <td>  149.690</td> <td>    8.005</td> <td> 0.000</td> <td>  904.812</td> <td> 1491.601</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Premium]</th>              <td> 1147.1417</td> <td>  152.896</td> <td>    7.503</td> <td> 0.000</td> <td>  847.464</td> <td> 1446.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Very Good]</th>            <td> 1011.3463</td> <td>  152.977</td> <td>    6.611</td> <td> 0.000</td> <td>  711.510</td> <td> 1311.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>color[T.E]</th>                  <td>  -59.4094</td> <td>  190.227</td> <td>   -0.312</td> <td> 0.755</td> <td> -432.256</td> <td>  313.437</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>color[T.F]</th>                  <td>  -86.0097</td> <td>  178.663</td> <td>   -0.481</td> <td> 0.630</td> <td> -436.191</td> <td>  264.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>color[T.G]</th>                  <td> -370.6455</td> <td>  178.642</td> <td>   -2.075</td> <td> 0.038</td> <td> -720.784</td> <td>  -20.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>color[T.H]</th>                  <td> -591.0922</td> <td>  179.786</td> <td>   -3.288</td> <td> 0.001</td> <td> -943.474</td> <td> -238.710</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>color[T.I]</th>                  <td>-1030.7417</td> <td>  201.485</td> <td>   -5.116</td> <td> 0.000</td> <td>-1425.655</td> <td> -635.829</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>color[T.J]</th>                  <td>-1210.6501</td> <td>  223.111</td> <td>   -5.426</td> <td> 0.000</td> <td>-1647.949</td> <td> -773.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Good]:color[T.E]</th>      <td>  -30.3553</td> <td>  212.126</td> <td>   -0.143</td> <td> 0.886</td> <td> -446.123</td> <td>  385.413</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Ideal]:color[T.E]</th>     <td> -211.3711</td> <td>  195.630</td> <td>   -1.080</td> <td> 0.280</td> <td> -594.807</td> <td>  172.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Premium]:color[T.E]</th>   <td>  -91.3261</td> <td>  199.440</td> <td>   -0.458</td> <td> 0.647</td> <td> -482.230</td> <td>  299.578</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Very Good]:color[T.E]</th> <td>  -45.2968</td> <td>  199.656</td> <td>   -0.227</td> <td> 0.821</td> <td> -436.625</td> <td>  346.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Good]:color[T.F]</th>      <td> -365.4060</td> <td>  202.035</td> <td>   -1.809</td> <td> 0.071</td> <td> -761.397</td> <td>   30.585</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Ideal]:color[T.F]</th>     <td> -198.0428</td> <td>  184.498</td> <td>   -1.073</td> <td> 0.283</td> <td> -559.661</td> <td>  163.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Premium]:color[T.F]</th>   <td> -322.8527</td> <td>  188.465</td> <td>   -1.713</td> <td> 0.087</td> <td> -692.246</td> <td>   46.540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Very Good]:color[T.F]</th> <td> -186.0519</td> <td>  189.090</td> <td>   -0.984</td> <td> 0.325</td> <td> -556.670</td> <td>  184.566</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Good]:color[T.G]</th>      <td>  -93.0430</td> <td>  202.404</td> <td>   -0.460</td> <td> 0.646</td> <td> -489.757</td> <td>  303.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Ideal]:color[T.G]</th>     <td>  -65.8579</td> <td>  183.980</td> <td>   -0.358</td> <td> 0.720</td> <td> -426.461</td> <td>  294.745</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Premium]:color[T.G]</th>   <td>   35.4302</td> <td>  187.596</td> <td>    0.189</td> <td> 0.850</td> <td> -332.260</td> <td>  403.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Very Good]:color[T.G]</th> <td>  -81.2595</td> <td>  188.786</td> <td>   -0.430</td> <td> 0.667</td> <td> -451.282</td> <td>  288.764</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Good]:color[T.H]</th>      <td>  137.0235</td> <td>  205.696</td> <td>    0.666</td> <td> 0.505</td> <td> -266.142</td> <td>  540.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Ideal]:color[T.H]</th>     <td>  -83.4763</td> <td>  186.060</td> <td>   -0.449</td> <td> 0.654</td> <td> -448.155</td> <td>  281.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Premium]:color[T.H]</th>   <td>  -44.4372</td> <td>  189.378</td> <td>   -0.235</td> <td> 0.814</td> <td> -415.620</td> <td>  326.745</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Very Good]:color[T.H]</th> <td>  -43.2485</td> <td>  190.851</td> <td>   -0.227</td> <td> 0.821</td> <td> -417.318</td> <td>  330.821</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Good]:color[T.I]</th>      <td>  331.4048</td> <td>  228.614</td> <td>    1.450</td> <td> 0.147</td> <td> -116.681</td> <td>  779.490</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Ideal]:color[T.I]</th>     <td>  106.2368</td> <td>  208.391</td> <td>    0.510</td> <td> 0.610</td> <td> -302.210</td> <td>  514.684</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Premium]:color[T.I]</th>   <td>  357.1453</td> <td>  212.341</td> <td>    1.682</td> <td> 0.093</td> <td>  -59.045</td> <td>  773.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Very Good]:color[T.I]</th> <td>  149.1555</td> <td>  213.697</td> <td>    0.698</td> <td> 0.485</td> <td> -269.693</td> <td>  568.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Good]:color[T.J]</th>      <td> -406.8484</td> <td>  256.938</td> <td>   -1.583</td> <td> 0.113</td> <td> -910.448</td> <td>   96.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Ideal]:color[T.J]</th>     <td> -330.0602</td> <td>  234.063</td> <td>   -1.410</td> <td> 0.159</td> <td> -788.826</td> <td>  128.706</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Premium]:color[T.J]</th>   <td> -156.8065</td> <td>  236.860</td> <td>   -0.662</td> <td> 0.508</td> <td> -621.055</td> <td>  307.442</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut[T.Very Good]:color[T.J]</th> <td> -381.5722</td> <td>  238.799</td> <td>   -1.598</td> <td> 0.110</td> <td> -849.620</td> <td>   86.475</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(carat)</th>               <td> 6630.7799</td> <td>   15.605</td> <td>  424.923</td> <td> 0.000</td> <td> 6600.195</td> <td> 6661.365</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>depth</th>                       <td>   -0.7353</td> <td>    5.961</td> <td>   -0.123</td> <td> 0.902</td> <td>  -12.418</td> <td>   10.948</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13993.592</td> <th>  Durbin-Watson:     </th> <td>   0.134</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>34739.732</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.432</td>   <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.693</td>   <th>  Cond. No.          </th> <td>7.08e+03</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 7.08e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.786\n",
       "Model:                            OLS   Adj. R-squared:                  0.786\n",
       "Method:                 Least Squares   F-statistic:                     4598.\n",
       "Date:                Thu, 04 Jun 2020   Prob (F-statistic):               0.00\n",
       "Time:                        11:35:16   Log-Likelihood:            -4.8222e+05\n",
       "No. Observations:               53940   AIC:                         9.645e+05\n",
       "Df Residuals:                   53896   BIC:                         9.649e+05\n",
       "Df Model:                          43                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================================\n",
       "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------\n",
       "Intercept                    2745.0643    415.804      6.602      0.000    1930.085    3560.043\n",
       "clarity[T.IF]                4916.7221     83.694     58.746      0.000    4752.681    5080.763\n",
       "clarity[T.SI1]               2686.1493     71.397     37.623      0.000    2546.210    2826.088\n",
       "clarity[T.SI2]               2060.8180     71.809     28.699      0.000    1920.072    2201.564\n",
       "clarity[T.VS1]               3710.1759     72.891     50.900      0.000    3567.309    3853.043\n",
       "clarity[T.VS2]               3438.3999     71.792     47.894      0.000    3297.687    3579.112\n",
       "clarity[T.VVS1]              4540.1420     77.314     58.724      0.000    4388.606    4691.678\n",
       "clarity[T.VVS2]              4343.0545     75.136     57.803      0.000    4195.788    4490.321\n",
       "cut[T.Good]                   708.5981    161.869      4.378      0.000     391.334    1025.862\n",
       "cut[T.Ideal]                 1198.2067    149.690      8.005      0.000     904.812    1491.601\n",
       "cut[T.Premium]               1147.1417    152.896      7.503      0.000     847.464    1446.820\n",
       "cut[T.Very Good]             1011.3463    152.977      6.611      0.000     711.510    1311.183\n",
       "color[T.E]                    -59.4094    190.227     -0.312      0.755    -432.256     313.437\n",
       "color[T.F]                    -86.0097    178.663     -0.481      0.630    -436.191     264.172\n",
       "color[T.G]                   -370.6455    178.642     -2.075      0.038    -720.784     -20.507\n",
       "color[T.H]                   -591.0922    179.786     -3.288      0.001    -943.474    -238.710\n",
       "color[T.I]                  -1030.7417    201.485     -5.116      0.000   -1425.655    -635.829\n",
       "color[T.J]                  -1210.6501    223.111     -5.426      0.000   -1647.949    -773.351\n",
       "cut[T.Good]:color[T.E]        -30.3553    212.126     -0.143      0.886    -446.123     385.413\n",
       "cut[T.Ideal]:color[T.E]      -211.3711    195.630     -1.080      0.280    -594.807     172.065\n",
       "cut[T.Premium]:color[T.E]     -91.3261    199.440     -0.458      0.647    -482.230     299.578\n",
       "cut[T.Very Good]:color[T.E]   -45.2968    199.656     -0.227      0.821    -436.625     346.031\n",
       "cut[T.Good]:color[T.F]       -365.4060    202.035     -1.809      0.071    -761.397      30.585\n",
       "cut[T.Ideal]:color[T.F]      -198.0428    184.498     -1.073      0.283    -559.661     163.575\n",
       "cut[T.Premium]:color[T.F]    -322.8527    188.465     -1.713      0.087    -692.246      46.540\n",
       "cut[T.Very Good]:color[T.F]  -186.0519    189.090     -0.984      0.325    -556.670     184.566\n",
       "cut[T.Good]:color[T.G]        -93.0430    202.404     -0.460      0.646    -489.757     303.671\n",
       "cut[T.Ideal]:color[T.G]       -65.8579    183.980     -0.358      0.720    -426.461     294.745\n",
       "cut[T.Premium]:color[T.G]      35.4302    187.596      0.189      0.850    -332.260     403.121\n",
       "cut[T.Very Good]:color[T.G]   -81.2595    188.786     -0.430      0.667    -451.282     288.764\n",
       "cut[T.Good]:color[T.H]        137.0235    205.696      0.666      0.505    -266.142     540.189\n",
       "cut[T.Ideal]:color[T.H]       -83.4763    186.060     -0.449      0.654    -448.155     281.202\n",
       "cut[T.Premium]:color[T.H]     -44.4372    189.378     -0.235      0.814    -415.620     326.745\n",
       "cut[T.Very Good]:color[T.H]   -43.2485    190.851     -0.227      0.821    -417.318     330.821\n",
       "cut[T.Good]:color[T.I]        331.4048    228.614      1.450      0.147    -116.681     779.490\n",
       "cut[T.Ideal]:color[T.I]       106.2368    208.391      0.510      0.610    -302.210     514.684\n",
       "cut[T.Premium]:color[T.I]     357.1453    212.341      1.682      0.093     -59.045     773.335\n",
       "cut[T.Very Good]:color[T.I]   149.1555    213.697      0.698      0.485    -269.693     568.004\n",
       "cut[T.Good]:color[T.J]       -406.8484    256.938     -1.583      0.113    -910.448      96.752\n",
       "cut[T.Ideal]:color[T.J]      -330.0602    234.063     -1.410      0.159    -788.826     128.706\n",
       "cut[T.Premium]:color[T.J]    -156.8065    236.860     -0.662      0.508    -621.055     307.442\n",
       "cut[T.Very Good]:color[T.J]  -381.5722    238.799     -1.598      0.110    -849.620      86.475\n",
       "np.log(carat)                6630.7799     15.605    424.923      0.000    6600.195    6661.365\n",
       "depth                          -0.7353      5.961     -0.123      0.902     -12.418      10.948\n",
       "==============================================================================\n",
       "Omnibus:                    13993.592   Durbin-Watson:                   0.134\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            34739.732\n",
       "Skew:                           1.432   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.693   Cond. No.                     7.08e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 7.08e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds = sm.datasets.get_rdataset('diamonds','ggplot2').data\n",
    "mod1 = smf.ols('price ~ np.log(carat) + clarity + depth + cut * color', data = diamonds)\n",
    "mod1 = mod1.fit() # Actually do the fit\n",
    "mod1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "```\n",
    "mod1 = smf.glm(\n",
    "    'price ~ np.log(carat) + clarity + depth + cut * color', \n",
    "    data = diamonds)\n",
    "```\n",
    "\n",
    "This formula will read as \n",
    "\n",
    "price depends on \n",
    "\n",
    "+ log(carat), \n",
    "+ clarity, depth, cut and color, \n",
    "+ and the interaction of cut and color\". "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "```\n",
    "mod1 = smf.glm(\n",
    "    'price ~ np.log(carat) + clarity + depth + cut * color', \n",
    "    data = diamonds)\n",
    "```\n",
    "\n",
    "color, clarity, and cut are all categorical variables. \n",
    "\n",
    "They actually need to be expanded into dummy variables, so we will have one column for each category level, which is 1 when the diamond is of that category and 0 otherwise. \n",
    "\n",
    "One less dummy than levels, since we keep a reference level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "```\n",
    "mod1 = smf.glm(\n",
    "    'price ~ np.log(carat) + clarity + depth + cut * color', \n",
    "    data = diamonds)\n",
    "```\n",
    "\n",
    "An intercept term is added"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "```\n",
    "mod1 = smf.glm(\n",
    "    'price ~ np.log(carat) + clarity + depth + cut * color', \n",
    "    data = diamonds)\n",
    "```\n",
    "\n",
    "The variable `carat` is transformed using `np.log`, i.e. the natural logarithm available in the `numpy` package. Generally, any valid Python function can be used here, even ones you create. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "```\n",
    "mod1 = smf.glm(\n",
    "    'price ~ np.log(carat) + clarity + depth + cut * color', \n",
    "    data = diamonds)\n",
    "```\n",
    "\n",
    "Interactions are computed. The syntax `cut * color` is a shortcut for `cut + color + cut:color`, where the `:` denotes interaction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "```\n",
    "mod2 = smf.ols(\n",
    "    'price ~ np.log(carat) + clarity + depth + \n",
    "     C(cut, Treatment(\"Ideal\")) * color', \n",
    "     data = diamonds).fit()\n",
    "```\n",
    "\n",
    "Here we specify that `cut` is a categorical variable, and we are using _treatment contrasts_ with _Ideal_ as the reference category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1313 entries, 0 to 1312\n",
      "Data columns (total 6 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Name      1313 non-null   object \n",
      " 1   PClass    1313 non-null   object \n",
      " 2   Age       756 non-null    float64\n",
      " 3   Sex       1313 non-null   object \n",
      " 4   Survived  1313 non-null   int64  \n",
      " 5   SexCode   1313 non-null   int64  \n",
      "dtypes: float64(1), int64(2), object(3)\n",
      "memory usage: 61.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic = sm.datasets.get_rdataset('Titanic','Stat2Data').data\n",
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Survived</td>     <th>  No. Observations:  </th>  <td>   756</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   751</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>     4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -347.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 04 Jun 2020</td> <th>  Deviance:          </th> <td>  695.14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>11:44:14</td>     <th>  Pearson chi2:      </th>  <td>  813.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>5</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>    1.8664</td> <td>    0.217</td> <td>    8.587</td> <td> 0.000</td> <td>    1.440</td> <td>    2.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sex[T.male]</th>   <td>   -2.6314</td> <td>    0.202</td> <td>  -13.058</td> <td> 0.000</td> <td>   -3.026</td> <td>   -2.236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PClass[T.1st]</th> <td>    1.8933</td> <td>    0.208</td> <td>    9.119</td> <td> 0.000</td> <td>    1.486</td> <td>    2.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PClass[T.2nd]</th> <td>    0.6013</td> <td>    0.148</td> <td>    4.052</td> <td> 0.000</td> <td>    0.310</td> <td>    0.892</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PClass[T.3rd]</th> <td>   -0.6282</td> <td>    0.132</td> <td>   -4.754</td> <td> 0.000</td> <td>   -0.887</td> <td>   -0.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Age</th>           <td>   -0.0392</td> <td>    0.008</td> <td>   -5.144</td> <td> 0.000</td> <td>   -0.054</td> <td>   -0.024</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:               Survived   No. Observations:                  756\n",
       "Model:                            GLM   Df Residuals:                      751\n",
       "Model Family:                Binomial   Df Model:                            4\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -347.57\n",
       "Date:                Thu, 04 Jun 2020   Deviance:                       695.14\n",
       "Time:                        11:44:14   Pearson chi2:                     813.\n",
       "No. Iterations:                     5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept         1.8664      0.217      8.587      0.000       1.440       2.292\n",
       "Sex[T.male]      -2.6314      0.202    -13.058      0.000      -3.026      -2.236\n",
       "PClass[T.1st]     1.8933      0.208      9.119      0.000       1.486       2.300\n",
       "PClass[T.2nd]     0.6013      0.148      4.052      0.000       0.310       0.892\n",
       "PClass[T.3rd]    -0.6282      0.132     -4.754      0.000      -0.887      -0.369\n",
       "Age              -0.0392      0.008     -5.144      0.000      -0.054      -0.024\n",
       "=================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_logistic = smf.glm('Survived ~ Age + Sex + PClass', data=titanic,\n",
    "  family = sm.families.Binomial()).fit()\n",
    "mod_logistic.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To get odds ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex[T.male]      0.071981\n",
       "PClass[T.1st]    6.640989\n",
       "PClass[T.2nd]    1.824486\n",
       "PClass[T.3rd]    0.533574\n",
       "Age              0.961581\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(mod_logistic.params.drop('Intercept'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## scikit-learn\n",
    "\n",
    "**scikit-learn** (`sklearn`) is the main machine learning package in Python\n",
    "\n",
    "1. `sklearn` requires that all inputs be numeric, and in fact, `numpy` arrays.\n",
    "1. `sklearn` requires that all categorical variables by replaced by 0/1 dummy variables\n",
    "1. `sklearn` requires us to separate the predictors from the outcome. We need to have one `X` matrix for the predictors and one `y` vector for the outcome.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "1. First of all, we know that all `pandas` Series and DataFrame objects can be converted to `numpy` arrays using the `values` or `to_numpy` functions. \n",
    "1. Second, we can easily extract a single variable from the data set using either the usual extracton methods or the \n",
    "`pop` function. \n",
    "1. Third, `pandas` gives us a way to convert all categorical values to numeric dummy variables using the `get_dummies` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "iris = sm.datasets.get_rdataset('iris').data\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Separate out outcome and predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = iris['Species']\n",
    "X = iris.drop('Species', axis = 1) # drops column, makes a copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = iris.pop('Species') # Now iris contains just the predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Transform `y` to be numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Transforming the predictors\n",
    "\n",
    "The usual transformations that we need for heterogeneous predictors is to create _dummy variables_ for the categorical predictors. \n",
    "\n",
    "We don't directly have a formula interface in `sklearn`, but we can use tools from `pandas` and `patsy` to take care of this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "cell_style": "split",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "diamonds = pd.read_csv('data/diamonds.csv.gz')\n",
    "\n",
    "y = diamonds.pop('price').values\n",
    "X = pd.get_dummies(diamonds)\n",
    "\n",
    "# Alternatively\n",
    "# import patsy\n",
    "# f = '~ np.log(carat) + clarity + depth + cut * color'\n",
    "# X = patsy.dmatrix(f, data=diamonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 26 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   carat          53940 non-null  float64\n",
      " 1   depth          53940 non-null  float64\n",
      " 2   table          53940 non-null  float64\n",
      " 3   x              53940 non-null  float64\n",
      " 4   y              53940 non-null  float64\n",
      " 5   z              53940 non-null  float64\n",
      " 6   cut_Fair       53940 non-null  uint8  \n",
      " 7   cut_Good       53940 non-null  uint8  \n",
      " 8   cut_Ideal      53940 non-null  uint8  \n",
      " 9   cut_Premium    53940 non-null  uint8  \n",
      " 10  cut_Very Good  53940 non-null  uint8  \n",
      " 11  color_D        53940 non-null  uint8  \n",
      " 12  color_E        53940 non-null  uint8  \n",
      " 13  color_F        53940 non-null  uint8  \n",
      " 14  color_G        53940 non-null  uint8  \n",
      " 15  color_H        53940 non-null  uint8  \n",
      " 16  color_I        53940 non-null  uint8  \n",
      " 17  color_J        53940 non-null  uint8  \n",
      " 18  clarity_I1     53940 non-null  uint8  \n",
      " 19  clarity_IF     53940 non-null  uint8  \n",
      " 20  clarity_SI1    53940 non-null  uint8  \n",
      " 21  clarity_SI2    53940 non-null  uint8  \n",
      " 22  clarity_VS1    53940 non-null  uint8  \n",
      " 23  clarity_VS2    53940 non-null  uint8  \n",
      " 24  clarity_VVS1   53940 non-null  uint8  \n",
      " 25  clarity_VVS2   53940 non-null  uint8  \n",
      "dtypes: float64(6), uint8(20)\n",
      "memory usage: 3.5 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Supervised machine learning methods\n",
    "\n",
    "| ML method               | Code to call it                                              |\n",
    "| ----------------------- | ------------------------------------------------------------ |\n",
    "| Decision Tree           | `sklearn.tree.DecisionTreeClassifier`, `sklearn.tree.DecisionTreeRegressor` |\n",
    "| Random Forest           | `sklearn.ensemble.RandomForestClassifier`, `sklearn.ensemble.RandomForestRegressor` |\n",
    "| Linear Regression       | `sklearn.linear_model.LinearRegression`                      |\n",
    "| Logistic Regression     | `sklearn.linear_model.LogisticRegression`                    |\n",
    "| Support Vector Machines | `sklearn.svm.LinearSVC`, `sklearn.svm.LinearSVR`             |\n",
    "|                         |                                                              |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The general method that the code will follow is :\n",
    "\n",
    "```\n",
    "from sklearn.... import Machine\n",
    "machine = Machine(*parameters*)\n",
    "machine.fit(X, y)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "diamonds = pd.read_csv('data/diamonds.csv.gz')\n",
    "y = diamonds.pop('price')\n",
    "d1 = diamonds.select_dtypes(include = 'number')\n",
    "d2 = diamonds.select_dtypes(exclude = 'number')\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "bl = pd.DataFrame(scale(d1))\n",
    "bl.columns = list(d1.columns)\n",
    "d1 = bl\n",
    "d2 = pd.get_dummies(d2)\n",
    "X = pd.concat([d1,d2], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Split the data into a training set and a test set (80/20)\n",
    "\n",
    "Model build on the training set **only**, and see predictive performance on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y , test_size = 0.2, random_state= 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Initialize 2 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "\n",
    "lm = LinearRegression()\n",
    "dt = DecisionTreeRegressor()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Fit the models to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm.fit(X_train, y_train)\n",
    "dt.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "See how the models do on the training set (don't really do or report this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression: 0.9202636015648039\n",
      "Decision tree: 0.9999965428396391\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import  r2_score\n",
    "\n",
    "print('Linear regression:', r2_score(y_train, lm.predict(X_train)))\n",
    "print('Decision tree:', r2_score(y_train, dt.predict(X_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We're almost certainly overfitting!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Visualizing a decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
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       "<graphviz.files.Source at 0x12d406d90>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "from sklearn import tree\n",
    "\n",
    "dt = DecisionTreeRegressor(max_depth=2) # Limit to 2 layers\n",
    "dt.fit(X_train, y_train)\n",
    "dot_data = tree.export_graphviz(dt, out_file=None, \n",
    "                                feature_names = X_train.columns,\n",
    "                                filled=True, rounded=True)\n",
    "graph = graphviz.Source(dot_data);\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Cross-validaton\n",
    "\n",
    "<center><img src = \"graphs/CV5.png\"/></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV average score = 0.829\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv_score = cross_val_score(dt, X_train, y_train, cv=5, scoring='r2')\n",
    "print(\"CV average score =\", np.round(np.mean(cv_score), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Tuning models using cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9645325340549133\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "import numpy.random as rnd\n",
    "rnd.RandomState(39358)\n",
    "\n",
    "param_grid = {'max_depth': [1,3,5,7, 10], 'min_samples_leaf': [1,5,10,20],\n",
    "  'max_features' : ['auto','sqrt']}\n",
    "\n",
    "clf = GridSearchCV(dt, param_grid, scoring = 'r2', cv = 5) # Tuning dt\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "clf.best_estimator_\n",
    "print(clf.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "How does this model do on the test set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9657241131204777"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = clf.best_estimator_.predict(X_test)\n",
    "r2_score(y_test, p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Unsupervised learning\n",
    "\n",
    "<img src=\"graphs/cluster_choice.png\" height=\"300\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### K-Means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 447.6x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = sm.datasets.get_rdataset('iris').data\n",
    "sns.relplot(data=iris, x = 'Sepal.Length',y = 'Sepal.Width', hue = 'Species');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 449.1x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
    "\n",
    "km = KMeans(n_clusters = 3)\n",
    "km.fit(iris[['Sepal.Length','Sepal.Width']]);\n",
    "\n",
    "iris['km_labels'] = km.labels_\n",
    "iris['km_labels'] = iris.km_labels.astype('category')\n",
    "\n",
    "sns.relplot(data=iris, x = 'Sepal.Length', y = 'Sepal.Width', \n",
    "           hue = 'km_labels');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Agglomerative (hierarchical) clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import dendrogram\n",
    "\n",
    "## The following is from https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html\n",
    "def plot_dendrogram(model, **kwargs):\n",
    "    # Create linkage matrix and then plot the dendrogram\n",
    "\n",
    "    # create the counts of samples under each node\n",
    "    counts = np.zeros(model.children_.shape[0])\n",
    "    n_samples = len(model.labels_)\n",
    "    for i, merge in enumerate(model.children_):\n",
    "        current_count = 0\n",
    "        for child_idx in merge:\n",
    "            if child_idx < n_samples:\n",
    "                current_count += 1  # leaf node\n",
    "            else:\n",
    "                current_count += counts[child_idx - n_samples]\n",
    "        counts[i] = current_count\n",
    "\n",
    "    linkage_matrix = np.column_stack([model.children_, model.distances_,\n",
    "                                      counts]).astype(float)\n",
    "\n",
    "    # Plot the corresponding dendrogram\n",
    "    dendrogram(linkage_matrix, **kwargs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hc = AgglomerativeClustering(distance_threshold=0, n_clusters=None, \n",
    "                             linkage='complete')\n",
    "hc.fit(iris[['Sepal.Length','Sepal.Width']])\n",
    "\n",
    "plot_dendrogram(hc, truncate_mode='level', p=3)\n",
    "plt.xlabel(\"Number of points in node (or index of point if no parenthesis).\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "hc = AgglomerativeClustering( n_clusters=3,\n",
    "                             linkage='average')\n",
    "\n",
    "hc.fit(iris[['Sepal.Length','Sepal.Width']]);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 446.1x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['hc_labels'] = pd.Series(hc.labels_).astype('category')\n",
    "\n",
    "sns.relplot(data=iris, x = 'Sepal.Length', y= 'Sepal.Width', \n",
    "           hue = 'hc_labels');"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "jupytext": {
   "formats": "ipynb,Rmd"
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  "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",
   "version": "3.8.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}