{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ciao"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print(\"hello!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ciao"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dqb-j4BkoKch"
   },
   "source": [
    "# -------------------------\n",
    "# Lesson 1\n",
    "#### Titolo2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V-iY-ex6oKcl"
   },
   "source": [
    "# Heading 1 \n",
    "\n",
    "this code does a lot of stuff because.... **really** lot of stuff\n",
    "*italic*\n",
    "\n",
    "***ciao***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "_aMpsHyEoKcl"
   },
   "outputs": [],
   "source": [
    "import cv2 \n",
    "import matplotlib\n",
    "import pandas\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "VoktUPgJoKcm",
    "outputId": "2d309756-16bb-42d3-9046-773380efad5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "print(\"hello world\")\n",
    "# comment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-AJfkGHBoKcn"
   },
   "source": [
    "# OpenCV read an image -  cv2.imread ()\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "f9KTQlH3oKcn"
   },
   "outputs": [],
   "source": [
    "immagine_gray = cv2.imread(\"eyes.png\", 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "OttsYdaqoKcn",
    "outputId": "9db75654-436a-4ca1-e80c-efb54458a83c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(412, 412)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immagine_gray.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0, ...,  0,  0,  0],\n",
       "       [ 0,  0,  0, ...,  0,  0,  0],\n",
       "       [ 0,  0,  0, ...,  0,  0,  0],\n",
       "       ...,\n",
       "       [21, 19, 17, ...,  0,  0,  0],\n",
       "       [21, 19, 17, ...,  0,  0,  0],\n",
       "       [21, 19, 17, ...,  0,  0,  0]], dtype=uint8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immagine_gray # [0-512]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "L-SI6iOIoKcn"
   },
   "outputs": [],
   "source": [
    "immagine_color = cv2.imread(\"eyes.png\", 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "66qT2Xf-oKco",
    "outputId": "f894db86-107a-4b72-9f12-da9f889fcbe5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(412, 412, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immagine_color.shape\n",
    "# RGB : RED GREEN BLUE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Ya8f1cKnoKco"
   },
   "outputs": [],
   "source": [
    "immagine_alfa = cv2.imread(\"eyes.png\", -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "udmATY9woKco",
    "outputId": "99b23432-c4df-46b2-9d4b-ed714c7f8565"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(412, 412, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immagine_alfa.shape\n",
    "# RGB + ALFA "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PbmoC77AoKco"
   },
   "source": [
    "# OpenCv Show an image - funzione cv2.imshow () \n",
    "The window automatically adjusts to the size of the image. The first argument is the name of the window. The second argument instead is our image. You can create as many windows as you like, but with different window names.\n",
    "\n",
    "- cv2.imshow(‘image’, img)\n",
    "- cv2.waitKey(0) \n",
    "- cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "68dFbeU0oKcp"
   },
   "outputs": [],
   "source": [
    "#cv2.imshow('image', immagine_color)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pFkAibw7oKcp"
   },
   "source": [
    "# OpenCv Save an image - cv2.imwrite () \n",
    "\n",
    "The first argument is the name of the file, the second is the image you want to save.\n",
    "\n",
    "cv2.imwrite(‘Lena.png’, img)\n",
    "\n",
    "This will save the image in PNG format in the working directory.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "w7UpiuvRoKcp",
    "outputId": "5a37079f-3a3a-46eb-b2f9-f429cc121274"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# image in gray sclae \n",
    "cv2.imwrite(\"eyes_grigi.png\", immagine_gray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "2xzFZgb4oKcp",
    "outputId": "b5bba605-54bf-493c-9e96-14937aeeb5dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# image with colors\n",
    "cv2.imwrite(\"eyes_colori.png\", immagine_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "id": "u-7oEpqnoKcp",
    "outputId": "f09ff5f1-3387-4bea-eb1a-16148a08e4d2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# image with colors and alpha channel\n",
    "cv2.imwrite(\"eyes_alfa.png\", immagine_alfa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RHRlFojgoKcq",
    "outputId": "6b5d21d4-1266-4c44-87c4-c462f32708eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0, ...,  0,  0,  0],\n",
       "       [ 0,  0,  0, ...,  0,  0,  0],\n",
       "       [ 0,  0,  0, ...,  0,  0,  0],\n",
       "       ...,\n",
       "       [21, 19, 17, ...,  0,  0,  0],\n",
       "       [21, 19, 17, ...,  0,  0,  0],\n",
       "       [21, 19, 17, ...,  0,  0,  0]], dtype=uint8)"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immagine_gray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cSIuavfMoKcq",
    "outputId": "082ba7c9-ced1-4089-ee26-739507073c8f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        ...,\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0]],\n",
       "\n",
       "       [[ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        ...,\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0]],\n",
       "\n",
       "       [[ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        ...,\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 3,  1, 68],\n",
       "        [ 2,  1, 64],\n",
       "        [ 0,  1, 57],\n",
       "        ...,\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0]],\n",
       "\n",
       "       [[ 3,  1, 68],\n",
       "        [ 2,  1, 64],\n",
       "        [ 0,  1, 57],\n",
       "        ...,\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0]],\n",
       "\n",
       "       [[ 3,  1, 68],\n",
       "        [ 2,  1, 64],\n",
       "        [ 0,  1, 57],\n",
       "        ...,\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0],\n",
       "        [ 0,  0,  0]]], dtype=uint8)"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immagine_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AoZ2d0CdoKcq",
    "outputId": "57eed209-4128-4c0b-dc04-3906100c9a70"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[  3,   1,  68, 255],\n",
       "        [  2,   1,  64, 255],\n",
       "        [  0,   1,  57, 255],\n",
       "        ...,\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0]],\n",
       "\n",
       "       [[  3,   1,  68, 255],\n",
       "        [  2,   1,  64, 255],\n",
       "        [  0,   1,  57, 255],\n",
       "        ...,\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0]],\n",
       "\n",
       "       [[  3,   1,  68, 255],\n",
       "        [  2,   1,  64, 255],\n",
       "        [  0,   1,  57, 255],\n",
       "        ...,\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0],\n",
       "        [  0,   0,   0,   0]]], dtype=uint8)"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immagine_alfa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zp2WSIzPoKcq"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "twiJgyJBoKcq"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VyD0eqsFoKcq"
   },
   "source": [
    "# Pandas\n",
    "\n",
    "Pandas is a library for manipulating data in sequential or tabular format. It is ideal for data analysis. Main features of Pandas are:\t\t\t\t\n",
    "- Loading and saving of table data, such as CSV, TSV, Excel files and database formats \n",
    "- Simplicity in performing indexing and data aggregation operations \n",
    "- Simplicity in the execution of numerical and statistical operations \n",
    "- Simplicity in viewing the results of operations \n",
    "\n",
    "\n",
    "The following command installs the latest version of Pandas:\n",
    "conda install -c anaconda pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "jL8O5LT0oKcr"
   },
   "outputs": [],
   "source": [
    "import pandas \n",
    "df = pandas.read_csv(\"new_file.csv\")   # dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "NzkE5zW2oKcr",
    "outputId": "3f2be817-98a6-44ea-d854-b76d2ddc6b19"
   },
   "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>Group</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>222</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group   F1   F2   F3 \n",
       "0    222    3    4     5\n",
       "1      0    1    1     1\n",
       "2      0    0    1     1\n",
       "3      1    1    0     0\n",
       "4      1    1    1     0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dWRD0YWfoKcr"
   },
   "source": [
    "# Save in cvs or json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "id": "9LiohQoDoKcr"
   },
   "outputs": [],
   "source": [
    "df.to_csv('a_file.csv')\n",
    "\n",
    "df.to_json('a_file.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{\"Group\":{\"0\":222,\"1\":0,\"2\":0,\"3\":1,\"4\":1},\" F1\":{\"0\":3,\"1\":1,\"2\":0,\"3\":1,\"4\":1},\" F2\":{\"0\":4,\"1\":1,\"2\":1,\"3\":0,\"4\":1},\" F3 \":{\"0\":5,\"1\":1,\"2\":1,\"3\":0,\"4\":0}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CIk-DlbqoKcr"
   },
   "source": [
    "# I want to split dataframes in two groups\n",
    "How can I do this split? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Group</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>222</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group   F1   F2   F3 \n",
       "0    222    3    4     5\n",
       "1      0    1    1     1\n",
       "2      0    0    1     1\n",
       "3      1    1    0     0\n",
       "4      1    1    1     0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "Zck-d954oKcs",
    "outputId": "4ba9dfe7-c5ba-4b27-f1dc-e6f5ebfaa279"
   },
   "outputs": [],
   "source": [
    "name_col =  df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Group'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_col[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Group', ' F1', ' F2', ' F3 '], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "ChnamiVkoKcs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group= df[\" F1\"].values  # In pandas posso selezionare come indice il nome della colonna \n",
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "hgYnkvK3oKcs",
    "outputId": "a5158332-b656-48ef-a811-b148665295e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "ApddGKW9oKcs",
    "outputId": "d4ff3f20-e198-42ec-8c6d-9b7a600d97a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[' F2'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "d9OWZe-RoKcs",
    "outputId": "d751d818-92ad-4e47-deee-a7c887697f68"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[222,   3,   4],\n",
       "       [  0,   1,   1],\n",
       "       [  0,   0,   1],\n",
       "       [  1,   1,   0],\n",
       "       [  1,   1,   1]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features = df[['Group', ' F1', ' F2']].values \n",
    "Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "voRebitpoKcu"
   },
   "source": [
    "# Matplotlib\n",
    "\n",
    "Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.\n",
    "\n",
    "Create publication quality plots.\n",
    "Make interactive figures that can zoom, pan, update.\n",
    "Customize visual style and layout.\n",
    "Export to many file formats.\n",
    "Embed in JupyterLab and Graphical User Interfaces.\n",
    "Use a rich array of third-party packages built on Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "sep-UsUmoKcv"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xjFqxHCDoKcv"
   },
   "source": [
    "Let's see some examples of Matplotlib graphs in which the data is displayed in an x-y Cartesian plane.\n",
    "In particular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "Y3iYSwQ2oKcv",
    "outputId": "82ab0e3e-3fb0-48f7-ab68-4ed099faa428"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fec42c33e10>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()  # Create a figure containing a single axes.\n",
    "ax.plot([1, 2, 3, 4, 5], [1, 4, 2, 3,0 ])  # Plot some data on the axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "id": "w0ElWjZSoKcv",
    "outputId": "ebe30b19-8362-4252-dc1d-ab0856a4f3bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()  # an empty figure with no Axes\n",
    "fig, ax = plt.subplots()  # a figure with a single Axes\n",
    "fig, axs = plt.subplots(2, 2)  # a figure with a 2x2 grid of Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "id": "rcB6-yuYoKcv"
   },
   "outputs": [],
   "source": [
    "#a = pandas.DataFrame(np.random.rand(4,5), columns = list('abcde')) # a matrix shape 4x5 of numbers [0,1]\n",
    "#a_asarray = a.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#a_asarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "id": "2-QNR0kaoKcv"
   },
   "outputs": [],
   "source": [
    "#b = np.matrix([[1, 2], [3, 4]])\n",
    "#b_asarray = np.asarray(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.02020202, 0.04040404, 0.06060606, 0.08080808,\n",
       "       0.1010101 , 0.12121212, 0.14141414, 0.16161616, 0.18181818,\n",
       "       0.2020202 , 0.22222222, 0.24242424, 0.26262626, 0.28282828,\n",
       "       0.3030303 , 0.32323232, 0.34343434, 0.36363636, 0.38383838,\n",
       "       0.4040404 , 0.42424242, 0.44444444, 0.46464646, 0.48484848,\n",
       "       0.50505051, 0.52525253, 0.54545455, 0.56565657, 0.58585859,\n",
       "       0.60606061, 0.62626263, 0.64646465, 0.66666667, 0.68686869,\n",
       "       0.70707071, 0.72727273, 0.74747475, 0.76767677, 0.78787879,\n",
       "       0.80808081, 0.82828283, 0.84848485, 0.86868687, 0.88888889,\n",
       "       0.90909091, 0.92929293, 0.94949495, 0.96969697, 0.98989899,\n",
       "       1.01010101, 1.03030303, 1.05050505, 1.07070707, 1.09090909,\n",
       "       1.11111111, 1.13131313, 1.15151515, 1.17171717, 1.19191919,\n",
       "       1.21212121, 1.23232323, 1.25252525, 1.27272727, 1.29292929,\n",
       "       1.31313131, 1.33333333, 1.35353535, 1.37373737, 1.39393939,\n",
       "       1.41414141, 1.43434343, 1.45454545, 1.47474747, 1.49494949,\n",
       "       1.51515152, 1.53535354, 1.55555556, 1.57575758, 1.5959596 ,\n",
       "       1.61616162, 1.63636364, 1.65656566, 1.67676768, 1.6969697 ,\n",
       "       1.71717172, 1.73737374, 1.75757576, 1.77777778, 1.7979798 ,\n",
       "       1.81818182, 1.83838384, 1.85858586, 1.87878788, 1.8989899 ,\n",
       "       1.91919192, 1.93939394, 1.95959596, 1.97979798, 2.        ])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0, 2, 100)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "id": "yPCzZN9goKcv",
    "outputId": "acbccd69-80c5-4443-cfc0-de3765539f10"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe52aa5c6d0>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "# Note that even in the OO-style, we use `.pyplot.figure` to create the figure.\n",
    "fig, ax = plt.subplots()  # Create a figure and an axes.\n",
    "ax.plot(x, x, label='linear')  # Plot some data on the axes.\n",
    "ax.plot(x, x**2, label='quadratic')  # Plot more data on the axes...\n",
    "ax.plot(x, x**3, label='cubic')  # ... and some more.\n",
    "ax.plot(x, x**4, label='four times')  # ... and some more.\n",
    "ax.set_xlabel('x label')  # Add an x-label to the axes.\n",
    "ax.set_ylabel('y label')  # Add a y-label to the axes.\n",
    "ax.set_title(\"Simple Plot\")  # Add a title to the axes.\n",
    "ax.legend()  # Add a legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "fKmeDc7toKcw"
   },
   "outputs": [],
   "source": [
    "# Pyplot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lsSQ4JoJoKcw"
   },
   "source": [
    "#. For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string. The default format string is 'b-', which is a solid blue line. For example, to plot the above with red circles, you would issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "n3T4TKP3oKcw",
    "outputId": "d0bb05f5-ba98-4623-c7d5-1bf78839a595"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# r = red; b = blue; k = black; g = green; y = yellow \n",
    "\n",
    "# o = circle; - = solid line; _ = underscore; -- = broken line; . = dot; , = small dot ; : = point line \n",
    "# ^ = triangle \n",
    "\n",
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'g.')\n",
    "plt.axis([0, 6, 0, 20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xLjeLxTxoKcy"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "id": "L3TCV0DtoKcz",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# evenly sampled time at 200ms intervals\n",
    "t = np.arange(0., 5., 0.2)\n",
    "\n",
    "# red dashes, blue squares and green triangles\n",
    "plt.plot(t, t, 'r.', t, t**2, 'bs', t, t**3, 'g^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "id": "Djion7BSoKcz",
    "outputId": "aef2dd54-ddce-41b1-a3a0-9d9271c16e36"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'a': np.arange(50), # integers in range [0, 50]\n",
    "        'c': np.random.randint(0, 50, 50), # 50 random integers in range [0, 50]\n",
    "        'd': np.random.randn(50)} # 50 random numbers in range [0,1]\n",
    "\n",
    "\n",
    "data['b'] = data['a'] + 10 * np.random.randn(50)\n",
    "data['d'] = np.abs(data['d']) * 100\n",
    "\n",
    "plt.scatter('a', 'b', c='c', s='d', data=data) \n",
    "\n",
    "# s marker size (can be scalar or array of size equal to size of x or y)\n",
    "# c color of sequence of colors for markers\n",
    "\n",
    "plt.xlabel('entry a')\n",
    "plt.ylabel('entry b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "id": "H_NJSiBZoKcz"
   },
   "outputs": [],
   "source": [
    " # histogram\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "id": "8nyqUR5RoKcz"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['group_a', 'group_b', 'group_c']\n",
    "values = [1, 10, 100]\n",
    "\n",
    "plt.figure(figsize=(9, 4))\n",
    "\n",
    "plt.subplot(131)  # that's the index of the subplot\n",
    "plt.bar(names, values)\n",
    "\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.scatter(names, values)\n",
    "\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.plot(names, values)\n",
    "\n",
    "\n",
    "\n",
    "plt.suptitle('Categorical Plotting')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "i8mHQ_o7oKcz"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "VyD0eqsFoKcq",
    "dWRD0YWfoKcr",
    "CIk-DlbqoKcr",
    "voRebitpoKcu",
    "lsSQ4JoJoKcw"
   ],
   "name": "1_tutorial.ipynb",
   "provenance": []
  },
  "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}