Python/Matplotlib/Python Matplotlib Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Matplotlib Tutorial\n",
    "Ten examples using Python 3.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 1. Simple plot with 4 numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 3, 2, 4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 2. Points have x and y values; add title and axis labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n",
    "plt.title('Test Plot', fontsize=8, color='g')\n",
    "plt.xlabel('number n')\n",
    "plt.ylabel('n^2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 3. Change figure size. plot red dots; set axis scales x: 0-6 and y: 0-20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 144x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2,5))                   # 2 inches wide x 5 inches tall\n",
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro') # red-o\n",
    "plt.axis([0, 6, 0, 20])                     # [xmin, xmax, ymin, ymax]\n",
    "plt.annotate('square it', (3,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 4. Bar chart with four bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADBdJREFUeJzt3X2MZXV9x/H3p6yUJ2tLmZoKxKGRaKxEoFNixZIWSKPFh9qaAEZT7MPWpvUpKKFpozZtaktpY1OrZotWiRSaok0NGEobRQQaYHhcccEYoIhoHWvSamuh4Ld/3LNhGGZ3Du7cufOdfb+SCXPP/c3Mdw5333P2zD13U1VIkvr4vlkPIEl6agy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmtk3jkx5xxBE1Pz8/jU8tSVvSLbfc8o2qmhuzdirhnp+fZ3FxcRqfWpK2pCT/Nnatp0okqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpmalcObkvQmY9wkwV/uPNkvbOI25JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNTMq3EneluSuJJ9PcmmSg6Y9mCRpdWuGO8mRwJuBhap6AXAAcNa0B5MkrW7sqZJtwMFJtgGHAA9NbyRJ0t6sGe6q+gpwIfAA8FXgP6vq6mkPJkla3ZhTJT8EvAo4BngWcGiS162ybnuSxSSLS0tL6z+pJAkYd6rkdOC+qlqqqv8DPgG8eOWiqtpRVQtVtTA3N7fec0qSBmPC/QDwoiSHJAlwGrBrumNJkvZkzDnuG4HLgVuBncPH7JjyXJKkPdg2ZlFVvQt415RnkSSN4JWTktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzRhuSWrGcEtSM4Zbkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzRhuSWpmVLiT/GCSy5PcnWRXkp+a9mCSpNVtG7nuL4Crquo1SQ4EDpniTJKkvVgz3El+ADgFOAegqh4BHpnuWJKkPRlzquTHgCXgb5LcluSiJIdOeS5J0h6MCfc24ETgA1V1AvDfwPkrFyXZnmQxyeLS0tI6jylJ2m1MuB8EHqyqG4fblzMJ+RNU1Y6qWqiqhbm5ufWcUZK0zJrnuKvqa0m+nOS5VXUPcBrwhemPJqmbnVf+2axHmKnjzjh3Q77O2GeVvAm4ZHhGyb3AG6Y3kiRpb0aFu6puBxamPIskaQSvnJSkZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzRhuSWpm7Mu6qotk1hPMVtWsJ5CmziNuSWrGcEtSM4Zbkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzYwOd5IDktyW5IppDiRJ2runcsT9FmDXtAaRJI0zKtxJjgLOAC6a7jiSpLWMPeJ+L3Ae8N0pziJJGmHNcCd5OfD1qrpljXXbkywmWVxaWlq3ASVJTzTmiPtk4JVJ7gcuA05N8rGVi6pqR1UtVNXC3NzcOo8pSdptzXBX1e9U1VFVNQ+cBXy6ql439ckkSavyedyS1My2p7K4qq4BrpnKJJKkUTzilqRmDLckNWO4JakZwy1JzTylX05KW12SWY8wU1U16xE0gkfcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzRhuSWrGcEtSM4Zbkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1Mya4U5ydJLPJNmV5K4kb9mIwSRJq9s2Ys2jwLlVdWuSpwO3JPnnqvrClGeTJK1izSPuqvpqVd06vP8tYBdw5LQHkySt7imd404yD5wA3DiNYSRJaxsd7iSHAR8H3lpV/7XK/duTLCZZXFpaWs8ZJUnLjAp3kqcxifYlVfWJ1dZU1Y6qWqiqhbm5ufWcUZK0zJhnlQT4ELCrqv58+iNJkvZmzBH3ycDrgVOT3D68/fyU55Ik7cGaTwesquuAbMAskqQRvHJSkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzRhuSWrGcEtSM4Zbkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc2MCneSlya5J8mXkpw/7aEkSXu2ZriTHAD8FfAy4PnA2UmeP+3BJEmrG3PEfRLwpaq6t6oeAS4DXjXdsSRJezIm3EcCX152+8FhmyRpBraNWJNVttWTFiXbge3DzW8nuWdfBpuhI4BvzOqLZ9Xd3cpM9x9x/+2LuP/20dv35YOfPXbhmHA/CBy97PZRwEMrF1XVDmDH2C+8WSVZrKqFWc/Rlftv37j/9s3+sv/GnCq5GTg2yTFJDgTOAj453bEkSXuy5hF3VT2a5LeBfwIOAD5cVXdNfTJJ0qrGnCqhqj4FfGrKs2wW7U/3zJj7b9+4//bNfrH/UvWk3zNKkjYxL3mXpGb2i3AneXWSSvK8EWvvT3LEKttvGP47n+S105hzM0ry7RW3z0nyvlnNs1Ws3K9aXZLHkty+7G0+yc8kuWLWs83SfhFu4GzgOibPiPmeVNWLh3fngf0m3NKMfaeqjl/2dv+sB9oMtny4kxwGnAz8KkO4h5/Y1yS5PMndSS7JiisPkhyc5Kokvz7c3n2E9MfATw8//d+2gd/KppPkFUluTHJbkn9J8sxh+7uTfHjYx/cmefOw/dAkVya5I8nnk5w52+9gtjLxp8O+2Ll8fyR5R5Kbk9yZ5PdnOedmluSkJDcMj8Ebkjx32H5AkguH/XpnkjcN208b1u4cHqPfP9vv4Hsz6lklzf0CcFVVfTHJN5OcOGw/AfhxJhcTXc8k7tcN9x3G5DVZLq6qi1d8vvOBt1fVy6c/+qZwcJLbl90+nMefx38d8KKqqiS/BpwHnDvc9zzgZ4GnA/ck+QDwUuChqjoDIMkzNuIb2MR+ETgeeCGTK/5uTnItcBxwLJPXCQrwySSnVNW1M5t0dpY//u6rqlevuP9u4JThacunA38E/BKTq7iPAU4Y7js8yUHAR4DThh5cDPwm8N4N+U7W0f4Q7rN5/H/MZcPtK4GbqupBgOGBMc/j4f5H4IKqumRjR92UvlNVx+++keQcYPeVaUcBf5fkR4EDgfuWfdyVVfUw8HCSrwPPBHYCFyb5E+CKqvrcRnwDm9hLgEur6jHg35N8FvhJ4BTg54DbhnWHMQn5/hjuJzz+VvEM4KNJjmXyUhxPG7afDnywqh4FqKpvJnkhk/h/cVjzUeC3aBjuLX2qJMkPA6cCFyW5H3gHcCaTo5iHly19jCf+ELseeNnK0yd6kr8E3ldVxwG/ARy07L4n7d/hD8xPMAn4e5K8c8Mm3Zz29PgK8J5l53WfU1Uf2sjBGvkD4DNV9QLgFTz+GAxPfk2lLfPneUuHG3gNk9Mdz66q+ao6mslR4UvW+Lh3Av8BvH+V+77F5K//mhztfGV4/5fXWpzkWcD/VNXHgAuBE9f4kK3uWuDM4XzsHJMj7ZuYXKX8K8PvZ0hyZJIfmeGcm9nyx+A5y7ZfDbwxyTaAJIczOa0yn+Q5w5rXA5/doDnX1VYP99nAP6zY9nHGPSv
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()          # clear figure\n",
    "x = np.arange(4)\n",
    "y = [8.8, 5.2, 3.6, 5.9]\n",
    "plt.xticks(x, ('Ankit', 'Hans', 'Joe', 'Flaco'))\n",
    "# plt.bar(x, y)\n",
    "# plt.bar(x, y, color='y')\n",
    "plt.bar(x, y, color=['lime', 'r', 'k', 'tan'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 5. Two sets of 10 random dots plotted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = {'Red O' : np.random.rand(10),\n",
    "     'Grn X' : np.random.rand(10)}\n",
    "df = pd.DataFrame(d)\n",
    "df.plot(style=['ro','gx'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 6. Time series - six months of random floats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(180), index=pd.date_range('1/1/2018', periods=180))\n",
    "df = pd.DataFrame(np.random.randn(180, 3), index=ts.index, columns=list('ABC'))\n",
    "df.cumsum().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 7. Random dots in a scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 50\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "sizes = (30 * np.random.rand(N))**2   # 0 to 15 point radii\n",
    "plt.scatter(x, y, s=sizes, c=colors, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 8. Load csv file and show multiple chart types\n",
    "Or use plt.figlegend() to show a legend outside the plot area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month  avg_high  avg_low  record_high  record_low  avg_precipitation\n",
      "0    Jan        58       42           74          22               2.95\n",
      "1    Feb        61       45           78          26               3.02\n",
      "2    Mar        65       48           84          25               2.34\n",
      "3    Apr        67       50           92          28               1.02\n",
      "4    May        71       53           98          35               0.48\n",
      "5    Jun        75       56          107          41               0.11\n",
      "6    Jul        77       58          105          44               0.00\n",
      "7    Aug        77       59          102          43               0.03\n",
      "8    Sep        77       57          103          40               0.17\n",
      "9    Oct        73       54           96          34               0.81\n",
      "10   Nov        64       48           84          30               1.70\n",
      "11   Dec        58       42           73          21               2.56\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('Fremont_weather.txt')\n",
    "print(df)\n",
    "plt.bar(df['month'], df['record_high'], color='r')\n",
    "plt.bar(df['month'], df['record_low'], color='c')\n",
    "plt.plot(df['month'], df['avg_high'], color='k')\n",
    "plt.plot(df['month'], df['avg_low'], color='b')\n",
    "plt.legend()\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 9.1. Subplots, part 1\n",
    "221 = top left subplot  \n",
    "222 = top right subplot  \n",
    "223 = bottom left subplot  \n",
    "224 = bottom right subplot  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('My SubPlots')\n",
    "fig.add_subplot(221)\n",
    "plt.plot([np.log(n) for n in range(1,10)])\n",
    "fig.add_subplot(222, facecolor='y')\n",
    "fig.add_subplot(223)\n",
    "fig.add_subplot(224) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 9.2. Subplots, part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, plots = plt.subplots(2, sharex=True)\n",
    "fig.suptitle('Sharing X axis')\n",
    "x = range(0,200,5)\n",
    "y = [n**0.8 for n in x]\n",
    "plots[0].plot(x, y, color='r')\n",
    "plots[1].scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### 10. Save figure to image file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3), dpi=100)\n",
    "plt.plot([245, 170, 148, 239, 161, 196, 112, 258])\n",
    "plt.axis([0, 7, 0, 300])\n",
    "plt.title('Flight Data')\n",
    "plt.xlabel('Speed')\n",
    "plt.savefig('Flights.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}