Intro To histograms The Engineering World_begin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Engineering WOrld - A Place For Learning And Exploring\n",
    "\n",
    "# Intro To histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as pp\n",
    "\n",
    "from IPython import display\n",
    "from ipywidgets import interact, widgets\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import mailbox\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "china1965 = pd.read_csv('income-1965-china.csv')\n",
    "china2015 = pd.read_csv('income-2015-china.csv')\n",
    "usa1965 = pd.read_csv('income-1965-usa.csv')\n",
    "usa2015 = pd.read_csv('income-2015-usa.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e6a0a8320>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W0ufrr62TtFbSj7OcsH7V/lPbv1f/9bZ9Rx7DA1kRbrSNiDgj6bOSBmy/IOkZnVu7/qqkgu0XdW4N/AsRMXP5M33I5yX11895QtJnmjs5sDhsBwSAxHDFDQCJIdwAkBjCDQCJIdwAkBjCDQCJIdwAkBjCDQCJIdwAkJj/B1FRBYTkC0DrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "china1965.income.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e6a16b240>"
      ]
     },
     "execution_count": 6,
     "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": [
    "pd.DataFrame({'usa':usa1965.income, 'china':china1965.income}).boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26e6a1d1198>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFstJREFUeJzt3X2QXNV55/HvgyREamDtxNiDhJBEbGp3JDmJkykSY5VLCrYx2GucxF4YuYIJs6hIBcXZDVaUdFUcszVb2FvllCK8NrOWCjkbxtpybCSbN9vS9DpaKi4LbLCkjheW4WUQFVbWBjPiRYz07B/TgpHokTTTzdxR3++nqmv63nu6z1HrTv/m3HPPvZGZSJLK54yiGyBJKoYBIEklZQBIUkkZAJJUUgaAJJWUASBJJWUASFJJGQCSVFIGgCSV1OyiG3Ai5557bi5evLjoZrSFgwcP0tHRUXQzpIbcP1vngQce2J+Zbz2Vsk0HQERcAHwVOA84AvRn5vrjyqwAtgJD9VXfyMybT/beixcvZteuXc02UUC1WmXFihVFN0NqyP2zdSLiiVMt24oewCjwp5n5YEScAzwQEd/NzL3HlfuHzPxwC+qTJLVA02MAmflMZj5Yf/48UAPOb/Z9JUlvrJYOAkfEYuBdwA8abH53RDwUEfdExNJW1itJmryWDQJHxNnA3wN/kpk/P27zg8CizByJiCuAO4GLJnif1cBqgM7OTqrVaquaWGojIyN+lpqx3D+LEa24H0BEzAG+DdyXmV84hfKPA92Zuf9E5bq7u9NB4NZwkE0zmftn60TEA5nZfSplmz4EFBEBbARqE335R8R59XJExMX1en/WbN06uYGBAZYtW8all17KsmXLGBgYKLpJkmaIVhwCeg/w+8BPIuLH9XV/ASwEyMwvAx8D/jAiRoEXgavTW5G94QYGBqhUKmzcuJHDhw8za9Ysent7Aejp6Sm4dZKK1nQAZOZOIE5S5lbg1mbr0uT09fWxceNGVq5c+WoXe+PGjaxZs8YAkOSlINpZrVZjeHj4mENAw8PD1Gq1opsmaQaY0ZeCUHPmz5/P2rVrueOOO149BLRq1Srmz59fdNMkzQD2ANpcfex9wmVJ5WUPoI3t27eP22+/nTVr1lCr1ejq6uJzn/sc1157bdFNkzQD2ANoY11dXSxYsIDdu3ezfft2du/ezYIFC+jq6iq6aZJmAAOgjVUqFXp7exkcHGR0dJTBwUF6e3upVCpFN03SDOAhoDbW09PD/fffz+WXX87LL7/M3Llzuf766z0FVBJgD6CtDQwMsGXLFubNm8cZZ5zBvHnz2LJli7OBJQEGQFtbu3Yts2fPZtOmTdx3331s2rSJ2bNns3bt2qKbJmkGMADa2PDwMJs3b2blypXMnj2blStXsnnzZoaHh4tumqQZwACQpJIyANrYggULuOaaa445C+iaa65hwYIFRTdN0gzgWUBt7POf/zyf+tSnuO6663jiiSdYtGgRhw8f5gtfOOktGySVgD2ANtbT08P69evp6OggIujo6GD9+vWeBioJsAfQ9np6eujp6fGOS5Jexx6AJJWUPYA2M9WrfXqDNql8WnFP4AsiYjAiahGxJyI+1aBMRMTfRMSjEfFwRPx6s/Wqscxs+Fj0Z9+ecJtf/lI5taIHMAr8aWY+GBHnAA9ExHczc++4MpcDF9Ufvwl8qf5TklSQpnsAmflMZj5Yf/48UAPOP67YlcBXc8w/Am+OiHnN1i1JmrqWDgJHxGLgXcAPjtt0PvDUuOVhXh8SkqRp1LJB4Ig4G/h74E8y8+fHb27wkoYHniNiNbAaoLOzk2q12qomlp6fpWaqkZER988CtCQAImIOY1/+f5eZ32hQZBi4YNzyAmBfo/fKzH6gH6C7uzs9d71F7r3LeQCasZynUoxWnAUUwEaglpkTXWNgG3BN/Wyg3wKey8xnmq1bkjR1regBvAf4feAnEfHj+rq/ABYCZOaXgbuBK4BHgReAP2hBvZKkJjQdAJm5k8bH+MeXSeCPmq1LktQ6XgpCkkrKAJCkkjIAJKmkDABJKikDQJJKygCQpJIyACSppAwASSopA0CSSsoAkKSSMgAkqaQMAEkqKQNAkkrKAJCkkjIAJKmkDABJKqmW3RRe0+tXP/sdnnvxlUm9ZvG6uyZV/k2/MIeHPvOBSb1G0umjVTeF3wR8GHg2M5c12L4C2AoM1Vd9IzNvbkXdZfXci6/w+C0fOuXyU7np9mQDQ9LppVU9gNuBW4GvnqDMP2Tmh1tUnySpSS0ZA8jM7wMHWvFekqTpMZ2DwO+OiIci4p6IWDqN9UqSGpiuQeAHgUWZORIRVwB3Ahc1KhgRq4HVAJ2dnVSr1Wlq4ulnMp/NyMjIlD5LP39Nh6nun2rOtARAZv583PO7I+K/RsS5mbm/Qdl+oB+gu7s7JztwWRr33jWpQd2pDAJPtg5pqqa0f6pp03IIKCLOi4ioP7+4Xu/PpqNuSVJjrToNdABYAZwbEcPAZ4A5AJn5ZeBjwB9GxCjwInB1ZmYr6pYkTU1LAiAze06y/VbGThNVi5zTtY53bl43uRdtnmwdAKc+10DS6cWZwKep52u3OBFMUlO8FpAklZQBIEklZQBIUkkZAJJUUgaAJJWUASBJJWUASFJJOQ/gNDbp8/TvnfwdwSS1LwPgNDWZSWAwFhaTfY2k9uYhIEkqKQNAkkrKAJCkkjIAJKmkDABJKikDQJJKygCQpJIyACSppFoSABGxKSKejYjdE2yPiPibiHg0Ih6OiF9vRb2SpKlrVQ/gduCDJ9h+OXBR/bEa+FKL6pUkTVFLAiAzvw8cOEGRK4Gv5ph/BN4cEfNaUbckaWqm61pA5wNPjVserq975viCEbGasV4CnZ2dVKvV6WhfKfhZaqYaGRlx/yzAdAVANFiXjQpmZj/QD9Dd3Z0rVqx4A5tVIvfehZ+lZqpqter+WYDpOgtoGLhg3PICYN801S1JamC6AmAbcE39bKDfAp7LzNcd/pEkTZ+WHAKKiAFgBXBuRAwDnwHmAGTml4G7gSuAR4EXgD9oRb2SpKlrSQBkZs9JtifwR62oS5LUGs4ElqSSMgAkqaQMAEkqKQNAkkrKAJCkkjIAJBVmYGCAZcuWcemll7Js2TIGBgaKblKpTNelIFSQgYEB+vr6eGJvjWXf7qJSqdDTc8KzdqVpMTAwQKVSYePGjRw+fJhZs2bR29sL4D46TWLsFP2Zqbu7O3ft2lV0M04rEY0uu3RyM3k/UHtatmwZGzZsYOXKla9eC2hwcJA1a9awe3fDW4voFETEA5nZfSplPQTUZjLz1cfSpUvZsWMHmcng4CCZyY4dO1i6dOkx5fzyVxFqtRrLly8/Zt3y5cup1WoFtah8DIA2VqvVGB4ePuYY6/DwsL9gmhG6urrYuXPnMet27txJV1dXQS0qH8cA2tj8+fNZu3Ytd9xxx6vHWFetWsX8+fOLbppEpVLhqquuoqOjgyeeeIJFixZx8OBB1q9fX3TTSsMAaHMvvfQS1113HU8++SQLFy7kpZde4uyzzy66WdIxpjp2peZ4CKiNPf3008yZMwd4bZB3zpw5PP3000U2SwKgr6+PLVu2MDQ0xPbt2xkaGmLLli309fUV3bTSMADa2Jlnnsm6desYGhpix44dDA0NsW7dOs4888yimyY5CDwDGABt7NChQ2zYsIHBwUFGR0cZHBxkw4YNHDp0qOimSQ4CzwCOAbSxJUuW8NGPfpQ1a9ZQq9Xo6uriE5/4BHfeeWfRTZOoVCr09va+OhFscHCQ3t5eDwFNIwOgjVUqlYYzLf0F00xwdLbv+D9Q+vr6nAU8jVoyEzgiPgisB2YBX8nMW47bfi3wX4Cjo4+3ZuZXTva+zgRu3tFLQRz9BfNSEJqJjs4EVvMmMxO46R5ARMwCvgi8HxgGfhgR2zJz73FFt2Tmjc3Wp8np6emhp6fHXzBJr9OKQeCLgUcz87HMPAR8DbiyBe8rSXoDtWIM4HzgqXHLw8BvNij3exHxXuB/A/8hM59qUIaIWA2sBujs7KRarbagiRoZGfGz1Izl/lmMVgRAoyl8xw8sfAsYyMyXI+IGYDPw243eLDP7gX4YGwPwsEVreAhIM5n7ZzFacQhoGLhg3PICYN/4Apn5s8x8ub7434DfaEG9kqQmtCIAfghcFBEXRsSZwNXAtvEFImLeuMWPAE71k6SCNX0IKDNHI+JG4D7GTgPdlJl7IuJmYFdmbgP+OCI+AowCB4Brm61X0unFmxXNPC2ZCJaZdwN3H7fuL8c9/3Pgz1tRl6TT04m+yBevu4vHb/nQNLZG4LWAJKm0DABJKikDQJJKygCQpJIyACSppAwASSopA0CSSsoAkKSSMgAkqaQMAEkqKQNAkkrKAJCkkjIAJKmkDABJKikDQJJKygCQpJIyACSppFoSABHxwYj4aUQ8GhHrGmyfGxFb6tt/EBGLW1GvJGnqmg6AiJgFfBG4HFgC9ETEkuOK9QL/LzPfAfw18Llm65UkNacVPYCLgUcz87HMPAR8DbjyuDJXApvrz78OXBpTvUO0JKklWhEA5wNPjVserq9rWCYzR4HngLe0oG5J0hTNbsF7NPpLPqdQZqxgxGpgNUBnZyfVarWpxmnMyMiIn6VmNPfP6deKABgGLhi3vADYN0GZ4YiYDbwJONDozTKzH+gH6O7uzhUrVrSgiapWq/hZasa69y73zwK04hDQD4GLIuLCiDgTuBrYdlyZbcAn688/BuzIzIY9AEnS9Gi6B5CZoxFxI3AfMAvYlJl7IuJmYFdmbgM2An8bEY8y9pf/1c3WK2lm+tXPfofnXnxl0q9bvO6uUy77pl+Yw0Of+cCk69CxWnEIiMy8G7j7uHV/Oe75S8DHW1GXpJntuRdf4fFbPjSp10z2EOVkwkITcyawJJWUASBJJWUASFJJGQCSVFIGgCSVlAEgSSVlAEhSSRkAklRSLZkIJklHndO1jnduft19oU5u88mLvFYHwOQmm+n1DABJLfV87RZnAp8mPAQkSSVlAEhSSRkAklRSBoAklZQBIEklZQBIUkkZAJJUUk0FQET8UkR8NyIeqf/8xQnKHY6IH9cfx98vWJJUgGZ7AOuA7Zl5EbC9vtzIi5n5a/XHR5qsU5LUAs3OBL4SWFF/vhmoAn/W5HtKOs1NaabuvZO7Kbya12wAdGbmMwCZ+UxEvG2CcmdFxC5gFLglM+9ssl5JM9RkLwMBY4ExldepOScNgIj4HnBeg02VSdSzMDP3RcQvAzsi4ieZ+X8mqG81sBqgs7OTarU6iWo0kZGRET9LzWjun9PvpAGQme+baFtE/HNEzKv/9T8PeHaC99hX//lYRFSBdwENAyAz+4F+gO7u7pzMBaI0sclebEuaVvfe5f5ZgGYHgbcBn6w//ySw9fgCEfGLETG3/vxc4D3A3ibrlSQ1qdkAuAV4f0Q8Ary/vkxEdEfEV+pluoBdEfEQMMjYGIABIEkFa2oQODN/BlzaYP0u4N/Xn98PvLOZeiRJredMYEkqKQNAkkrKAJCkkjIAJKmkDABJKikDQJJKygCQpJIyACSppAwASSopA0CSSsoAkKSSMgAkqaQMAEkqKQNAkkrKAJCkkjIAJKmkDABJKqmmAiAiPh4ReyLiSER0n6DcByPipxHxaESsa6ZOSVJrNNsD2A38LvD9iQpExCzgi8DlwBKgJyKWNFmvJKlJzd4TuAYQEScqdjHwaGY+Vi/7NeBKwBvDS1KBpmMM4HzgqXHLw/V1kqQCnbQHEBHfA85rsKmSmVtPoY5G3YM8QX2rgdUAnZ2dVKvVU6hCJzMyMuJnqRnN/XP6nTQAMvN9TdYxDFwwbnkBsO8E9fUD/QDd3d25YsWKJqsXjP1y+VlqphkYGKCvr48n9ta48dtdVCoVenp6im5WaTQ1BnCKfghcFBEXAk8DVwOrpqFeSTPIScYK2bNnD6tWrWLVqmO/HjInPGCgJjV7GujvRMQw8G7groi4r75+fkTcDZCZo8CNwH1ADfgfmbmnuWZLOt1k5jGPpUuXUqlUWLp0KWecccYxy+PL6Y3T7FlA3wS+2WD9PuCKcct3A3c3U5ek9rJ3716effZZOjo6yEwOHjxIf38/+/fvL7pppeFMYEmFmDVrFi+88ALw2uGhF154gVmzZhXZrFKZjjEASXqd0dFRjhw5wpo1a1iyZAl79+7l05/+NEeOHCm6aaVhAEgqzNvf/nZuuukmMpOI4B3veAePPPJI0c0qDQ8BSSrMI488wg033MC3vvUtbrjhBr/8p5k9AEmFOeuss7jnnnu47bbbWLhwIWeddRYvvfRS0c0qDXsAkgpz9tlnA6+d6390WdPDAJBUiLlz53LZZZfR0dFBRNDR0cFll13G3Llzi25aaRgAkgpx/fXXMzAwwP79+zly5Aj79+9nYGCA66+/vuimlYYBIKkQl1xyCR0dHRw4cACAAwcO0NHRwSWXXFJwy8rDAJBUiL6+PrZu3cqhQ4cYHBzk0KFDbN26lb6+vqKbVhoGgKRC1Go1li9ffsy65cuXU6vVCmpR+RgAkgrR1dXFzp07j1m3c+dOurq6CmpR+TgPQFIhKpUKV111FR0dHTz55JMsXLiQgwcPsn79+qKbVhr2ACQVzss+F8MAkFSIvr4+tmzZwtDQEDt27GBoaIgtW7Y4CDyNDABJhXAQuHgGgKRCOAhcPANAUiEqlQq9vb0MDg4yOjrK4OAgvb29VCqVoptWGk2dBRQRHwf+CugCLs7MXROUexx4HjgMjGZmdzP1Sjr99fT0ALBmzRpqtRpdXV309fW9ul5vvGZPA90N/C5w2ymUXZmZ3uxT0qt6enro6emhWq2yYsWKoptTOs3eFL4Gr93PU5J0+piuiWAJfCciErgtM/snKhgRq4HVAJ2dnVSr1elpYZsbGRnxs9SM5f5ZjJMGQER8DzivwaZKZm49xXrek5n7IuJtwHcj4p8y8/uNCtbDoR+gu7s77Ra2hl1szWTun8U4aQBk5vuarSQz99V/PhsR3wQuBhoGgCRperzhp4FGREdEnHP0OfABxgaPJUkFimauwRERvwNsAN4K/Avw48y8LCLmA1/JzCsi4peBb9ZfMhu4IzNPaa53RPxf4IkpN1DjnQt4FpZmKvfP1lmUmW89lYJNBYBOHxGxy/kXmqncP4vhTGBJKikDQJJKygAojwnnXkgzgPtnARwDkKSSsgcgSSVlAEh6w0XE7RHxsQbr50fE14tok7wpvKQC1a8S8Lpg0PSwB9AmImJxROwet3xTRPxVRPxxROyNiIcj4mv1bRdHxP0R8aP6z39dXMvVjiLimvo+91BE/G199Xvr+9tjR3sD4/fbiLg2Ir4REfdGxCMR8flx7/eliNgVEXsi4rMF/JPakj2A9rcOuDAzX46IN9fX/RPw3swcjYj3Af8Z+L3CWqi2EhFLgQpjF4HcHxG/BHwBmAcsB/4NsA1odOjn14B3AS8DP42IDZn5FGMXnzwQEbOA7RHxK5n58HT8e9qZAdD+Hgb+LiLuBO6sr3sTsDkiLmLsUt1zimqc2tJvA18/egOo+hc3wJ2ZeQTYGxGdE7x2e2Y+BxARe4FFwFPAv6tfKn42Y0GyhLF9W03wEFD7GOXY/8+z6j8/BHwR+A3ggYiYDfwnYDAzlwH/dlxZqRWCsT8sjvfycWUaGV/mMDA7Ii4EbgIuzcxfAe7CfbYlDID28c/A2yLiLRExF/gwY/+/F2TmILAWeDNwNmM9gKfrr7u2gLaqvW1n7C/2twDUDwE1418BB4Hn6j2Hy5t8P9V5CKhNZOYrEXEz8ANgiLHj/LOA/x4Rb2LsL66/zsx/qQ+ubY6I/wjsKKzRakuZuSci+oD/GRGHgR81+X4PRcSPgD3AY8D/akEzhTOBJam0PAQkSSVlAEhSSRkAklRSBoAklZQBIEklZQBIUkkZAJJUUgaAJJXU/wc46Fc7K0eOvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame({'usa':usa1965. log10_income, 'china':china1965. log10_income}).boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x26e6b308860>"
      ]
     },
     "execution_count": 9,
     "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": [
    "china1965.income.plot(kind='hist', histtype='step', bins=30)\n",
    "\n",
    "pp.axvline(china1965.income.mean(), c='C1')\n",
    "pp.axvline(china1965.income.median(), c='C1', linestyle= '--')\n",
    "pp.axvline(china1965.income.quantile(0.25), c='C1', linestyle= ':')\n",
    "pp.axvline(china1965.income.quantile(0.75), c='C1', linestyle= ':')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 3, 0.0, 1.2342999882944066)"
      ]
     },
     "execution_count": 13,
     "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": [
    "china1965.income.plot(kind='hist', histtype='step', bins=30, density=True)\n",
    "china1965.income.plot.density(bw_method=0.5)\n",
    "\n",
    "pp.axis(xmin=0, xmax=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "china1965.log10_income.plot.hist(histtype='step', bins=20)\n",
    "usa1965.log10_income.plot.hist(histtype='step', bins=20)\n",
    "\n",
    "levels = [0.25,0.5,1,2,4,8,16,32,64]\n",
    "pp.xticks(np.log10(levels), levels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "china2015.log10_income.plot.hist(histtype='step', bins=20)\n",
    "usa2015.log10_income.plot.hist(histtype='step', bins=20)\n",
    "\n",
    "levels = [0.25,0.5,1,2,4,8,16,32,64]\n",
    "pp.xticks(np.log10(levels), levels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "gapminder =pd.read_csv('gapminder.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>age5_surviving</th>\n",
       "      <th>babies_per_woman</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>gdp_per_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [country, year, region, population, life_expectancy, age5_surviving, babies_per_woman, gdp_per_capita, gdp_per_day]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder.query('country == \"china\" and year == 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}