Organizing code

Plotting the historical data on USD BRL on matplotlib.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "72.87\n",
+      "9147.87991648\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0, 0)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "#show(p)\n",
+    "import pandas as pd\n",
+    "\n",
+    "countries = ['Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda',\n",
+    "             'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan',\n",
+    "             'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus',\n",
+    "             'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia']\n",
+    "\n",
+    "life_expectancy_values = pd.Series([74.7,  75. ,  83.4,  57.6,  74.6,  75.4,  72.3,  81.5,  80.2,\n",
+    "                          70.3,  72.1,  76.4,  68.1,  75.2,  69.8,  79.4,  70.8,  62.7,\n",
+    "                          67.3,  70.6])\n",
+    "\n",
+    "gdp_values = pd.Series([ 1681.61390973,   2155.48523109,  21495.80508273,    562.98768478,\n",
+    "              13495.1274663 ,   9388.68852258,   1424.19056199,  24765.54890176,\n",
+    "              27036.48733192,   1945.63754911,  21721.61840978,  13373.21993972,\n",
+    "                483.97086804,   9783.98417323,   2253.46411147,  25034.66692293,\n",
+    "               3680.91642923,    366.04496652,   1175.92638695,   1132.21387981])\n",
+    "\n",
+    "\n",
+    "# Life expectancy and gdp data in 2007 for 20 countries\n",
+    "life_expectancy = pd.Series(life_expectancy_values)\n",
+    "gdp = pd.Series(gdp_values)\n",
+    "\n",
+    "# Change False to True for each block of code to see what it does\n",
+    "\n",
+    "# Accessing elements and slicing\n",
+    "if False:\n",
+    "    print life_expectancy[0]\n",
+    "    print gdp[3:6]\n",
+    "    \n",
+    "# Looping\n",
+    "if False:\n",
+    "    for country_life_expectancy in life_expectancy:\n",
+    "        print 'Examining life expectancy {}'.format(country_life_expectancy)\n",
+    "        \n",
+    "# Pandas functions\n",
+    "if False:\n",
+    "    print life_expectancy.mean()\n",
+    "    print life_expectancy.std()\n",
+    "    print gdp.max()\n",
+    "    print gdp.sum()\n",
+    "\n",
+    "# Vectorized operations and index arrays\n",
+    "if False:\n",
+    "    a = pd.Series([1, 2, 3, 4])\n",
+    "    b = pd.Series([1, 2, 1, 2])\n",
+    "  \n",
+    "    print a + b\n",
+    "    print a * 2\n",
+    "    print a >= 3\n",
+    "    print a[a >= 3]\n",
+    "   \n",
+    "\n",
+    "\n",
+    "def variable_correlation(variable1, variable2):\n",
+    "    '''\n",
+    "    Fill in this function to calculate the number of data points for which\n",
+    "    the directions of variable1 and variable2 relative to the mean are the\n",
+    "    same, and the number of data points for which they are different.\n",
+    "    Direction here means whether each value is above or below its mean.\n",
+    "    \n",
+    "    You can classify cases where the value is equal to the mean for one or\n",
+    "    both variables however you like.\n",
+    "    \n",
+    "    Each argument will be a Pandas series.\n",
+    "    \n",
+    "    For example, if the inputs were pd.Series([1, 2, 3, 4]) and\n",
+    "    pd.Series([4, 5, 6, 7]), then the output would be (4, 0).\n",
+    "    This is because 1 and 4 are both below their means, 2 and 5 are both\n",
+    "    below, 3 and 6 are both above, and 4 and 7 are both above.\n",
+    "    \n",
+    "    On the other hand, if the inputs were pd.Series([1, 2, 3, 4]) and\n",
+    "    pd.Series([7, 6, 5, 4]), then the output would be (0, 4).\n",
+    "    This is because 1 is below its mean but 7 is above its mean, and\n",
+    "    so on.\n",
+    "    '''\n",
+    "    mean1 = variable1.mean()\n",
+    "    mean2 = variable2.mean()\n",
+    "    print mean1\n",
+    "    print mean2\n",
+    "    \n",
+    "    #variable1 = variable1[variable1 > mean1]\n",
+    "    #variable2 = variable2[variable2 > mean2]\n",
+    "\n",
+    "    \n",
+    "    num_same_direction = 0        # Replace this with your code\n",
+    "    num_different_direction = 0   # Replace this with your code\n",
+    "    \n",
+    "    #for index, value in variable1.iteritems():\n",
+    "     #   if value > mean1:\n",
+    "      #      if variable2[index] > mean2:\n",
+    "       #         num_same_direction = num_same_direction + 1\n",
+    "        #elif value < mean1:\n",
+    "         #   if variable2[index] < mean2:\n",
+    "          #      num_same_direction = num_same_direction + 1\n",
+    "        #elif value > mean1:\n",
+    "         #   if variable2[index] < mean2:\n",
+    "          #      num_different_direction = num_different_direction + 1\n",
+    "        #elif value < mean1:\n",
+    "         #   if variable2[index] > mean2:\n",
+    "          #      num_different_direction = num_different_direction + 1\n",
+    "    \n",
+    "\n",
+    "    \n",
+    "        \n",
+    "                \n",
+    "            \n",
+    "        \n",
+    "\n",
+    "    \n",
+    "    return (num_same_direction, num_different_direction)\n",
+    "\n",
+    "\n",
+    "variable_correlation(life_expectancy_values, gdp_values)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

README.md

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+# Kamaji
+#### Documentation provided in IPython Notebook - Algar Kamaji Documentation

Untitled.ipynb

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+{
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+ "metadata": {},
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+}

Untitled1.ipynb

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+}