E01-Python&Numpy&Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 01.1\n",
    "\n",
    "by [Sergio Castelblanco](http://www.linkedin.com/in/sergio-castelblanco/) -- [Jhon Gonzalez](http://linkedin.com/in/jhon-franklin-gonzalez-gamboa-b6123230/)\n",
    "\n",
    "\n",
    "version 2.0, June 20 2018\n",
    "\n",
    "Create a function that receives two inputs a and b, and returns the product of the a decimal of pi and the b decimal of pi.\n",
    "\n",
    "```\n",
    "i.e, \n",
    "pi = 3.14159\n",
    "if a = 2 and b = 4\n",
    "result = 4 * 5\n",
    "result = 20\n",
    "```\n",
    "\n",
    "Caveats:\n",
    "- a and b are between 1 and 15\n",
    "- decimals positions 1 and 2 are 1 and 4, respectively. (remember that python start indexing in 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import pi\n",
    "def mult_dec_pi(a, b):\n",
    "# restandole la parte entera a la parte decimal para obtener solo los numeros decimales\n",
    "    number_dec = str(pi-int(pi))[1:]\n",
    "    result = float(number_dec[a])* float(number_dec[b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import pi\n",
    "def mult_dec_pi(a, b):\n",
    "    if a > 15:\n",
    "        return 'Error'\n",
    "    elif b > 15:\n",
    "        return 'Error'\n",
    "    else:\n",
    "        number_dec = str(pi-int(pi))[1:]\n",
    "        result = float(number_dec[a])* float(number_dec[b])\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult_dec_pi(a=2, b=4)\n",
    "# 20.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult_dec_pi(a=5, b=10)\n",
    "# 45.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult_dec_pi(a=14, b=1)\n",
    "# 9.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult_dec_pi(a=6, b=8)\n",
    "# 10.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Error'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bonus\n",
    "mult_dec_pi(a=16, b=4)\n",
    "# 'Error'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 01.2\n",
    "\n",
    "Using the given dataset. Estimate a linear regression between Employed and GNP.\n",
    "\n",
    "$$Employed = b_0 + b_1 * GNP $$\n",
    "\n",
    "$$\\hat b = (X^TX)^{-1}X^TY$$\n",
    "$$Y = Employed$$\n",
    "$$X = [1  \\quad GNP]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Employed')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11296bc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "# Import data\n",
    "raw_data = \"\"\"\n",
    "Year,Employed,GNP\n",
    "1947,60.323,234.289\n",
    "1948,61.122,259.426\n",
    "1949,60.171,258.054\n",
    "1950,61.187,284.599\n",
    "1951,63.221,328.975\n",
    "1952,63.639,346.999\n",
    "1953,64.989,365.385\n",
    "1954,63.761,363.112\n",
    "1955,66.019,397.469\n",
    "1956,67.857,419.18\n",
    "1957,68.169,442.769\n",
    "1958,66.513,444.546\n",
    "1959,68.655,482.704\n",
    "1960,69.564,502.601\n",
    "1961,69.331,518.173\n",
    "1962,70.551,554.894\"\"\"\n",
    "\n",
    "data = []\n",
    "for line in raw_data.splitlines()[2:]:\n",
    "    words = line.split(',')\n",
    "    data.append(words)\n",
    "data = np.array(data, dtype=np.float)\n",
    "n_obs = data.shape[0]\n",
    "plt.plot(data[:, 2], data[:, 1], 'bo')\n",
    "plt.xlabel(\"GNP\")\n",
    "plt.ylabel(\"Employed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1947.   ,    60.323,   234.289],\n",
       "       [ 1948.   ,    61.122,   259.426],\n",
       "       [ 1949.   ,    60.171,   258.054],\n",
       "       [ 1950.   ,    61.187,   284.599],\n",
       "       [ 1951.   ,    63.221,   328.975],\n",
       "       [ 1952.   ,    63.639,   346.999],\n",
       "       [ 1953.   ,    64.989,   365.385],\n",
       "       [ 1954.   ,    63.761,   363.112],\n",
       "       [ 1955.   ,    66.019,   397.469],\n",
       "       [ 1956.   ,    67.857,   419.18 ],\n",
       "       [ 1957.   ,    68.169,   442.769],\n",
       "       [ 1958.   ,    66.513,   444.546],\n",
       "       [ 1959.   ,    68.655,   482.704],\n",
       "       [ 1960.   ,    69.564,   502.601],\n",
       "       [ 1961.   ,    69.331,   518.173],\n",
       "       [ 1962.   ,    70.551,   554.894]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# h\n",
    "def lr_h(beta,x):\n",
    "    return np.dot(beta, x.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 60.323,  61.122,  60.171,  61.187,  63.221,  63.639,  64.989,\n",
       "         63.761,  66.019,  67.857,  68.169,  66.513,  68.655,  69.564,\n",
       "         69.331,  70.551]), array([[   1.   ,  234.289],\n",
       "        [   1.   ,  259.426],\n",
       "        [   1.   ,  258.054],\n",
       "        [   1.   ,  284.599],\n",
       "        [   1.   ,  328.975],\n",
       "        [   1.   ,  346.999],\n",
       "        [   1.   ,  365.385],\n",
       "        [   1.   ,  363.112],\n",
       "        [   1.   ,  397.469],\n",
       "        [   1.   ,  419.18 ],\n",
       "        [   1.   ,  442.769],\n",
       "        [   1.   ,  444.546],\n",
       "        [   1.   ,  482.704],\n",
       "        [   1.   ,  502.601],\n",
       "        [   1.   ,  518.173],\n",
       "        [   1.   ,  554.894]]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1])\n",
    "b = np.array(data[:,2])\n",
    "X = np.column_stack((a, b))\n",
    "y = data[:, 1]\n",
    "y, X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5.18435898e+01,   3.47522943e-02])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta = np.dot(np.linalg.inv(np.dot(X.T, X)),np.dot(X.T, y))\n",
    "beta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 01.3\n",
    "\n",
    "Analyze the baby names dataset using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# Load dataset\n",
    "import csv\n",
    "import zipfile\n",
    "with open('../Talleres/baby-names2.csv', 'r') as z:\n",
    "    names = pd.io.parsers.read_table(z, sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>John</td>\n",
       "      <td>0.081541</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880</td>\n",
       "      <td>William</td>\n",
       "      <td>0.080511</td>\n",
       "      <td>boy</td>\n",
       "      <td>W450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1880</td>\n",
       "      <td>James</td>\n",
       "      <td>0.050057</td>\n",
       "      <td>boy</td>\n",
       "      <td>J520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1880</td>\n",
       "      <td>Charles</td>\n",
       "      <td>0.045167</td>\n",
       "      <td>boy</td>\n",
       "      <td>C642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1880</td>\n",
       "      <td>George</td>\n",
       "      <td>0.043292</td>\n",
       "      <td>boy</td>\n",
       "      <td>G620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     name      prop  sex soundex\n",
       "0  1880     John  0.081541  boy    J500\n",
       "1  1880  William  0.080511  boy    W450\n",
       "2  1880    James  0.050057  boy    J520\n",
       "3  1880  Charles  0.045167  boy    C642\n",
       "4  1880   George  0.043292  boy    G620"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113000</th>\n",
       "      <td>1993</td>\n",
       "      <td>Michael</td>\n",
       "      <td>0.024010</td>\n",
       "      <td>boy</td>\n",
       "      <td>M240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113001</th>\n",
       "      <td>1993</td>\n",
       "      <td>Christopher</td>\n",
       "      <td>0.018572</td>\n",
       "      <td>boy</td>\n",
       "      <td>C623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113002</th>\n",
       "      <td>1993</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>0.017332</td>\n",
       "      <td>boy</td>\n",
       "      <td>M300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113003</th>\n",
       "      <td>1993</td>\n",
       "      <td>Joshua</td>\n",
       "      <td>0.016268</td>\n",
       "      <td>boy</td>\n",
       "      <td>J200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113004</th>\n",
       "      <td>1993</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>0.014439</td>\n",
       "      <td>boy</td>\n",
       "      <td>T460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year         name      prop  sex soundex\n",
       "113000  1993      Michael  0.024010  boy    M240\n",
       "113001  1993  Christopher  0.018572  boy    C623\n",
       "113002  1993      Matthew  0.017332  boy    M300\n",
       "113003  1993       Joshua  0.016268  boy    J200\n",
       "113004  1993        Tyler  0.014439  boy    T460"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[names.year == 1993].head()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### segment the data into boy and girl names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boys = names[names.sex == 'boy'].copy()    \n",
    "girls = names[names.sex == 'girl'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129000, 5)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the popularity of a name over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2pIQ4fnb6MB772ni276ng7L9O49EPVmt+/zZIRV9EIuaYA7N5/XtHc+Sgbvxy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\nUdEXEYkhKvoiIjFERV9EJIb8P+G/MymBxQkMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149688d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "william = boys[boys['name']=='William']\n",
    "\n",
    "plt.plot(range(william.shape[0]), william['prop'])\n",
    "plt.xticks(range(william.shape[0])[::5], william['year'].values[::5], rotation='vertical')\n",
    "plt.ylim([0, 0.1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3mtmJ7p4+7MUJQGuG+mIzOzZsP+buTWH7HWaW6Szq/m4/l+dHz/3waX+4P/ff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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e3c64e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Daniel = boys[boys['name']=='Daniel']\n",
    "\n",
    "plt.plot(range(Daniel.shape[0]), Daniel['prop'])\n",
    "plt.xticks(range(Daniel.shape[0])[::5], Daniel['year'].values[::5], rotation='vertical')\n",
    "plt.ylim([0, 0.1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 01.3\n",
    "\n",
    "Which has been the most popular boy name every decade?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>John</td>\n",
       "      <td>0.081541</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880</td>\n",
       "      <td>William</td>\n",
       "      <td>0.080511</td>\n",
       "      <td>boy</td>\n",
       "      <td>W450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1880</td>\n",
       "      <td>James</td>\n",
       "      <td>0.050057</td>\n",
       "      <td>boy</td>\n",
       "      <td>J520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1880</td>\n",
       "      <td>Charles</td>\n",
       "      <td>0.045167</td>\n",
       "      <td>boy</td>\n",
       "      <td>C642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1880</td>\n",
       "      <td>George</td>\n",
       "      <td>0.043292</td>\n",
       "      <td>boy</td>\n",
       "      <td>G620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1880</td>\n",
       "      <td>Frank</td>\n",
       "      <td>0.027380</td>\n",
       "      <td>boy</td>\n",
       "      <td>F652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1880</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>0.022229</td>\n",
       "      <td>boy</td>\n",
       "      <td>J210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1880</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>0.021401</td>\n",
       "      <td>boy</td>\n",
       "      <td>T520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1880</td>\n",
       "      <td>Henry</td>\n",
       "      <td>0.020641</td>\n",
       "      <td>boy</td>\n",
       "      <td>H560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1880</td>\n",
       "      <td>Robert</td>\n",
       "      <td>0.020404</td>\n",
       "      <td>boy</td>\n",
       "      <td>R163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     name      prop  sex soundex\n",
       "0  1880     John  0.081541  boy    J500\n",
       "1  1880  William  0.080511  boy    W450\n",
       "2  1880    James  0.050057  boy    J520\n",
       "3  1880  Charles  0.045167  boy    C642\n",
       "4  1880   George  0.043292  boy    G620\n",
       "5  1880    Frank  0.027380  boy    F652\n",
       "6  1880   Joseph  0.022229  boy    J210\n",
       "7  1880   Thomas  0.021401  boy    T520\n",
       "8  1880    Henry  0.020641  boy    H560\n",
       "9  1880   Robert  0.020404  boy    R163"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "      <th>decade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>John</td>\n",
       "      <td>0.081541</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880</td>\n",
       "      <td>William</td>\n",
       "      <td>0.080511</td>\n",
       "      <td>boy</td>\n",
       "      <td>W450</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1880</td>\n",
       "      <td>James</td>\n",
       "      <td>0.050057</td>\n",
       "      <td>boy</td>\n",
       "      <td>J520</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1880</td>\n",
       "      <td>Charles</td>\n",
       "      <td>0.045167</td>\n",
       "      <td>boy</td>\n",
       "      <td>C642</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1880</td>\n",
       "      <td>George</td>\n",
       "      <td>0.043292</td>\n",
       "      <td>boy</td>\n",
       "      <td>G620</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     name      prop  sex soundex  decade\n",
       "0  1880     John  0.081541  boy    J500    1880\n",
       "1  1880  William  0.080511  boy    W450    1880\n",
       "2  1880    James  0.050057  boy    J520    1880\n",
       "3  1880  Charles  0.045167  boy    C642    1880\n",
       "4  1880   George  0.043292  boy    G620    1880"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys['decade'] = boys.year - boys.year%10\n",
    "boys.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupando los niños por el máximo de los, esto permitirá conocer cuál ha sido el valor con mayor proporción de los años durante cada decada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decade</th>\n",
       "      <th>name_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1890</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1900</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1910</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1920</td>\n",
       "      <td>Robert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1930</td>\n",
       "      <td>Robert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1940</td>\n",
       "      <td>James</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1950</td>\n",
       "      <td>James</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1960</td>\n",
       "      <td>Michael</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1970</td>\n",
       "      <td>Michael</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1980</td>\n",
       "      <td>Michael</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1990</td>\n",
       "      <td>Michael</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2000</td>\n",
       "      <td>Jacob</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    decade   name_x\n",
       "0     1880     John\n",
       "1     1890     John\n",
       "2     1900     John\n",
       "3     1910     John\n",
       "4     1920   Robert\n",
       "5     1930   Robert\n",
       "6     1940    James\n",
       "7     1950    James\n",
       "8     1960  Michael\n",
       "9     1970  Michael\n",
       "10    1980  Michael\n",
       "11    1990  Michael\n",
       "12    2000    Jacob"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1 = boys.groupby(\"decade\",as_index=False).max()\n",
    "bpop = pd.merge(boys,b1, on=['decade','prop'])\n",
    "bpop[['decade','name_x']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como se puede apreciar se obtuvo el nombre más comun por cada decada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 01.4\n",
    "\n",
    "Which has been the most popular girl name?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g1 = girls.groupby(by=['name'], sort=True, as_index=False)['prop'].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1.prop = g1.prop/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2=g1.prop.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45118599999999998"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2729</th>\n",
       "      <td>Mary</td>\n",
       "      <td>0.451186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name      prop\n",
       "2729  Mary  0.451186"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g1.loc[g1['prop']==g2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con lo anterior se tiene que el nombre más popular para la mujer en todos los tiempos es Mary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 01.5\n",
    "\n",
    "What is the most popular new girl name? (new is a name that appears only in the 2000's)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>256030</th>\n",
       "      <td>2007</td>\n",
       "      <td>Nevaeh</td>\n",
       "      <td>0.003208</td>\n",
       "      <td>girl</td>\n",
       "      <td>N100</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year    name      prop   sex soundex  new\n",
       "256030  2007  Nevaeh  0.003208  girl    N100  new"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list to store the data\n",
    "new = []\n",
    "\n",
    "# For each row in the column,\n",
    "for row in girls['year']:\n",
    "    # if more than a value,\n",
    "    if row < 2000:\n",
    "        # Append a letter grade\n",
    "        new.append('old')\n",
    "    # otherwise,\n",
    "    else:\n",
    "        # Append a failing grade\n",
    "        new.append('new')\n",
    "girls['new'] = new\n",
    "old_names = girls.loc[girls['new'] == \"old\"].name.unique()\n",
    "gnew_max = girls[~girls.name.isin(old_names)]\n",
    "gnew_max[gnew_max['prop']== max(gnew_max.prop)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con lo anterior se tiene que el nombre más popular para la mujer después del 2000 es Nevaeh"
   ]
  }
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