Analytics Foundation - Data Science with Python Bootcamp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basics\n",
    "# lambda functions: on the fly\n",
    "a = lambda x: x**2\n",
    "a(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Structures\n",
    "# list\n",
    "c = [1,1,2,3]\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index the first element\n",
    "c[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sets\n",
    "set(c)\n",
    "# removes duplicates in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 1, 'two': 2}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {}\n",
    "d['one'] = 1\n",
    "d['two'] = 2\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy and some important packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lists can store different data types\n",
    "a = [1, 'sdf', lambda x: x**2, [1,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 'sdf', <function <lambda> at 0x11c550950>, list([1, 2, 3])],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate pairwaise distance matrix between following points:\n",
    "(0,0),(4,0),(4,3),(0,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [4, 0],\n",
       "       [4, 3],\n",
       "       [0, 3]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts = np.array([[0,0],[4,0],[4,3],[0,3]])\n",
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check array length\n",
    "len(pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function\n",
    "def distance_matrix_py(pts):\n",
    "    n = len(pts)\n",
    "    p = len(pts[0])\n",
    "    m = np.zeros((n,n))\n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            s = 0\n",
    "            for k in range(p):\n",
    "                s += (pts[i,k] - pts[j,k]) ** 2\n",
    "            m[i,j] = np.sqrt(s)\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 4., 5., 3.],\n",
       "       [4., 0., 3., 5.],\n",
       "       [5., 3., 0., 4.],\n",
       "       [3., 5., 4., 0.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_matrix_py(pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.98870576, 0.91346192],\n",
       "       [0.06963591, 0.70112785],\n",
       "       [0.85088984, 0.96810796],\n",
       "       [0.33905919, 0.2555476 ],\n",
       "       [0.86032617, 0.861572  ],\n",
       "       [0.82192747, 0.0646557 ],\n",
       "       [0.52215052, 0.42845579],\n",
       "       [0.11827122, 0.01218488],\n",
       "       [0.38605915, 0.25854531],\n",
       "       [0.34321638, 0.78951171]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create random numbers\n",
    "r = np.random.rand(10,2)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.94327894, 0.14825457, 0.92460365, 0.13846979,\n",
       "        0.86503584, 0.67298197, 1.25297908, 0.88999927, 0.65728243],\n",
       "       [0.94327894, 0.        , 0.82561255, 0.52070209, 0.80680445,\n",
       "        0.98541331, 0.52831764, 0.69065752, 0.54406156, 0.28750301],\n",
       "       [0.14825457, 0.82561255, 0.        , 0.87733282, 0.10695305,\n",
       "        0.90391637, 0.63189714, 1.20437493, 0.848261  , 0.53817187],\n",
       "       [0.92460365, 0.52070209, 0.87733282, 0.        , 0.79936527,\n",
       "        0.51923164, 0.25183264, 0.32859206, 0.04709546, 0.53398029],\n",
       "       [0.13846979, 0.80680445, 0.10695305, 0.79936527, 0.        ,\n",
       "        0.79784087, 0.54950197, 1.12787589, 0.76718341, 0.52210652],\n",
       "       [0.86503584, 0.98541331, 0.90391637, 0.51923164, 0.79784087,\n",
       "        0.        , 0.47139869, 0.70560988, 0.47704756, 0.86866595],\n",
       "       [0.67298197, 0.52831764, 0.63189714, 0.25183264, 0.54950197,\n",
       "        0.47139869, 0.        , 0.57999996, 0.21769344, 0.40296253],\n",
       "       [1.25297908, 0.69065752, 1.20437493, 0.32859206, 1.12787589,\n",
       "        0.70560988, 0.57999996, 0.        , 0.36387338, 0.80922019],\n",
       "       [0.88999927, 0.54406156, 0.848261  , 0.04709546, 0.76718341,\n",
       "        0.47704756, 0.21769344, 0.36387338, 0.        , 0.53269205],\n",
       "       [0.65728243, 0.28750301, 0.53817187, 0.53398029, 0.52210652,\n",
       "        0.86866595, 0.40296253, 0.80922019, 0.53269205, 0.        ]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_matrix_py(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datetime module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usually needed for time series analysis\n",
    "# 3 Objects: datetime, date, time\n",
    "# key points:\n",
    "# 1. timezone aware v no aware"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "from pytz import timezone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set time zone\n",
    "# set using yyyy, mm, dd\n",
    "t1 = datetime.datetime(2019,12,12)\n",
    "t2 = datetime.datetime(2020,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(20)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check difference between 2 times\n",
    "t2 - t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check difference in days\n",
    "(t2-t1).days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set timezone\n",
    "t2 = datetime.datetime(2020,1,2, tzinfo=timezone('UTC'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analytics w Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Series gives us an index and a value\n",
    "a = pd.Series(data = [1,2,3], name = 'wage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: wage, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slice using index\n",
    "a.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=3, step=1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show indeces \n",
    "a.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'wage'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.0\n",
       "mean     2.0\n",
       "std      1.0\n",
       "min      1.0\n",
       "25%      1.5\n",
       "50%      2.0\n",
       "75%      2.5\n",
       "max      3.0\n",
       "Name: wage, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics\n",
    "a.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    4\n",
       "2    9\n",
       "Name: wage, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.apply(lambda x: x ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: wage, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3\n",
       "1    2\n",
       "0    1\n",
       "Name: wage, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: wage, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "D    2\n",
       "Name: wage, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add\n",
    "a.loc['D'] = 2\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "D     True\n",
       "Name: wage, dtype: bool"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dupes\n",
    "a.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3\n",
       "0    1\n",
       "Name: wage, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Know the data\n",
    "# Evaluate the model\n",
    "# Interpret the result\n",
    "# Present the result\n",
    "# Use R2 to assess model significance \n",
    "# Higher R2 means more linear relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "D    2\n",
       "Name: wage, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# do a pie chart\n",
    "%matplotlib inline\n",
    "a.plot.pie()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d8dbbe0>"
      ]
     },
     "execution_count": 63,
     "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": [
    "# do bar chart\n",
    "a.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plot\n",
    "r = np.random.rand(1000,2)\n",
    "rDF = pd.DataFrame(data=r)\n",
    "rDF.plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11dde2c18>"
      ]
     },
     "execution_count": 71,
     "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": [
    "# Histogram\n",
    "rDF[0].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rDF.plot(x=0, y=1, kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K3JVI6ykURLZCoi7JmD8+ypr1nwTV99y2gLt/MEq7mUrGajEUzGwI9Wcrl7l7XaPPH+XuD8VsTiSTlTz+Cpc/+GJw/cFf6M3M4pEROxLZepsNBTM7DzgHWAbcZGbnu/v9qbcvBxQKkneqamr5zs2lvLH2o6B6Ay4+ZgjFh2rdgWS+lkYKE4H93L3OzAYCd5nZQHefRsBZCSK55id3LeGO8pXB9RceuTvfHlGodQeSNVoKhYJPbxm5+2tmdjj1wbALCgXJI1U1tVx457MsWVkbVF9U2JPpp++vMJCs01IorDazYe6+GCA1YjgOuBnYK3p3IhngvNsWMXvJqqBa3SqSbNdSKJwOfOaEWHffAJxuZtOjdSWSAapqapk4o5zqxLqg+jMO3IVzR++m0YFktc2Ggrtv8uapuz/Z9u2IZIbWzh1MPXFPxo3YJWJHIumhdQoiTZw3q4LZS1cH1fboXMBjF+mcZMkdCgWRlPLqBONuKCVwUTITDxnElGOHxm1KJM0UCpL3yqsTnPP3RdTUhq07KOzVmXvPOUSjA8lJCgXJa4f9fh7L3/0wqLZbpw789bsHaAM7yWkKBclL5dUJTppeGlw/dKduPDDp8HgNiWQIhYLklXmVq7nwjsW892HYBnbbdoCZE7W9teQPhYLkjcOvmM9rifXB9drATvKRQkHywnF/WhAcCHt+vgdXn7KvtreWvKRQkJxWVVPLSX95Muh2kQGPXHCowkDymkJBclJ5dYJfzH6BylVhG9h17QSVlx0buSuRzBctFMxsADAD2AnYCJSkttxurnZ/oBQ4xd3vitWT5IevX7uQRa+vDa4fP6KQy07U/o4iEHeksAG40N0XmVkPoMLMHnH3ysZFZlYA/A6YG7EXyQPl1QnOve1ZVr2fDKo/8Au9uObb+2kRmkgj0ULB3VcBq1Kva81sGfXHelY2KT0XuBvYP1Yvkvu+9qcFLH0z7FbRdtsWcJfOSRZpVlrmFFKntu0LlDX5fH/gRODLKBRkCyTqkoy9diGvrwlblXzAwO244/ujInclkr2ih4KZdad+JDDJ3d9v8vbVwE/d/ROzTR/kZmbFQDFAYWFhrFYly1x0x7PcuejN4PoT9unHtG8Nj9iRSPYzd4/3m5t1AuYAc939ymber+a/x3r2AdYBxe5+36Z+z6KiIi8vL4/RrmSJ8uoEJ08vJfQn94CBvbj8xL10u0jymplVuHtRS3Uxnz4y4CZgWXOBAODugxrV3wrM2VwgiJx2YykLqxJBtTtv15lbJ4xQGIi0QszbR6OA8cBSM1uc+txkoBDA3a+P+L0lx1TV1HLtoy8HB8L5Xx7MBV/ZI3JXIrkn5tNHC/nvraGQ+jNi9SLZK1GXpHjGM1SsCFt3sHf/HtwyYYQeMxXZQlrRLBlr6pxKblhYHVx/wrB+TDtVE8kiW0OhIBnnvkWv89O7nyMZtrs1BcBc7Vkk0iYUCpJRii57mHc++DiotnD7bSk++AucdtCglotFJIhCQTJCVU0t37n538GBMHJQL/5x1kGRuxLJPwoFaVeJuiTnzKqgtHpN8NecP3owFxypJ4tEYlAoSLspefwVLn/wxeD6scM+zyXHDdWTRSIRKRQk7RJ1ScbfVBZ81kFRYU+mn76/wkAkDRQKklY/v3cpM8tWBNXu3LMzt35XK5JF0kmhIGmRqEty0G/mkfwkbMeivfv3YPa5h0buSkSaUihIdK2dO9BEskj7UShINOXVCS68cwnL310fVN+nWyfmXnCY5g5E2pFCQaJozUloABMPHsSU44ZG7EhEQigUpE3Nq1zNpH88S+1HG4Pq9+n/OW6ecIBGByIZQqEgbSJRl+SrVz7GO+s2BNUb8Ij2KxLJOAoF2WqzSpcz5b7ng2q7bWOcd8RunHXEbpG7EpEtoVCQrdKa7a33K+zJ3WcfHLkjEdkaCgXZIom6JKdMf4qqt9e1WLt91wJKxu9P0aDeaehMRLaGQkFaJVGX5M/z/sOtTy8Pqj9ijz7cMmFE5K5EpK0oFCTY9Mdf4bcPvkjYmmSYfMwQig/dNWpPItK2FArSokRdkrNnVVAWuL21zkkWyV4KBdmsWaXLueT+59kYODyYfPQQig/T6EAkWykUpFnzKlczbd5/eO6N94Pqtb21SG5QKMhnJOqSHDttAatrPwr+Gs0diOQOhYI0aO1upkN26Mas4gM1OhDJIQoFAeD82xZx/5JVQbW779CNy0/cS+sORHKQQiHPzatczaVzXmD5ux+2WHvcnjsy6cg9tF+RSA5TKOSpqppavvGXhaz9MGw300mjBzNJB9+I5DyFQh46a0Y5cytrguu/WdRfgSCSJxQKeSRRl+Rrf36CN9cmg+o7AA9re2uRvKJQyBOtebKoZ+cCzj58V21vLZKHooWCmQ0AZgA7ARuBEnef1qRmHPDT1Id1wA/cfUmsnvJReXWCyfcu5eW3PgiqH9i7C49d9OXIXYlIpoo5UtgAXOjui8ysB1BhZo+4e2WjmmrgMHdfY2ZHAyWAttRsI8dcvYDK1a04J/mQQUw5Vucki+SzaKHg7quAVanXtWa2DOgPVDaqearRl5QCO8fqJ98ccNnDvPXBx0G1Y/fpxyVf+5IWoYlIeuYUzGwgsC9QtpmyM4EHN/H1xUAxQGFhYRt3lzsSdUnuWbSSq+e9xAfJlnew6921Iw//6HCFgYg0iB4KZtYduBuY5O7N7q5mZkdQHwrNntXo7iXU31qiqKgodDv/vHL/4jc4/x+Lg+uP2KMvt0w4IGJHIpKNooaCmXWiPhBmufs9m6jZG7gRONrdEzH7yVXl1YngQDjyizvw06OG6DFTEWlWzKePDLgJWObuV26iphC4Bxjv7i/H6iVXJeqSnH7j07ywOuzJoqlj92TcyF0idyUi2SzmSGEUMB5Yamaf/jV2MlAI4O7XA78AegPX1WcIG9y9KGJPOWP646/wm8B1BwN6bct95xysuQMRaVHMp48WAtZCzfeA78XqIRdV1dQyccYzVCfWB9VrzyIRaQ2taM4SibokxTOeoWLF2qD6/XbpScl4nYQmIq2jUMgCs0qXM+W+54Nqv9CnKyXjizSRLCJbRKGQwcqrE/x+7ov8+7X3guq7dTLm//iIyF2JSC5TKGSgqppazrz1GZavCZs3ADh5v89zxcn7RuxKRPKBQiHDfPeWMua/9E5wfc9tC5j/4yM0dyAibUKhkCESdUnG/PFR1qz/JKi+o8EfTt6bscMHRO5MRPKJQiEDXPXwS0ybXxVc37/nNjx58ZEROxKRfKVQaEeJuiTfKnk6+KyDQb278PNjhzJ66E6ROxORfKVQaCdT51Ryw8LqoNqdPteZmWeO0GOmIhKdQiHNEnVJvnrV47wTeNZB145QOnlM5K5EROopFNKoNYvQAEYM3I7bvz8qYkciIp+lUEiDqppafvfQMh5Z9nZQ/Q49OvH37x2o20UiknYKhchOvGYhz64M268I4PzRg7lAG9iJSDtRKESSqEtS9Ot/EXpM3MhB23PtuOFahCYi7Uqh0MYSdUnOnllB2Wtrgr/mhH36Me1bwyN2JSISRqHQhkoef4XLAw++Adh5u87cOkGPmopI5lAotJFxJaU8+WrYEdO779CNy0/ci6JBvSN3JSLSOgqFrdTadQeTjxlC8aG7Ru5KRGTLKBS2UKIuyY1PvMpfHn81qF67mYpINlAobIGf37uUmWUrgutvOn0/7VckIllBodAKVTW1HDdtAR9uDKvvsW0Hlv7q6LhNiYi0IYVCoAk3l/Hoy+GH3xyxRx9umTAiYkciIm1PodCC8uoE428uZX3YPDJDduzGNd/eT4+ZikhWUihsxtevW8iiFWFbVBw4qBeXjd1LYSAiWU2h0Iyqmlq+P7OcqrfXBdUP7tuV2846KHJXIiLxKRSaOP+2Rdy/ZFVQ7bYFcO04PVkkIrlDoZAyr3I1F925mHfXf9JibecCuE5hICI5KO9DoaqmlhOufYIPPgrbz/TzPbfhqYuPjNyViEj7yOtQOOtvzzD3hbeCanfo1onffGNvjQ5EJKdFCwUzGwDMAHYCNgIl7j6tSY0B04BjgHXAGe6+KFZPn6qqqWX8jaWsqv0oqP6EYf2Ydqq2thaR3BdzpLABuNDdF5lZD6DCzB5x98pGNUcDu6V+jQD+kvpnND+5czF3VLwRVFvYqzM3n6GtrUUkf0QLBXdfBaxKva41s2VAf6BxKJwAzHB3B0rNbDsz65f62jY3/fFXggNh+64FLPjpmBhtiIhkrA7p+CZmNhDYFyhr8lZ/4PVGH69Mfa7NJeqSXDH3paDa00YUsugXR8VoQ0Qko0WfaDaz7sDdwCR3f7/p2818yf88BmRmxUAxQGFh4Rb1sXLNejoWGBs2bvopoy4FsPDiMdreWkTyVtSRgpl1oj4QZrn7Pc2UrAQGNPp4Z+DNpkXuXuLuRe5e1Ldv3y3qZedeXfDNPHU6fkQhy6Yeq0AQkbwWLRRSTxbdBCxz9ys3UTYbON3qjQTWxppP6N29M1ectDedCv47ODHgjAN3oeLnY7jsxL1ifFsRkawS8/bRKGA8sNTMFqc+NxkoBHD364EHqH8ctYr6R1InROyH44f1Z9TgPrzw5lrA+NLnP6eRgYhIIzGfPlpI83MGjWscOCdWD83p3b0zh+6+Qzq/pYhI1kjL00ciIpIdFAoiItJAoSAiIg0UCiIi0kChICIiDRQKIiLSQKEgIiINzDe390MGMrO3geVb+dv0Ad5pg3ayha439+XbNet6W28Xd29xn6CsC4W2YGbl7l7U3n2ki6439+XbNet649HtIxERaaBQEBGRBvkaCiXt3UCa6XpzX75ds643krycUxARkebl60hBRESakdOhYGZHmdlLZlZlZj9r5v3OZnZ76v2y1FnSWSvgen9kZpVm9pyZzTOzXdqjz7bS0vU2qjvJzNzMsvpplZDrNbNvpv6MXzCzv6e7x7YW8DNdaGaPmtmzqZ/rY9qjz7ZgZjeb2Vtm9vwm3jcz+1Pq38VzZjY8SiPunpO/gALgFeALwDbAEmBok5qzgetTr08Fbm/vviNf7xFA19TrH+T69abqegALgFKgqL37jvznuxvwLNAr9fEO7d13Gq65BPhB6vVQ4LX27nsrrvdQYDjw/CbePwZ4kPpzakYCZTH6yOWRwgFAlbu/6u4fAf8ATmhScwLw19Tru4DRqWNEs1GL1+vuj7r7utSHpdSfiZ2tQv58AS4Dfg98mM7mIgi53onAte6+BsDd30pzj20t5Jod+FzqdU+aOeM9W7j7AuDdzZScAMzweqXAdmbWr637yOVQ6A+83ujjlanPNVvj7huAtUDvtHTX9kKut7Ezqf9bR7Zq8XrNbF9ggLvPSWdjkYT8+e4O7G5mT5pZqZkdlbbu4gi55l8Bp5nZSuqP9z03Pa21i9b+N75FYp7R3N6a+xt/00etQmqyRfC1mNlpQBFwWNSO4trs9ZpZB+Aq4Ix0NRRZyJ9vR+pvIR1O/SjwCTPb093fi9xbLCHX/C3gVnf/o5kdCPwtdc0b47eXdmn5/1UujxRWAgMafbwz/zu0bKgxs47UDz83N3zLZCHXi5mNAaYAx7t7Mk29xdDS9fYA9gQeM7PXqL8HOzuLJ5tDf57vd/eP3b0aeIn6kMhWIdd8JnAHgLs/DWxL/T5BuSjov/Gtlcuh8Aywm5kNMrNtqJ9Int2kZjbwndTrk4D5nprRyUItXm/qdsp06gMh2+83b/Z63X2tu/dx94HuPpD6OZTj3b28fdrdaiE/z/dR/zABZtaH+ttJr6a1y7YVcs0rgNEAZvZF6kPh7bR2mT6zgdNTTyGNBNa6+6q2/iY5e/vI3TeY2Q+BudQ/xXCzu79gZpcC5e4+G7iJ+uFmFfUjhFPbr+OtE3i9VwDdgTtT8+kr3P34dmt6KwReb84IvN65wFfMrBL4BLjI3RPt1/XWCbzmC4EbzOwC6m+lnJGtf7Ezs9uov/XXJzVH8kugE4C7X0/9nMkxQBWwDpgQpY8s/fcnIiIR5PLtIxERaSWFgoiINFAoiIhIA4WCiIg0UCiIiEgDhYJIG2hph0uRbKFQEGkbtwLZvteQiEJBpC0E7HApkhUUCiIi0kChICIiDRQKIiLSQKEgIiINFApGVU6cAAAATElEQVQibSC1w+XTwB5mttLMzmzvnkS2hHZJFRGRBhopiIhIA4WCiIg0UCiIiEgDhYKIiDRQKIiISAOFgoiINFAoiIhIA4WCiIg0+P90Y1B4O9NrUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rDF[2] = rDF[1] + 2\n",
    "rDF.plot(x=1, y=2, kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data = [[1,2,3], [4,5,6]], index=['A','B'], columns = ['a','b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "A  1  2  3\n",
       "B  4  5  6"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "A  1  2  3\n",
       "B  4  5  6"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# square everything\n",
    "df.apply(lambda x:x**2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['d'] = df['a'] * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c  d\n",
       "A  1  2  3  2\n",
       "B  4  5  6  8"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>a</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  c\n",
       "B  4  6"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"(a>2) and (b<8)\")[['a','c']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>-25</td>\n",
       "      <td>-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b   c   d\n",
       "A  1  2   3   2\n",
       "B  4  5 -25 -25"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace\n",
    "df.mask(df > 5, -25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    3\n",
       "B    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aggregation\n",
    "# Group by sum of the numbers and show the mean\n",
    "# Group by a, calculate mean\n",
    "g = df.groupby(df.a).agg('mean').sort_values(by='b', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>a</th>\n",
       "      <th>b_x</th>\n",
       "      <th>c_x</th>\n",
       "      <th>d_x</th>\n",
       "      <th>b_y</th>\n",
       "      <th>c_y</th>\n",
       "      <th>d_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b_x  c_x  d_x  b_y  c_y  d_y\n",
       "0  1    2    3    2    2    3    2\n",
       "1  4    5    6    8    5    6    8"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Joining AKA merge\n",
    "df.merge(g, how='inner', left_on='a', right_on='a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>a</th>\n",
       "      <th>b_x</th>\n",
       "      <th>c_x</th>\n",
       "      <th>d_x</th>\n",
       "      <th>b_y</th>\n",
       "      <th>c_y</th>\n",
       "      <th>d_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a  b_x  c_x  d_x  b_y  c_y  d_y\n",
       "0  1.0  2.0  3.0  2.0  2.0  3.0  2.0\n",
       "1  4.0  5.0  6.0  8.0  NaN  NaN  NaN\n",
       "2  NaN  NaN  NaN  NaN  5.0  6.0  8.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.merge(g, how='outer', left_on='b', right_on='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Titanic Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for na\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop cabin bc it has so many nas\n",
    "new_data = data.drop('Cabin', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "4      35.0\n",
       "5      30.0\n",
       "6      54.0\n",
       "7       2.0\n",
       "8      27.0\n",
       "9      14.0\n",
       "10      4.0\n",
       "11     58.0\n",
       "12     20.0\n",
       "13     39.0\n",
       "14     14.0\n",
       "15     55.0\n",
       "16      2.0\n",
       "17     30.0\n",
       "18     31.0\n",
       "19     30.0\n",
       "20     35.0\n",
       "21     34.0\n",
       "22     15.0\n",
       "23     28.0\n",
       "24      8.0\n",
       "25     38.0\n",
       "26     30.0\n",
       "27     19.0\n",
       "28     30.0\n",
       "29     30.0\n",
       "       ... \n",
       "861    21.0\n",
       "862    48.0\n",
       "863    30.0\n",
       "864    24.0\n",
       "865    42.0\n",
       "866    27.0\n",
       "867    31.0\n",
       "868    30.0\n",
       "869     4.0\n",
       "870    26.0\n",
       "871    47.0\n",
       "872    33.0\n",
       "873    47.0\n",
       "874    28.0\n",
       "875    15.0\n",
       "876    20.0\n",
       "877    19.0\n",
       "878    30.0\n",
       "879    56.0\n",
       "880    25.0\n",
       "881    33.0\n",
       "882    22.0\n",
       "883    28.0\n",
       "884    25.0\n",
       "885    39.0\n",
       "886    27.0\n",
       "887    19.0\n",
       "888    30.0\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill nas in Age\n",
    "data['Age'].fillna(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    233\n",
       "male      109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.groupby('Sex').sum()['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>233</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>109</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived      \n",
       "            sum count\n",
       "Sex                  \n",
       "female      233   314\n",
       "male        109   577"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.groupby('Sex').agg({'Survived':['sum','count']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data.groupby('Sex').count()['PassengerId'].plot.pie()\n",
    "plt.show()"
   ]
  }
 ],
 "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
}