SIT719_Prac02_Task01_numpy_pandas_data_analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SIT719: Security and Privacy Issues in Analytics\n",
    "**(Week 02: Programming Python using numpy, pandas)**\n",
    "\n",
    "---\n",
    "- Materials in this module include resources collected from various open-source online repositories.\n",
    "- You are free to use, change and distribute this package.\n",
    "- Created by Adnan Anwar\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Weekly Demo Python List\n",
    "L1 = [5, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L1[1] # 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L1[0] # 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-63d346bc1a39>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Will generate error as outside of the range\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mL1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m# raises IndexError\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "#Will generate error as outside of the range\n",
    "L1[2] # raises IndexError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "L2 = ['a', 1, [3, 4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L2[0] # 'a'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L2[2][0] # 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L2[-1] # last element: [3,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L2[-2] # second to last: 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#list(range(n)) \n",
    "list(range(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(['a', 1, 2, 34])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = ['a', 'b', 'c']\n",
    "\n",
    "L[-1] # 'c'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 'd']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.append('d')\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L[-1] # 'd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#WEEKLY DEMO CODE Dictionary\n",
    "truck_wheel = {'name':'wheel','mass':5.7, 'Ix':20.0,'Iy':1.,'Iz':17.,'center of mass':[0.,0.,0.]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'wheel'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truck_wheel['name'] # returns 'wheel'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.7"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truck_wheel['mass'] # returns 5.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.arange(15).reshape(3, 5)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'int32'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 7, 8])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([6, 7, 8])\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas demo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  #import pandas library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2\n",
       "0     1     3\n",
       "1     2     4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'col1': [1, 2], 'col2': [3, 4]}\n",
    "df = pd.DataFrame(data=d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  1  2  3\n",
       "1  4  5  6\n",
       "2  7  8  9"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the data with `read_csv()`\n",
    "iris_data = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0    1    2    3               4\n",
      "0    5.1  3.5  1.4  0.2     Iris-setosa\n",
      "1    4.9  3.0  1.4  0.2     Iris-setosa\n",
      "2    4.7  3.2  1.3  0.2     Iris-setosa\n",
      "3    4.6  3.1  1.5  0.2     Iris-setosa\n",
      "4    5.0  3.6  1.4  0.2     Iris-setosa\n",
      "5    5.4  3.9  1.7  0.4     Iris-setosa\n",
      "6    4.6  3.4  1.4  0.3     Iris-setosa\n",
      "7    5.0  3.4  1.5  0.2     Iris-setosa\n",
      "8    4.4  2.9  1.4  0.2     Iris-setosa\n",
      "9    4.9  3.1  1.5  0.1     Iris-setosa\n",
      "10   5.4  3.7  1.5  0.2     Iris-setosa\n",
      "11   4.8  3.4  1.6  0.2     Iris-setosa\n",
      "12   4.8  3.0  1.4  0.1     Iris-setosa\n",
      "13   4.3  3.0  1.1  0.1     Iris-setosa\n",
      "14   5.8  4.0  1.2  0.2     Iris-setosa\n",
      "15   5.7  4.4  1.5  0.4     Iris-setosa\n",
      "16   5.4  3.9  1.3  0.4     Iris-setosa\n",
      "17   5.1  3.5  1.4  0.3     Iris-setosa\n",
      "18   5.7  3.8  1.7  0.3     Iris-setosa\n",
      "19   5.1  3.8  1.5  0.3     Iris-setosa\n",
      "20   5.4  3.4  1.7  0.2     Iris-setosa\n",
      "21   5.1  3.7  1.5  0.4     Iris-setosa\n",
      "22   4.6  3.6  1.0  0.2     Iris-setosa\n",
      "23   5.1  3.3  1.7  0.5     Iris-setosa\n",
      "24   4.8  3.4  1.9  0.2     Iris-setosa\n",
      "25   5.0  3.0  1.6  0.2     Iris-setosa\n",
      "26   5.0  3.4  1.6  0.4     Iris-setosa\n",
      "27   5.2  3.5  1.5  0.2     Iris-setosa\n",
      "28   5.2  3.4  1.4  0.2     Iris-setosa\n",
      "29   4.7  3.2  1.6  0.2     Iris-setosa\n",
      "..   ...  ...  ...  ...             ...\n",
      "120  6.9  3.2  5.7  2.3  Iris-virginica\n",
      "121  5.6  2.8  4.9  2.0  Iris-virginica\n",
      "122  7.7  2.8  6.7  2.0  Iris-virginica\n",
      "123  6.3  2.7  4.9  1.8  Iris-virginica\n",
      "124  6.7  3.3  5.7  2.1  Iris-virginica\n",
      "125  7.2  3.2  6.0  1.8  Iris-virginica\n",
      "126  6.2  2.8  4.8  1.8  Iris-virginica\n",
      "127  6.1  3.0  4.9  1.8  Iris-virginica\n",
      "128  6.4  2.8  5.6  2.1  Iris-virginica\n",
      "129  7.2  3.0  5.8  1.6  Iris-virginica\n",
      "130  7.4  2.8  6.1  1.9  Iris-virginica\n",
      "131  7.9  3.8  6.4  2.0  Iris-virginica\n",
      "132  6.4  2.8  5.6  2.2  Iris-virginica\n",
      "133  6.3  2.8  5.1  1.5  Iris-virginica\n",
      "134  6.1  2.6  5.6  1.4  Iris-virginica\n",
      "135  7.7  3.0  6.1  2.3  Iris-virginica\n",
      "136  6.3  3.4  5.6  2.4  Iris-virginica\n",
      "137  6.4  3.1  5.5  1.8  Iris-virginica\n",
      "138  6.0  3.0  4.8  1.8  Iris-virginica\n",
      "139  6.9  3.1  5.4  2.1  Iris-virginica\n",
      "140  6.7  3.1  5.6  2.4  Iris-virginica\n",
      "141  6.9  3.1  5.1  2.3  Iris-virginica\n",
      "142  5.8  2.7  5.1  1.9  Iris-virginica\n",
      "143  6.8  3.2  5.9  2.3  Iris-virginica\n",
      "144  6.7  3.3  5.7  2.5  Iris-virginica\n",
      "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
      "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
      "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
      "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
      "149  5.9  3.0  5.1  1.8  Iris-virginica\n",
      "\n",
      "[150 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# Print out `iris_data`\n",
    "print(iris_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WEKKLY DEMO scikit learn\n",
    "#we will learn more in week 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
      "       [4.9, 3. , 1.4, 0.2],\n",
      "       [4.7, 3.2, 1.3, 0.2],\n",
      "       [4.6, 3.1, 1.5, 0.2],\n",
      "       [5. , 3.6, 1.4, 0.2],\n",
      "       [5.4, 3.9, 1.7, 0.4],\n",
      "       [4.6, 3.4, 1.4, 0.3],\n",
      "       [5. , 3.4, 1.5, 0.2],\n",
      "       [4.4, 2.9, 1.4, 0.2],\n",
      "       [4.9, 3.1, 1.5, 0.1],\n",
      "       [5.4, 3.7, 1.5, 0.2],\n",
      "       [4.8, 3.4, 1.6, 0.2],\n",
      "       [4.8, 3. , 1.4, 0.1],\n",
      "       [4.3, 3. , 1.1, 0.1],\n",
      "       [5.8, 4. , 1.2, 0.2],\n",
      "       [5.7, 4.4, 1.5, 0.4],\n",
      "       [5.4, 3.9, 1.3, 0.4],\n",
      "       [5.1, 3.5, 1.4, 0.3],\n",
      "       [5.7, 3.8, 1.7, 0.3],\n",
      "       [5.1, 3.8, 1.5, 0.3],\n",
      "       [5.4, 3.4, 1.7, 0.2],\n",
      "       [5.1, 3.7, 1.5, 0.4],\n",
      "       [4.6, 3.6, 1. , 0.2],\n",
      "       [5.1, 3.3, 1.7, 0.5],\n",
      "       [4.8, 3.4, 1.9, 0.2],\n",
      "       [5. , 3. , 1.6, 0.2],\n",
      "       [5. , 3.4, 1.6, 0.4],\n",
      "       [5.2, 3.5, 1.5, 0.2],\n",
      "       [5.2, 3.4, 1.4, 0.2],\n",
      "       [4.7, 3.2, 1.6, 0.2],\n",
      "       [4.8, 3.1, 1.6, 0.2],\n",
      "       [5.4, 3.4, 1.5, 0.4],\n",
      "       [5.2, 4.1, 1.5, 0.1],\n",
      "       [5.5, 4.2, 1.4, 0.2],\n",
      "       [4.9, 3.1, 1.5, 0.2],\n",
      "       [5. , 3.2, 1.2, 0.2],\n",
      "       [5.5, 3.5, 1.3, 0.2],\n",
      "       [4.9, 3.6, 1.4, 0.1],\n",
      "       [4.4, 3. , 1.3, 0.2],\n",
      "       [5.1, 3.4, 1.5, 0.2],\n",
      "       [5. , 3.5, 1.3, 0.3],\n",
      "       [4.5, 2.3, 1.3, 0.3],\n",
      "       [4.4, 3.2, 1.3, 0.2],\n",
      "       [5. , 3.5, 1.6, 0.6],\n",
      "       [5.1, 3.8, 1.9, 0.4],\n",
      "       [4.8, 3. , 1.4, 0.3],\n",
      "       [5.1, 3.8, 1.6, 0.2],\n",
      "       [4.6, 3.2, 1.4, 0.2],\n",
      "       [5.3, 3.7, 1.5, 0.2],\n",
      "       [5. , 3.3, 1.4, 0.2],\n",
      "       [7. , 3.2, 4.7, 1.4],\n",
      "       [6.4, 3.2, 4.5, 1.5],\n",
      "       [6.9, 3.1, 4.9, 1.5],\n",
      "       [5.5, 2.3, 4. , 1.3],\n",
      "       [6.5, 2.8, 4.6, 1.5],\n",
      "       [5.7, 2.8, 4.5, 1.3],\n",
      "       [6.3, 3.3, 4.7, 1.6],\n",
      "       [4.9, 2.4, 3.3, 1. ],\n",
      "       [6.6, 2.9, 4.6, 1.3],\n",
      "       [5.2, 2.7, 3.9, 1.4],\n",
      "       [5. , 2. , 3.5, 1. ],\n",
      "       [5.9, 3. , 4.2, 1.5],\n",
      "       [6. , 2.2, 4. , 1. ],\n",
      "       [6.1, 2.9, 4.7, 1.4],\n",
      "       [5.6, 2.9, 3.6, 1.3],\n",
      "       [6.7, 3.1, 4.4, 1.4],\n",
      "       [5.6, 3. , 4.5, 1.5],\n",
      "       [5.8, 2.7, 4.1, 1. ],\n",
      "       [6.2, 2.2, 4.5, 1.5],\n",
      "       [5.6, 2.5, 3.9, 1.1],\n",
      "       [5.9, 3.2, 4.8, 1.8],\n",
      "       [6.1, 2.8, 4. , 1.3],\n",
      "       [6.3, 2.5, 4.9, 1.5],\n",
      "       [6.1, 2.8, 4.7, 1.2],\n",
      "       [6.4, 2.9, 4.3, 1.3],\n",
      "       [6.6, 3. , 4.4, 1.4],\n",
      "       [6.8, 2.8, 4.8, 1.4],\n",
      "       [6.7, 3. , 5. , 1.7],\n",
      "       [6. , 2.9, 4.5, 1.5],\n",
      "       [5.7, 2.6, 3.5, 1. ],\n",
      "       [5.5, 2.4, 3.8, 1.1],\n",
      "       [5.5, 2.4, 3.7, 1. ],\n",
      "       [5.8, 2.7, 3.9, 1.2],\n",
      "       [6. , 2.7, 5.1, 1.6],\n",
      "       [5.4, 3. , 4.5, 1.5],\n",
      "       [6. , 3.4, 4.5, 1.6],\n",
      "       [6.7, 3.1, 4.7, 1.5],\n",
      "       [6.3, 2.3, 4.4, 1.3],\n",
      "       [5.6, 3. , 4.1, 1.3],\n",
      "       [5.5, 2.5, 4. , 1.3],\n",
      "       [5.5, 2.6, 4.4, 1.2],\n",
      "       [6.1, 3. , 4.6, 1.4],\n",
      "       [5.8, 2.6, 4. , 1.2],\n",
      "       [5. , 2.3, 3.3, 1. ],\n",
      "       [5.6, 2.7, 4.2, 1.3],\n",
      "       [5.7, 3. , 4.2, 1.2],\n",
      "       [5.7, 2.9, 4.2, 1.3],\n",
      "       [6.2, 2.9, 4.3, 1.3],\n",
      "       [5.1, 2.5, 3. , 1.1],\n",
      "       [5.7, 2.8, 4.1, 1.3],\n",
      "       [6.3, 3.3, 6. , 2.5],\n",
      "       [5.8, 2.7, 5.1, 1.9],\n",
      "       [7.1, 3. , 5.9, 2.1],\n",
      "       [6.3, 2.9, 5.6, 1.8],\n",
      "       [6.5, 3. , 5.8, 2.2],\n",
      "       [7.6, 3. , 6.6, 2.1],\n",
      "       [4.9, 2.5, 4.5, 1.7],\n",
      "       [7.3, 2.9, 6.3, 1.8],\n",
      "       [6.7, 2.5, 5.8, 1.8],\n",
      "       [7.2, 3.6, 6.1, 2.5],\n",
      "       [6.5, 3.2, 5.1, 2. ],\n",
      "       [6.4, 2.7, 5.3, 1.9],\n",
      "       [6.8, 3. , 5.5, 2.1],\n",
      "       [5.7, 2.5, 5. , 2. ],\n",
      "       [5.8, 2.8, 5.1, 2.4],\n",
      "       [6.4, 3.2, 5.3, 2.3],\n",
      "       [6.5, 3. , 5.5, 1.8],\n",
      "       [7.7, 3.8, 6.7, 2.2],\n",
      "       [7.7, 2.6, 6.9, 2.3],\n",
      "       [6. , 2.2, 5. , 1.5],\n",
      "       [6.9, 3.2, 5.7, 2.3],\n",
      "       [5.6, 2.8, 4.9, 2. ],\n",
      "       [7.7, 2.8, 6.7, 2. ],\n",
      "       [6.3, 2.7, 4.9, 1.8],\n",
      "       [6.7, 3.3, 5.7, 2.1],\n",
      "       [7.2, 3.2, 6. , 1.8],\n",
      "       [6.2, 2.8, 4.8, 1.8],\n",
      "       [6.1, 3. , 4.9, 1.8],\n",
      "       [6.4, 2.8, 5.6, 2.1],\n",
      "       [7.2, 3. , 5.8, 1.6],\n",
      "       [7.4, 2.8, 6.1, 1.9],\n",
      "       [7.9, 3.8, 6.4, 2. ],\n",
      "       [6.4, 2.8, 5.6, 2.2],\n",
      "       [6.3, 2.8, 5.1, 1.5],\n",
      "       [6.1, 2.6, 5.6, 1.4],\n",
      "       [7.7, 3. , 6.1, 2.3],\n",
      "       [6.3, 3.4, 5.6, 2.4],\n",
      "       [6.4, 3.1, 5.5, 1.8],\n",
      "       [6. , 3. , 4.8, 1.8],\n",
      "       [6.9, 3.1, 5.4, 2.1],\n",
      "       [6.7, 3.1, 5.6, 2.4],\n",
      "       [6.9, 3.1, 5.1, 2.3],\n",
      "       [5.8, 2.7, 5.1, 1.9],\n",
      "       [6.8, 3.2, 5.9, 2.3],\n",
      "       [6.7, 3.3, 5.7, 2.5],\n",
      "       [6.7, 3. , 5.2, 2.3],\n",
      "       [6.3, 2.5, 5. , 1.9],\n",
      "       [6.5, 3. , 5.2, 2. ],\n",
      "       [6.2, 3.4, 5.4, 2.3],\n",
      "       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': 'c:\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\iris.csv'}\n"
     ]
    }
   ],
   "source": [
    "# Import `datasets` from `sklearn`\n",
    "from sklearn import datasets\n",
    "# Load in the `digits` data\n",
    "iris_dataset = datasets.load_iris()\n",
    "# Print the `digits` data \n",
    "print(iris_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WEEKLY DEMO MatplotLib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([1, 2, 3, 4])\n",
    "plt.ylabel('some numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# evenly sampled time at 200ms intervals\n",
    "t = np.arange(0., 5., 0.2)\n",
    "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#WEEKLy DEMO Data Cleaning\n",
    "\n",
    "import pandas as pd\n",
    "from numpy import nan as NA\n",
    "data = pd.Series([1, NA, 3.5, NA, 7])\n",
    "\n",
    "data.isnull()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    0.0\n",
       "2    3.5\n",
       "3    0.0\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.000000\n",
       "1    3.833333\n",
       "2    3.500000\n",
       "3    3.833333\n",
       "4    7.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(data.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEMO CODE TRANSFORMATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Name  Age     Sex\n",
      "0  Adrian   24    Male\n",
      "1     Bob   28    Male\n",
      "2    Liza   18  Female\n",
      "3     Bob   28    Male\n"
     ]
    }
   ],
   "source": [
    "data = {\"Name\": [\"Adrian\", \"Bob\", \"Liza\", \"Bob\"],\n",
    "\t\t\"Age\": [24, 28, 18, 28],\n",
    "\t\t\"Sex\": [\"Male\", \"Male\", \"Female\", \"Male\"]}\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Name  Age     Sex\n",
      "0  Adrian   24    Male\n",
      "1     Bob   28    Male\n",
      "2    Liza   18  Female\n"
     ]
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adrian</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Liza</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age     Sex\n",
       "0  Adrian   24       M\n",
       "1     Bob   28       M\n",
       "2    Liza   18  Female"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.replace(to_replace =\"Male\", value =\"M\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adrian</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Liza</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age Sex\n",
       "0  Adrian   24   M\n",
       "1     Bob   28   M\n",
       "2    Liza   18   F"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.replace(to_replace =\"Female\", value =\"F\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.022022</td>\n",
       "      <td>0.007063</td>\n",
       "      <td>-0.012863</td>\n",
       "      <td>0.000241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.994765</td>\n",
       "      <td>1.005192</td>\n",
       "      <td>1.007881</td>\n",
       "      <td>0.985998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.719664</td>\n",
       "      <td>-0.621347</td>\n",
       "      <td>-0.669460</td>\n",
       "      <td>-0.650659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.060619</td>\n",
       "      <td>-0.014776</td>\n",
       "      <td>-0.001646</td>\n",
       "      <td>-0.016853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.631490</td>\n",
       "      <td>0.691372</td>\n",
       "      <td>0.685581</td>\n",
       "      <td>0.661554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean     -0.022022     0.007063    -0.012863     0.000241\n",
       "std       0.994765     1.005192     1.007881     0.985998\n",
       "min      -3.000000    -3.000000    -3.000000    -3.000000\n",
       "25%      -0.719664    -0.621347    -0.669460    -0.650659\n",
       "50%      -0.060619    -0.014776    -0.001646    -0.016853\n",
       "75%       0.631490     0.691372     0.685581     0.661554\n",
       "max       3.000000     3.000000     3.000000     3.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.DataFrame(np.random.randn(1000, 4))\n",
    "data.describe()\n",
    "col = data[2]\n",
    "col[np.abs(col) > 3]\n",
    "\n",
    "data[np.abs(data) > 3] = np.sign(data) * 3\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WEEKLy DEMO data Merge Combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>key</th>\n",
       "      <th>data1_x</th>\n",
       "      <th>data1_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1_x  data1_y\n",
       "0   b        0        1\n",
       "1   b        1        1\n",
       "2   b        6        1\n",
       "3   a        2        0\n",
       "4   a        4        0\n",
       "5   a        5        0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "df1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],'data1': range(7)})\n",
    "df2 = pd.DataFrame({'key': ['a', 'b', 'd'],'data1': range(3)})\n",
    "pd.merge(df1, df2, on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>key</th>\n",
       "      <th>data1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1\n",
       "0   b      0\n",
       "1   b      1\n",
       "2   a      2\n",
       "3   c      3\n",
       "4   a      4\n",
       "5   a      5\n",
       "6   b      6\n",
       "0   a      0\n",
       "1   b      1\n",
       "2   d      2"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>key</th>\n",
       "      <th>data1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1\n",
       "0   a      0\n",
       "1   a      1\n",
       "2   a      2\n",
       "3   a      3\n",
       "4   b      4\n",
       "5   b      5\n",
       "6   b      6"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'key': ['a', 'a', 'a', 'a', 'b', 'b', 'b'], 'data1': range(7)})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
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       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1\n",
       "0   a      0\n",
       "1   a      1\n",
       "2   a      2\n",
       "3   a      3"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_b=df1.iloc[0:4]\n",
    "df_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\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>key</th>\n",
       "      <th>data1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1\n",
       "4   b      4\n",
       "5   b      5\n",
       "6   b      6"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_b=df1.iloc[4:]\n",
    "df_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>number</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "number    one  two  three\n",
       "state                    \n",
       "Ohio        0    1      2\n",
       "Colorado    3    4      5"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.arange(6).reshape((2, 3)), index=pd.Index(['Ohio', 'Colorado'], name='state'),   columns=pd.Index(['one', 'two', 'three'],   name='number'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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