AppScrip DS Assignment Part 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTING THE LIBRARIES\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. How to convert the index of a series into a column of a dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Input\n",
    "mylist = list('chitychitybangbang')\n",
    "myarr = np.arange(26)\n",
    "mydict = dict(zip(mylist, myarr))\n",
    "ser = pd.Series(mydict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SOLUTION 1\n",
    "df1 = pd.DataFrame(ser)\n",
    "df1= pd.DataFrame.transpose(df)\n",
    "\n",
    "#SOLUTION 2\n",
    "df2 = pd.DataFrame(ser).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. How to get the items of series A not present in series B?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Input\n",
    "ser1 = pd.Series([1, 2, 3, 4, 5] )\n",
    "ser2 = pd.Series([4, 5, 6, 7, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "5    6\n",
       "6    7\n",
       "7    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SOLUTION\n",
    "pd.concat([ser1,ser2]).drop_duplicates().reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. How to get frequency counts of unique items of a series?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Input \n",
    "ser = pd.Series(np.take(list('abcdefgh'), np.random.randint(8, size=30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    6\n",
       "a    5\n",
       "h    4\n",
       "c    4\n",
       "f    4\n",
       "g    3\n",
       "e    3\n",
       "d    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SOLUTION\n",
    "ser.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. How to convert a numpy array to a dataframe of given shape?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Input\n",
    "ser = pd.Series(np.random.randint(1, 10, 35))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser_5by7 = np.array(ser).reshape((5,7))\n",
    "ser_7by5 = np.array(ser).reshape((7,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 X 7\n",
      "[[5 5 3 2 7 6 6]\n",
      " [4 2 8 8 5 5 5]\n",
      " [2 3 3 1 8 8 9]\n",
      " [7 4 3 7 8 9 8]\n",
      " [9 1 5 9 7 6 7]]\n",
      "7 x 5\n",
      "[[5 5 3 2 7]\n",
      " [6 6 4 2 8]\n",
      " [8 5 5 5 2]\n",
      " [3 3 1 8 8]\n",
      " [9 7 4 3 7]\n",
      " [8 9 8 9 1]\n",
      " [5 9 7 6 7]]\n"
     ]
    }
   ],
   "source": [
    "print('5 X 7')\n",
    "print(ser_5by7)\n",
    "print('7 x 5')\n",
    "print(ser_7by5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 5, 3, 2, 7],\n",
       "       [6, 6, 4, 2, 8],\n",
       "       [8, 5, 5, 5, 2],\n",
       "       [3, 3, 1, 8, 8],\n",
       "       [9, 7, 4, 3, 7],\n",
       "       [8, 9, 8, 9, 1],\n",
       "       [5, 9, 7, 6, 7]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_7by5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Calculate the Mean Square Error on a truth and predicted series?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Input -\n",
    "truth = pd.Series(range(10))\n",
    "pred = pd.Series(range(10)) + np.random.random(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean Square Error\n",
    "def mse(y_true, y_pred):\n",
    "    return np.square(y_true- y_pred).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3691"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(mse(truth, pred), 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. CRUD operations on Mongo DB or Postgres using any backend framework(flask or Django)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The respective project was generated in visual studio"
   ]
  }
 ],
 "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
}