From 1afdd2b76a959f60f9d8244359f5babbb84f4255 Mon Sep 17 00:00:00 2001 From: Kunal Dudhavat <35394933+kunald11@users.noreply.github.com> Date: Wed, 30 May 2018 01:14:22 +0530 Subject: [PATCH 1/3] Add files via upload --- python-assn/assn-01/Kunal_Dudhavat-01.ipynb | 441 ++++++++++++++++++++ 1 file changed, 441 insertions(+) create mode 100644 python-assn/assn-01/Kunal_Dudhavat-01.ipynb diff --git a/python-assn/assn-01/Kunal_Dudhavat-01.ipynb b/python-assn/assn-01/Kunal_Dudhavat-01.ipynb new file mode 100644 index 0000000..eb04fed --- /dev/null +++ b/python-assn/assn-01/Kunal_Dudhavat-01.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 1: Strings, Lists and Files\n", + "\n", + "This assignment is based on basic python operations and to help you get a better understanding of lists, dictionaries, string operations, file I/O and other basic python operations. All the best!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q1) Given an positive integer, write a function to print the smallest integer that can be formed by rearranging the digits of the given number.**\n", + "\n", + "For e.g.:\n", + "```\n", + " >>> least_int(54235)\n", + " 23455\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def least_int(num):\n", + " #write your code here\n", + " numList = [int(d) for d in str(num)]\n", + " numList.sort()\n", + " snum = int(''.join(map(str,numList)))\n", + " return snum\n", + "least_int(554698)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**Q2) Given a string consisting of words and whitespaces, write a function to print a word consisting of the last letters of all the words beginning with the letter 'a'. Assume that all the alphabets in the strings are lower case.**\n", + "\n", + "For e.g.: \n", + "```\n", + ">>> mystring = 'apple banana aeroplace machine learning python append'\n", + ">>> function_a(mystring)\n", + "eed\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def function_a(string1):\n", + " #write your code here\n", + " lst = string1.split()\n", + " sr = \"\"\n", + " for i in range(0,len(lst)):\n", + " s = lst[i]\n", + " l = len(s)\n", + " if s[0] == 'a':\n", + " sr += s[l - 1]\n", + " return sr \n", + "function_a(\"apple banana aeroplace machine learning python append\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "**Q3) Given a string in the form of a sentence, remove all duplicate words from the sentence.(Order of words in the return value can differ from original)**\n", + "For e.g.:\n", + "```\n", + ">>> mystr = 'Vedang Pro is Pro at ML'\n", + ">>> remove_dup(mystr)\n", + "'Vedang at ML Pro is'\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def remove_dup(mystring):\n", + " #write your code here\n", + " mylist = mystring.split()\n", + " newlist = []\n", + " for string in mylist:\n", + " if string not in newlist:\n", + " newlist.append(string)\n", + " mystring = \" \".join(newlist)\n", + " return mystring \n", + "\n", + "remove_dup(\"Vedang Pro is Pro at ML\") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "______\n", + "**Q4) Write a single line of code to check if a given string ```mystring``` is a palindrome or not.**\n", + "For e.g.:\n", + "```\n", + ">>> mystring = 'SatanoscillatemymetallicsonataS'\n", + "#your code\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mystring = 'SatanoscillatemymetallicsonataS' #give any random string\n", + "#write your code here\n", + "def isPalindrome(mystring):\n", + " rev = mystring[::-1]\n", + " if mystring == rev:\n", + " print(\"It is a Palindrome!\")\n", + " else:\n", + " print(\"It is not a Palindrome!\")\n", + "isPalindrome(mystring)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "**Q5) Given a list of integers, create a new list where the i'th entry in the list is the average of previous i - 1 entries in the original list.**\n", + "For e.g.:\n", + "```\n", + ">>> a = [1, 2, 3, 4, 5]\n", + ">>> list_of_avgs(a)\n", + "[1.0, 1.5, 2.0, 2.5, 3.0]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def list_of_avgs(a):\n", + " #write your code here\n", + " sum_var=0\n", + " result=[]\n", + " for i in range(len(a)):\n", + " sum_var=sum_var+a[i]\n", + " result.append(sum_var/(i+1))\n", + " print(result)\n", + " return result\n", + "list_of_avgs([1, 2, 3, 4, 5]) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "**Q6) Given a list of random integers, modify the list such that all the elements in even places are in ascending order and all those in the odd places appear in a decreasing order. Assume labeling of the places start from the number zero.**\n", + "\n", + "For e.g.\n", + "```\n", + ">>> a = [1, 8, 4, 5, 2, 5, 3, 9, 4]\n", + ">>> asc_desc_list(a)\n", + "[1, 9, 2, 8, 3, 5, 4, 5, 4]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def asc_decc_list(a):\n", + " #write your code here\n", + " evenlist = a[::2]\n", + " oddlist = a[1::2]\n", + " oddlist.sort(reverse = True)\n", + " evenlist.sort()\n", + " newlist = [None]*(len(oddlist)+len(evenlist))\n", + " newlist[::2] = evenlist\n", + " newlist[1::2] = oddlist\n", + " return newlist\n", + "asc_decc_list([1, 8, 4, 5, 2, 5, 3, 9, 4])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "**Q7) Write an iterator function to return the elements of the Fibonacci series.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#write your code here\n", + "nterms = int(input(\"How many terms? \"))\n", + "\n", + "n1 = 0\n", + "n2 = 1\n", + "count = 0\n", + "\n", + "if nterms <= 0:\n", + " print(\"Please enter a positive integer\")\n", + "elif nterms == 1:\n", + " print(\"Fibonacci sequence upto\",nterms,\":\")\n", + " print(n1)\n", + "else:\n", + " print(\"Fibonacci sequence upto\",nterms,\":\")\n", + " while count < nterms:\n", + " print(n1,end=' , ')\n", + " nth = n1 + n2\n", + " \n", + " n1 = n2\n", + " n2 = nth\n", + " count += 1 \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "**Q8) Print all the sublists of a list, seperated by a comma and a whitespace.**\n", + "For e.g.:\n", + "```\n", + ">>> a = [1, 2]\n", + ">>> sub_lists(a)\n", + "[], [1], [1, 2] [2][\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def sub_lists(a):\n", + " #write your code here\n", + " sublist = [[]]\n", + " for i in range(len(a)):\n", + " for j in range(i + 1, len(a)):\n", + " sub = a[i:j]\n", + " sublist.append(sub)\n", + " return sublist\n", + " \n", + "\n", + "l1 = [1, 2, 3, 4]\n", + "print(sub_lists(l1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "**Q9) Write two functions, each of which take a list as an argument. The first function modifies the list by appending the sum of entries of the list passed as an argument. The second function reassigns a new list to the argument passed, where each entry in the new list is one more than the corresponding entry given as an argument. Is the change reflected outside the functions in both cases? Why/Why not?**\n", + "Note: For reassign_list(a), you have to reassign a new list to the argument, and not change the existing list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def modify_list(a):\n", + " #write your code here\n", + " lsum = sum(a)\n", + " a.append(lsum)\n", + " return a\n", + "\n", + "def reassign_list(a):\n", + " #write your code here\n", + " new_a = [x+1 for x in a]\n", + "a = [1, 2, 3]\n", + "modify_list(a)\n", + "print(a)\n", + "\n", + "a = [1, 2, 3]\n", + "reassign_list(a)\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "**Q10) Given a string, return a dictionary consisting of each character in the string and the position at which it occurs. (Positions are to be taken as keys, and position numbering starts from 1)**\n", + "\n", + "For e.g.)\n", + "\n", + "```\n", + ">>> a = 'Machine Learning'\n", + ">>> char_position(a)\n", + "{1: 'M', 2: 'a', 3: 'c', 4: 'h', 5: 'i', 6: 'n', 7: 'e', 9: 'L', 10: 'e', 11: 'a', 12: 'r', 13: 'n', 14: 'i', 15: 'n', 16: 'g'}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def char_position(a):\n", + " #write your code here\n", + " b = dict((i+1,a[i]) for i in range(len(a)) if (a[i]!= \" \"))\n", + " return b\n", + "char_position(\"Machine Learning\") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "**Q11) Given a string, arrange the characters in increasing order of their frequency at which they appear(Ignore any whitespaces)**\n", + "\n", + "For e.g.:\n", + "```\n", + ">>> a = 'ifadhaiusbdufbugfsdbfubgsidf'\n", + ">>> sort_by_freq(a)\n", + "'haaggiiisssdddduuuubbbbfffff'\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def sort_by_freq(a):\n", + " #write your code here\n", + " import string\n", + " char_freq = dict.fromkeys(string.ascii_lowercase,0)\n", + " for x in a:\n", + " char_freq[x]=char_freq[x]+1\n", + " \n", + " for key in char_freq:\n", + " min_freq=char_freq[key]\n", + " for key2 in char_freq:\n", + " if char_freq[key2]>min_freq and min_freq==0:\n", + " min_freq=char_freq[key2]\n", + " elif char_freq[key2]0:\n", + " min_freq=char_freq[key2]\n", + "\n", + " for key2 in char_freq:\n", + " if char_freq[key2]==min_freq:\n", + " for i in range(char_freq[key2]):\n", + " print(key2,end='')\n", + " char_freq[key2]=0\n", + " break\n", + "\n", + "sort_by_freq(\"ifadhaiusbdufbugfsdbfubgsidf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "---\n", + "**Q12)**\n", + "\n", + "**A) Create a file called myfile.txt**\n", + "\n", + "**B) Enter some a random paragraph into the file.**\n", + "\n", + "**C) Take an integer input n from the user. Create a file with the name as the nth word in myfile.txt.(Assume that n is less than the number of words in your file.)**\n", + "\n", + "**D) Enter the paragraph from myfile.txt into your new file, in reverse order. \n", + "Note: In part (D) only the order of the words should change, the words are not reversed in themselves.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Q12\n", + "n=int(input())\n", + "read_file = open(\"myfile.txt\",\"r+\")\n", + "words=[x for x in read_file.read().split(\" \")]\n", + "write_file = open(words[n-1], \"w+\")\n", + "for i in range(1,len(words),1):\n", + " write_file.write(words[len((words))-i-1])\n", + " write_file.write(\" \")\n", + "write_file.close()" + ] + } + ], + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e80288883de0429ad716e7936057bff4301e87a8 Mon Sep 17 00:00:00 2001 From: Kunal Dudhavat <35394933+kunald11@users.noreply.github.com> Date: Wed, 30 May 2018 01:14:59 +0530 Subject: [PATCH 2/3] Add files via upload --- python-assn/assn-02/Kunal_Dudhavat-02.ipynb | 423 ++++++++++++++++++++ 1 file changed, 423 insertions(+) create mode 100644 python-assn/assn-02/Kunal_Dudhavat-02.ipynb diff --git a/python-assn/assn-02/Kunal_Dudhavat-02.ipynb b/python-assn/assn-02/Kunal_Dudhavat-02.ipynb new file mode 100644 index 0000000..715f61d --- /dev/null +++ b/python-assn/assn-02/Kunal_Dudhavat-02.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Numpy exercise\n", + "===\n", + "\n", + "This exercise is designed to get you familiarised with numpy and shows the most common operations that you will perform in the future.\n", + "\n", + "You will need the Iris.csv file from the GitHub repositry to perform this exercise.\n", + "\n", + "Write your code in the following way:\n", + "#################################
\n", + "\n", + "Your code here\n", + "#################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A=np.arange(16) #This creates a vector with values from 0 to 15.\n", + "\n", + "#Reshape A into a 4x4 numpy array and print the array\n", + "#################################\n", + "A.shape = (4,4)\n", + "print(A)\n", + "#################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Expected output:
\n", + "[[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]\n", + " [12 13 14 15]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Create another array B which is made of square of each elements of B and print B.\n", + "#################################\n", + "B=np.empty([4,4],dtype='int')\n", + "B=A**2\n", + "print(B)\n", + "#################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Expected output:
\n", + "[[ 0 1 4 9]\n", + " [ 16 25 36 49]\n", + " [ 64 81 100 121]\n", + " [144 169 196 225]]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 0 0 0 0 0 0 0 240 206 217 81 0 0 0 0 218 2\n", + " 0 0 0 0 0 0 255 255 255 255]\n", + " [255 255 255 255 228 101 93 10 32 91 70 97 0 0 0 0 0 0\n", + " 0 0 32 32 32 49 32 32 51 53]\n", + " [ 32 32 54 55 32 49 49 52 32 49 48 49 32 32 57 55 32 49\n", + " 49 54 32 49 48 49 32 32 51 50]\n", + " [ 32 32 57 55 32 32 51 50 32 49 49 48 32 49 49 55 32 49\n", + " 48 57 32 49 49 50 32 49 50 49]\n", + " [ 32 32 51 50 32 32 57 55 10 32 49 49 52 32 49 49 52 32\n", + " 32 57 55 32 49 50 49 32 32 51]\n", + " [ 50 32 49 49 49 32 49 48 50 32 32 51 50 32 49 49 53 32\n", + " 49 48 52 32 32 57 55 32 49 49]\n", + " [ 50 32 49 48 49 32 32 51 50 32 32 52 48 32 32 53 48 32\n", + " 32 53 54 32 32 52 52 10 32 32]\n", + " [ 53 48 32 32 53 54 32 32 52 49 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49 55 32 49 48 53 32 49 49 48 32 49 49\n", + " 54 32 32 53 54 32 32 51 57 32 32 52 49 32 32 49 48 32\n", + " 49 49 50 32 49 49 52 32 49 48 53 32 49 49 48 32 49 49\n", + " 54 32 32 52 48 32 49 50 48 32 32 52 49 32 32 49 48 32\n", + " 32 51 53 10 32 32 51 53 32 32 51 53 32 32 51 53 32 32\n", + " 51 53 32 32 51 53 32 32 51 53 32 32 51 53 32 32 51 53\n", + " 32 32 51 53 32 32 51 53 32 32 51 53 32 32 51 53 32 32\n", + " 51 53 32 32 51 53 32 32 51 53 32 32 51 53 32 32 51 53\n", + " 32 32 51 53 10 0 10 112 114 105 110]\n", + "[[-1.70959053e-02 2.59759698e-03 1.37182524e-02 ... -1.75612468e-02\n", + " 1.07571267e-02 2.40498293e-02]\n", + " [ 3.40539936e-03 -5.21691889e-04 1.21487599e-02 ... -1.88415975e-07\n", + " 1.03528476e-03 1.31321075e-02]\n", + " [-9.43320303e-03 -1.28074581e-02 5.71105194e-03 ... 6.19979477e-03\n", + " 5.07207269e-04 -6.50443912e-03]\n", + " ...\n", + " [ 7.76289696e-03 2.92500197e-03 -1.24003235e-02 ... 1.36733358e-02\n", + " 1.29526136e-02 5.02107937e-03]\n", + " [-1.40916544e-02 2.57908976e-02 -4.13449709e-04 ... -5.02315078e-03\n", + " -9.43358502e-04 1.37318601e-02]\n", + " [-2.74217129e-03 -5.60632340e-03 -3.41017567e-02 ... 1.27718592e-02\n", + " -5.89726637e-03 -1.47913861e-02]]\n", + "(10, 785)\n", + "[ 20.16197655 11.17914099 -0.38878155 -27.72195217 23.27443093\n", + " 15.05961274 1.38725079 -2.16423043 22.14473215 -8.27130715]\n" + ] + } + ], + "source": [ + "#Create a numpy array of shape (28,28) whose values are chosen randomly from 0-255\n", + "#################################\n", + "x=np.empty([28,28],dtype='uint8')\n", + "print(x)\n", + "#################################\n", + "\n", + "#Convert x into a vector (Make it of single dimension)\n", + "#################################\n", + "x.shape=(x.size)\n", + "print(x)\n", + "#################################\n", + "\n", + "#Add the number 1 at the start of x\n", + "# e.g.: If x is [3,2,5,7,5,...], new x should be [1,3,2,5,7,5,...]\n", + "#################################\n", + "x=np.insert(x,0,1)\n", + "print(x)\n", + "#################################\n", + "\n", + "#Create vector W of shape (785,10) whose values follow a normal distribution with mean=0 and standard dev=0.01\n", + "#################################\n", + "w = np.random.normal(0, 0.01, (785, 10))\n", + "print(w)\n", + "#################################\n", + "\n", + "#Multiply the 2 matrices W and x. (Keep in mind the shape of the two)\n", + "#################################\n", + "print(w.T.shape)\n", + "if w.T.shape[1]==x.shape[0]:\n", + " mul = np.dot(w.T,x)\n", + " print(mul)\n", + "else :\n", + " print(\"Cannot be multiplied\") \n", + "#################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Load the Iris.csv file into numpy array 'data' and shuffle the data.(The data array should contain rows in random order)\n", + "#################################\n", + "import csv \n", + "import numpy as np\n", + "with open('Iris.csv', 'r') as f:\n", + " reader = csv.reader(f)\n", + " count=0\n", + " data = np.ones([150,5]) \n", + " for row in reader:\n", + " data[count]=row\n", + " count +=1\n", + " np.random.shuffle(data) \n", + " print(data)\n", + "#################################\n", + "\n", + "\n", + "#Create a numpy array X that contains the firts 4 columns of data. Keep the last column of data in numpy array y.\n", + "#################################\n", + "x = np.ones([150,4])\n", + "y = np.ones([150,1])\n", + "x = data[...,0:4]\n", + "y[...,0] = data[...,4]\n", + "\n", + "print(x)\n", + "print(y)\n", + "#################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the following:\n", + "# 1) First 10 values of data\n", + "# 2) Last 10 values of data\n", + "# 3) All rows of X whose corresponding value in y is 2\n", + "\n", + "#################################\n", + "print(data[0:10])\n", + "print(data[-10:])\n", + "'''\n", + "for i in range(150):\n", + " if y[i]==2:\n", + " print(x[i])\n", + "'''\n", + "print(x[(y==2).flatten()])\n", + "#################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a numpy array y1 of same length as y and number of columns 3. \n", + "# Values of y1 are such that,\n", + "# If y contains 0, y1 should have (1,0,0)\n", + "# If y contains 1, y1 should have (0,1,0)\n", + "# If y contains 2, y1 should have (0,0,1)\n", + "# This type of encoding is known as one-hot encoding\n", + "#################################\n", + "y1 = np.zeros([150,3])\n", + "\"\"\"for i in range(150):\n", + " if y[i]==0:\n", + " y1[i,0]=1\n", + " elif y[i]==1:\n", + " y1[i,1]=1\n", + " elif y[i]==2:\n", + " y1[i,2]=1\"\"\"\n", + "y1[(y==0).flatten(),0]=1\n", + "y1[(y==1).flatten(),1]=1\n", + "y1[(y==2).flatten(),2]=1\n", + "print(y1) \n", + "#################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "A=np.random.randint(100,size=(32,64,3))\n", + "# Convert A into shape (1,32,64,3) without using np.reshape()\n", + "#################################\n", + "B = np.zeros([1,32,64,3],dtype='i1')\n", + "B[0:1,0:32,0:64,0:3] = A[0:32,0:64,0:3]\n", + "A = B\n", + "print(A.shape)\n", + "#################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Expected Output:
(1,32,64,3)" + ] + } + ], + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 3aaad50101a3836eeae52feb9807d7742443c99d Mon Sep 17 00:00:00 2001 From: Kunal Dudhavat <35394933+kunald11@users.noreply.github.com> Date: Wed, 30 May 2018 01:15:46 +0530 Subject: [PATCH 3/3] Add files via upload --- .../assn-03/Kunal_Dudhavat dataframe.csv | 151 ++ python-assn/assn-03/Kunal_Dudhavat model.pkl | Bin 0 -> 936 bytes python-assn/assn-03/Kunal_Dudhavat-03.ipynb | 2269 +++++++++++++++++ 3 files changed, 2420 insertions(+) create mode 100644 python-assn/assn-03/Kunal_Dudhavat dataframe.csv create mode 100644 python-assn/assn-03/Kunal_Dudhavat model.pkl create mode 100644 python-assn/assn-03/Kunal_Dudhavat-03.ipynb diff --git a/python-assn/assn-03/Kunal_Dudhavat dataframe.csv b/python-assn/assn-03/Kunal_Dudhavat dataframe.csv new file mode 100644 index 0000000..819dfde --- /dev/null +++ b/python-assn/assn-03/Kunal_Dudhavat dataframe.csv @@ -0,0 +1,151 @@ +,sepal length (cm),sepal width (cm),petal length (cm),petal width (cm),target +0,5.1,3.5,1.4,0.2,0 +1,4.9,3.0,1.4,0.2,0 +2,4.7,3.2,1.3,0.2,0 +3,4.6,3.1,1.5,0.2,0 +4,5.0,3.6,1.4,0.2,0 +5,5.4,3.9,1.7,0.4,0 +6,4.6,3.4,1.4,0.3,0 +7,5.0,3.4,1.5,0.2,0 +8,4.4,2.9,1.4,0.2,0 +9,4.9,3.1,1.5,0.1,0 +10,5.4,3.7,1.5,0.2,0 +11,4.8,3.4,1.6,0.2,0 +12,4.8,3.0,1.4,0.1,0 +13,4.3,3.0,1.1,0.1,0 +14,5.8,4.0,1.2,0.2,0 +15,5.7,4.4,1.5,0.4,0 +16,5.4,3.9,1.3,0.4,0 +17,5.1,3.5,1.4,0.3,0 +18,5.7,3.8,1.7,0.3,0 +19,5.1,3.8,1.5,0.3,0 +20,5.4,3.4,1.7,0.2,0 +21,5.1,3.7,1.5,0.4,0 +22,4.6,3.6,1.0,0.2,0 +23,5.1,3.3,1.7,0.5,0 +24,4.8,3.4,1.9,0.2,0 +25,5.0,3.0,1.6,0.2,0 +26,5.0,3.4,1.6,0.4,0 +27,5.2,3.5,1.5,0.2,0 +28,5.2,3.4,1.4,0.2,0 +29,4.7,3.2,1.6,0.2,0 +30,4.8,3.1,1.6,0.2,0 +31,5.4,3.4,1.5,0.4,0 +32,5.2,4.1,1.5,0.1,0 +33,5.5,4.2,1.4,0.2,0 +34,4.9,3.1,1.5,0.1,0 +35,5.0,3.2,1.2,0.2,0 +36,5.5,3.5,1.3,0.2,0 +37,4.9,3.1,1.5,0.1,0 +38,4.4,3.0,1.3,0.2,0 +39,5.1,3.4,1.5,0.2,0 +40,5.0,3.5,1.3,0.3,0 +41,4.5,2.3,1.3,0.3,0 +42,4.4,3.2,1.3,0.2,0 +43,5.0,3.5,1.6,0.6,0 +44,5.1,3.8,1.9,0.4,0 +45,4.8,3.0,1.4,0.3,0 +46,5.1,3.8,1.6,0.2,0 +47,4.6,3.2,1.4,0.2,0 +48,5.3,3.7,1.5,0.2,0 +49,5.0,3.3,1.4,0.2,0 +50,7.0,3.2,4.7,1.4,1 +51,6.4,3.2,4.5,1.5,1 +52,6.9,3.1,4.9,1.5,1 +53,5.5,2.3,4.0,1.3,1 +54,6.5,2.8,4.6,1.5,1 +55,5.7,2.8,4.5,1.3,1 +56,6.3,3.3,4.7,1.6,1 +57,4.9,2.4,3.3,1.0,1 +58,6.6,2.9,4.6,1.3,1 +59,5.2,2.7,3.9,1.4,1 +60,5.0,2.0,3.5,1.0,1 +61,5.9,3.0,4.2,1.5,1 +62,6.0,2.2,4.0,1.0,1 +63,6.1,2.9,4.7,1.4,1 +64,5.6,2.9,3.6,1.3,1 +65,6.7,3.1,4.4,1.4,1 +66,5.6,3.0,4.5,1.5,1 +67,5.8,2.7,4.1,1.0,1 +68,6.2,2.2,4.5,1.5,1 +69,5.6,2.5,3.9,1.1,1 +70,5.9,3.2,4.8,1.8,1 +71,6.1,2.8,4.0,1.3,1 +72,6.3,2.5,4.9,1.5,1 +73,6.1,2.8,4.7,1.2,1 +74,6.4,2.9,4.3,1.3,1 +75,6.6,3.0,4.4,1.4,1 +76,6.8,2.8,4.8,1.4,1 +77,6.7,3.0,5.0,1.7,1 +78,6.0,2.9,4.5,1.5,1 +79,5.7,2.6,3.5,1.0,1 +80,5.5,2.4,3.8,1.1,1 +81,5.5,2.4,3.7,1.0,1 +82,5.8,2.7,3.9,1.2,1 +83,6.0,2.7,5.1,1.6,1 +84,5.4,3.0,4.5,1.5,1 +85,6.0,3.4,4.5,1.6,1 +86,6.7,3.1,4.7,1.5,1 +87,6.3,2.3,4.4,1.3,1 +88,5.6,3.0,4.1,1.3,1 +89,5.5,2.5,4.0,1.3,1 +90,5.5,2.6,4.4,1.2,1 +91,6.1,3.0,4.6,1.4,1 +92,5.8,2.6,4.0,1.2,1 +93,5.0,2.3,3.3,1.0,1 +94,5.6,2.7,4.2,1.3,1 +95,5.7,3.0,4.2,1.2,1 +96,5.7,2.9,4.2,1.3,1 +97,6.2,2.9,4.3,1.3,1 +98,5.1,2.5,3.0,1.1,1 +99,5.7,2.8,4.1,1.3,1 +100,6.3,3.3,6.0,2.5,2 +101,5.8,2.7,5.1,1.9,2 +102,7.1,3.0,5.9,2.1,2 +103,6.3,2.9,5.6,1.8,2 +104,6.5,3.0,5.8,2.2,2 +105,7.6,3.0,6.6,2.1,2 +106,4.9,2.5,4.5,1.7,2 +107,7.3,2.9,6.3,1.8,2 +108,6.7,2.5,5.8,1.8,2 +109,7.2,3.6,6.1,2.5,2 +110,6.5,3.2,5.1,2.0,2 +111,6.4,2.7,5.3,1.9,2 +112,6.8,3.0,5.5,2.1,2 +113,5.7,2.5,5.0,2.0,2 +114,5.8,2.8,5.1,2.4,2 +115,6.4,3.2,5.3,2.3,2 +116,6.5,3.0,5.5,1.8,2 +117,7.7,3.8,6.7,2.2,2 +118,7.7,2.6,6.9,2.3,2 +119,6.0,2.2,5.0,1.5,2 +120,6.9,3.2,5.7,2.3,2 +121,5.6,2.8,4.9,2.0,2 +122,7.7,2.8,6.7,2.0,2 +123,6.3,2.7,4.9,1.8,2 +124,6.7,3.3,5.7,2.1,2 +125,7.2,3.2,6.0,1.8,2 +126,6.2,2.8,4.8,1.8,2 +127,6.1,3.0,4.9,1.8,2 +128,6.4,2.8,5.6,2.1,2 +129,7.2,3.0,5.8,1.6,2 +130,7.4,2.8,6.1,1.9,2 +131,7.9,3.8,6.4,2.0,2 +132,6.4,2.8,5.6,2.2,2 +133,6.3,2.8,5.1,1.5,2 +134,6.1,2.6,5.6,1.4,2 +135,7.7,3.0,6.1,2.3,2 +136,6.3,3.4,5.6,2.4,2 +137,6.4,3.1,5.5,1.8,2 +138,6.0,3.0,4.8,1.8,2 +139,6.9,3.1,5.4,2.1,2 +140,6.7,3.1,5.6,2.4,2 +141,6.9,3.1,5.1,2.3,2 +142,5.8,2.7,5.1,1.9,2 +143,6.8,3.2,5.9,2.3,2 +144,6.7,3.3,5.7,2.5,2 +145,6.7,3.0,5.2,2.3,2 +146,6.3,2.5,5.0,1.9,2 +147,6.5,3.0,5.2,2.0,2 +148,6.2,3.4,5.4,2.3,2 +149,5.9,3.0,5.1,1.8,2 diff --git a/python-assn/assn-03/Kunal_Dudhavat model.pkl b/python-assn/assn-03/Kunal_Dudhavat model.pkl new file mode 100644 index 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--- /dev/null +++ b/python-assn/assn-03/Kunal_Dudhavat-03.ipynb @@ -0,0 +1,2269 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Using Pandas, Matplotlib and Scikit-Learn**\n", + "\n", + "NOTE: SAVE FILE BEFORE ATTEMPTING.\n", + "\n", + "Since the outputs will get cleared as you run cells. Take screenshots to compare answers to expected answers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Using Pandas**\n", + "\n", + "We start out by practicing how to handle data given to us. The rows in any dataset are called tuples or instances and the columns are the features with one of the columns being the label (there may also not be a label if it is an Unsupervised learning task). \n", + "\n", + "Storing this data as a list of lists might be cumbersome and difficult depending on the complexity and size of the data.\n", + "\n", + "Pandas offers a data structure called a DataFrame, which makes it easy to handle data and visualise the intricacies of it.\n", + "\n", + "In this example we'll try to handle and analyse the Iris dataset and build a simple model for learning it using Scikit-Learn. The problem is to look at a few features of the data (sepal length, sepal width, etc.) and determine which of the three given flowers (here called 0, 1 and 2) are we looking at." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "# Just to load the data. Don't worry about scikit-learn for now.\n", + "from sklearn.datasets import load_iris\n", + "data = load_iris()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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, 0.1],\n", + " [5. , 3.2, 1.2, 0.2],\n", + " [5.5, 3.5, 1.3, 0.2],\n", + " [4.9, 3.1, 1.5, 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]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We retrieve the X vector which contains the features of each tuple\n", + "X = data['data']\n", + "\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "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])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# And the y vector which is the numpy array containing the labels.\n", + "y = data['target']\n", + "\n", + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['sepal length (cm)',\n", + " 'sepal width (cm)',\n", + " 'petal length (cm)',\n", + " 'petal width (cm)']" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Here we store the names of the features for our understanding later.\n", + "headers = data['feature_names']\n", + "\n", + "headers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are also a lot of other keys in the dictionary that is the variable ```data```, but we'll not use these.\n", + "\n", + "We'll now make a DataFrame object to handle the data properly." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
55.43.91.70.4
64.63.41.40.3
75.03.41.50.2
84.42.91.40.2
94.93.11.50.1
105.43.71.50.2
114.83.41.60.2
124.83.01.40.1
134.33.01.10.1
145.84.01.20.2
155.74.41.50.4
165.43.91.30.4
175.13.51.40.3
185.73.81.70.3
195.13.81.50.3
205.43.41.70.2
215.13.71.50.4
224.63.61.00.2
235.13.31.70.5
244.83.41.90.2
255.03.01.60.2
265.03.41.60.4
275.23.51.50.2
285.23.41.40.2
294.73.21.60.2
...............
1206.93.25.72.3
1215.62.84.92.0
1227.72.86.72.0
1236.32.74.91.8
1246.73.35.72.1
1257.23.26.01.8
1266.22.84.81.8
1276.13.04.91.8
1286.42.85.62.1
1297.23.05.81.6
1307.42.86.11.9
1317.93.86.42.0
1326.42.85.62.2
1336.32.85.11.5
1346.12.65.61.4
1357.73.06.12.3
1366.33.45.62.4
1376.43.15.51.8
1386.03.04.81.8
1396.93.15.42.1
1406.73.15.62.4
1416.93.15.12.3
1425.82.75.11.9
1436.83.25.92.3
1446.73.35.72.5
1456.73.05.22.3
1466.32.55.01.9
1476.53.05.22.0
1486.23.45.42.3
1495.93.05.11.8
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150 rows × 4 columns

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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
55.43.91.70.4
64.63.41.40.3
75.03.41.50.2
84.42.91.40.2
94.93.11.50.1
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", + "0 5.1 3.5 1.4 0.2\n", + "1 4.9 3.0 1.4 0.2\n", + "2 4.7 3.2 1.3 0.2\n", + "3 4.6 3.1 1.5 0.2\n", + "4 5.0 3.6 1.4 0.2\n", + "5 5.4 3.9 1.7 0.4\n", + "6 4.6 3.4 1.4 0.3\n", + "7 5.0 3.4 1.5 0.2\n", + "8 4.4 2.9 1.4 0.2\n", + "9 4.9 3.1 1.5 0.1" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Return the first 10 rows in df\n", + "df[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
1406.73.15.62.4
1416.93.15.12.3
1425.82.75.11.9
1436.83.25.92.3
1446.73.35.72.5
1456.73.05.22.3
1466.32.55.01.9
1476.53.05.22.0
1486.23.45.42.3
1495.93.05.11.8
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", + "140 6.7 3.1 5.6 2.4\n", + "141 6.9 3.1 5.1 2.3\n", + "142 5.8 2.7 5.1 1.9\n", + "143 6.8 3.2 5.9 2.3\n", + "144 6.7 3.3 5.7 2.5\n", + "145 6.7 3.0 5.2 2.3\n", + "146 6.3 2.5 5.0 1.9\n", + "147 6.5 3.0 5.2 2.0\n", + "148 6.2 3.4 5.4 2.3\n", + "149 5.9 3.0 5.1 1.8" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Return the last 10 rows in df\n", + "df[-10:]" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(150, 4)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get to know the shape of the data\n", + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us there are 150 rows and 4 columns in ```df```." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
count150.000000150.000000150.000000150.000000
mean5.8433333.0540003.7586671.198667
std0.8280660.4335941.7644200.763161
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
75%6.4000003.3000005.1000001.800000
max7.9000004.4000006.9000002.500000
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) \\\n", + "count 150.000000 150.000000 150.000000 \n", + "mean 5.843333 3.054000 3.758667 \n", + "std 0.828066 0.433594 1.764420 \n", + "min 4.300000 2.000000 1.000000 \n", + "25% 5.100000 2.800000 1.600000 \n", + "50% 5.800000 3.000000 4.350000 \n", + "75% 6.400000 3.300000 5.100000 \n", + "max 7.900000 4.400000 6.900000 \n", + "\n", + " petal width (cm) \n", + "count 150.000000 \n", + "mean 1.198667 \n", + "std 0.763161 \n", + "min 0.100000 \n", + "25% 0.300000 \n", + "50% 1.300000 \n", + "75% 1.800000 \n", + "max 2.500000 " + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get a more detailed description of the dataset\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives us the statistics of each of features in detail. ```count``` is the number of values in that feature, ```mean``` and ```std``` are the mean and standard deviation and the other values are the minimum, maximum and values for each of the quartiles (1st, 2nd and 3rd).\n", + "\n", + "Other methods:" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 5.1\n", + "1 4.9\n", + "2 4.7\n", + "3 4.6\n", + "4 5.0\n", + "5 5.4\n", + "6 4.6\n", + "7 5.0\n", + "8 4.4\n", + "9 4.9\n", + "10 5.4\n", + "11 4.8\n", + "12 4.8\n", + "13 4.3\n", + "14 5.8\n", + "15 5.7\n", + "16 5.4\n", + "17 5.1\n", + "18 5.7\n", + "19 5.1\n", + "20 5.4\n", + "21 5.1\n", + "22 4.6\n", + "23 5.1\n", + "24 4.8\n", + "25 5.0\n", + "26 5.0\n", + "27 5.2\n", + "28 5.2\n", + "29 4.7\n", + " ... \n", + "120 6.9\n", + "121 5.6\n", + "122 7.7\n", + "123 6.3\n", + "124 6.7\n", + "125 7.2\n", + "126 6.2\n", + "127 6.1\n", + "128 6.4\n", + "129 7.2\n", + "130 7.4\n", + "131 7.9\n", + "132 6.4\n", + "133 6.3\n", + "134 6.1\n", + "135 7.7\n", + "136 6.3\n", + "137 6.4\n", + "138 6.0\n", + "139 6.9\n", + "140 6.7\n", + "141 6.9\n", + "142 5.8\n", + "143 6.8\n", + "144 6.7\n", + "145 6.7\n", + "146 6.3\n", + "147 6.5\n", + "148 6.2\n", + "149 5.9\n", + "Name: sepal length (cm), Length: 150, dtype: float64" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Retrieve only sepal_length from df\n", + "df[df.columns[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sepal length (cm) 5.0\n", + "sepal width (cm) 3.6\n", + "petal length (cm) 1.4\n", + "petal width (cm) 0.2\n", + "Name: 4, dtype: float64" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Return 5th row of df\n", + "df.loc[4]" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.4" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Return petal length of 5th row of df\n", + "df.iloc[4,2]" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
414.52.31.30.3
535.52.34.01.3
546.52.84.61.5
555.72.84.51.3
574.92.43.31.0
595.22.73.91.4
605.02.03.51.0
626.02.24.01.0
675.82.74.11.0
686.22.24.51.5
695.62.53.91.1
716.12.84.01.3
726.32.54.91.5
736.12.84.71.2
766.82.84.81.4
795.72.63.51.0
805.52.43.81.1
815.52.43.71.0
825.82.73.91.2
836.02.75.11.6
876.32.34.41.3
895.52.54.01.3
905.52.64.41.2
925.82.64.01.2
935.02.33.31.0
945.62.74.21.3
985.12.53.01.1
995.72.84.11.3
1015.82.75.11.9
1064.92.54.51.7
1086.72.55.81.8
1116.42.75.31.9
1135.72.55.02.0
1145.82.85.12.4
1187.72.66.92.3
1196.02.25.01.5
1215.62.84.92.0
1227.72.86.72.0
1236.32.74.91.8
1266.22.84.81.8
1286.42.85.62.1
1307.42.86.11.9
1326.42.85.62.2
1336.32.85.11.5
1346.12.65.61.4
1425.82.75.11.9
1466.32.55.01.9
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", + "41 4.5 2.3 1.3 0.3\n", + "53 5.5 2.3 4.0 1.3\n", + "54 6.5 2.8 4.6 1.5\n", + "55 5.7 2.8 4.5 1.3\n", + "57 4.9 2.4 3.3 1.0\n", + "59 5.2 2.7 3.9 1.4\n", + "60 5.0 2.0 3.5 1.0\n", + "62 6.0 2.2 4.0 1.0\n", + "67 5.8 2.7 4.1 1.0\n", + "68 6.2 2.2 4.5 1.5\n", + "69 5.6 2.5 3.9 1.1\n", + "71 6.1 2.8 4.0 1.3\n", + "72 6.3 2.5 4.9 1.5\n", + "73 6.1 2.8 4.7 1.2\n", + "76 6.8 2.8 4.8 1.4\n", + "79 5.7 2.6 3.5 1.0\n", + "80 5.5 2.4 3.8 1.1\n", + "81 5.5 2.4 3.7 1.0\n", + "82 5.8 2.7 3.9 1.2\n", + "83 6.0 2.7 5.1 1.6\n", + "87 6.3 2.3 4.4 1.3\n", + "89 5.5 2.5 4.0 1.3\n", + "90 5.5 2.6 4.4 1.2\n", + "92 5.8 2.6 4.0 1.2\n", + "93 5.0 2.3 3.3 1.0\n", + "94 5.6 2.7 4.2 1.3\n", + "98 5.1 2.5 3.0 1.1\n", + "99 5.7 2.8 4.1 1.3\n", + "101 5.8 2.7 5.1 1.9\n", + "106 4.9 2.5 4.5 1.7\n", + "108 6.7 2.5 5.8 1.8\n", + "111 6.4 2.7 5.3 1.9\n", + "113 5.7 2.5 5.0 2.0\n", + "114 5.8 2.8 5.1 2.4\n", + "118 7.7 2.6 6.9 2.3\n", + "119 6.0 2.2 5.0 1.5\n", + "121 5.6 2.8 4.9 2.0\n", + "122 7.7 2.8 6.7 2.0\n", + "123 6.3 2.7 4.9 1.8\n", + "126 6.2 2.8 4.8 1.8\n", + "128 6.4 2.8 5.6 2.1\n", + "130 7.4 2.8 6.1 1.9\n", + "132 6.4 2.8 5.6 2.2\n", + "133 6.3 2.8 5.1 1.5\n", + "134 6.1 2.6 5.6 1.4\n", + "142 5.8 2.7 5.1 1.9\n", + "146 6.3 2.5 5.0 1.9" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Return all instances in df where sepal width is less than 2.9 and store it in a different DataFrame, df1\n", + "### CODE HERE ###\n", + "df1 = df[(df[df.columns[1]] < 2.9)]\n", + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)5.843333
sepal width (cm)3.054000
petal length (cm)3.758667
petal width (cm)1.198667
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" + ], + "text/plain": [ + " 0\n", + "sepal length (cm) 5.843333\n", + "sepal width (cm) 3.054000\n", + "petal length (cm) 3.758667\n", + "petal width (cm) 1.198667" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get mean of each column in df\n", + "# Hint: use numpy.mean\n", + "import numpy as np\n", + "v = [np.mean(df['sepal length (cm)']),np.mean(df['sepal width (cm)']),np.mean(df['petal length (cm)']),np.mean(df['petal width (cm)'])]\n", + "f = pd.DataFrame(Z,index=['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)'])\n", + "f\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While we can always get the mean using ```df.describe()```, ```df.apply()``` can be used to apply any function to each column, such as ```np.sum```, ```np.max``` and so on." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Using Matplotlib**\n", + "\n", + "It is important to visualise the data we have so we can get useful inferences from it, such as by plotting one feature against another. If we can guess beforehand which features are more likely to decide the value of the target variable, we'll be able to create a better model accordingly.\n", + "\n", + "```matplotlib.pyplot``` is the library for creating 2D graphs in Python. It mimics the functions used in Matlab, which is a much more comprehensive tool for a wide range of experimental purposes." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Only for jupyter-notebooks; don't bother about this line elsewhere.\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll try to plot the features from the Iris dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "sl = df['sepal length (cm)']" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a plt.figure object\n", + "fig = plt.figure('Sepal length vs y')\n", + "\n", + "ax0 = fig.add_subplot(111) # This means there's only 1 plot in the whole figure. 211 would mean 2 plots\n", + "# and the top plot is being added. 212 means 2 plots and bottom plot is being added.\n", + "ax0.scatter(sl, y, c = 'b', marker='o', label='Sepal length')\n", + "ax0.set_xlabel('sepal length (cm)')\n", + "ax0.set_ylabel('flower')\n", + "ax0.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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sA1HqANXY2Pk2CxZkzyGkDvALFuSvM2GC+8SJ6b5WrQp1Vq1K933WWWFJ1amuDgfsr389lFtb0y/QkSNh3eTJoVxZGcqjR4fy5Mmh3NISXrAhQ9LxjB4d1jU0pOtkHtzd3SdNSq/rrO9589LvGFLPc9SosKT6WrUqJIT58zt/nsXsdymJYpNCMXMKLxOuDHoE2Aw84b03hNRl9fX13tTUVK7uZSBzzx6ySJZzaW/vOB6ea04h15h5ZtvJOqn/u8w6yfH4trawXeY8Q3JcPzlf0Noa+sns68iR7LmB1tbQduZ2Bw6E65nz9Z3sK9d8QXLuJNfzLGa/S6/rzTmFk919IE8vigTJA1ExB6ZkAkiWu1snV99DE/+OFRXZB1joOK6feZDO1QZkJ4RUnWS9zITQWd/JvnI9z+Q2uZ6nEkK/VsyH1040s7ujTye/YWZ3mdmkkkcmIiJ9rpikcAOwCTgRmAj8R7ROREQGmWKSwnh3v8HdW6PlRmB8ieMSEZEyKCYpvG1mnzGzimj5DLC71IGJiEjfKyYp/A3w58DvCZeYfipaJyIig0wx39H8GrCkD2IREZEy6zQpmNnafBu6+6W9H46IiJRTvjOFTxLuinos8Ic89UREZJDIlxTeAx4iXI76x30SjYiIlFW+pHAd8DPgJMJdUlMM8Gi9iIgMIp1efeTua939Q8B6dz8pY5nq7koIIiKDUMFLUt19eV8EIiIi5VfM5xREROR9QklBRERiSgoiIhJTUhARkZiSgoiIxJQUREQkpqQgIiIxJQUREYkpKYiISExJQUREYkoKIiISU1IQEZGYkoKIiMSUFEREJKakICIiMSUFERGJKSmIiEhMSUFERGJKCiIiElNSEBGRmJKCiIjElBRERCSmpCAiIjElBRERiSkpiIhITElBRERiJU0KZrbYzF4ws5fM7Iocj3/OzN4ysy3R8vlSxiMiIvkNLVXDZlYB/AD4MLADeMrMNrn784mqt7n7JaWKQ0REilfKM4XZwEvu/oq7HwFuBT5ewv5ERKSHSpkUJgLbM8o7onVJf2pmz5jZnWY2uYTxiIhIAaVMCpZjnSfK/wHUuPsM4H7gppwNmS0zsyYza3rrrbd6OUwREUkpZVLYAWS+858E7Mys4O673f1wVPwRUJerIXdf5+717l4/fvz4kgQrIiKlTQpPASeb2VQzGwZcAGzKrGBmJ2QUlwBbSxiPiIgUULKrj9y91cwuAX4OVADr3f03ZvYPQJO7bwIuNbMlQCvwDvC5UsUjIiKFmXtymL9/q6+v96ampnKHISIyoJhZs7vXF6qnTzSLiEhMSUFERGJKCiIiElNSEBGRmJKCiIjElBRERCSmpCAiIjElBRERiSkpiIhITElBRERiSgoiIhJTUhARkZiSgoiIxJQUREQkpqQgIiIxJQUREYkpKYiISExJQUREYiX7jmYRef9qaWlhx44dHDp0qNyhvO9UVVUxadIkKisru7W9koKI9LodO3YwevRoampqMLNyh/O+4e7s3r2bHTt2MHXq1G61oeEjEel1hw4dYuzYsUoIfczMGDt2bI/O0JQURKQklBDKo6f7XUlBRAaliooKamtrOe200/izP/szDhw4kLf+P/7jPxbVbk1NDW+//XaX4/n85z/P888/32H9jTfeyCWXXALAPffck1Vn4cKFNDU1dbmvnlBSEJFBacSIEWzZsoXnnnuOYcOGcd111+WtX2xS6K7rr7+eadOm5a2TTArloKQgImW3cSPU1MCQIeHnxo29235DQwMvvfQSABs2bGD27NnU1tbyhS98gba2Nq644goOHjxIbW0tS5cuBeATn/gEdXV1TJ8+nXXr1uVt//bbb+dLX/oSAGvWrOGkk04C4OWXX2bevHlA9rv+G264gVNOOYUFCxbw6KOPAvDYY4+xadMmvvKVr1BbW8vLL78MwB133MHs2bM55ZRT2Lx5c+/umByUFESkrDZuhGXLYNs2cA8/ly3rvcTQ2trKvffey+mnn87WrVu57bbbePTRR9myZQsVFRVs3LiR7373u/GZxcao4/Xr19Pc3ExTUxNr165l9+7dnfYxf/78+IC9efNmxo4dy+uvv84jjzxCQ0NDVt1du3bR2NjIo48+yn333RefGZx99tksWbKE733ve2zZsoUPfvCDcfxPPvkk11xzDd/4xjd6Z6fkoaQgImV15ZWQHO4/cCCs74nUO//6+nqqq6u56KKLeOCBB2hububMM8+ktraWBx54gFdeeSXn9mvXrmXmzJnMmTOH7du38+KLL3ba14QJE9i3bx979+5l+/btXHjhhTz88MNs3ry5Q1J44oknWLhwIePHj2fYsGH8xV/8Rd7n8clPfhKAuro6Xn311a7thG7Q5xREpKxee61r64uVeuefyd357Gc/y3e+85282z700EPcf//9PP7444wcOZKFCxcWvMxz7ty53HDDDZx66qk0NDSwfv16Hn/8cf7lX/6lQ92uXCE0fPhwIEyct7a2Fr1dd+lMQUTKqrq6a+t7YtGiRdx55528+eabALzzzjts27YNgMrKSlpaWgDYs2cPxx57LCNHjuS3v/0tv/zlLwu2PX/+fP75n/+Z+fPnc8YZZ/Dggw8yfPhwxowZk1XvrLPO4qGHHmL37t20tLRwxx13xI+NHj2avXv39tbT7RYlBREpq29/G0aOzF43cmRY39umTZvGt771LT7ykY8wY8YMPvzhD7Nr1y4Ali1bxowZM1i6dCmLFy+mtbWVGTNm8PWvf505c+YUbLuhoYHt27czf/58KioqmDx5cjzJnOmEE05g9erVzJ07l3PPPZdZs2bFj11wwQV873vf44wzzognmvuauXtZOu6u+vp67+vrdkWka7Zu3cqHPvShoutv3BjmEF57LZwhfPvbEF0EJN2Qa/+bWbO71xfaVnMKIlJ2S5cqCfQXGj4SEZGYkoKIiMSUFEREJKakICIiMSUFERGJKSmIyKDU17fO7u4ttfN59dVXufnmm+Ny5m22S0VJQUTKL/l5qV74/FR/u3V2dySTQl8oaVIws8Vm9oKZvWRmV+R4fLiZ3RY9/oSZ1ZQynn4t+U/Q3t7x8eS6gwezy4cPw7592et27swuv/deqJdp//7scq77qyT7TpZFumv1arj88vT/gHsor17da12U+tbZSbn6ADjqqKO48sor4xvtvfHGG0C4xfacOXM488wzWbVqFUcddRQAV1xxBZs3b6a2tparr74agJ07d7J48WJOPvlkvvrVr/bK/sni7iVZgArgZeAkYBjwa2Baos7FwHXR7xcAtxVqt66uzrvjmGPcw19b58uIEYUf37DBfcoUd7Pwc/ny7PKGDd0IrrHRfeVK9/b2UF61yr22Nvx0D+snTnSfMMG9rS2sSwU1d24oHzqUXrd3b3YdCOU9e9LlQ4fCuqqq7DotLWHdmDHp+BYsCPGk+m5rC+UFC7rxZOX94Pnnny+uYnt7+NuH9P9AstxNo0aNcnf3lpYWX7Jkif/whz/0559/3s8//3w/cuSIu7svX77cb7rppqz6Kbt373Z39wMHDvj06dP97bffdnf3KVOm+FtvvdWhv9T6fH0AvmnTJnd3/8pXvuLf/OY33d39vPPO85tvvtnd3a+99to4lgcffNDPO++8uI8bbrjBp06d6u+++64fPHjQq6ur/bXXXusQS679DzR5EcfuUn6ieTbwkru/AmBmtwIfBzK/VujjwOro9zuB75uZRU+g1xx7LLz7buF6yTfeuR7/zGfS5W3b4Nprs8vLloXfi/50pnsIbs2aUL7qKti0CVJ3d2xshMsug9dfD+W6OnjssfT2jz8eAsu8eczo0en6KTt3wsSJ6XJVVTiryLzz4/79MG5cel1ra/jWkz17Qjx1ddDcHH5u2QK1teGMYYhGIaWbzCB6B8yaNen/g5Urw/oefN9w6p0/hDOFiy66iHXr1sW3zk7VOf7443Nuv3btWu6++26A+NbZY8eOLdhv5u25k30MGzaM888/Hwi3wr7vvvsAePzxx7nnnnsAuPDCC/nyl7/cafuLFi2Kb7I3bdo0tm3bxuTJkwvGVaxSJoWJwPaM8g7grM7quHurme0BxgK9OltTTELoLan7wBedFDr7p6itDQfeiopQXrECNm8O63LdPSwpMwHkKgNEp6gdylVVsHcvDI3+PDITQSqe2tqwXglBeir1P5D624ceJwTo+1tnF9NHZWVlfNvs7t4KO3Ur7Z60kU8p/6NzvaLJM4Bi6mBmy8ysycya3nrrrV4JrpS6fB/4zMSQ0tycXV6zpuO65NUUhw6Fg3mm5BnDnj3ZZwfQcR4iMyFAOPAn+1ZCkN6SmkPIlDnH0ItKeevsYvrozJw5c7jrrrsAuPXWW+P15biVdin/q3cAmec0k4CdndUxs6HAGOCdZEPuvs7d6929fvz48SUKt/d0+T7wuf4p6uqyyytXdlyXPEOoqgpDR5mSZwhjxoR6mZJnDKNHZ082t7d37LuuTpPN0nOpv/01a8LfeHt7+LlmTUkSQylvnV1MH5255ppruOqqq5g9eza7du2Kh4dmzJjB0KFDmTlzZjzRXHLFTDx0ZyEMTb0CTCU90Tw9UeeLZE80316o3e5MNBczydxby8iRXZxsTk6spSZxIT25u2JFuoPaWvcDB7I7TZbB/fXX85fBfd++juXUxHNVVZh0zhVPsiySUPREs3vHCy1S/xONjaUIrV/av3+/t0fP/5ZbbvElS5b0qL2eTDSXLCmEGPgY8DvCVUhXRuv+AVgS/V4F3AG8BDwJnFSoTV19pKuPpP/rUlJw73iVUQ+uOhqIHn74YZ8xY4affvrp3tDQ4C+++GKP2utJUtCX7PQX7tkTa8mrelKH7sx1Bw/CiBHp8uHD0NKSPRy0cyeceGK6/N57MHx4WFL274dRo9Ll1tbsOYVc8eiqI8mjq1+yI71LX7IzGCSvtEgecM061slMCNDxYA/ZCQHg6KM79p2ZEKBjQsgVjxKCyKCk/2wRKYmBNgoxWPR0vyspiEivq6qqYvfu3UoMfczd2b17N1XJKwy7QMNHItLrJk2axI4dOxgInysabKqqqpg0aVK3t1dSEJFeV1lZydSpU8sdhnSDho9ERCSmpCAiIjElBRERiQ24D6+Z2VtA/rtLpY2jl++42gcGWswDLV5QzH1hoMULAy/mrsY7xd0L3jxuwCWFrjCzpmI+wdefDLSYB1q8oJj7wkCLFwZezKWKV8NHIiISU1IQEZHYYE8KXfu27f5hoMU80OIFxdwXBlq8MPBiLkm8g3pOQUREumawnymIiEgXDNqkYGaLzewFM3vJzK4odzyFmNl6M3vTzJ4rdyzFMLPJZvagmW01s9+Y2cpyx1SImVWZ2ZNm9uso5m+UO6ZimFmFmf3KzH5S7liKYWavmtmzZrbFzPr9l5+Y2TFmdqeZ/Tb6e55b7pjyMbNTo32bWt4zs8t6rf3BOHxkZhWEb3z7MOF7oJ8CPu3uz5c1sDzMbD6wD/h3dz+t3PEUYmYnACe4+9NmNhpoBj7Rz/exAaPcfZ+ZVQKPACvdvfhvZS8DM/sSUA8c7e7nlzueQszsVaDe3QfENf9mdhOw2d2vN7NhwEh3f7fccRUjOta9Dpzl7sV+fiuvwXqmMBt4yd1fcfcjwK3Ax8scU17u/jDwTrnjKJa773L3p6Pf9wJbgYnljSq/6FsJ90XFymjp1++KzGwScB5wfbljGYzM7GhgPvBjAHc/MlASQmQR8HJvJQQYvElhIrA9o7yDfn7AGsjMrAY4A3iivJEUFg3FbAHeBO5z9/4e8zXAV4H2cgfSBQ78wsyazWxZuYMp4CTgLeCGaIjuejMbVWijfuQC4JbebHCwJgXLsa5fvyMcqMzsKOAu4DJ3f6/c8RTi7m3uXgtMAmabWb8dqjOz84E33b253LF00TnuPgv4KPDFaGi0vxoKzAKudfczgP1Av5+DBIiGupYAd/Rmu4M1KewAJmeUJwE7yxTLoBWNy98FbHT3/1vueLoiGiJ4CFhc5lDyOQdYEo3R3wr8iZltKG9Ihbn7zujnm8DdhOHc/moHsCPjjPFOQpIYCD4KPO3ub/Rmo4M1KTwFnGxmU6NsegGwqcwxDSrRpO2Pga0nNFVzAAAFYklEQVTuflW54ymGmY03s2Oi30cA5wK/LW9UnXP3/+Puk9y9hvA3/F/u/pkyh5WXmY2KLjwgGob5CNBvr6hz998D283s1GjVIqDfXiyR8Gl6eegIBuk3r7l7q5ldAvwcqADWu/tvyhxWXmZ2C7AQGGdmO4BGd/9xeaPK6xzgL4FnozF6gK+5+0/LGFMhJwA3RVdsDAFud/cBcZnnAPIB4O7wnoGhwM3u/rPyhlTQCmBj9AbyFeCvyxxPQWY2knB15Rd6ve3BeEmqiIh0z2AdPhIRkW5QUhARkZiSgoiIxJQUREQkpqQgIiIxJQXpl8zsc2Z2YhH1bjSzTxW7vhfi+lrG7zX57mprZnPN7EdFtHmimd3ZyWMPmVl9V/tOtHGZmf1VMXULtHOJmfX7yzWlZ5QUpL/6HFAwKZTB1wpXiS0GCl6j7+473b2YBNaVvgEws6HA3wA3d3XbHNYDl/ZCO9KPKSlIyUXvan9rZjeZ2TPRvetHRo/Vmdl/RzdP+7mZnRC9w68nfKBoi5mNMLNVZvaUmT1nZuuiT1QX23+HPqL1D5nZP0XfsfA7M2uI1o80s9ujWG8zsyfMrN7MvguMiGLaGDVfYWY/svD9DL+IPimdsgi438x+amYzorZ/ZWarot+/aWafz3zXHz3XW1N9AyOi9V3tO+VPCLdCaI3a+SMzu9/Cd0o8bWYfNLOF0f65PdoP3zWzpdF+edbMPgjg7geAV82sP9+2QnpISUH6yqnAOnefAbwHXBzdO+lfgU+5ex3hnei33f1OoAlY6u617n4Q+L67nxl918QIoKjvFeisj4wqQ919NnAZ0Bituxj4QxTrN4E6AHe/AjgYxbQ0qnsy8AN3nw68C/xp1O84oMXd9wAPAw0WbtPcSvg0OMA8YHMi5OXAgajvb3en74RzCN91kbIx2mYmcDawK1o/E1gJnE74pPop0X65nvCJ35QmoCFHPzJIKClIX9nu7o9Gv28gHBBPBU4D7otulfH3hJsX5vLH0Tv2ZwnvfqcX2W+hPlI38msGaqLf5xFuQIe7Pwc8k6f9/3H31G0+Mtv4CPCL6PfNhHv2zwP+EzgqOlOqcfcXEu3NJ+wf3P2Zbvad6QTCraGJ7kk00d3vjto/FL37B3gq+o6Mw8DLGbE/m2j3TfrnsJ70kkF57yPpl5L3U3HCLc5/4+55v/7QzKqAHxK+zWu7ma0Gqorst1Afh6OfbaT/H4oemsrYPtVGagjno0DqRoFPEYbDXgHuA8YB/5vsd/CZir33TGd9ZzpIel/le16ZbbVnlNvJPk5URW3KIKUzBekr1Zb+7ttPE74K8wVgfGq9mVWaWeoMYC8wOvo9dVB728L3N3TlqqJ8fXTmEeDPo/rTCEMqKS3RkFSnovmOGcAWCN/mRfjSpz8Hfkk4c/gyHYeOIAw1LY3aOS1qp+i+c9gK/FEUx3vADjP7RNT+8NTcThecQj++66n0nJKC9JWtwGfN7BngOMKXmhwhHOD/ycx+TTiInh3VvxG4LhryOQz8iDCUcQ/hnXdRCvTRmR8SEskzwN8RhnD2RI+tA57JmOzNpQ74lWffbXIz8EY0XLOZMISVKylcSxheeobwjWtPZjxWTN9J9xKGpFL+Erg0av8xYEIX2oIwR3F/F7eRAUR3SZWSs/B1nT+JJon7PQu31q5090PRlTcPECZejxS5/d8TviP81lLGWSwzuxv4qru/2MN2zgC+5O5/2TuRSX+kOQWRjkYCD0ZDNQYsLzYhALj7t0oWWfdcQZhw7lFSIMyFfL3n4Uh/pjMFERGJaU5BRERiSgoiIhJTUhARkZiSgoiIxJQUREQkpqQgIiKx/w8UjRPEB9uGUwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot petal width vs y with blue o's and petal length vs y with red x's\n", + "fig = plt.figure('Petal width vs y')\n", + "pw = df['petal width (cm)']\n", + "pl = df['petal length (cm)']\n", + "ax0 = fig.add_subplot(111)\n", + "ax0.scatter(pw, y, c = 'b', marker='o', label='Petal width')\n", + "ax0.scatter(pl, y, c = 'r', marker='x', label='Petal length')\n", + "ax0.set_xlabel('petal length/width (cm)')\n", + "ax0.set_ylabel('flower')\n", + "ax0.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot sepal length vs petal length for each instance\n", + "fig = plt.figure('Sepal length vs petal length')\n", + "ax0 = fig.add_subplot(111)\n", + "ax0.scatter(sl, pl, c = 'm', marker='o', label='Sepal length')\n", + "ax0.set_xlabel('sepal length (cm)')\n", + "ax0.set_ylabel('petal length (cm)')\n", + "ax0.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot sepal length as a line graph for each instance\n", + "# Hint: use numpy.arange() for x axis\n", + "number = np.arange(150)\n", + "sl = df['sepal length (cm)']\n", + "fig = plt.figure('Sepal length')\n", + "ax0 = fig.add_subplot(111)\n", + "ax0.plot(number, sl, c = 'g', label='Sepal length')\n", + "ax0.set_xlabel('instance')\n", + "ax0.set_ylabel('sepal length (cm)')\n", + "ax0.legend()\n", + "\n", + "plt.show()\n", + "### CODE HERE ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Using Scikit-Learn**\n", + "\n", + "Scikit-Learn offers a variety of classifiers for learning the data and creating a model for it. This task is a classification task and hence a regression method such as Linear Regression cannot be used for it. We'll use Logistic Regression to create a classifier for this data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before using a model on the data, we will need some data to test the model on. For this, we'll keep some data aside called the 'test set'.\n", + "\n", + "Also, we can have some problems in learning the data if the data is not randomly shuffled and chosen first. (More on this later)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'shuffle' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'shuffle' is not defined" + ] + } + ], + "source": [ + "# Join target variable to dataframe\n", + "df['target']=y\n", + "# Randomly shuffle dataframe\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "df = shuffle(df)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", + " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# We divide df into training and testing sets such that the training set is 80% of df and test set is 20%\n", + "train, test = train_test_split(df, test_size=0.2)\n", + "\n", + "y_train = train['target']\n", + "y_test = test['target']\n", + "X_train = train[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']]\n", + "X_test = test[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']]\n", + "\n", + "# Create a classifier\n", + "clf = LogisticRegression()\n", + "# Train it on the training set\n", + "clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 2, 1, 1, 2, 0, 2, 0, 0, 2, 0, 2, 1, 1, 2, 2, 0, 0, 1, 1,\n", + " 1, 0, 2, 2, 0, 2, 2, 1])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate the predictions on the test set\n", + "pred = clf.predict(X_test)\n", + "\n", + "pred" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9333333333333333" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Find the accuracy on the test set\n", + "accuracy_score(pred, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the accuracy is high, we can assume this is a good model we have created. However, there are a lot of factors that decide how relevant the accuracy is to the actual performance of the model. (Again, more on this later!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Saving data and model**\n", + "\n", + "To save the data, we'll use the csv format, which is quite popular with data scientists since it is easy to read from and write to. \n", + "\n", + "To store the model, we'll use the pickle module in python to save it to disk.\n", + "\n", + "Both of these shall be in the same directory as this." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dataframe.csv Kunal_Dudhavat-01.ipynb Kunal_Dudhavat-03.ipynb\r\n", + "Iris.csv Kunal_Dudhavat-02.ipynb model.pkl\r\n" + ] + } + ], + "source": [ + "ls" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "# Save dataframe to csv\n", + "df.to_csv('dataframe.csv')\n", + "\n", + "# Save model to model.pkl\n", + "filename = 'model.pkl'\n", + "outfile = open('model.pkl','wb')\n", + "pickle.dump(clf,outfile)\n", + "outfile.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is recommended you practice these functions to get a good idea of how to create a model for a dataset in Python. Hope you enjoyed it!" + ] + } + ], + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}