ipynb/NumPy Exercises Part 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome back, folks! This post is the last in the series of NumPy exercises. In this post, we will see intermediate and advanced level exercises. Remember, the more you practice, the more you will understand NumPy and will use it with ease in your ML projects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now start by importing the NumPy as usual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 41: Convert a numeric to a categorical (text) array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Bin the petal length (3rd) column of the_data_2d to form a text array, such that if petal length is:\n",
    "\n",
    "   * Less than 3 we change it to 'small.'\n",
    "   * between 3 and 5 we change it to 'medium.'\n",
    "   * Greater or equal to 5 we change it to 'large.'\n",
    "   \n",
    "Finally, print the first four values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "the_data_2d = np.genfromtxt(url, delimiter=',', dtype='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#['small', 'small', 'small', 'small']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "petal_length_bins = np.digitize(the_data_2d[:,2].astype(\"float\"),[0,3,5,10])\n",
    "label_map = {1: 'small', 2: 'medium', 3: 'large', 4: np.nan}\n",
    "petal_lenght_categorized = [label_map[x] for x in petal_lenght_bins]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['small', 'small', 'small', 'small']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "petal_lenght_categorized[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by selecting the sepal length column and cast it to float. Then we use the digitize method to create bins or, as I like to call them, containers for the corresponding range of values in the column. \n",
    "\n",
    "So we are assigning the following values:\n",
    "* 1 to all the elements between 0 and 3 exclusive (3 not included) in the petal length column. \n",
    "* 2 to all the elements between 3 and 5 exclusive.\n",
    "* Finally, 3 to all the elements between 5 to 10 exclusive.\n",
    "\n",
    "We then now create a dictionary with key-value pairs where each key has a corresponding text. 1 is associated with small, 2 is associated with medium, and finally, 3 is associated with large.\n",
    "\n",
    "Finally, we use list comprehension to go through the bins and replace each instance of 1, 2, and 3 by the corresponding text and print the first four elements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 42: Create a new column from existing columns of a NumPy array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Create a new column for volume in the_data_2d, where volume is (pi x petal length(3rd column) x sepal length(1st column)^2)/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "the_data_2d = np.loadtxt(url, delimiter=',', dtype=float,usecols=[0,1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([[ 5.1       ,  3.5       ,  1.4       ,  0.2       , 38.13265163],\n",
    "#        [ 4.9       ,  3.        ,  1.4       ,  0.2       , 35.20049849],\n",
    "#        [ 4.7       ,  3.2       ,  1.3       ,  0.2       , 30.07237208],\n",
    "#        [ 4.6       ,  3.1       ,  1.5       ,  0.2       , 33.23805027],\n",
    "#        [ 5.        ,  3.6       ,  1.4       ,  0.2       , 36.65191429]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "volume = (np.pi * the_data_2d[:,2] * pow(the_data_2d[:,0],2))/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 38.13,  35.2 ,  30.07,  33.24,  36.65,  51.91,  31.02,  39.27,\n",
       "        28.38,  37.71,  45.8 ,  38.6 ,  33.78,  21.3 ,  42.27,  51.04,\n",
       "        39.7 ,  38.13,  57.84,  40.86,  51.91,  40.86,  22.16,  46.3 ,\n",
       "        45.84,  41.89,  41.89,  42.47,  39.64,  37.01,  38.6 ,  45.8 ,\n",
       "        42.47,  44.35,  37.71,  31.42,  41.18,  37.71,  26.36,  40.86,\n",
       "        34.03,  27.57,  26.36,  41.89,  51.75,  33.78,  43.58,  31.02,\n",
       "        44.12,  36.65, 241.17, 193.02, 244.3 , 126.71, 203.52, 153.11,\n",
       "       195.35,  82.97, 209.83, 110.43,  91.63, 153.1 , 150.8 , 183.14,\n",
       "       118.22, 206.84, 147.78, 144.43, 181.14, 128.08, 174.97, 155.86,\n",
       "       203.66, 183.14, 184.44, 200.71, 232.43, 235.04, 169.65, 119.08,\n",
       "       120.38, 117.21, 137.39, 192.27, 137.41, 169.65, 220.94, 182.88,\n",
       "       134.64, 126.71, 139.38, 179.24, 140.91,  86.39, 137.93, 142.9 ,\n",
       "       142.9 , 173.09,  81.71, 139.5 , 249.38, 179.66, 311.46, 232.75,\n",
       "       256.62, 399.21, 113.14, 351.57, 272.65, 331.15, 225.64, 227.33,\n",
       "       266.32, 170.12, 179.66, 227.33, 243.34, 415.99, 428.41, 188.5 ,\n",
       "       284.19, 160.92, 415.99, 203.66, 267.95, 325.72, 193.22, 190.93,\n",
       "       240.2 , 314.86, 349.8 , 418.28, 240.2 , 211.97, 218.21, 378.74,\n",
       "       232.75, 235.91, 180.96, 269.23, 263.25, 254.27, 179.66, 285.69,\n",
       "       267.95, 244.45, 207.82, 230.07, 217.37, 185.91])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first get the volume by multiplying the pie constant with the third column times the power by two of the first column then finally dividing the whole by 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But there is an issue; the volume is a one-dimensional array, while the_data_2d is a two-dimensional array. It means that we can not stack them together because they are of different dimensions. We have to change the dimension of volume to a two-dimensional array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "volume = volume[:,np.newaxis]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the newaxis method, which will increase a dimension to the next dimension. In our case, because we had a one-dimensional array, we would have a two-dimensional array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 1)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_data_2d.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can append the volume to the the_data_2d array. There are two ways of achieving this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1st Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1       ,  3.5       ,  1.4       ,  0.2       , 38.13265163],\n",
       "       [ 4.9       ,  3.        ,  1.4       ,  0.2       , 35.20049849],\n",
       "       [ 4.7       ,  3.2       ,  1.3       ,  0.2       , 30.07237208],\n",
       "       [ 4.6       ,  3.1       ,  1.5       ,  0.2       , 33.23805027],\n",
       "       [ 5.        ,  3.6       ,  1.4       ,  0.2       , 36.65191429]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(suppress=True)\n",
    "np.append(the_data_2d,volume,axis=1)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't want to print the exponential; we suppress it using the set_printoption method. We append the volume column to the_data_2d column-wise by setting axis to 1 (column-wise or horizontally) and print the first five rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2nd Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1       ,  3.5       ,  1.4       ,  0.2       , 38.13265163],\n",
       "       [ 4.9       ,  3.        ,  1.4       ,  0.2       , 35.20049849],\n",
       "       [ 4.7       ,  3.2       ,  1.3       ,  0.2       , 30.07237208],\n",
       "       [ 4.6       ,  3.1       ,  1.5       ,  0.2       , 33.23805027],\n",
       "       [ 5.        ,  3.6       ,  1.4       ,  0.2       , 36.65191429]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack((the_data_2d,volume))[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use hstack to stack column-wise the_data_2d and volume and print the first five rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 43: Probabilistic sampling in numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Randomly sample iris's species such that setose is twice the number of versicolor and virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "the_data_2d = np.genfromtxt(url, delimiter=',', dtype='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desired output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use np.random.seed(100)\n",
    "#(array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype='<U15'), array([77, 37, 36]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "the_data_2d[:,4]\n",
    "np.random.seed(100)\n",
    "a = np.array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])\n",
    "species_out = np.random.choice(a,size=150,p=[0.5,0.25,0.25])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first create an array with the three types of flowers, then use this array to create a random sample with p being the probabilities associated with each entry of size 150. The first entry (Iris-Setosa) has double the probabilities than the two other entries (Iris-versicolor and Iris-virginica). That's why we have set the first element in p to 0.5 and the rest 0.25."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype='<U15'), array([77, 37, 36]))\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(species_out,return_counts=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now print out unique values from the species_out and return the count of each value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 44: Get the second largest value of an column in an array when grouped by another column?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: What is the value of the second-longest petal length of species setosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "the_data_2d = np.genfromtxt(url, delimiter=',', dtype='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 1.5 1.6 1.4 1.1 1.2 1.5 1.3 1.4\n",
      " 1.7 1.5 1.7 1.5 1.  1.7 1.9 1.6 1.6 1.5 1.4 1.6 1.6 1.5 1.5 1.4 1.5 1.2\n",
      " 1.3 1.5 1.3 1.5 1.3 1.3 1.3 1.6 1.9 1.4 1.6 1.4 1.5 1.4]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.7"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "petal_lght_set = the_data_2d[the_data_2d[:,4]==b'Iris-setosa',2].astype(\"float\")\n",
    "print(petal_lght_set)\n",
    "petal_lght_set_sorted = np.sort(petal_lght_set)\n",
    "petal_lght_set_sorted_unq = np.unique(petal_lght_set_sorted)\n",
    "petal_lght_set_sorted_unq[-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first select the 5th column (species column) to extract only the iris-setosa species. This expression will return a boolean array, which will be indexed in the original array to get back all the rows where we have the iris-setosa as the species. Now we pass in 2 as the second element in the index to get the petal length column for only the iris-setosa species and cast the array to float.\n",
    "\n",
    "Then we proceed by sorting and removing duplicates in the column. After getting an ordered list of unique elements, we use indexing to get the second last element by passing in -2 which correspond to the second-longest petal length of setosa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 45: How to sort a 2D array by a column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Sort the iris dataset based on the sepal length column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "the_data_2d = np.genfromtxt(url, delimiter=',', dtype='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([[b'4.3', b'3.0', b'1.1', b'0.1', b'Iris-setosa'],\n",
    "#        [b'4.4', b'3.2', b'1.3', b'0.2', b'Iris-setosa'],\n",
    "#        [b'4.4', b'3.0', b'1.3', b'0.2', b'Iris-setosa'],\n",
    "#        [b'4.4', b'2.9', b'1.4', b'0.2', b'Iris-setosa'],\n",
    "#        [b'4.5', b'2.3', b'1.3', b'0.3', b'Iris-setosa'],\n",
    "#        [b'4.6', b'3.6', b'1.0', b'0.2', b'Iris-setosa'],\n",
    "#        [b'4.6', b'3.1', b'1.5', b'0.2', b'Iris-setosa'],\n",
    "#        [b'4.6', b'3.4', b'1.4', b'0.3', b'Iris-setosa'],\n",
    "#        [b'4.6', b'3.2', b'1.4', b'0.2', b'Iris-setosa'],\n",
    "#        [b'4.7', b'3.2', b'1.3', b'0.2', b'Iris-setosa']], dtype=object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[b'4.3', b'3.0', b'1.1', b'0.1', b'Iris-setosa'],\n",
       "       [b'4.4', b'3.2', b'1.3', b'0.2', b'Iris-setosa'],\n",
       "       [b'4.4', b'3.0', b'1.3', b'0.2', b'Iris-setosa'],\n",
       "       [b'4.4', b'2.9', b'1.4', b'0.2', b'Iris-setosa'],\n",
       "       [b'4.5', b'2.3', b'1.3', b'0.3', b'Iris-setosa'],\n",
       "       [b'4.6', b'3.6', b'1.0', b'0.2', b'Iris-setosa'],\n",
       "       [b'4.6', b'3.1', b'1.5', b'0.2', b'Iris-setosa'],\n",
       "       [b'4.6', b'3.4', b'1.4', b'0.3', b'Iris-setosa'],\n",
       "       [b'4.6', b'3.2', b'1.4', b'0.2', b'Iris-setosa'],\n",
       "       [b'4.7', b'3.2', b'1.3', b'0.2', b'Iris-setosa']], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_data_2d[the_data_2d[:,0].astype(\"float\").argsort()][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first select the column we want to sort (in our case the sepal length), cast it to float and use the argsort method to sort and get back the position index of each sorted element. \n",
    "\n",
    "Then pass the expression to the original array by using indexing, which will sort the whole array using the sepal length values. Finally, print the first 10 rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 46: How to find the most frequent value in a NumPy column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Find the most frequent value of petal length (3rd column) in the the_data_2d dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "the_data_2d = np.genfromtxt(url, delimiter=',', dtype='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desired output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_num, unique_count = np.unique(the_data_2d[:,2].astype(\"float64\"),return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_num[np.argmax(unique_count)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are using the unique value to get back unique elements (unduplicated elements) and set the total count to True, which will return the counts of how many times an element is duplicated in the column.\n",
    "\n",
    "With these two variables, we can get the element that was duplicated the most by using the argmax method and give it the argument unique_count and get back the position index of the greatest number. Then we use that position in the unique_num array to get the most reoccurring value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 47: Find the position and the first occurrence of a value greater than a given value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Find the position and the exact value of the first occurrence greater than 1.0 in the petal width column (4th column) of the_data_2d dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "the_data_2d = np.genfromtxt(url, delimiter=',', dtype='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desired output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The element is 1.4 and is located at position 50 in the petal width column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_position = np.where(the_data_2d[:,3].astype(\"float\")>1)\n",
    "larg_num_position = all_position[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "larg_num = the_data_2d[larg_num_position,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The element is 1.4 and is located at position 50 in the petal width column\n"
     ]
    }
   ],
   "source": [
    "print(\"The element is {} and is located at position {} in the petal width column\".format(float(larg_num),larg_num_position))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get all the elements in the petal width column and cast them to float, then compare each element to 1 and get back all the positions where the elements are greater than 1 using the numpy where method.\n",
    "\n",
    "At this stage, we have a 2D array with all the positions of elements higher than 1. To select the first element that corresponds to the first occurrence of a value greater than 1, we use [0] to get the first array within the 2D array and then another [0] right after to get the first element in the nested 1D array.\n",
    "\n",
    "Now that we managed to get the position of that number, we can get the value itself by using NumPy indexing in the 4th column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 48: Replace all values greater than a given value to a given cutoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: From array_a, replace all values greater than 30 to 30 and less than 10 to 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.62684215, 14.64009987, 21.80136195, 42.39403048,  1.23122395,\n",
       "        6.95688692, 33.86670515, 41.466785  ,  7.69862289, 29.17957314,\n",
       "       44.67477576, 11.25090398, 10.08108276,  6.31046763, 11.76517714,\n",
       "       48.95256545, 40.77247431,  9.42510962, 40.99501269, 14.42961361])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "array_a = np.random.uniform(1,50, 20)\n",
    "array_a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([27.62684215, 14.64009987, 21.80136195, 30.        , 10.        ,\n",
    "#        10.        , 30.        , 30.        , 10.        , 29.17957314,\n",
    "#        30.        , 11.25090398, 10.08108276, 10.        , 11.76517714,\n",
    "#        30.        , 30.        , 10.        , 30.        , 14.42961361])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1st Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.62684215, 14.64009987, 21.80136195, 30.        , 10.        ,\n",
       "       10.        , 30.        , 30.        , 10.        , 29.17957314,\n",
       "       30.        , 11.25090398, 10.08108276, 10.        , 11.76517714,\n",
       "       30.        , 30.        , 10.        , 30.        , 14.42961361])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(array_a>30,30,np.where(array_a<10,10,array_a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first way to approach this problem is to use the where method, which takes in as the first argument, the condition we are looking for; in this case, we want to target elements in the array greater than 30. Then, the second argument is the value that will replace each position where the condition is satisfied. It means that all the elements greater than 30 will be replaced by number 30 in the array. \n",
    "\n",
    "Finally, the second argument is the value that will be placed in each position where the condition from the first argument is not met. But in our case, we want to change each value less than 10 to 10 that is why we are using another where method to change all the numbers less than 10 to 10 just like we did for 30. The only difference is that this time for the third argument in the nested where method, we pass in the original array, which leaves all the numbers greater than 10 untouched. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2nd Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.62684215, 14.64009987, 21.80136195, 30.        , 10.        ,\n",
       "       10.        , 30.        , 30.        , 10.        , 29.17957314,\n",
       "       30.        , 11.25090398, 10.08108276, 10.        , 11.76517714,\n",
       "       30.        , 30.        , 10.        , 30.        , 14.42961361])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.clip(array_a,10,30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the clip method, which is easier to understand. We pass in the array itself, and then as a second argument, we place the number 10, which will replace all the numbers less than 10 by 10. The third argument is the maximum number, which will replace all the numbers greater than 30 by 30. That's all!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 49: How to get the positions of top n values from a NumPy array?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Get the positions of top 5 maximum values in a given array a."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(100)\n",
    "a = np.random.uniform(1,50, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "#array([18,  7,  3, 10, 15])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18,  7,  3, 10, 15])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argsort(a)[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the argsort method, which returns the position from the smallest to the highest elements. To get the position of 5 maximum element, we extract the five last elements using indexing [-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 50: How to compute the row-wise counts of all possible values in an array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Compute the counts of unique values row-wise.\n",
    "\n",
    "\n",
    "Instruction: Output contains 10 columns representing numbers from 1 to 10. The values in the solution are the counts of the numbers in the respective rows.\n",
    "\n",
    "For example, Cell(0,2) in the solution has value 2, which means that the number 3 occurs precisely 2 times in the 1st row. \n",
    "\n",
    "Keep in mind that we are generating a random array from 1 to 10, not from 0 to 10. It means that in the output solution, 1 is in the first column, 2 in the second column, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9,  9,  4,  8,  8,  1,  5,  3,  6,  3],\n",
       "       [ 3,  3,  2,  1,  9,  5,  1, 10,  7,  3],\n",
       "       [ 5,  2,  6,  4,  5,  5,  4,  8,  2,  2],\n",
       "       [ 8,  8,  1,  3, 10, 10,  4,  3,  6,  9],\n",
       "       [ 2,  1,  8,  7,  3,  1,  9,  3,  6,  2],\n",
       "       [ 9,  2,  6,  5,  3,  9,  4,  6,  1, 10]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "arr = np.random.randint(1,11,size=(6, 10))\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [[1, 0, 2, 1, 1, 1, 0, 2, 2, 0],\n",
    "#  [2, 1, 3, 0, 1, 0, 1, 0, 1, 1],\n",
    "#  [0, 3, 0, 2, 3, 1, 0, 1, 0, 0],\n",
    "#  [1, 0, 2, 1, 0, 1, 0, 2, 1, 2],\n",
    "#  [2, 2, 2, 0, 0, 1, 1, 1, 1, 0],\n",
    "#  [1, 1, 1, 1, 1, 2, 0, 0, 2, 1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 0, 2, 1, 1, 1, 0, 2, 2, 0],\n",
       " [2, 1, 3, 0, 1, 0, 1, 0, 1, 1],\n",
       " [0, 3, 0, 2, 3, 1, 0, 1, 0, 0],\n",
       " [1, 0, 2, 1, 0, 1, 0, 2, 1, 2],\n",
       " [2, 2, 2, 0, 0, 1, 1, 1, 1, 0],\n",
       " [1, 1, 1, 1, 1, 2, 0, 0, 2, 1]]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def count_all_repeat(arr):\n",
    "    uni_count_arr = [np.unique(row,return_counts=True) for row in arr]\n",
    "    final_array = [[int(counts[uniques==i]) if i in uniques else 0 for i in np.unique(arr)] for uniques, counts in uni_count_arr]\n",
    "    return final_array\n",
    "count_all_repeat(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first create a function and then pass in the array. We search for unique elements and the number of times those elements occur in each row of the array. That is why we are using the list comprehension to go through all the rows to get the uni_count_arr.\n",
    "\n",
    "The next line is where the magic is happening! We start by going through all the uniques elements and counts from uni_count_arr variable. Then we create a nested list comprehension inside a list comprehension. Inside this list comprehension, we loop through all the unique elements in the array, which will between 1 to 10 and use an if statement to see if a specific number is in the uniques array. \n",
    "\n",
    "If it is not found, 0 will be returned, which means that the number is not in the row. If the number is found, we are using indexing on the counts array to get the position where there is a match between uniques value and i value, and cast it to an integer. Finally, we return the multidimensional array.\n",
    "\n",
    "Note: We could also have used a nested for loop with an if & else statement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 51: How to convert an array of arrays into a flat 1d array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Convert array_of_arrays into a flat linear 1d array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.arange(3)\n",
    "arr2 = np.arange(3,7)\n",
    "arr3 = np.arange(7,10)\n",
    "\n",
    "array_of_arrays = np.array([arr1, arr2, arr3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate(array_of_arrays)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the concatenate method to merge all the arrays into one array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 52: How to generate one-hot encoding for an array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Compute the one-hot encoding (dummy binary variables for each unique value in the array)\n",
    "\n",
    "Generating a one-hot encoding is creating a multidimensional array where the number of rows corresponds to the number of elements in the array. 1 will be placed in the position corresponding to the value of the component of the array and 0 will be placed in the remaining positions. for eg, if we have 2 as a value in the original array, the corresponding row will [ 0.,  1.,  0.] where 1 is placed at position 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 2, 2, 2, 1])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(101) \n",
    "arr = np.random.randint(1,4, size=6)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "#array([[ 0.,  1.,  0.],\n",
    "#       [ 0.,  0.,  1.],\n",
    "#       [ 0.,  1.,  0.],\n",
    "#       [ 0.,  1.,  0.],\n",
    "#       [ 0.,  1.,  0.],\n",
    "#       [ 1.,  0.,  0.]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.]])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def one_hot_encodings(arr):\n",
    "    the_uniques = np.unique(arr)\n",
    "    the_out = np.zeros((arr.shape[0],the_uniques.shape[0]))\n",
    "    for i,element in enumerate(arr):\n",
    "        the_out[i,element-1] = 1\n",
    "    return the_out\n",
    "\n",
    "one_hot_encodings(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "aWe create first a function that takes in the original array. We get all the uniques elements from the array and create a multidimensional array using the np.zeros method, where the first element in the tuple corresponds to the row which is the numbers of elements in the original array and the second element is the columns corresponding to the uniques array(maximum is 3).\n",
    "\n",
    "We then create a for loop with the enumerate function, where each element in the array is assigned a number starting from 0. Then we use indexing on the_out variable to traverse all the rows using i value and the element minus 1 as the position of the column; we do subtract one to get the index in the row depending on the value of the element value. For example, for the second row, the corresponding element in the original array is 3, so 3 - 1 gives 2, which is the position to place 1.\n",
    "\n",
    "After targeting that specific row and column, we are going replacing 0 by 1 to that particular position. After the loop is done, we return the array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 53: How to create row numbers grouped by a categorical variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Create row numbers grouped by a categorical variable. Use the following sample from iris species as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-virginica'], dtype='<U15')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "species = np.genfromtxt(url, delimiter=',', dtype='str', usecols=4)\n",
    "np.random.seed(100)\n",
    "species_sm_sorted = np.sort(np.random.choice(species,size=20))\n",
    "species_sm_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desired output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "#[0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1st Method: using the list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for unique_val in np.unique(species_sm_sorted) for i,_ in enumerate(species_sm_sorted[species_sm_sorted==unique_val])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first way to go about to resolve this is to use a list comprehension. We get all the 3 unique species values and place them in the unique_val. \n",
    "\n",
    "We create another for loop with i value as the count from the enumerate function, which will count the number of occurrences of the unique_val variable in species_sm_sorted array starting from 0. For each species, the count is reset.\n",
    "\n",
    "Finally, we return the i variable in the array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2nd Method: using the for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "output = []\n",
    "\n",
    "for val in np.unique(species_sm_sorted):\n",
    "    for i,_ in enumerate(species_sm_sorted[species_sm_sorted==val]):\n",
    "        output.append(i)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a for loop to loop through the uniques elements in the array species_sm_sorted which are Iris-setosa, Iris-versicolor, and Iris-virginica.\n",
    "\n",
    "Then we create a nested for loop with i as the count from the enumerate function, which will count the number of occurrences of the unique_val variable in species_sm_sorted array starting from 0. For each species, the count is reset just as we did with the list comprehension method.\n",
    "\n",
    "Finally, we return the i variable in the array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 54: How to create group ids based on a given categorical variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Create group ids based on a given categorical variable. Use the following sample from iris species as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-virginica'], dtype='<U15')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "species = np.genfromtxt(url, delimiter=',', dtype='str', usecols=4)\n",
    "np.random.seed(100)\n",
    "species_sm_sorted = np.sort(np.random.choice(species,size=20))\n",
    "species_sm_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desired output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1st Method: Using list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_id = [np.argwhere(np.unique(species_sm_sorted)==each_unique)[0][0] for each_unique in species_sm_sorted]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method is the same as using the for loop method just that, in this case, we are using a list comprehension. Please refer to for loop method for the explanation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2nd Method: Using for loop "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]\n"
     ]
    }
   ],
   "source": [
    "output = []\n",
    "\n",
    "for each_unique in species_sm_sorted:\n",
    "    group_id = np.argwhere(np.unique(species_sm_sorted)==each_unique)[0][0]\n",
    "    output.append(group_id)\n",
    "        \n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first loop through each element of species_sm_sorted array. Then we use the np.argwhere method to get the positions where the condition inside the parenthesis is met. The condition is that uniques elements in species_sm_sorted array are equal to each_unique variable. \n",
    "\n",
    "We get back the positions where each_unique variables are found as standalone arrays. We use indexing [0][0] to the value inside the 2D array. So that means that Iris-setosa is found 5 times at position 0, then that Iris-versicolor is found 9 times at position 1 and finally that Iris-virginica is found 6 times at position 2.\n",
    "\n",
    "Finally, we append to the empty list output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 55: How to rank items in an array using NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Create the ranks for the given numeric array a."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9,  4, 15,  0, 17, 16, 17,  8,  9,  0])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(10)\n",
    "arr = np.random.randint(20, size=10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "#[4 2 6 0 8 7 9 3 5 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 6, 0, 8, 7, 9, 3, 5, 1])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.argsort().argsort()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the rank of an array, we apply the argsort method on the array twice. \n",
    "\n",
    "The first time we apply the argsort method, we get back an array with the position that would sort the original array. \n",
    "\n",
    "Applying the argsort again on the previous array will return an array where those positions are sorted for the second time; to get back the ranks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 56: How to rank items in a multidimensional array using NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Create a rank array of the same shape as a given numeric array a."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9,  4, 15,  0, 17],\n",
       "       [16, 17,  8,  9,  0]])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(10)\n",
    "arr = np.random.randint(20, size=[2,5])\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([[4, 2, 6, 0, 8],\n",
    "#        [7, 9, 3, 5, 1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 2, 6, 0, 8],\n",
       "       [7, 9, 3, 5, 1]])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.ravel().argsort().argsort().reshape(arr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first flatten the array to a 1D array by using the ravel method, then get the rank just as we did in the previous exercise using two argsort method.\n",
    "\n",
    "Finally, reshape it to the original array shape using the original array's shape as the argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 57: How to find the maximum value in each row of a NumPy array 2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Compute the maximum for each row in the given array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 9, 4],\n",
       "       [8, 8, 1],\n",
       "       [5, 3, 6],\n",
       "       [3, 3, 3],\n",
       "       [2, 1, 9]])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "the_arr = np.random.randint(1,10, [5,3])\n",
    "the_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "#array([9, 8, 6, 3, 9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1st Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 8, 6, 3, 9])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_arr.max(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first way to solve this question is by using the max method, which returns the highest number in an array. In our case, it is row-wise because we have specified the axis to be 1. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2nd Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 8, 6, 3, 9])"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.apply_along_axis(np.max,axis=1,arr=the_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second way is to use the apply_along_axis method, which will apply the function passed as an argument to the array row-wise because we set the axis parameter to 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 58: How to compute the min-by-max for each row for a NumPy array 2d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Compute the min-by-max for each row for a given 2d NumPy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 9, 4],\n",
       "       [8, 8, 1],\n",
       "       [5, 3, 6],\n",
       "       [3, 3, 3],\n",
       "       [2, 1, 9]])"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "the_arr = np.random.randint(1,10, size=[5,3])\n",
    "the_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desired solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([0.44444444, 0.125     , 0.5       , 1.        , 0.11111111])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1st Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "the_mins = np.amin(the_arr,axis=1)\n",
    "the_maxs = np.amax(the_arr,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 3, 3, 1])"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 8, 6, 3, 9])"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_maxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.44444444, 0.125     , 0.5       , 1.        , 0.11111111])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_mins / the_maxs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The first method is straight forward; we get the minimum and the maximum elements on each row by using the amin and the amax method, pass in the array, and set the axis to 1, which correspond to the row to get back the lowest and highest values in the array.\n",
    "\n",
    "Then divide the_mins and the_maxs values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2nd Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.44, 0.12, 0.5 , 1.  , 0.11])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.apply_along_axis(lambda x: np.min(x)/np.max(x),axis=1,arr=the_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second method is by using the apply_along_axis, pass in a lambda expression, which will return the division of each minimum number by maximum number, then specified that we are dealing with the row by setting axis to 1, then finally pass in the array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 59: How to find the duplicate records in a NumPy array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Find the duplicate entries (2nd occurrence onwards) in the given NumPy array and mark them as True. First time occurrences should be False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 3, 0, 2, 4, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "arr = np.random.randint(0, 5, 10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [False  True False  True False False  True  True  True  True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_True = np.full(arr.shape[0],fill_value=True)\n",
    "all_True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 2, 5])"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_pos = np.unique(arr,return_index=True)[1]\n",
    "unique_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False,  True, False, False,  True,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_True[unique_pos] = False\n",
    "all_True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of NumPy version 1.16.4, There is no specific method to perform this task. We have to figure out how to use different means to solve this issue.\n",
    "\n",
    "We first create an array comprised of only True values using the full method. The number of elements in this newly created array is the same as the number of elements in the original array. \n",
    "\n",
    "Then we use the unique method to get back the indices (or positions) of each unique (not duplicated) value in the array by setting return_index to True.\n",
    "\n",
    "Finally, we change all True values in the all_True array at the unique value position by False."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 60: How to find the grouped mean in NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Find the mean of sepal width (2nd column) grouped by a categorical column in a 2D NumPy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "the_data_2d = np.genfromtxt(url, delimiter=',', dtype='object')\n",
    "names = ('sepallength', 'sepalwidth', 'petallength', 'petalwidth', 'species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [[b'Iris-setosa', 3.418],\n",
    "# [b'Iris-versicolor', 2.770],\n",
    "# [b'Iris-virginica', 2.974]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1st Method: using list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_column = the_data_2d[:,1].astype(\"float\")\n",
    "species_column = the_data_2d[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[b'Iris-setosa', 3.418],\n",
       " [b'Iris-versicolor', 2.7700000000000005],\n",
       " [b'Iris-virginica', 2.974]]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[uniques_species,numerical_column[species_column==uniques_species].mean()] for uniques_species in np.unique(species_column)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first get the sepal width column and cast it to float type and then extract the species column. Then we create a list comprehension expression and loop through the unique species in the column, which are Iris-setosa, Iris-versicolor, Iris-virginica. \n",
    "\n",
    "After the loop, we place the unique species value as the first element in the list, and as the second element, we use indexing to get the mean of the sepal length for each species type by applying the mean function to the numerical_column value where species_column equals uniques_species."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2nd Method: using for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[b'Iris-setosa', 3.418],\n",
       " [b'Iris-versicolor', 2.7700000000000005],\n",
       " [b'Iris-virginica', 2.974]]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_species_grouped = []\n",
    "for uniques_species in np.unique(species_column):\n",
    "    mean_species_grouped.append([uniques_species,numerical_column[species_column==uniques_species].mean()])\n",
    "mean_species_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the loop, it is almost similar to using the list comprehension. The only difference is that we create an empty list, and then we loop through the unique species.\n",
    "\n",
    "Then append to the empty list the unique species as the first element and as the second element, the mean of sepal length for each unique species just as we did for the list comprehension. Finally, we return the list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 61:  How to convert a PIL image to NumPy array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Import the image from the following URL and convert it to a NumPy array and optionally convert it back to an image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'https://upload.wikimedia.org/wikipedia/commons/8/8b/Denali_Mt_McKinley.jpg'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([[[  9,  72, 125],\n",
    "#         [  9,  72, 125],\n",
    "#         [  9,  72, 125],\n",
    "#         ...,\n",
    "#         [ 42, 103, 147],\n",
    "#         [ 42, 103, 147],\n",
    "#         [ 43, 104, 148]],\n",
    "\n",
    "#        [[  9,  72, 125],\n",
    "#         [  9,  72, 125],\n",
    "#         [ 10,  73, 126],\n",
    "#         ...,\n",
    "#         [ 42, 103, 147],\n",
    "#         [ 42, 103, 147],\n",
    "#         [ 43, 104, 148]],\n",
    "\n",
    "#        [[  9,  72, 125],\n",
    "#         [ 10,  73, 126],\n",
    "#         [ 10,  73, 126],\n",
    "#         ...,\n",
    "#         [ 44, 105, 150],\n",
    "#         [ 45, 106, 151],\n",
    "#         [ 45, 106, 151]],\n",
    "\n",
    "#        ...,\n",
    "\n",
    "#        [[ 21,  41,  50],\n",
    "#         [ 29,  51,  64],\n",
    "#         [ 28,  54,  69],\n",
    "#         ...,\n",
    "#         [ 27,  58,  79],\n",
    "#         [ 22,  53,  74],\n",
    "#         [ 19,  47,  69]],\n",
    "\n",
    "#        [[ 27,  51,  63],\n",
    "#         [ 29,  55,  68],\n",
    "#         [ 27,  56,  72],\n",
    "#         ...,\n",
    "#         [ 33,  70,  97],\n",
    "#         [ 27,  64,  91],\n",
    "#         [ 22,  57,  85]],\n",
    "\n",
    "#        [[ 18,  45,  56],\n",
    "#         [ 20,  48,  62],\n",
    "#         [ 21,  52,  70],\n",
    "#         ...,\n",
    "#         [ 15,  51,  77],\n",
    "#         [ 12,  48,  74],\n",
    "#         [ 11,  45,  72]]], dtype=uint8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import BytesIO\n",
    "from PIL import Image\n",
    "import PIL, requests\n",
    "\n",
    "\n",
    "the_response = requests.get(URL)\n",
    "the_image = Image.open(BytesIO(the_response.content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=452x303 at 0x11AACBE48>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "Image_arr = np.array(the_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  9,  72, 125],\n",
       "        [  9,  72, 125],\n",
       "        [  9,  72, 125],\n",
       "        ...,\n",
       "        [ 42, 103, 147],\n",
       "        [ 42, 103, 147],\n",
       "        [ 43, 104, 148]],\n",
       "\n",
       "       [[  9,  72, 125],\n",
       "        [  9,  72, 125],\n",
       "        [ 10,  73, 126],\n",
       "        ...,\n",
       "        [ 42, 103, 147],\n",
       "        [ 42, 103, 147],\n",
       "        [ 43, 104, 148]],\n",
       "\n",
       "       [[  9,  72, 125],\n",
       "        [ 10,  73, 126],\n",
       "        [ 10,  73, 126],\n",
       "        ...,\n",
       "        [ 44, 105, 150],\n",
       "        [ 45, 106, 151],\n",
       "        [ 45, 106, 151]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 21,  41,  50],\n",
       "        [ 29,  51,  64],\n",
       "        [ 28,  54,  69],\n",
       "        ...,\n",
       "        [ 27,  58,  79],\n",
       "        [ 22,  53,  74],\n",
       "        [ 19,  47,  69]],\n",
       "\n",
       "       [[ 27,  51,  63],\n",
       "        [ 29,  55,  68],\n",
       "        [ 27,  56,  72],\n",
       "        ...,\n",
       "        [ 33,  70,  97],\n",
       "        [ 27,  64,  91],\n",
       "        [ 22,  57,  85]],\n",
       "\n",
       "       [[ 18,  45,  56],\n",
       "        [ 20,  48,  62],\n",
       "        [ 21,  52,  70],\n",
       "        ...,\n",
       "        [ 15,  51,  77],\n",
       "        [ 12,  48,  74],\n",
       "        [ 11,  45,  72]]], dtype=uint8)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "the_image_2 = PIL.Image.fromarray(Image_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=452x303 at 0x11AE7FEF0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_image_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example, we are importing a couple of libraries first the PIL (python imagery library), the requests library for making HTTP requests in Python, and finally, the io library for input and output.\n",
    "\n",
    "We start by getting the response from the URL given of the picture file by using the get method and store it in the_response variable.\n",
    "\n",
    "To print the image locally, we apply the content method to the response to get the content of the response, then change it into bytes using the BytesIO method and then open it as an image file using Image.open and store it in the the_image variable.\n",
    "\n",
    "To cast the image to an array, we use the np.array method and pass in the_image variable.\n",
    "\n",
    "Optionally, to convert back the array to an image file, this time, we use PIL.Image.fromarray method and pass in the Image_arr variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 62: How to drop all missing values from a NumPy array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Drop all nan values from a 1D numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3., nan,  5.,  6.,  7., nan])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_arr = np.array([1,2,3,np.nan,5,6,7,np.nan])\n",
    "the_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desired output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([ 1.,  2.,  3.,  5.,  6.,  7.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 5., 6., 7.])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_arr[~np.isnan(the_arr)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "np.isnan method will return a boolean array where True is the position of the nan value in the array.\n",
    "\n",
    "To drop nan values, we are going to use the ~ operator to inverse the boolean array. This time we will get False on the position where nan is found and True on the rest of the positions. It means that if we use indexing on the original array, we will get back only values that are not nan values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 63: How to compute the euclidean distance between two arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Compute the Euclidean distance between two arrays a and b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3,4,5])\n",
    "b = np.array([4,5,6,7,8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6.708203932499369"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "euclidean_dist = np.linalg.norm(a-b,ord=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.708203932499369\n"
     ]
    }
   ],
   "source": [
    "print(euclidean_dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Theory Behind this exercise is from [Introduction to Data Mining](https://www.amazon.com/dp/0321321367/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](/blog/assets/post_cont_image/ex63.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function can return one of eight different matrix norms, or one of an infinite number of vector norms (described above), depending on the value of the ord parameter.\n",
    "\n",
    "It works because Euclidean distance is l2 norm and the value of ord parameter in numpy.linalg.norm method is 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 64: How to find all the local maxima (or peaks) in a 1d array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Find all the peaks in a 1D NumPy array a. Peaks are points surrounded by smaller values on both sides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1, 3, 7, 1, 2, 6, 0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([2, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.signal import argrelextrema\n",
    "\n",
    "maxInd = argrelextrema(a, np.greater)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 5])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxInd[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the extrema of a NumPy array, we use the argrelextrema method (from scipy library) and pass in the array as the first argument and then as the second argument we use the np.greater function which is a function to compare the greatest number along with two elements in the array.\n",
    "\n",
    "A tuple is returned and used [0] to get back the positions we are looking for."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 65: How to subtract a 1D array from a 2D array, where each item of 1D array subtracts from respective row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Subtract the 1D array b_1d from the 2D array a_2d, such that each item (element) of b_1d subtracts from the corresponding element in a row of a_2d."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_2d = np.array([[3,3,3],[4,4,4],[5,5,5]])\n",
    "b_1d = np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [[2 2 2]\n",
    "#  [2 2 2]\n",
    "#  [2 2 2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 2, 2],\n",
       "       [2, 2, 2],\n",
       "       [2, 2, 2]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_2d[:,:] - b_1d[:,None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are taking each item in rows from the 2D array and subtract each item in each row by every element in the 1D array. \n",
    "\n",
    "So we are taking all the 3s in the first row of the 2D array and subtract them by 1 from the 1D array and the results are placed in a new array, then all the 4s are subtracted by 2 and finally all the 5s by 3.\n",
    "\n",
    "We will be left with a 2D array only comprised by 2s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 66: How to subtract a 1D array from a 2D array, where each item of 2D array subtracts from respective column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Subtract the 1D array arr_1d from the 2D array arr_2d, such that each item (element) of arr_1d subtracts from the respective element in a column of arr_2d."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_2d = np.array([[3,3,3],[4,4,4],[5,5,5]])\n",
    "b_1d = np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desired output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([[2, 1, 0],\n",
    "#        [3, 2, 1],\n",
    "#        [4, 3, 2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1, 0],\n",
       "       [3, 2, 1],\n",
       "       [4, 3, 2]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_2d[:,:] - b_1d[None,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one is similar to the previous one, but the difference is that we are subtracting each item in the 2D array by the corresponding item in the 1D array. \n",
    "\n",
    "So for the first row in the 2D array, we take 3 and subtract it by 1 from the 1D array, then we take the second 3 and subtract it by 2 in the 1D array and finally we take 3 at the third position and subtract it to 3 in the 1D array. We do the same for the rest of the arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 67: How to find the index of n'th repetition of an item in an array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Find the index of 5th repetition of number 1 in x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "the_arr = np.array([1, 2, 1, 1, 3, 4, 3, 1, 1, 2, 1, 1, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1st Method: NumPy way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rep_pos = np.where(the_arr==1)[0]\n",
    "rep_pos[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the where method to get the position of all the 1s in the array by using the condition the_arr == 1. We get back a tuple that is why we used [0] to select the first argument. \n",
    "\n",
    "To select the 5th repetition of 1, we then use indexing on the array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2nd Method: Pythonic way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1), (2, 1), (3, 1), (7, 1), (8, 1), (10, 1), (11, 1)]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple_pos_repeated = [i for i in enumerate(the_arr) if i[1] == 1]\n",
    "tuple_pos_repeated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple_pos_repeated[5][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second way to approach this problem is by using list comprehension. We use the for loop and enumerate function to traverse the array and then use an if statement to only extract elements equal to 1. if you want to refresh your mind about loops and list comprehension, please read more [here](https://semasuka.github.io/blog/2019/02/03/control-flow-part-2.html).\n",
    "\n",
    "Now that we have a tuple of all the positions of number 1 in the array, we can proceed by using indexing on tuple by using[5] to get the 5th repetition of 1 and then indexing again by adding [0] to get only the position we are looking for."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 68: How to convert NumPy's datetime64 object to datetime's datetime object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Convert NumPy's datetime64 object to datetime's datetime object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1519596610"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt64 = np.datetime64('2018-02-25 22:10:10')\n",
    "dt64.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desired output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datetime.datetime(2018, 2, 25, 22, 10, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2018, 2, 25, 22, 10, 10)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "datetime.utcfromtimestamp(dt64.astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first import the datetime function from the datetime module, then pass the Numpy datetime object (converted into int type) which returns the UTC datetime object. Yeah, it is that simple!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 69: How to compute the moving average of a NumPy array?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Compute the moving average of window size 3, for the given a random 1D array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 8, 3, 7, 7, 0, 4, 2, 5, 2])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "oneD_arr = np.random.randint(10, size=10)\n",
    "oneD_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# moving average:  [ 6.33  6.    5.67  4.67  3.67  2.    3.67  3.  ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.33, 6.  , 5.67, 4.67, 3.67, 2.  , 3.67, 3.  ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(precision=2)\n",
    "def moving_average(arr,w=3):\n",
    "    accum_arr = np.cumsum(arr, dtype=float)\n",
    "    accum_arr[w:] = accum_arr[w:] - accum_arr[:-w]\n",
    "    return accum_arr[w-1:]/w\n",
    "moving_average(oneD_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the moving average of a set of data at a particular window, we first create a function that takes in the array itself and then set the window w by default to 3. Then we calculate the cumulative sum of the elements in the array, which in this case will be all the elements in the row axis since we only have a 1D array.\n",
    "\n",
    "Now comes the fun part! we subtract each element in the accum_arr starting from index 4 (which correspond to window 3 of the moving average) by each element in another accum_arr array, this time ending at the 4th last element inclusive. The result is assigned to accum_arr starting from index 4.\n",
    "\n",
    "Finally, we take the previous array this time starting from index 3(w-1) and we divide all the elements in that array by the window 3 to finally get the moving average of window 3 of that array.\n",
    "\n",
    "These steps above are from the formula to compute the moving average of a NumPy. Please refer [this Wikipedia page](https://en.wikipedia.org/wiki/Moving_average) for more information. Refer [to this link](https://stackoverflow.com/questions/14313510/how-to-calculate-moving-average-using-numpy) on StackOverflow where this exercise is inspired from."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 70: How to create a NumPy array sequence given only the starting point, length and the step?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Create a NumPy array of length 10, starting from 5 and has a step of 3 between consecutive numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_num = 5\n",
    "step_num = 3\n",
    "size_num = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array([ 5,  8, 11, 14, 17, 20, 23, 26, 29, 32])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  8, 11, 14, 17, 20, 23, 26, 29, 32])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_num = 5\n",
    "step_num = 3\n",
    "size_num = 10\n",
    "\n",
    "def seq(start,step,size):\n",
    "    stop = start + (size*step)\n",
    "    return np.arange(start,stop,step)\n",
    "seq(start_num,step_num,size_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an interesting exercise because we have the length of the array, the start and the step. To use the arange method on the NumPy variable, we need the start, stop and step. In this case, we are missing the stop value.\n",
    "So to get the stop, we can use the size, the step and the start. \n",
    "\n",
    "That is what we did by creating a new function that takes as parameter the start, the step and the size. To calculate the stop, we add the start and the product of the size and the step.\n",
    "\n",
    "Then returned the np.arange method with the start, the previously calculated stop and the step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 71: How to fill in missing dates in an irregular series of NumPy dates?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: Given an array of a non-continuous sequence of dates. Make it a continuous sequence of dates, by filling in the missing dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2018-02-01', '2018-02-03', '2018-02-05', '2018-02-07',\n",
       "       '2018-02-09', '2018-02-11', '2018-02-13', '2018-02-15',\n",
       "       '2018-02-17', '2018-02-19', '2018-02-21', '2018-02-23'],\n",
       "      dtype='datetime64[D]')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = np.arange(np.datetime64('2018-02-01'), np.datetime64('2018-02-25'), 2)\n",
    "dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array(['2018-02-01', '2018-02-02', '2018-02-03', '2018-02-04',\n",
    "#        '2018-02-05', '2018-02-06', '2018-02-07', '2018-02-08',\n",
    "#        '2018-02-09', '2018-02-10', '2018-02-11', '2018-02-12',\n",
    "#        '2018-02-13', '2018-02-14', '2018-02-15', '2018-02-16',\n",
    "#        '2018-02-17', '2018-02-18', '2018-02-19', '2018-02-20',\n",
    "#        '2018-02-21', '2018-02-22', '2018-02-23'], dtype='datetime64[D]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1st Method: using list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "cont_no_last_date = np.array([np.arange(date,(date+d)) for date,d in zip(dates, np.diff(dates))]).reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2018-02-01', '2018-02-02', '2018-02-03', '2018-02-04',\n",
       "       '2018-02-05', '2018-02-06', '2018-02-07', '2018-02-08',\n",
       "       '2018-02-09', '2018-02-10', '2018-02-11', '2018-02-12',\n",
       "       '2018-02-13', '2018-02-14', '2018-02-15', '2018-02-16',\n",
       "       '2018-02-17', '2018-02-18', '2018-02-19', '2018-02-20',\n",
       "       '2018-02-21', '2018-02-22', '2018-02-23'], dtype='datetime64[D]')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont_with_last_date = np.hstack((cont_no_last_date,dates[-1]))\n",
    "cont_with_last_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the ways to go about solving this issue is to use the list comprehension, we first loop through each date and the difference between those dates d (which is 2) and create a NumPy range starting from the date to a new date found using date + d and place those dates in an array. we end up with a continuous range of dates from '2018-02-01' to '2018-02-22' (notice how it is not '2018-02-23') and reshape it using -1 which means that it is an unknown dimension and we want NumPy to figure out the correct dimension to use.\n",
    "\n",
    "Now we need to add the last date by stacking it horizontally (column-wise) using the hstack function to cont_no_last_date and pass in the last date using indexing -1. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2nd Method: using for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "cont_no_last_date = []\n",
    "for date,d in zip(dates, np.diff(dates)):\n",
    "    cont_no_last_date.append(np.arange(date,date+d))\n",
    "cont_no_last_date = np.array(cont_no_last_date).reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2018-02-01', '2018-02-02', '2018-02-03', '2018-02-04',\n",
       "       '2018-02-05', '2018-02-06', '2018-02-07', '2018-02-08',\n",
       "       '2018-02-09', '2018-02-10', '2018-02-11', '2018-02-12',\n",
       "       '2018-02-13', '2018-02-14', '2018-02-15', '2018-02-16',\n",
       "       '2018-02-17', '2018-02-18', '2018-02-19', '2018-02-20',\n",
       "       '2018-02-21', '2018-02-22', '2018-02-23'], dtype='datetime64[D]')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont_with_last_date = np.hstack((cont_no_last_date,dates[-1]))\n",
    "cont_with_last_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second way to approach this problem is by using the for loop statement. we first create an empty list and then loop through the dates and d value from zip method of the dates and the difference between the dates. Then inside the loop, we append the rage from the date to the date + d just as we did for the list comprehension.\n",
    "\n",
    "After the loop, we cast the list to an array, reshape and assign it to the cont_no_last_date value. Then we add the last date just as we did for the list comprehension method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ex 72: How to create strides from a given 1D array?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q: From the given 1D array arr, generate a 2D matrix using strides, with a window length of 4 and strides of 2, like [[0,1,2,3], [2,3,4,5], [4,5,6,7]..]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(15) \n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desire output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [[ 0  1  2  3]\n",
    "#  [ 2  3  4  5]\n",
    "#  [ 4  5  6  7]\n",
    "#  [ 6  7  8  9]\n",
    "#  [ 8  9 10 11]\n",
    "#  [10 11 12 13]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 2,  3,  4,  5],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 6,  7,  8,  9],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [10, 11, 12, 13]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gen_strides(arr, stride_len=2, window_len=4):\n",
    "    n_strides = ((arr.size - window_len)//stride_len) + 1\n",
    "    # n_strides = 6\n",
    "    print(stride_len)\n",
    "    return np.array([arr[s:(s+window_len)] for s in np.arange(0,n_strides*stride_len,stride_len)])\n",
    "gen_strides(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first create a function that takes in the array, set the stride length to 2 and then window length to 4. Then we calculate the n_strides value by subtracting the array size to window_len and then dividing it (using floor division or integer division) by stride_len and adding 1 to it.\n",
    "\n",
    "Finally, we create a list comprehension that goes through a range from 0 to 12 (n_strides * stride_len) by stepping by 2 using s value which corresponds to the 6 lists that we are creating. We use indexing on the arr with the starting position s and the last element s + window_len in each of the 6 lists. At last, we place the 6 strides lists in an array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There you go! 72 exercise on NumPy to solidify your understanding of NumPy. I can guaranty that if you completed all these exercises you are more than ready to start using NumPy in an end-to-end Machine Learning project.\n",
    "\n",
    "The next post will be an introduction to one of my favorite Machine learning libraries, pandas Library for tabular data manipulation. Stay tuned and don't forget to subscribe to get notified for the next post.\n",
    "\n",
    "Thank you again for doing these exercises with me. I hope you have learned one or two things. If you like this post, please subscribe to stay updated with new posts, and if you have a thought or a question, I would love to hear it by commenting below. Cheers, and keep on learning!"
   ]
  }
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