diff --git a/Copy_of_Numpy_Exercises.ipynb b/Copy_of_Numpy_Exercises.ipynb new file mode 100644 index 0000000..d5ac3ec --- /dev/null +++ b/Copy_of_Numpy_Exercises.ipynb @@ -0,0 +1,371 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of Numpy_Exercises.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/dnc2k/Assignment-2/blob/dnc2k/Copy_of_Numpy_Exercises.ipynb)" + ] + }, + { + "metadata": { + "id": "a_4UupTr9fbX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Numpy Exercises\n", + "\n", + "1) Create a uniform subdivision of the interval -1.3 to 2.5 with 64 subdivisions" + ] + }, + { + "metadata": { + "id": "LIP5u4zi0Nmg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 311 + }, + "outputId": "23b1a117-1e5b-4256-d322-3b95cec3d1c5" + }, + "cell_type": "code", + "source": [ + "import numpy as np #import numpy\n", + "a=np.linspace(-1.3,2.5,64).reshape(8,8)\n", + "print (a)" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[-1.3 -1.23968254 -1.17936508 -1.11904762 -1.05873016 -0.9984127\n", + " -0.93809524 -0.87777778]\n", + " [-0.81746032 -0.75714286 -0.6968254 -0.63650794 -0.57619048 -0.51587302\n", + " -0.45555556 -0.3952381 ]\n", + " [-0.33492063 -0.27460317 -0.21428571 -0.15396825 -0.09365079 -0.03333333\n", + " 0.02698413 0.08730159]\n", + " [ 0.14761905 0.20793651 0.26825397 0.32857143 0.38888889 0.44920635\n", + " 0.50952381 0.56984127]\n", + " [ 0.63015873 0.69047619 0.75079365 0.81111111 0.87142857 0.93174603\n", + " 0.99206349 1.05238095]\n", + " [ 1.11269841 1.17301587 1.23333333 1.29365079 1.35396825 1.41428571\n", + " 1.47460317 1.53492063]\n", + " [ 1.5952381 1.65555556 1.71587302 1.77619048 1.83650794 1.8968254\n", + " 1.95714286 2.01746032]\n", + " [ 2.07777778 2.13809524 2.1984127 2.25873016 2.31904762 2.37936508\n", + " 2.43968254 2.5 ]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "dBoH_A7M9jjL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "2) Generate an array of length 3n filled with the cyclic pattern 1, 2, 3" + ] + }, + { + "metadata": { + "id": "4TxT66309n1o", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "13ce8763-8b0e-4de1-ad88-7f870e808022" + }, + "cell_type": "code", + "source": [ + "a1=np.array([1,2,3])\n", + "a2=np.resize(a1,3)\n", + "print (a2)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1 2 3]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "Vh-UKizx9oTp", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "3) Create an array of the first 10 odd integers." + ] + }, + { + "metadata": { + "id": "ebhEUZq29r32", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "0efbd9b4-7bc8-4978-e6f6-0fe847326a45" + }, + "cell_type": "code", + "source": [ + "a2=np.arange(1,20,2)\n", + "print (a2)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[ 1 3 5 7 9 11 13 15 17 19]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "QfJRdMat90f4", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "4) Find intersection of a and b" + ] + }, + { + "metadata": { + "id": "gOlfuJCo-JwF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "766a60e1-d170-41e5-8526-8e13d55546a3" + }, + "cell_type": "code", + "source": [ + "#expected output array([2, 4])\n", + "a = np.array([1,2,3,2,3,4,3,4,5,6])\n", + "b = np.array([7,2,10,2,7,4,9,4,9,8])\n", + "I = np.intersect1d(a,b)\n", + "print ('I',I)\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "I [2 4]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "RtVCf0UoCeB8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "5) Reshape 1d array a to 2d array of 2X5" + ] + }, + { + "metadata": { + "id": "2E8b55_2Cjx5", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "outputId": "0321e16b-580e-4f68-e128-ef07b70086f1" + }, + "cell_type": "code", + "source": [ + "a = np.arange(10)\n", + "a1= a.reshape(2,5)\n", + "print ('After reshape \\n',a1)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "After reshape \n", + " [[0 1 2 3 4]\n", + " [5 6 7 8 9]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "dVrSBW1zEjp2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "6) Create a numpy array to list and vice versa" + ] + }, + { + "metadata": { + "id": "tcBCyhXPEp9C", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + }, + "outputId": "b11709dd-14c1-441f-ea05-42815cc546d8" + }, + "cell_type": "code", + "source": [ + "a = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "a1=np.array(a)\n", + "print (\"1-D array:\\n\" , a1)\n", + "l1=a1.tolist()\n", + "print (\"Array to List:\\n\", l1)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "1-D array:\n", + " [1 2 3 4 5 6 7 8 9]\n", + "Array to List:\n", + " [1, 2, 3, 4, 5, 6, 7, 8, 9]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "JNqX8wnz9sQJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "7) Create a 10 x 10 arrays of zeros and then \"frame\" it with a border of ones." + ] + }, + { + "metadata": { + "id": "4bjP3JAc9vRD", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 201 + }, + "outputId": "2618d44e-9f7a-423c-f4f4-ba5b2cc4b2b9" + }, + "cell_type": "code", + "source": [ + "a=np.zeros(shape=(10,10),dtype=int)\n", + "a[0,:]=1\n", + "a[:,0]=1\n", + "a[9,:]=1\n", + "a[:,9]=1\n", + "print (a)" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[1 1 1 1 1 1 1 1 1 1]\n", + " [1 0 0 0 0 0 0 0 0 1]\n", + " [1 0 0 0 0 0 0 0 0 1]\n", + " [1 0 0 0 0 0 0 0 0 1]\n", + " [1 0 0 0 0 0 0 0 0 1]\n", + " [1 0 0 0 0 0 0 0 0 1]\n", + " [1 0 0 0 0 0 0 0 0 1]\n", + " [1 0 0 0 0 0 0 0 0 1]\n", + " [1 0 0 0 0 0 0 0 0 1]\n", + " [1 1 1 1 1 1 1 1 1 1]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "xaQgf8tT9v-n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "8) Create an 8 x 8 array with a checkerboard pattern of zeros and ones using a slicing+striding approach." + ] + }, + { + "metadata": { + "id": "No7fx0Xy9zEh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 182 + }, + "outputId": "0e8bcd3c-2881-467b-ffdb-6c57153a46c0" + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "x=np.zeros(shape=(8,8),dtype=int)\n", + "x[1::2,::2]=1\n", + "x[::2,1::2]=1\n", + "print (\"Checkerboard Pattern:\\n\",x)" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Checkerboard Pattern:\n", + " [[0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]\n", + " [0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]\n", + " [0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]\n", + " [0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]]\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file diff --git a/Copy_of_dnc2k.ipynb b/Copy_of_dnc2k.ipynb new file mode 100644 index 0000000..c048631 --- /dev/null +++ b/Copy_of_dnc2k.ipynb @@ -0,0 +1,93 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of dnc2k.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/dnc2k/Assignment-2/blob/dnc2k/Copy_of_dnc2k.ipynb)" + ] + }, + { + "metadata": { + "id": "lqNVh-T-sXGj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 201 + }, + "outputId": "2830c962-2b72-4da3-8646-cad7e83ab7b8" + }, + "cell_type": "code", + "source": [ + "dummy_list=[23,82,98,62,16]\n", + "\n", + "print (dummy_list)\n", + "\n", + "dummy_list.reverse()\n", + "print (dummy_list)\n", + "\n", + "dummy_list_2 = [2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n", + "for item in dummy_list_2:\n", + " dummy_list.append(item)\n", + "print (dummy_list)\n", + "\n", + "dummy_dict={16:2, 62:1, 98:1, 82:1, 23:1, 2:1, 200:1, 4:1, 1:1, 0:1, 9.45:1, 45.67:1, 90:1, 12.01:1, 12.02:1}\n", + "\n", + "print (dummy_dict)\n", + "\n", + "dummy_list.sort()\n", + "print (dummy_list)\n", + "dummy_list.sort(reverse=True)\n", + "print (dummy_list)\n", + "\n", + "x = 16\n", + "dummy_list.remove(x)\n", + "print (dummy_list)\n", + "\n", + "x=int(input())\n", + "del dummy_list[x]\n", + "print (dummy_list)\n", + "\n", + "dummy_list.clear()\n", + "print (dummy_list)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[23, 82, 98, 62, 16]\n", + "[16, 62, 98, 82, 23]\n", + "[16, 62, 98, 82, 23, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n", + "{16: 2, 62: 1, 98: 1, 82: 1, 23: 1, 2: 1, 200: 1, 4: 1, 1: 1, 0: 1, 9.45: 1, 45.67: 1, 90: 1, 12.01: 1, 12.02: 1}\n", + "[0, 1, 2, 4, 9.45, 12.01, 12.02, 16, 16, 23, 45.67, 62, 82, 90, 98, 200]\n", + "[200, 98, 90, 82, 62, 45.67, 23, 16, 16, 12.02, 12.01, 9.45, 4, 2, 1, 0]\n", + "[200, 98, 90, 82, 62, 45.67, 23, 16, 12.02, 12.01, 9.45, 4, 2, 1, 0]\n", + "3\n", + "[200, 98, 90, 62, 45.67, 23, 16, 12.02, 12.01, 9.45, 4, 2, 1, 0]\n", + "[]\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file diff --git a/Get_to_know_your_Data.ipynb b/Get_to_know_your_Data.ipynb new file mode 100644 index 0000000..9065529 --- /dev/null +++ b/Get_to_know_your_Data.ipynb @@ -0,0 +1,2354 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Get to know your Data.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/dnc2k/Assignment-2/blob/dnc2k/Get_to_know_your_Data.ipynb)" + ] + }, + { + "metadata": { + "id": "J82LU53m_OU0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Get to know your Data\n", + "\n", + "\n", + "#### Import necessary modules\n" + ] + }, + { + "metadata": { + "id": "ZyO1UXL8mtSj", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yXTzTowtnwGI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Loading CSV Data to a DataFrame" + ] + }, + { + "metadata": { + "id": "H1Bjlb5wm9f-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "KE-k7b_Mn5iN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### See the top 10 rows\n" + ] + }, + { + "metadata": { + "id": "HY2Ps7xMn4ao", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "outputId": "b3885b8d-976d-4d86-b736-455a1b4dacbd" + }, + "cell_type": "code", + "source": [ + "iris_df.head()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "ZQXekIodqOZu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Find number of rows and columns\n" + ] + }, + { + "metadata": { + "id": "6Y-A-lbFqR82", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "outputId": "16f316ca-d432-4e85-c69e-858b96ac6223" + }, + "cell_type": "code", + "source": [ + "print(iris_df.shape)\n", + "\n", + "#first is row and second is column\n", + "#select row by simple indexing\n", + "\n", + "print(iris_df.shape[0])\n", + "print(iris_df.shape[1])" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(150, 5)\n", + "150\n", + "5\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "4ckCiGPhrC_t", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Print all columns" + ] + }, + { + "metadata": { + "id": "S6jgMyRDrF2a", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "outputId": "fafa4cc7-b62d-4f89-80c2-9675b7e27275" + }, + "cell_type": "code", + "source": [ + "print(iris_df.columns)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n", + " 'species'],\n", + " dtype='object')\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "kVav5-ACtIqS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Check Index\n" + ] + }, + { + "metadata": { + "id": "iu3I9zIGtLDX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "bb648e8c-d63a-48a6-d120-ecb0fa35dc9b" + }, + "cell_type": "code", + "source": [ + "print(iris_df.index)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RangeIndex(start=0, stop=150, step=1)\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "psCc7PborOCQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Right now the iris_data set has all the species grouped together let's shuffle it" + ] + }, + { + "metadata": { + "id": "Bxc8i6avrZPw", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 237 + }, + "outputId": "83f93cef-be1a-446d-e113-7dd23d70f087" + }, + "cell_type": "code", + "source": [ + "#generate a random permutaion on index\n", + "\n", + "print(iris_df.head())\n", + "\n", + "new_index = np.random.permutation(iris_df.index)\n", + "iris_df = iris_df.reindex(index = new_index)\n", + "\n", + "print(iris_df.head())" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + " sepal_length sepal_width petal_length petal_width species\n", + "97 6.2 2.9 4.3 1.3 versicolor\n", + "103 6.3 2.9 5.6 1.8 virginica\n", + "6 4.6 3.4 1.4 0.3 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "58 6.6 2.9 4.6 1.3 versicolor\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "j32h8022sRT8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### We can also apply an operation on whole column of iris_df" + ] + }, + { + "metadata": { + "id": "seYXHXsYsYJI", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 348 + }, + "outputId": "1101114c-d7bc-4667-ec14-a215ed860945" + }, + "cell_type": "code", + "source": [ + "#original\n", + "\n", + "print(iris_df.head())\n", + "\n", + "iris_df['sepal_width'] *= 10\n", + "\n", + "#changed\n", + "\n", + "print(iris_df.head())\n", + "\n", + "#lets undo the operation\n", + "\n", + "iris_df['sepal_width'] /= 10\n", + "\n", + "print(iris_df.head())" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "97 6.2 2.9 4.3 1.3 versicolor\n", + "103 6.3 2.9 5.6 1.8 virginica\n", + "6 4.6 3.4 1.4 0.3 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "58 6.6 2.9 4.6 1.3 versicolor\n", + " sepal_length sepal_width petal_length petal_width species\n", + "97 6.2 29.0 4.3 1.3 versicolor\n", + "103 6.3 29.0 5.6 1.8 virginica\n", + "6 4.6 34.0 1.4 0.3 setosa\n", + "1 4.9 30.0 1.4 0.2 setosa\n", + "58 6.6 29.0 4.6 1.3 versicolor\n", + " sepal_length sepal_width petal_length petal_width species\n", + "97 6.2 2.9 4.3 1.3 versicolor\n", + "103 6.3 2.9 5.6 1.8 virginica\n", + "6 4.6 3.4 1.4 0.3 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "58 6.6 2.9 4.6 1.3 versicolor\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "R-Ca-LBLzjiF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Show all the rows where sepal_width > 3.3" + ] + }, + { + "metadata": { + "id": "WJ7W-F-d0AoZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1179 + }, + "outputId": "f304de3d-0e95-4b9f-c3d5-e835e62d33f8" + }, + "cell_type": "code", + "source": [ + "iris_df[iris_df['sepal_width']>3.3]" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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64.63.41.40.3setosa
335.54.21.40.2setosa
165.43.91.30.4setosa
445.13.81.90.4setosa
365.53.51.30.2setosa
405.03.51.30.3setosa
275.23.51.50.2setosa
205.43.41.70.2setosa
195.13.81.50.3setosa
1486.23.45.42.3virginica
435.03.51.60.6setosa
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1177.73.86.72.2virginica
315.43.41.50.4setosa
224.63.61.00.2setosa
215.13.71.50.4setosa
856.03.44.51.6versicolor
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145.84.01.20.2setosa
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185.73.81.70.3setosa
1097.23.66.12.5virginica
155.74.41.50.4setosa
244.83.41.90.2setosa
285.23.41.40.2setosa
75.03.41.50.2setosa
114.83.41.60.2setosa
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175.13.51.40.3setosa
105.43.71.50.2setosa
55.43.91.70.4setosa
485.33.71.50.2setosa
465.13.81.60.2setosa
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
856.03.44.51.6versicolor
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "85 6.0 3.4 4.5 1.6 versicolor" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "id": "1lmnB3ot2u7I", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Sorting a column by value" + ] + }, + { + "metadata": { + "id": "K7KIj6fv2zWP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1992 + }, + "outputId": "9c54e5a0-60ce-4853-f6c7-5311daa91e60" + }, + "cell_type": "code", + "source": [ + "iris_df.sort_values(by='sepal_width')#, ascending = False)\n", + "#pass ascending = False for descending order" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
605.02.03.51.0versicolor
1196.02.25.01.5virginica
686.22.24.51.5versicolor
626.02.24.01.0versicolor
535.52.34.01.3versicolor
876.32.34.41.3versicolor
935.02.33.31.0versicolor
414.52.31.30.3setosa
574.92.43.31.0versicolor
815.52.43.71.0versicolor
805.52.43.81.1versicolor
895.52.54.01.3versicolor
1086.72.55.81.8virginica
985.12.53.01.1versicolor
1466.32.55.01.9virginica
1064.92.54.51.7virginica
726.32.54.91.5versicolor
695.62.53.91.1versicolor
1135.72.55.02.0virginica
905.52.64.41.2versicolor
1187.72.66.92.3virginica
795.72.63.51.0versicolor
1346.12.65.61.4virginica
925.82.64.01.2versicolor
825.82.73.91.2versicolor
595.22.73.91.4versicolor
1116.42.75.31.9virginica
945.62.74.21.3versicolor
1015.82.75.11.9virginica
675.82.74.11.0versicolor
..................
205.43.41.70.2setosa
1486.23.45.42.3virginica
395.13.41.50.2setosa
856.03.44.51.6versicolor
64.63.41.40.3setosa
244.83.41.90.2setosa
365.53.51.30.2setosa
275.23.51.50.2setosa
05.13.51.40.2setosa
175.13.51.40.3setosa
435.03.51.60.6setosa
405.03.51.30.3setosa
224.63.61.00.2setosa
1097.23.66.12.5virginica
45.03.61.40.2setosa
215.13.71.50.4setosa
105.43.71.50.2setosa
485.33.71.50.2setosa
465.13.81.60.2setosa
445.13.81.90.4setosa
1177.73.86.72.2virginica
185.73.81.70.3setosa
195.13.81.50.3setosa
1317.93.86.42.0virginica
55.43.91.70.4setosa
165.43.91.30.4setosa
145.84.01.20.2setosa
325.24.11.50.1setosa
335.54.21.40.2setosa
155.74.41.50.4setosa
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150 rows × 5 columns

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976.22.94.31.3versicolor
586.62.94.61.3versicolor
766.82.84.81.4versicolor
955.73.04.21.2versicolor
845.43.04.51.5versicolor
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "97 6.2 2.9 4.3 1.3 versicolor\n", + "58 6.6 2.9 4.6 1.3 versicolor\n", + "76 6.8 2.8 4.8 1.4 versicolor\n", + "95 5.7 3.0 4.2 1.2 versicolor\n", + "84 5.4 3.0 4.5 1.5 versicolor" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "metadata": { + "id": "7tumfZ3DotPG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "outputId": "ed1e77a9-5413-40d5-9af7-5f6d31b60128" + }, + "cell_type": "code", + "source": [ + "# do the same for other 2 species \n", + "versicolor = iris_df[iris_df['species'] == species[1]]\n", + "\n", + "versicolor.head()" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "count 50.00000 50.000000 50.000000 50.00000\n", + "mean 5.00600 3.418000 1.464000 0.24400\n", + "std 0.35249 0.381024 0.173511 0.10721\n", + "min 4.30000 2.300000 1.000000 0.10000\n", + "25% 4.80000 3.125000 1.400000 0.20000\n", + "50% 5.00000 3.400000 1.500000 0.20000\n", + "75% 5.20000 3.675000 1.575000 0.30000\n", + "max 5.80000 4.400000 1.900000 0.60000" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "metadata": { + "id": "Vdu0ulZWtr09", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Let's plot and see the difference" + ] + }, + { + "metadata": { + "id": "PEVMzRvpttmD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "##### import matplotlib.pyplot " + ] + }, + { + "metadata": { + "id": "rqDXuuAtt7C3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 402 + }, + "outputId": "021c56dd-a9fb-4313-e553-f75a57683298" + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "#hist creates a histogram there are many more plots(see the documentation) you can play with it.\n", + "\n", + "plt.hist(setosa['sepal_length'])\n", + "plt.hist(versicolor['sepal_length'])\n", + "plt.hist(virginica['sepal_length'])" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([ 4., 1., 6., 5., 12., 8., 4., 5., 2., 3.]),\n", + " array([4.3 , 4.45, 4.6 , 4.75, 4.9 , 5.05, 5.2 , 5.35, 5.5 , 5.65, 5.8 ]),\n", + " )" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file diff --git a/Numpy_Examples_1.ipynb b/Numpy_Examples_1.ipynb new file mode 100644 index 0000000..2827a4b --- /dev/null +++ b/Numpy_Examples_1.ipynb @@ -0,0 +1,513 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Numpy_Examples 1.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/dnc2k/Assignment-2/blob/dnc2k/Numpy_Examples_1.ipynb)" + ] + }, + { + "metadata": { + "id": "3pSVAeWfuPcq", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Numpy Examples\n", + "\n", + "## What is numpy?\n", + "\n", + "#### Python has built-in:\n", + "\n", + "- containers: lists (costless insertion and append), dictionnaries (fast lookup)\n", + "- high-level number objects: integers, floating point\n", + "\n", + "#### Numpy is:\n", + "\n", + " - extension package to Python for multidimensional arrays\n", + " - closer to hardware (efficiency)\n", + " - designed for scientific computation (convenience)\n", + "\n", + "\n", + "#### Import numpy\n", + "\n" + ] + }, + { + "metadata": { + "id": "ozUi4_X55UHE", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "3-1ghFDF5N2z", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Uncomment Print statement and run each cell to see the output\n", + "\n", + "#### Create numpy arrays\n" + ] + }, + { + "metadata": { + "id": "atYpk2ert0b-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + }, + "outputId": "4ff1f20c-14bb-4cb8-ceca-c7c1cd6a24c5" + }, + "cell_type": "code", + "source": [ + "a = np.array([1, 2, 3]) # Create a rank 1 array\n", + "print(a)\n", + "print(type(a)) #print type of a\n", + "\n", + "b = np.array([[1,2,3],[4,5,6]]) # Create a rank 2 array\n", + "print(b.shape) # Prints \"(2, 3)\"\n", + "print(b[0, 0], b[0, 1], b[1, 0])" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1 2 3]\n", + "\n", + "(2, 3)\n", + "1 2 4\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "Kro5ZOwXue5n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Some basic functions for creating arrays. Print all the defined arrays and see the results." + ] + }, + { + "metadata": { + "id": "V3rdzgr9uhHS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 237 + }, + "outputId": "e25a51e1-4c70-4177-af60-164aa8901f45" + }, + "cell_type": "code", + "source": [ + "a = np.zeros(shape=(2,2))\n", + "b = np.ones(shape = (3,3))\n", + "c = np.eye(2)\n", + "d = np.full(shape=(3,3), fill_value=5)\n", + "e = np.random.random((2,2))\n", + "\n", + "print('a', a)\n", + "print('b',b)\n", + "print('c',c)\n", + "print('d',d)\n", + "print('e',e)" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "a [[0. 0.]\n", + " [0. 0.]]\n", + "b [[1. 1. 1.]\n", + " [1. 1. 1.]\n", + " [1. 1. 1.]]\n", + "c [[1. 0.]\n", + " [0. 1.]]\n", + "d [[5 5 5]\n", + " [5 5 5]\n", + " [5 5 5]]\n", + "e [[0.16463418 0.51575979]\n", + " [0.87927607 0.06306851]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "8RPW_SutukjF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Execute and understand :)" + ] + }, + { + "metadata": { + "id": "-8JuqYt4upeo", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "outputId": "c54a2f16-6ccb-41f7-825d-21b527156b3d" + }, + "cell_type": "code", + "source": [ + "a = np.arange(10)\n", + "b = np.linspace(0,10, num=6)\n", + "print(a)\n", + "print(b)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0 1 2 3 4 5 6 7 8 9]\n", + "[ 0. 2. 4. 6. 8. 10.]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "MRHhbjx4uvYN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Array Indexing" + ] + }, + { + "metadata": { + "id": "grF5_yUSuxVK", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + }, + "outputId": "00a0aace-b1a6-4652-cadb-4fa881762e71" + }, + "cell_type": "code", + "source": [ + "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n", + "\n", + "# Use slicing to pull out the subarray consisting of the first 2 rows\n", + "# and columns 1 and 2; b is the following array of shape (2, 2):\n", + "# [[2 3]\n", + "# [6 7]]\n", + "b = a[:2, 1:3]\n", + "print (b)\n", + "# A slice of an array is a view into the same data, so modifying it\n", + "# will modify the original array.\n", + "\n", + "print(a[0, 1]) # Prints \"2\"\n", + "\n", + "b[0, 0] = 77 # b[0, 0] is the same piece of data as a[0, 1]\n", + "print(a[0, 1]) # Prints \"77\"" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[2 3]\n", + " [6 7]]\n", + "2\n", + "77\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "s400Gijxu0kO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Slicing" + ] + }, + { + "metadata": { + "id": "kubpegh2u4zF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 127 + }, + "outputId": "79290990-bd7a-4cde-a777-34e3a6da9241" + }, + "cell_type": "code", + "source": [ + "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n", + "\n", + "row_r1 = a[1, :] # Rank 1 view of the second row of a\n", + "row_r2 = a[1:2, :] # Rank 2 view of the second row of a\n", + "\n", + "print(row_r1, row_r1.shape) # Prints \"[5 6 7 8] (4,)\"\n", + "print(row_r2, row_r2.shape) # Prints \"[[5 6 7 8]] (1, 4)\"\n", + "\n", + "col_r1 = a[:, 1]\n", + "col_r2 = a[:, 1:2]\n", + "\n", + "print(col_r1, col_r1.shape) # Prints \"[ 2 6 10] (3,)\"\n", + "print(col_r2, col_r2.shape)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[5 6 7 8] (4,)\n", + "[[5 6 7 8]] (1, 4)\n", + "[ 2 6 10] (3,)\n", + "[[ 2]\n", + " [ 6]\n", + " [10]] (3, 1)\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "TmGnCO3AvE8t", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Aritmetic operations" + ] + }, + { + "metadata": { + "id": "YvBw3ImjvGqD", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "outputId": "85c537ca-e089-4714-a430-911232c66ef1" + }, + "cell_type": "code", + "source": [ + "x = np.array([[1,2],[3,4]])\n", + "\n", + "print(np.sum(x)) # Compute sum of all elements; prints \"10\"\n", + "print(np.sum(x, axis=0)) # Compute sum of each column; prints \"[4 6]\"\n", + "print(np.sum(x, axis=1)) # Compute sum of each row; prints \"[3 7]\"" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "10\n", + "[4 6]\n", + "[3 7]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "uaVY3ZzD4pC2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Using Boolean Mask" + ] + }, + { + "metadata": { + "id": "-PNfOMvh4_Gp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "outputId": "da128d9b-2bd3-4832-f0e9-1fc395281fbe" + }, + "cell_type": "code", + "source": [ + "b = np.arange(10)\n", + "\n", + "print(b)\n", + "\n", + "mask = b%2!=0 #perform computations on the list \n", + "\n", + "print(mask)\n", + "\n", + "print(b[mask]) #applying the mask on the numpy array\n" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0 1 2 3 4 5 6 7 8 9]\n", + "[False True False True False True False True False True]\n", + "[1 3 5 7 9]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "HbEPBbz-5J9K", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "bbf0f757-9b46-411d-a516-8ab550cb86bf" + }, + "cell_type": "code", + "source": [ + "modified_b = b\n", + "modified_b[mask] = -1\n", + "\n", + "print(modified_b)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[ 0 -1 2 -1 4 -1 6 -1 8 -1]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "zgSd71EEAHC7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Swapping two columns in a 2d numpy array" + ] + }, + { + "metadata": { + "id": "-cvqeXd_AGo1", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 127 + }, + "outputId": "bed3a174-f512-4eb6-a7de-00a6e7a53087" + }, + "cell_type": "code", + "source": [ + "a = np.arange(9).reshape(3,3)\n", + "print(a)\n", + "\n", + "print(a[:, [1,0,2]])" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[0 1 2]\n", + " [3 4 5]\n", + " [6 7 8]]\n", + "[[1 0 2]\n", + " [4 3 5]\n", + " [7 6 8]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "U7ifiLY3Ayky", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Swapping two rows in a 2d numpy array" + ] + }, + { + "metadata": { + "id": "0FrOURRDAZNP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 127 + }, + "outputId": "0407ee33-bb36-42ac-e542-e0076bc3847d" + }, + "cell_type": "code", + "source": [ + "a = np.arange(9).reshape(3,3)\n", + "print(a)\n", + "\n", + "print(a[[1,0,2], :])" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[0 1 2]\n", + " [3 4 5]\n", + " [6 7 8]]\n", + "[[3 4 5]\n", + " [0 1 2]\n", + " [6 7 8]]\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file diff --git a/dnc2k.ipynb b/dnc2k.ipynb index 9e2543a..078df9a 100644 --- a/dnc2k.ipynb +++ b/dnc2k.ipynb @@ -1,32 +1,93 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "dnc2k.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/dnc2k/Assignment-2/blob/dnc2k/dnc2k.ipynb)" + ] + }, + { + "metadata": { + "id": "VSHhNc1q4AJF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 201 + }, + "outputId": "eff259d6-8576-4bf5-ad11-6d59c2eb9bac" + }, + "cell_type": "code", + "source": [ + "dummy_list=[23,82,98,62,16]\n", + "\n", + "print (dummy_list)\n", + "\n", + "dummy_list.reverse()\n", + "print (dummy_list)\n", + "\n", + "dummy_list_2 = [2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n", + "for item in dummy_list_2:\n", + " dummy_list.append(item)\n", + "print (dummy_list)\n", + "\n", + "dummy_dict={16:2, 62:1, 98:1, 82:1, 23:1, 2:1, 200:1, 4:1, 1:1, 0:1, 9.45:1, 45.67:1, 90:1, 12.01:1, 12.02:1}\n", + "\n", + "print (dummy_dict)\n", + "\n", + "dummy_list.sort()\n", + "print (dummy_list)\n", + "dummy_list.sort(reverse=True)\n", + "print (dummy_list)\n", + "\n", + "x = 16\n", + "dummy_list.remove(x)\n", + "print (dummy_list)\n", + "\n", + "x=int(input())\n", + "del dummy_list[x]\n", + "print (dummy_list)\n", + "\n", + "dummy_list.clear()\n", + "print (dummy_list)" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[23, 82, 98, 62, 16]\n", + "[16, 62, 98, 82, 23]\n", + "[16, 62, 98, 82, 23, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n", + "{16: 2, 62: 1, 98: 1, 82: 1, 23: 1, 2: 1, 200: 1, 4: 1, 1: 1, 0: 1, 9.45: 1, 45.67: 1, 90: 1, 12.01: 1, 12.02: 1}\n", + "[0, 1, 2, 4, 9.45, 12.01, 12.02, 16, 16, 23, 45.67, 62, 82, 90, 98, 200]\n", + "[200, 98, 90, 82, 62, 45.67, 23, 16, 16, 12.02, 12.01, 9.45, 4, 2, 1, 0]\n", + "[200, 98, 90, 82, 62, 45.67, 23, 16, 12.02, 12.01, 9.45, 4, 2, 1, 0]\n", + "2\n", + "[200, 98, 82, 62, 45.67, 23, 16, 12.02, 12.01, 9.45, 4, 2, 1, 0]\n", + "[]\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file