{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "9afe08a5", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "c5856fa5", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Index(...) must be called with a collection of some kind, 2 was passed", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df\u001b[38;5;241m=\u001b[39m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n", "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/series.py:380\u001b[0m, in \u001b[0;36mSeries.__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m 376\u001b[0m \u001b[38;5;66;03m# uncomment the line below when removing the FutureWarning\u001b[39;00m\n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# dtype = np.dtype(object)\u001b[39;00m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 380\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[43mensure_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 382\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 383\u001b[0m data \u001b[38;5;241m=\u001b[39m {}\n", "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:7043\u001b[0m, in \u001b[0;36mensure_index\u001b[0;34m(index_like, copy)\u001b[0m\n\u001b[1;32m 7041\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Index\u001b[38;5;241m.\u001b[39m_with_infer(index_like, copy\u001b[38;5;241m=\u001b[39mcopy, tupleize_cols\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 7042\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 7043\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mIndex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_with_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:680\u001b[0m, in \u001b[0;36mIndex._with_infer\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 678\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m 679\u001b[0m warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.*the Index constructor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mFutureWarning\u001b[39;00m)\n\u001b[0;32m--> 680\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 682\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m _dtype_obj \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m result\u001b[38;5;241m.\u001b[39m_is_multi:\n\u001b[1;32m 683\u001b[0m \u001b[38;5;66;03m# error: Argument 1 to \"maybe_convert_objects\" has incompatible type\u001b[39;00m\n\u001b[1;32m 684\u001b[0m \u001b[38;5;66;03m# \"Union[ExtensionArray, ndarray[Any, Any]]\"; expected\u001b[39;00m\n\u001b[1;32m 685\u001b[0m \u001b[38;5;66;03m# \"ndarray[Any, Any]\"\u001b[39;00m\n\u001b[1;32m 686\u001b[0m values \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mmaybe_convert_objects(result\u001b[38;5;241m.\u001b[39m_values) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n", "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:508\u001b[0m, in \u001b[0;36mIndex.__new__\u001b[0;34m(cls, data, dtype, copy, name, tupleize_cols, **kwargs)\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m klass\u001b[38;5;241m.\u001b[39m_simple_new(arr, name)\n\u001b[1;32m 507\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_scalar(data):\n\u001b[0;32m--> 508\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_scalar_data_error(data)\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(data, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__array__\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 510\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Index(np\u001b[38;5;241m.\u001b[39masarray(data), dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, name\u001b[38;5;241m=\u001b[39mname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", "\u001b[0;31mTypeError\u001b[0m: Index(...) must be called with a collection of some kind, 2 was passed" ] } ], "source": [ "df=pd.Series(1,2,3,4,5)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 1, "id": "422aaa0b", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'pd' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dataset\u001b[38;5;241m=\u001b[39m\u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAdvertising.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined" ] } ], "source": [ "dataset=pd.read_csv('Advertising.csv')" ] }, { "cell_type": "code", "execution_count": 2, "id": "6b4ba291", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "dataset=pd.read_csv('Advertising.csv')" ] }, { "cell_type": "code", "execution_count": 3, "id": "2ec925f7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Unnamed: 0 TV radio newspaper sales\n", "0 1 230.1 37.8 69.2 22.1\n", "1 2 44.5 39.3 45.1 10.4\n", "2 3 17.2 45.9 69.3 9.3\n", "3 4 151.5 41.3 58.5 18.5\n", "4 5 180.8 10.8 58.4 12.9\n" ] } ], "source": [ "print(dataset.head())" ] }, { "cell_type": "code", "execution_count": 4, "id": "4d33fa27", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 200 entries, 0 to 199\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Unnamed: 0 200 non-null int64 \n", " 1 TV 200 non-null float64\n", " 2 radio 200 non-null float64\n", " 3 newspaper 200 non-null float64\n", " 4 sales 200 non-null float64\n", "dtypes: float64(4), int64(1)\n", "memory usage: 7.9 KB\n", "None\n" ] } ], "source": [ "print(dataset.info())" ] }, { "cell_type": "code", "execution_count": 6, "id": "d9d1a560", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " animal_name hair feathers eggs milk airborne aquatic predator \\\n", "0 aardvark 1 0 0 1 0 0 1 \n", "1 antelope 1 0 0 1 0 0 0 \n", "2 bass 0 0 1 0 0 1 1 \n", "3 bear 1 0 0 1 0 0 1 \n", "4 boar 1 0 0 1 0 0 1 \n", "\n", " toothed backbone breathes venomous fins legs tail domestic catsize \\\n", "0 1 1 1 0 0 4 0 0 1 \n", "1 1 1 1 0 0 4 1 0 1 \n", "2 1 1 0 0 1 0 1 0 0 \n", "3 1 1 1 0 0 4 0 0 1 \n", "4 1 1 1 0 0 4 1 0 1 \n", "\n", " class_type \n", "0 1 \n", "1 1 \n", "2 4 \n", "3 1 \n", "4 1 \n" ] } ], "source": [ "dataset2=pd.read_csv('zoo.csv')\n", "print(dataset2.head())" ] }, { "cell_type": "code", "execution_count": 7, "id": "9a48dde4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 101 entries, 0 to 100\n", "Data columns (total 18 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 animal_name 101 non-null object\n", " 1 hair 101 non-null int64 \n", " 2 feathers 101 non-null int64 \n", " 3 eggs 101 non-null int64 \n", " 4 milk 101 non-null int64 \n", " 5 airborne 101 non-null int64 \n", " 6 aquatic 101 non-null int64 \n", " 7 predator 101 non-null int64 \n", " 8 toothed 101 non-null int64 \n", " 9 backbone 101 non-null int64 \n", " 10 breathes 101 non-null int64 \n", " 11 venomous 101 non-null int64 \n", " 12 fins 101 non-null int64 \n", " 13 legs 101 non-null int64 \n", " 14 tail 101 non-null int64 \n", " 15 domestic 101 non-null int64 \n", " 16 catsize 101 non-null int64 \n", " 17 class_type 101 non-null int64 \n", "dtypes: int64(17), object(1)\n", "memory usage: 14.3+ KB\n", "None\n" ] } ], "source": [ "print(dataset2.info())" ] }, { "cell_type": "code", "execution_count": 8, "id": "a2ba3d6c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Unnamed: 0 TV radio newspaper sales\n", "0 1 230.1 37.8 69.2 22.1\n", "1 2 44.5 39.3 45.1 10.4\n", "2 3 17.2 45.9 69.3 9.3\n", "3 4 151.5 41.3 58.5 18.5\n", "4 5 180.8 10.8 58.4 12.9\n", "5 6 8.7 48.9 75.0 7.2\n", "6 7 57.5 32.8 23.5 11.8\n", "7 8 120.2 19.6 11.6 13.2\n", "8 9 8.6 2.1 1.0 4.8\n", "9 10 199.8 2.6 21.2 10.6\n", "10 11 66.1 5.8 24.2 8.6\n", "11 12 214.7 24.0 4.0 17.4\n", "12 13 23.8 35.1 65.9 9.2\n", "13 14 97.5 7.6 7.2 9.7\n", "14 15 204.1 32.9 46.0 19.0\n", "15 16 195.4 47.7 52.9 22.4\n", "16 17 67.8 36.6 114.0 12.5\n", "17 18 281.4 39.6 55.8 24.4\n", "18 19 69.2 20.5 18.3 11.3\n", "19 20 147.3 23.9 19.1 14.6\n", "20 21 218.4 27.7 53.4 18.0\n", "21 22 237.4 5.1 23.5 12.5\n", "22 23 13.2 15.9 49.6 5.6\n", "23 24 228.3 16.9 26.2 15.5\n", "24 25 62.3 12.6 18.3 9.7\n", "25 26 262.9 3.5 19.5 12.0\n", "26 27 142.9 29.3 12.6 15.0\n", "27 28 240.1 16.7 22.9 15.9\n", "28 29 248.8 27.1 22.9 18.9\n", "29 30 70.6 16.0 40.8 10.5\n", "30 31 292.9 28.3 43.2 21.4\n", "31 32 112.9 17.4 38.6 11.9\n", "32 33 97.2 1.5 30.0 9.6\n", "33 34 265.6 20.0 0.3 17.4\n", "34 35 95.7 1.4 7.4 9.5\n", "35 36 290.7 4.1 8.5 12.8\n", "36 37 266.9 43.8 5.0 25.4\n", "37 38 74.7 49.4 45.7 14.7\n", "38 39 43.1 26.7 35.1 10.1\n", "39 40 228.0 37.7 32.0 21.5\n", "40 41 202.5 22.3 31.6 16.6\n", "41 42 177.0 33.4 38.7 17.1\n", "42 43 293.6 27.7 1.8 20.7\n", "43 44 206.9 8.4 26.4 12.9\n", "44 45 25.1 25.7 43.3 8.5\n", "45 46 175.1 22.5 31.5 14.9\n", "46 47 89.7 9.9 35.7 10.6\n", "47 48 239.9 41.5 18.5 23.2\n", "48 49 227.2 15.8 49.9 14.8\n", "49 50 66.9 11.7 36.8 9.7\n", "50 51 199.8 3.1 34.6 11.4\n", "51 52 100.4 9.6 3.6 10.7\n", "52 53 216.4 41.7 39.6 22.6\n", "53 54 182.6 46.2 58.7 21.2\n", "54 55 262.7 28.8 15.9 20.2\n", "55 56 198.9 49.4 60.0 23.7\n", "56 57 7.3 28.1 41.4 5.5\n", "57 58 136.2 19.2 16.6 13.2\n", "58 59 210.8 49.6 37.7 23.8\n", "59 60 210.7 29.5 9.3 18.4\n", "60 61 53.5 2.0 21.4 8.1\n", "61 62 261.3 42.7 54.7 24.2\n", "62 63 239.3 15.5 27.3 15.7\n", "63 64 102.7 29.6 8.4 14.0\n", "64 65 131.1 42.8 28.9 18.0\n", "65 66 69.0 9.3 0.9 9.3\n", "66 67 31.5 24.6 2.2 9.5\n", "67 68 139.3 14.5 10.2 13.4\n", "68 69 237.4 27.5 11.0 18.9\n", "69 70 216.8 43.9 27.2 22.3\n", "70 71 199.1 30.6 38.7 18.3\n", "71 72 109.8 14.3 31.7 12.4\n", "72 73 26.8 33.0 19.3 8.8\n", "73 74 129.4 5.7 31.3 11.0\n", "74 75 213.4 24.6 13.1 17.0\n", "75 76 16.9 43.7 89.4 8.7\n", "76 77 27.5 1.6 20.7 6.9\n", "77 78 120.5 28.5 14.2 14.2\n", "78 79 5.4 29.9 9.4 5.3\n", "79 80 116.0 7.7 23.1 11.0\n", "80 81 76.4 26.7 22.3 11.8\n", "81 82 239.8 4.1 36.9 12.3\n", "82 83 75.3 20.3 32.5 11.3\n", "83 84 68.4 44.5 35.6 13.6\n", "84 85 213.5 43.0 33.8 21.7\n", "85 86 193.2 18.4 65.7 15.2\n", "86 87 76.3 27.5 16.0 12.0\n", "87 88 110.7 40.6 63.2 16.0\n", "88 89 88.3 25.5 73.4 12.9\n", "89 90 109.8 47.8 51.4 16.7\n", "90 91 134.3 4.9 9.3 11.2\n", "91 92 28.6 1.5 33.0 7.3\n", "92 93 217.7 33.5 59.0 19.4\n", "93 94 250.9 36.5 72.3 22.2\n", "94 95 107.4 14.0 10.9 11.5\n", "95 96 163.3 31.6 52.9 16.9\n", "96 97 197.6 3.5 5.9 11.7\n", "97 98 184.9 21.0 22.0 15.5\n", "98 99 289.7 42.3 51.2 25.4\n", "99 100 135.2 41.7 45.9 17.2\n", "100 101 222.4 4.3 49.8 11.7\n", "101 102 296.4 36.3 100.9 23.8\n", "102 103 280.2 10.1 21.4 14.8\n", "103 104 187.9 17.2 17.9 14.7\n", "104 105 238.2 34.3 5.3 20.7\n", "105 106 137.9 46.4 59.0 19.2\n", "106 107 25.0 11.0 29.7 7.2\n", "107 108 90.4 0.3 23.2 8.7\n", "108 109 13.1 0.4 25.6 5.3\n", "109 110 255.4 26.9 5.5 19.8\n", "110 111 225.8 8.2 56.5 13.4\n", "111 112 241.7 38.0 23.2 21.8\n", "112 113 175.7 15.4 2.4 14.1\n", "113 114 209.6 20.6 10.7 15.9\n", "114 115 78.2 46.8 34.5 14.6\n", "115 116 75.1 35.0 52.7 12.6\n", "116 117 139.2 14.3 25.6 12.2\n", "117 118 76.4 0.8 14.8 9.4\n", "118 119 125.7 36.9 79.2 15.9\n", "119 120 19.4 16.0 22.3 6.6\n", "120 121 141.3 26.8 46.2 15.5\n", "121 122 18.8 21.7 50.4 7.0\n", "122 123 224.0 2.4 15.6 11.6\n", "123 124 123.1 34.6 12.4 15.2\n", "124 125 229.5 32.3 74.2 19.7\n", "125 126 87.2 11.8 25.9 10.6\n", "126 127 7.8 38.9 50.6 6.6\n", "127 128 80.2 0.0 9.2 8.8\n", "128 129 220.3 49.0 3.2 24.7\n", "129 130 59.6 12.0 43.1 9.7\n", "130 131 0.7 39.6 8.7 1.6\n", "131 132 265.2 2.9 43.0 12.7\n", "132 133 8.4 27.2 2.1 5.7\n", "133 134 219.8 33.5 45.1 19.6\n", "134 135 36.9 38.6 65.6 10.8\n", "135 136 48.3 47.0 8.5 11.6\n", "136 137 25.6 39.0 9.3 9.5\n", "137 138 273.7 28.9 59.7 20.8\n", "138 139 43.0 25.9 20.5 9.6\n", "139 140 184.9 43.9 1.7 20.7\n", "140 141 73.4 17.0 12.9 10.9\n", "141 142 193.7 35.4 75.6 19.2\n", "142 143 220.5 33.2 37.9 20.1\n", "143 144 104.6 5.7 34.4 10.4\n", "144 145 96.2 14.8 38.9 11.4\n", "145 146 140.3 1.9 9.0 10.3\n", "146 147 240.1 7.3 8.7 13.2\n", "147 148 243.2 49.0 44.3 25.4\n", "148 149 38.0 40.3 11.9 10.9\n", "149 150 44.7 25.8 20.6 10.1\n", "150 151 280.7 13.9 37.0 16.1\n", "151 152 121.0 8.4 48.7 11.6\n", "152 153 197.6 23.3 14.2 16.6\n", "153 154 171.3 39.7 37.7 19.0\n", "154 155 187.8 21.1 9.5 15.6\n", "155 156 4.1 11.6 5.7 3.2\n", "156 157 93.9 43.5 50.5 15.3\n", "157 158 149.8 1.3 24.3 10.1\n", "158 159 11.7 36.9 45.2 7.3\n", "159 160 131.7 18.4 34.6 12.9\n", "160 161 172.5 18.1 30.7 14.4\n", "161 162 85.7 35.8 49.3 13.3\n", "162 163 188.4 18.1 25.6 14.9\n", "163 164 163.5 36.8 7.4 18.0\n", "164 165 117.2 14.7 5.4 11.9\n", "165 166 234.5 3.4 84.8 11.9\n", "166 167 17.9 37.6 21.6 8.0\n", "167 168 206.8 5.2 19.4 12.2\n", "168 169 215.4 23.6 57.6 17.1\n", "169 170 284.3 10.6 6.4 15.0\n", "170 171 50.0 11.6 18.4 8.4\n", "171 172 164.5 20.9 47.4 14.5\n", "172 173 19.6 20.1 17.0 7.6\n", "173 174 168.4 7.1 12.8 11.7\n", "174 175 222.4 3.4 13.1 11.5\n", "175 176 276.9 48.9 41.8 27.0\n", "176 177 248.4 30.2 20.3 20.2\n", "177 178 170.2 7.8 35.2 11.7\n", "178 179 276.7 2.3 23.7 11.8\n", "179 180 165.6 10.0 17.6 12.6\n", "180 181 156.6 2.6 8.3 10.5\n", "181 182 218.5 5.4 27.4 12.2\n", "182 183 56.2 5.7 29.7 8.7\n", "183 184 287.6 43.0 71.8 26.2\n", "184 185 253.8 21.3 30.0 17.6\n", "185 186 205.0 45.1 19.6 22.6\n", "186 187 139.5 2.1 26.6 10.3\n", "187 188 191.1 28.7 18.2 17.3\n", "188 189 286.0 13.9 3.7 15.9\n", "189 190 18.7 12.1 23.4 6.7\n", "190 191 39.5 41.1 5.8 10.8\n", "191 192 75.5 10.8 6.0 9.9\n", "192 193 17.2 4.1 31.6 5.9\n", "193 194 166.8 42.0 3.6 19.6\n", "194 195 149.7 35.6 6.0 17.3\n", "195 196 38.2 3.7 13.8 7.6\n", "196 197 94.2 4.9 8.1 9.7\n", "197 198 177.0 9.3 6.4 12.8\n", "198 199 283.6 42.0 66.2 25.5\n", "199 200 232.1 8.6 8.7 13.4\n" ] } ], "source": [ "dataset=pd.read_csv('Advertising.csv')\n", "print(dataset.to_string())" ] }, { "cell_type": "code", "execution_count": 9, "id": "7967d3ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Unnamed: 0 TV radio newspaper sales\n", "190 191 39.5 41.1 5.8 10.8\n", "191 192 75.5 10.8 6.0 9.9\n", "192 193 17.2 4.1 31.6 5.9\n", "193 194 166.8 42.0 3.6 19.6\n", "194 195 149.7 35.6 6.0 17.3\n", "195 196 38.2 3.7 13.8 7.6\n", "196 197 94.2 4.9 8.1 9.7\n", "197 198 177.0 9.3 6.4 12.8\n", "198 199 283.6 42.0 66.2 25.5\n", "199 200 232.1 8.6 8.7 13.4\n" ] } ], "source": [ "print(dataset.tail(10))" ] }, { "cell_type": "code", "execution_count": 11, "id": "1347c424", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "60\n" ] } ], "source": [ "print(pd.options.display.max_rows)" ] }, { "cell_type": "code", "execution_count": 12, "id": "0053340d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 200 entries, 0 to 199\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Unnamed: 0 200 non-null int64 \n", " 1 TV 200 non-null float64\n", " 2 radio 200 non-null float64\n", " 3 newspaper 200 non-null float64\n", " 4 sales 200 non-null float64\n", "dtypes: float64(4), int64(1)\n", "memory usage: 7.9 KB\n", "None\n" ] } ], "source": [ "print(dataset.info())\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "ed6b5c85", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 101 entries, 0 to 100\n", "Data columns (total 18 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 animal_name 101 non-null object\n", " 1 hair 101 non-null int64 \n", " 2 feathers 101 non-null int64 \n", " 3 eggs 101 non-null int64 \n", " 4 milk 101 non-null int64 \n", " 5 airborne 101 non-null int64 \n", " 6 aquatic 101 non-null int64 \n", " 7 predator 101 non-null int64 \n", " 8 toothed 101 non-null int64 \n", " 9 backbone 101 non-null int64 \n", " 10 breathes 101 non-null int64 \n", " 11 venomous 101 non-null int64 \n", " 12 fins 101 non-null int64 \n", " 13 legs 101 non-null int64 \n", " 14 tail 101 non-null int64 \n", " 15 domestic 101 non-null int64 \n", " 16 catsize 101 non-null int64 \n", " 17 class_type 101 non-null int64 \n", "dtypes: int64(17), object(1)\n", "memory usage: 14.3+ KB\n", "None\n" ] } ], "source": [ "print(dataset2.info())" ] }, { "cell_type": "code", "execution_count": 14, "id": "109d99a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "23.264000000000024\n" ] } ], "source": [ "print(dataset['radio'].mean())" ] }, { "cell_type": "code", "execution_count": 15, "id": "2ff8fd70", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 4.1\n", "1 5.7\n", "Name: radio, dtype: float64\n" ] } ], "source": [ "print(dataset['radio'].mode())" ] }, { "cell_type": "code", "execution_count": null, "id": "ec82cd0a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 16, "id": "fd136034", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "22.9\n" ] } ], "source": [ "print(dataset['radio'].median())" ] }, { "cell_type": "code", "execution_count": 1, "id": "b0f24bb1", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 7, "id": "31734cdf", "metadata": {}, "outputs": [], "source": [ "import matplotlib as mt" ] }, { "cell_type": "code", "execution_count": 19, "id": "682ce274", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Duration Pulse Maxpulse Calories\n", "0 60 110 130 409.1\n", "1 60 117 145 479.0\n", "2 60 103 135 340.0\n", "3 45 109 175 282.4\n", "4 45 117 148 406.0\n" ] }, { "ename": "AttributeError", "evalue": "module 'matplotlib' has no attribute 'show'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "Input \u001b[0;32mIn [19]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mhead())\n\u001b[1;32m 3\u001b[0m df\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscatter\u001b[39m\u001b[38;5;124m'\u001b[39m, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDuration\u001b[39m\u001b[38;5;124m'\u001b[39m,y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMaxpulse\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m \u001b[43mmt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m()\n", "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/matplotlib/_api/__init__.py:222\u001b[0m, in \u001b[0;36mcaching_module_getattr..__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m props:\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m props[name]\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__get__\u001b[39m(instance)\n\u001b[0;32m--> 222\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 223\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__module__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib' has no attribute 'show'" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df=pd.read_csv('data.csv')\n", "print(df.head())\n", "df.plot(kind='scatter', x='Duration',y='Maxpulse')\n", "mt.show()" ] }, { "cell_type": "code", "execution_count": 21, "id": "297abc8e", "metadata": {}, "outputs": [], "source": [ "df2=pd.read_csv('loan_data.csv')" ] }, { "cell_type": "code", "execution_count": 22, "id": "5c902c2a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " model mpg cyl disp hp drat wt qsec vs am gear \\\n", "0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 \n", "1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 \n", "2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 \n", "3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 \n", "4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 \n", "\n", " carb \n", "0 4 \n", "1 4 \n", "2 1 \n", "3 1 \n", "4 2 \n" ] } ], "source": [ "print(df2.head())" ] }, { "cell_type": "code", "execution_count": 23, "id": "a8d6cd28", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 32 entries, 0 to 31\n", "Data columns (total 12 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 model 32 non-null object \n", " 1 mpg 32 non-null float64\n", " 2 cyl 32 non-null int64 \n", " 3 disp 32 non-null float64\n", " 4 hp 32 non-null int64 \n", " 5 drat 32 non-null float64\n", " 6 wt 32 non-null float64\n", " 7 qsec 32 non-null float64\n", " 8 vs 32 non-null int64 \n", " 9 am 32 non-null int64 \n", " 10 gear 32 non-null int64 \n", " 11 carb 32 non-null int64 \n", "dtypes: float64(5), int64(6), object(1)\n", "memory usage: 3.1+ KB\n", "None\n" ] } ], "source": [ "print(df2.info())" ] }, { "cell_type": "code", "execution_count": null, "id": "7cce9436", "metadata": {}, "outputs": [], "source": [ "df2" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }