1lab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello world\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[76 23 62 90 91 97 20 20  1 57 53 16 36 57 66 94 99 62 98 97 12 78 99 54\n",
      "  3 60  1 22 72 76 32 94 28 74 64 28 65 45 18 66 34  5 87 24 12 94 77 18\n",
      " 44 66  7 55 86 60 24 80 43 96 83 37 61 10 75 44 97 86 15 75  7 36 49 94\n",
      "  0 32 28 98 47 78 64 13 83 91 32 82 95 93 53  2 58 70 55 66 74 54 96 13\n",
      " 40 15 99 89]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numpy import random\n",
    "\n",
    "x=random.randint(100, size=(100)) # https://www.w3schools.com/python/numpy/numpy_random.asp\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[29 99 83 95 71 97 93 25  7 91 23 99 56 62 48  0 91 77 77 50 85 19 19  5\n",
      " 53  5 66 21  1 66  7 38 33 62 43 68 52 37  8 89 93  8 50 98 64 49  7 24\n",
      " 28 78  8 86 25 88 11 80 85 48 77 70 45 98 98 62 67 95 20 40 87 22 16 78\n",
      " 37 12 99 28 65 86 13 88 68 83 22 82 62  9 75 84 35  7 39  0 55 31 65 71\n",
      " 48 60  2 51]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import random\n",
    "\n",
    "x=random.randint(100, size=(100)) \n",
    "print(x)\n",
    "plt.plot(x) # https://www.w3schools.com/python/matplotlib_plotting.asp\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 9)\n",
      "Times Pregnant              0\n",
      "Plasma Glucose              0\n",
      "Diastolic Blood Pressure    0\n",
      "Skin Thickness              0\n",
      "Serum Insulin               0\n",
      "Body mass                   0\n",
      "Diabetes Pedigree           0\n",
      "Age                         0\n",
      "Class Variable              0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "print(info.shape)\n",
    "print(info.isnull().sum()) # https://medium.com/analytics-vidhya/python-finding-missing-values-in-a-data-frame-3030aaf0e4fd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Times Pregnant              111\n",
      "Plasma Glucose                5\n",
      "Diastolic Blood Pressure     35\n",
      "Skin Thickness              227\n",
      "Serum Insulin               374\n",
      "Body mass                    11\n",
      "Diabetes Pedigree             0\n",
      "Age                           0\n",
      "Class Variable              500\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "#print(info.columns)\n",
    "\n",
    "#pregnant = info[\"Times Pregnant\"] # https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html\n",
    "#print(np.where(pregnant<=-1)) # https://stackoverflow.com/questions/13869173/numpy-find-index-of-the-elements-within-range\n",
    "#glucose = info[\"Plasma Glucose\"]\n",
    "#print(np.where(glucose<=0))\n",
    "\n",
    "missing_data = (info[columns] == 0).sum()\n",
    "\n",
    "print(missing_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average: \n",
      " Times Pregnant                3.845052\n",
      "Plasma Glucose              120.894531\n",
      "Diastolic Blood Pressure     69.105469\n",
      "Skin Thickness               20.536458\n",
      "Serum Insulin                79.799479\n",
      "Body mass                    31.992578\n",
      "Diabetes Pedigree             0.471876\n",
      "Age                          33.240885\n",
      "Class Variable                0.348958\n",
      "dtype: float64 \n",
      "\n",
      "Variance: \n",
      " Times Pregnant                 11.354056\n",
      "Plasma Glucose               1022.248314\n",
      "Diastolic Blood Pressure      374.647271\n",
      "Skin Thickness                254.473245\n",
      "Serum Insulin               13281.180078\n",
      "Body mass                      62.159984\n",
      "Diabetes Pedigree               0.109779\n",
      "Age                           138.303046\n",
      "Class Variable                  0.227483\n",
      "dtype: float64 \n",
      "\n",
      "Standart Deviation \n",
      " Times Pregnant                3.369578\n",
      "Plasma Glucose               31.972618\n",
      "Diastolic Blood Pressure     19.355807\n",
      "Skin Thickness               15.952218\n",
      "Serum Insulin               115.244002\n",
      "Body mass                     7.884160\n",
      "Diabetes Pedigree             0.331329\n",
      "Age                          11.760232\n",
      "Class Variable                0.476951\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "# num_of_rows = len(info)\n",
    "\n",
    "# sum = (info[columns]).sum()\n",
    "# avg_sum = sum/num_of_rows\n",
    "\n",
    "print(\"Average:\", \"\\n\", info.mean(), \"\\n\") #https://www.geeksforgeeks.org/create-the-mean-and-standard-deviation-of-the-data-of-a-pandas-series/\n",
    "print(\"Variance:\", \"\\n\", info.var(), \"\\n\") #https://www.w3schools.com/python/pandas/ref_df_std.asp#:~:text=The%20var()%20method%20calculates,the%20variance%20for%20each%20row.\n",
    "print(\"Standart Deviation\", \"\\n\", info.std()) #https://www.geeksforgeeks.org/create-the-mean-and-standard-deviation-of-the-data-of-a-pandas-series/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "plt.hist(info[\"Times Pregnant\"])\n",
    "plt.xlabel(\"Times Pregnant\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Times Pregnant\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "plt.hist(info[\"Plasma Glucose\"])\n",
    "plt.xlabel(\"Plasma Glucose\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Plasma Glucose\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "plt.hist(info[\"Diastolic Blood Pressure\"])\n",
    "plt.xlabel(\"Blood Pressure\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Blood Pressure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "plt.hist(info[\"Skin Thickness\"])\n",
    "plt.xlabel(\"Skin Thickness\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Skin Thickness\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "plt.hist(info[\"Serum Insulin\"])\n",
    "plt.xlabel(\"Serum Insulin\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Serum Insulin\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "plt.hist(info[\"Body mass\"])\n",
    "plt.xlabel(\"Body mass\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Body mass\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "plt.hist(info[\"Diabetes Pedigree\"])\n",
    "plt.xlabel(\"Diabetes Pedigree\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Diabetes Pedigree\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "plt.hist(info[\"Age\"])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "columns = [\"Times Pregnant\", \"Plasma Glucose\", \"Diastolic Blood Pressure\", \n",
    "                \"Skin Thickness\", \"Serum Insulin\", \"Body mass\",\n",
    "                \"Diabetes Pedigree\", \"Age\", \"Class Variable\"]\n",
    "\n",
    "info = pd.read_csv(url, names=columns)\n",
    "\n",
    "plt.hist(info[\"Class Variable\"])\n",
    "plt.xlabel(\"Class Variable\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Class Variable\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}