saif_khi_python_assignment1

saif_khi_python_assignment1.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Windows\\system32\n",
+      "Index(['age', 'bp', 'sg', 'al', 'su', 'rbc', 'pc', 'pcc', 'ba', 'bgr', 'bu',\n",
+      "       'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc', 'htn', 'dm', 'cad',\n",
+      "       'appet', 'pe', 'ane', 'class'],\n",
+      "      dtype='object')\n",
+      "[nan 'yes' 'no' ' yes' '\\tno' '\\tyes']\n",
+      "['nan' 'yes' 'no']\n",
+      "60\n",
+      "47\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa212940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "180.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "\n",
+    "print(os.getcwd())\n",
+    "\n",
+    "def print_column_unique(df):\n",
+    "    for x in range(0,len(df.columns)):\n",
+    "        print(str(df.columns[x])+\" \" +str(df.iloc[:,x].unique()))\n",
+    "def clean(df):\n",
+    "    func=lambda x: str(x).strip()\n",
+    "    df=df.applymap(func)\n",
+    "    df=df.replace(\"?\",float(\"nan\"))\n",
+    "    return df\n",
+    "\n",
+    "df = pd.read_csv(\"C:/Users/muhammad.saif/Documents/Updated Dataset/Dataset/chronic_kidney_disease_updated.csv\",sep=',', na_values=['?'])\n",
+    "print(df.columns)\n",
+    "#print_column_unique(df)\n",
+    "\n",
+    "print(df.dm.unique())\n",
+    "df=clean(df)\n",
+    "print(df.dm.unique())\n",
+    "\n",
+    "df[['age', 'bp', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc']]=df[['age', 'bp', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc']].astype(float)\n",
+    "print(len(df[(df[\"class\"]==\"ckd\") & (df[\"rbc\"]==\"normal\")]))\n",
+    "print(len(df[(df[\"class\"]==\"ckd\") & (df[\"rbc\"]==\"abnormal\")]))\n",
+    "df[df[\"class\"]==\"ckd\"].rbc.value_counts().plot(kind=\"bar\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(df.bp.max())\n",
+    "#print_column_unique(df)\n",
+    "#print(df.dm.unique())\n",
+    "df.to_csv(\"clean_chronic_kidney_disease.csv\",sep=\",\",index=False,encoding=\"utf-8\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}