Add pandas dataframe methods and create flights data CSV file

Author: Sarmad426

9-Pandas/3-pandas.ipynb

@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pandas Essentials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   FlightID           Airline Destination  Duration  Delay\n",
+      "0         1  American Airline     Sharjah       330     18\n",
+      "1         2      Tata Airline      Lahore       320     17\n",
+      "2         3               PIA  Washington       297     93\n",
+      "3         4     Japan Airways      Alaska       199     84\n",
+      "4         5     Japan Airways      Madina       146      2\n",
+      "                    Duration      Delay\n",
+      "Airline                                \n",
+      "American Airline  265.166667  50.777778\n",
+      "Emirates          226.500000  61.700000\n",
+      "Japan Airways     228.526316  55.736842\n",
+      "PIA               227.125000  64.312500\n",
+      "Qatar Airways     170.000000  65.875000\n",
+      "Saudi Airline     213.500000  45.500000\n",
+      "Tata Airline      215.444444  68.555556\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Create a random flights data CSV file\n",
+    "np.random.seed(0)\n",
+    "num_records = 100\n",
+    "\n",
+    "AIRLINES = ['PIA', 'Qatar Airways', 'Emirates',\"Japan Airways\",\"American Airline\",\"Tata Airline\", \"Saudi Airline\"]\n",
+    "\n",
+    "CITIES = [\n",
+    "    \"Dubai\",\n",
+    "    \"Delhi\",\n",
+    "    \"Karachi\",\n",
+    "    \"Riyad\",\n",
+    "    \"Makkah\",\n",
+    "    \"Madina\",\n",
+    "    \"Kuwait\",\n",
+    "    \"Lahore\",\n",
+    "    \"Colombo\",\n",
+    "    \"Dhaka\",\n",
+    "    \"Sharjah\",\n",
+    "    \"Mumbai\",\n",
+    "    \"Auckland\",\n",
+    "    \"Alaska\",\n",
+    "    \"San Francisco\",\n",
+    "    \"Washington\",\n",
+    "]\n",
+    "\n",
+    "flights_data = {\n",
+    "    'FlightID': np.arange(1, num_records + 1),\n",
+    "    'Airline': np.random.choice(AIRLINES, num_records),\n",
+    "    'Destination': np.random.choice(CITIES, num_records),\n",
+    "    'Duration': np.random.randint(60, 360, num_records),  # Duration in minutes\n",
+    "    'Delay': np.random.randint(0, 120, num_records)  # Delay in minutes\n",
+    "}\n",
+    "\n",
+    "flights_df = pd.DataFrame(flights_data)\n",
+    "flights_df.to_csv('random_flights_data.csv', index=False)\n",
+    "\n",
+    "# Read the CSV file using pandas\n",
+    "flights_df = pd.read_csv('random_flights_data.csv')\n",
+    "\n",
+    "# Display the first few rows of the dataframe\n",
+    "print(flights_df.head())\n",
+    "\n",
+    "# Perform operations on the data\n",
+    "# Example: Calculate the average duration and delay for each airline\n",
+    "average_stats = flights_df.groupby('Airline')[['Duration', 'Delay']].mean()\n",
+    "print(average_stats)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}