Histogram to check the frequency distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2a13343f",
   "metadata": {
    "papermill": {
     "duration": 0.002832,
     "end_time": "2024-06-13T15:22:35.070894",
     "exception": false,
     "start_time": "2024-06-13T15:22:35.068062",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Aim: (4) For the given dataset mtcars.csv (www.kaggle.com/ruiromanini/mtcars), plot a histogram to check the frequency distribution of the variable ‘mpg’ (Miles per gallon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "315e5688",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-13T15:22:35.077029Z",
     "iopub.status.busy": "2024-06-13T15:22:35.076586Z",
     "iopub.status.idle": "2024-06-13T15:22:36.395222Z",
     "shell.execute_reply": "2024-06-13T15:22:36.393986Z"
    },
    "papermill": {
     "duration": 1.324786,
     "end_time": "2024-06-13T15:22:36.397839",
     "exception": false,
     "start_time": "2024-06-13T15:22:35.073053",
     "status": "completed"
    },
    "tags": []
   },
   "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 matplotlib.pyplot as plt\n",
    "# Load the dataset\n",
    "mtcars = pd.read_csv('/kaggle/input/mtcars/mtcars.csv') # Replace 'path_to_your_mtcars.csv' with the actual path to your mtcars.csv file\n",
    "# Plotting the histogram\n",
    "plt.hist(mtcars['mpg'], bins=10, color='skyblue', edgecolor='black')\n",
    "# Adding labels and title\n",
    "plt.xlabel('Miles per gallon (mpg)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Miles per gallon (mpg)')\n",
    "# Displaying the plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 5206454,
     "sourceId": 8684246,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30732,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "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": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 5.049368,
   "end_time": "2024-06-13T15:22:36.921540",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-06-13T15:22:31.872172",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}