Data-Science-For-Beginners/translations/tr/4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Kış ve Yaz Aylarında NYC Taksi Verileri\n",
    "\n",
    "Sağlanan sütunlar hakkında daha fazla bilgi edinmek için [Veri sözlüğüne](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) göz atabilirsiniz.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "#Install the pandas library\r\n",
    "!pip install pandas"
   ],
   "outputs": [],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "import pandas as pd\r\n",
    "\r\n",
    "path = '../../data/taxi.csv'\r\n",
    "\r\n",
    "#Load the csv file into a dataframe\r\n",
    "df = pd.read_csv(path)\r\n",
    "\r\n",
    "#Print the dataframe\r\n",
    "print(df)\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "     VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
      "0         2.0  2019-07-15 16:27:53   2019-07-15 16:44:21              3.0   \n",
      "1         2.0  2019-07-17 20:26:35   2019-07-17 20:40:09              6.0   \n",
      "2         2.0  2019-07-06 16:01:08   2019-07-06 16:10:25              1.0   \n",
      "3         1.0  2019-07-18 22:32:23   2019-07-18 22:35:08              1.0   \n",
      "4         2.0  2019-07-19 14:54:29   2019-07-19 15:19:08              1.0   \n",
      "..        ...                  ...                   ...              ...   \n",
      "195       2.0  2019-01-18 08:42:15   2019-01-18 08:56:57              1.0   \n",
      "196       1.0  2019-01-19 04:34:45   2019-01-19 04:43:44              1.0   \n",
      "197       2.0  2019-01-05 10:37:39   2019-01-05 10:42:03              1.0   \n",
      "198       2.0  2019-01-23 10:36:29   2019-01-23 10:44:34              2.0   \n",
      "199       2.0  2019-01-30 06:55:58   2019-01-30 07:07:02              5.0   \n",
      "\n",
      "     trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
      "0             2.02         1.0                  N           186           233   \n",
      "1             1.59         1.0                  N           141           161   \n",
      "2             1.69         1.0                  N           246           249   \n",
      "3             0.90         1.0                  N           229           141   \n",
      "4             4.79         1.0                  N           237           107   \n",
      "..             ...         ...                ...           ...           ...   \n",
      "195           1.18         1.0                  N            43           237   \n",
      "196           2.30         1.0                  N           148           234   \n",
      "197           0.83         1.0                  N           237           263   \n",
      "198           1.12         1.0                  N           144           113   \n",
      "199           2.41         1.0                  N           209           107   \n",
      "\n",
      "     payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
      "0             1.0         12.0    1.0      0.5        4.08           0.0   \n",
      "1             2.0         10.0    0.5      0.5        0.00           0.0   \n",
      "2             2.0          8.5    0.0      0.5        0.00           0.0   \n",
      "3             1.0          4.5    3.0      0.5        1.65           0.0   \n",
      "4             1.0         19.5    0.0      0.5        5.70           0.0   \n",
      "..            ...          ...    ...      ...         ...           ...   \n",
      "195           1.0         10.0    0.0      0.5        2.16           0.0   \n",
      "196           1.0          9.5    0.5      0.5        2.15           0.0   \n",
      "197           1.0          5.0    0.0      0.5        1.16           0.0   \n",
      "198           2.0          7.0    0.0      0.5        0.00           0.0   \n",
      "199           1.0         10.5    0.0      0.5        1.00           0.0   \n",
      "\n",
      "     improvement_surcharge  total_amount  congestion_surcharge  \n",
      "0                      0.3         20.38                   2.5  \n",
      "1                      0.3         13.80                   2.5  \n",
      "2                      0.3         11.80                   2.5  \n",
      "3                      0.3          9.95                   2.5  \n",
      "4                      0.3         28.50                   2.5  \n",
      "..                     ...           ...                   ...  \n",
      "195                    0.3         12.96                   0.0  \n",
      "196                    0.3         12.95                   0.0  \n",
      "197                    0.3          6.96                   0.0  \n",
      "198                    0.3          7.80                   0.0  \n",
      "199                    0.3         12.30                   0.0  \n",
      "\n",
      "[200 rows x 18 columns]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Aşağıdaki hücreleri kendi Keşifsel Veri Analizinizi yapmak için kullanın\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.7 64-bit ('venv': venv)"
  },
  "language_info": {
   "mimetype": "text/x-python",
   "name": "python",
   "pygments_lexer": "ipython3",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.9.7",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "name": "04-nyc-taxi-join-weather-in-pandas",
  "notebookId": 1709144033725344,
  "interpreter": {
   "hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
  },
  "coopTranslator": {
   "original_hash": "7bca1c1abc1e55842817b62e44e1a963",
   "translation_date": "2025-09-02T08:33:37+00:00",
   "source_file": "4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb",
   "language_code": "tr"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}