{ "cells": [ { "cell_type": "markdown", "source": [ "# Données des taxis de NYC en hiver et en été\n", "\n", "Consultez le [dictionnaire des données](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) pour en savoir plus sur les colonnes fournies.\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": [ "# Utilisez les cellules ci-dessous pour effectuer votre propre analyse exploratoire des données\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**Avertissement** : \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de recourir à une traduction professionnelle réalisée par un humain. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\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": "2025-09-01T22:22:09+00:00", "source_file": "4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb", "language_code": "fr" } }, "nbformat": 4, "nbformat_minor": 2 }