{ "cells": [ { "cell_type": "markdown", "source": [ "# Dados de táxis de Nova Iorque no Inverno e no Verão\n", "\n", "Consulte o [Dicionário de Dados](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) para saber mais sobre as colunas fornecidas.\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", "metadata": {}, "source": [ "\n---\n\n**Aviso**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos pela precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte autoritária. Para informações críticas, recomenda-se uma tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes da utilização desta tradução.\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": "3bd4c20c4e8f3158f483f0f1cc543bb1", "translation_date": "2025-09-02T08:35:14+00:00", "source_file": "4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb", "language_code": "pt" } }, "nbformat": 4, "nbformat_minor": 2 }