{ "cells": [ { "cell_type": "markdown", "source": [ "# 冬と夏のNYCタクシーデータ\n", "\n", "提供されている列について詳しく知りたい場合は、[データ辞書](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)を参照してください。\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": [ "# 以下のセルを使用して独自の探索的データ分析を行ってください\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**免責事項**: \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を追求しておりますが、自動翻訳には誤りや不正確な部分が含まれる可能性があることをご承知ください。元の言語で記載された文書が正式な情報源とみなされるべきです。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の使用に起因する誤解や誤解釈について、当方は一切の責任を負いません。\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": "2025-09-01T22:22:51+00:00", "source_file": "4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb", "language_code": "ja" } }, "nbformat": 4, "nbformat_minor": 2 }