{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# NYC taxi data in winter and summer\n",
        "\n",
        "A client has approached your team for help in investigating a taxi customer's seasonal spending habits in New York City. They want to know: **Do yellow taxi passengers in New York City tip drivers more in the winter or summer?**\n",
        "\n",
        "Your team is in the [capturing](../../data-science/data-science-lifecycle/introduction.md#Capturing) stage of the Data Science Lifecycle and you are in charge of handling the dataset. You have been provided a notebook and <a href=\"../../assets/data/taxi.csv\">data</a> to explore.\n",
        "\n",
        "We will use Python to load yellow taxi trip data from the [NYC Taxi & Limousine Commission](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets).\n",
        "You can also open the taxi data file in a text editor or spreadsheet software like Excel."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Instructions\n",
        "\n",
        "- Assess whether or not the data in this dataset can help answer the question.\n",
        "- Explore the [NYC Open Data catalog](https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93). Identify an additional dataset that could potentially be helpful in answering the client's question.\n",
        "- Write 3 questions that you would ask the client for more clarification and a better understanding of the problem. \n",
        "\n",
        "Refer to the [dataset's dictionary](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) and [user guide](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf) for more information about the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "path = '../../assets/data/taxi.csv'\n",
        "\n",
        "#Load the csv file into a dataframe\n",
        "df = pd.read_csv(path)\n",
        "\n",
        "#Print the dataframe\n",
        "print(df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Rubric\n",
        "\n",
        "Exemplary | Adequate | Needs Improvement\n",
        "--- | --- | -- |\n",
        "--- | --- | -- |\n",
        "\n",
        "## Acknowledgments\n",
        "\n",
        "Thanks to Microsoft for creating the open-source course [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners). It inspires the majority of the content in this chapter."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.9.13 64-bit",
      "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": "3.9.13"
    },
    "name": "04-nyc-taxi-join-weather-in-pandas",
    "notebookId": 1709144033725344,
    "vscode": {
      "interpreter": {
        "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}