{
  "id": 346096,
  "title": "🌦 NYC Daily Weather Dataset ",
  "url": "/competitions/big-data-derby-2022/discussion/346096",
  "author_name": "",
  "post_date": "2022-08-17T23:47:31.503546600Z",
  "votes": 73,
  "comment_count": 10,
  "views": 0,
  "content": "<p>Hello guys, I uploaded a <a href=\"https://www.kaggle.com/datasets/alejopaullier/new-york-city-weather-data-2019\" target=\"_blank\">dataset</a> containing daily weather information from NYC from 2019, like temperature, snowfall and precipitation. </p>\n<p>Since horses are affected by weather conditions this dataset might be useful for this competition.</p>\n<p>Hope you like it.</p>",
  "messages": [
    {
      "id": "1904076",
      "postDate": "08/17/2022 23:47:31",
      "content": "<p>Hello guys, I uploaded a <a href=\"https://www.kaggle.com/datasets/alejopaullier/new-york-city-weather-data-2019\" target=\"_blank\">dataset</a> containing daily weather information from NYC from 2019, like temperature, snowfall and precipitation. </p>\n<p>Since horses are affected by weather conditions this dataset might be useful for this competition.</p>\n<p>Hope you like it.</p>",
      "rawMarkdown": "Hello guys, I uploaded a [dataset](https://www.kaggle.com/datasets/alejopaullier/new-york-city-weather-data-2019) containing daily weather information from NYC from 2019, like temperature, snowfall and precipitation. \n\nSince horses are affected by weather conditions this dataset might be useful for this competition.\n\nHope you like it.",
      "votes": null
    },
    {
      "id": "1905774",
      "postDate": "08/19/2022 09:38:32",
      "content": "<p>This is interesting!  It is worth a shot!</p>",
      "rawMarkdown": "This is interesting!  It is worth a shot!",
      "votes": null
    },
    {
      "id": "1905795",
      "postDate": "08/19/2022 09:59:14",
      "content": "<p>Thanks for sharing, the 7 day rolling-mean for avg-temp and percipitation seem to have some predictive value for what I'm trying to do.</p>",
      "rawMarkdown": "Thanks for sharing, the 7 day rolling-mean for avg-temp and percipitation seem to have some predictive value for what I'm trying to do.",
      "votes": null
    },
    {
      "id": "1906461",
      "postDate": "08/19/2022 22:46:07",
      "content": "<p>I also made this <a href=\"https://www.kaggle.com/code/alejopaullier/new-york-city-weather-2019\" target=\"_blank\">notebook</a>, its an EDA with dynamic plotly graphs to get a better understanding of the data.</p>",
      "rawMarkdown": "I also made this [notebook](https://www.kaggle.com/code/alejopaullier/new-york-city-weather-2019), its an EDA with dynamic plotly graphs to get a better understanding of the data.",
      "votes": null
    },
    {
      "id": "1907134",
      "postDate": "08/20/2022 13:44:33",
      "content": "<p>Thanks for this resource, what is the best way to combine this data with the nyra_complete.csv by date? Can you join the weather data to the race data based on the date. Thanks. </p>\n<p>E.g some pseudocode would be.   new_df = pandas concatenate (nyra_complete, weather, join by common date values)</p>",
      "rawMarkdown": "Thanks for this resource, what is the best way to combine this data with the nyra_complete.csv by date? Can you join the weather data to the race data based on the date. Thanks. \n\nE.g some pseudocode would be.   new_df = pandas concatenate (nyra_complete, weather, join by common date values)",
      "votes": null
    },
    {
      "id": "1907140",
      "postDate": "08/20/2022 13:52:00",
      "content": "<p>Thank you for sharing!</p>",
      "rawMarkdown": "Thank you for sharing!",
      "votes": null
    },
    {
      "id": "1907359",
      "postDate": "08/20/2022 17:44:38",
      "content": "<p>I think you need to set the date as index using pandas <code>set_index()</code> and then make a left join using pandas <code>merge()</code> function, where the left table is the NYRA table and the right table is the weather data. Hope it helps</p>",
      "rawMarkdown": "I think you need to set the date as index using pandas `set_index()` and then make a left join using pandas `merge()` function, where the left table is the NYRA table and the right table is the weather data. Hope it helps",
      "votes": null
    },
    {
      "id": "1907452",
      "postDate": "08/20/2022 19:46:50",
      "content": "<p>Looks interesting. Will give it a try</p>",
      "rawMarkdown": "Looks interesting. Will give it a try",
      "votes": null
    },
    {
      "id": "1908668",
      "postDate": "08/21/2022 21:43:50",
      "content": "<p>Thank you :)</p>",
      "rawMarkdown": "Thank you :)",
      "votes": null
    },
    {
      "id": "1909383",
      "postDate": "08/22/2022 15:20:27",
      "content": "<p>Thank you for sharing, this is worth a try, upvote!</p>",
      "rawMarkdown": "Thank you for sharing, this is worth a try, upvote!",
      "votes": null
    },
    {
      "id": "1960634",
      "postDate": "09/28/2022 17:16:44",
      "content": "<blockquote>\n  <p>nyra_2019.set_index(\"race_date\").merge(ny_weather.set_index(\"date\"),how=\"left\", left_index=True, right_index=True)</p>\n</blockquote>",
      "rawMarkdown": "> nyra_2019.set_index(\"race_date\").merge(ny_weather.set_index(\"date\"),how=\"left\", left_index=True, right_index=True)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1905774,
      "author_name": "mayanjabrian",
      "author_url": "",
      "post_date": "08/19/2022 09:38:32",
      "content": "<p>This is interesting!  It is worth a shot!</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1905795,
      "author_name": "lennarthaupts",
      "author_url": "",
      "post_date": "08/19/2022 09:59:14",
      "content": "<p>Thanks for sharing, the 7 day rolling-mean for avg-temp and percipitation seem to have some predictive value for what I'm trying to do.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1906461,
      "author_name": "alejopaullier",
      "author_url": "",
      "post_date": "08/19/2022 22:46:07",
      "content": "<p>I also made this <a href=\"https://www.kaggle.com/code/alejopaullier/new-york-city-weather-2019\" target=\"_blank\">notebook</a>, its an EDA with dynamic plotly graphs to get a better understanding of the data.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1907134,
      "author_name": "johnscanlan",
      "author_url": "",
      "post_date": "08/20/2022 13:44:33",
      "content": "<p>Thanks for this resource, what is the best way to combine this data with the nyra_complete.csv by date? Can you join the weather data to the race data based on the date. Thanks. </p>\n<p>E.g some pseudocode would be.   new_df = pandas concatenate (nyra_complete, weather, join by common date values)</p>",
      "votes": null,
      "replies": [
        {
          "id": 1907359,
          "author_name": "alejopaullier",
          "author_url": "",
          "post_date": "08/20/2022 17:44:38",
          "content": "<p>I think you need to set the date as index using pandas <code>set_index()</code> and then make a left join using pandas <code>merge()</code> function, where the left table is the NYRA table and the right table is the weather data. Hope it helps</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 1908668,
          "author_name": "johnscanlan",
          "author_url": "",
          "post_date": "08/21/2022 21:43:50",
          "content": "<p>Thank you :)</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 1960634,
          "author_name": "naiborhujosua",
          "author_url": "",
          "post_date": "09/28/2022 17:16:44",
          "content": "<blockquote>\n  <p>nyra_2019.set_index(\"race_date\").merge(ny_weather.set_index(\"date\"),how=\"left\", left_index=True, right_index=True)</p>\n</blockquote>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 1907140,
      "author_name": "antonkozyriev",
      "author_url": "",
      "post_date": "08/20/2022 13:52:00",
      "content": "<p>Thank you for sharing!</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1907452,
      "author_name": "meghna1809",
      "author_url": "",
      "post_date": "08/20/2022 19:46:50",
      "content": "<p>Looks interesting. Will give it a try</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1909383,
      "author_name": "",
      "author_url": "",
      "post_date": "08/22/2022 15:20:27",
      "content": "<p>Thank you for sharing, this is worth a try, upvote!</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1904076": "Hello guys, I uploaded a [dataset](https://www.kaggle.com/datasets/alejopaullier/new-york-city-weather-data-2019) containing daily weather information from NYC from 2019, like temperature, snowfall and precipitation. \n\nSince horses are affected by weather conditions this dataset might be useful for this competition.\n\nHope you like it.",
    "1905774": "This is interesting!  It is worth a shot!",
    "1905795": "Thanks for sharing, the 7 day rolling-mean for avg-temp and percipitation seem to have some predictive value for what I'm trying to do.",
    "1906461": "I also made this [notebook](https://www.kaggle.com/code/alejopaullier/new-york-city-weather-2019), its an EDA with dynamic plotly graphs to get a better understanding of the data.",
    "1907134": "Thanks for this resource, what is the best way to combine this data with the nyra_complete.csv by date? Can you join the weather data to the race data based on the date. Thanks. \n\nE.g some pseudocode would be.   new_df = pandas concatenate (nyra_complete, weather, join by common date values)",
    "1907140": "Thank you for sharing!",
    "1907359": "I think you need to set the date as index using pandas `set_index()` and then make a left join using pandas `merge()` function, where the left table is the NYRA table and the right table is the weather data. Hope it helps",
    "1907452": "Looks interesting. Will give it a try",
    "1908668": "Thank you :)",
    "1909383": "Thank you for sharing, this is worth a try, upvote!",
    "1960634": "> nyra_2019.set_index(\"race_date\").merge(ny_weather.set_index(\"date\"),how=\"left\", left_index=True, right_index=True)"
  },
  "source": "meta"
}