{ "id": 307804, "title": "JSON -> PANDAS📊", "url": "/competitions/herbarium-2022-fgvc9/discussion/307804", "author_name": "Sanskar Hasija", "post_date": "2022-02-15T17:30:56.523000", "votes": 18, "comment_count": 8, "views": 0, "content": "

Hello Everyone!
\nI have created two Pandas dataframes for train and test data from the .json files provided. The dataframes contains directories and categories for the images which will come in handy while creating generators or tf Datasets.
\nLink to Dataset - https://www.kaggle.com/odins0n/herbarium-2022-pandas

\n

The train data has 4 columns : Image_id, Directory, Genus_id, Category
\nThe test dataframe has 2 columns : Image_id, Directory

\n

Script for creating dataframes - https://www.kaggle.com/odins0n/json-pandas-herbarium-2022/

\n

(If you have any feedback, please let me know in the comments 🙂)

", "messages": [ { "id": 1691923, "postDate": "2022-02-15T17:30:56.523Z", "content": "

Hello Everyone!
\nI have created two Pandas dataframes for train and test data from the .json files provided. The dataframes contains directories and categories for the images which will come in handy while creating generators or tf Datasets.
\nLink to Dataset - https://www.kaggle.com/odins0n/herbarium-2022-pandas

\n

The train data has 4 columns : Image_id, Directory, Genus_id, Category
\nThe test dataframe has 2 columns : Image_id, Directory

\n

Script for creating dataframes - https://www.kaggle.com/odins0n/json-pandas-herbarium-2022/

\n

(If you have any feedback, please let me know in the comments 🙂)

", "rawMarkdown": "Hello Everyone! \nI have created two Pandas dataframes for train and test data from the .json files provided. The dataframes contains directories and categories for the images which will come in handy while creating generators or tf Datasets.\nLink to Dataset - https://www.kaggle.com/odins0n/herbarium-2022-pandas\n\nThe train data has 4 columns : `Image_id, Directory, Genus_id, Category `\nThe test dataframe has 2 columns : `Image_id, Directory `\n\nScript for creating dataframes - https://www.kaggle.com/odins0n/json-pandas-herbarium-2022/\n\n(If you have any feedback, please let me know in the comments 🙂)", "votes": 18 }, { "id": 1703112, "postDate": "2022-02-24T07:29:40.283Z", "content": "

Great idea @odins0n
\nThanks a lot 👏

", "rawMarkdown": "Great idea @odins0n \nThanks a lot 👏", "votes": 1, "replies": [ { "id": 1703215, "postDate": "2022-02-24T09:54:57.080Z", "content": "

Thanks @datascientistfp

", "rawMarkdown": "Thanks @datascientistfp ", "votes": 1 } ] }, { "id": 1701211, "postDate": "2022-02-22T15:34:49.800Z", "content": "

thank you for sharing!

", "rawMarkdown": "thank you for sharing!", "votes": 1, "replies": [ { "id": 1703050, "postDate": "2022-02-24T06:40:34.967Z", "content": "

Welcome @andriyuru

", "rawMarkdown": "Welcome @andriyuru " } ] }, { "id": 1692715, "postDate": "2022-02-16T07:45:16.913Z", "content": "

that is nice, we have similar approach in our baseline…
\n🌿Herbarium: EDA 🔎 & baseline Flash⚡EfficientNet

", "rawMarkdown": "that is nice, we have similar approach in our baseline...\n[🌿Herbarium: EDA 🔎 & baseline Flash⚡EfficientNet](https://www.kaggle.com/jirkaborovec/herbarium-eda-baseline-flash-efficientnet)", "votes": 1, "replies": [ { "id": 1692814, "postDate": "2022-02-16T08:54:08.377Z", "content": "

Thanks @jirkaborovec
\nI will check out your notebook.

", "rawMarkdown": "Thanks @jirkaborovec \nI will check out your notebook.", "votes": 1 } ] }, { "id": 1692742, "postDate": "2022-02-16T08:00:23.583Z", "content": "

Hello, @odins0n! Good work! By the way, you also can use my functions from my notebook to create a data frame with all metadata.

", "rawMarkdown": "Hello, @odins0n! Good work! By the way, you also can use my functions from [my notebook](https://www.kaggle.com/vad13irt/herbarium-2022-fast-exploratory-data-analysis/notebook) to create a data frame with all metadata. ", "votes": 2, "replies": [ { "id": 1692813, "postDate": "2022-02-16T08:53:31.137Z", "content": "

Thanks, @vad13irt.
\nI will check out your notebook

", "rawMarkdown": "Thanks, @vad13irt. \nI will check out your notebook ", "votes": 1 } ] } ], "comments": [ { "id": 1703112, "author_name": "FPiotro", "author_url": "", "post_date": "2022-02-24T07:29:40.283000", "content": "

Great idea @odins0n
\nThanks a lot 👏

", "votes": 1, "replies": [ { "id": 1703215, "author_name": "Sanskar Hasija", "author_url": "", "post_date": "2022-02-24T09:54:57.080000", "content": "

Thanks @datascientistfp

", "votes": 1, "replies": [] } ] }, { "id": 1701211, "author_name": "", "author_url": "", "post_date": "2022-02-22T15:34:49.800000", "content": "

thank you for sharing!

", "votes": 1, "replies": [ { "id": 1703050, "author_name": "Sanskar Hasija", "author_url": "", "post_date": "2022-02-24T06:40:34.967000", "content": "

Welcome @andriyuru

", "votes": 0, "replies": [] } ] }, { "id": 1692715, "author_name": "Jirka", "author_url": "", "post_date": "2022-02-16T07:45:16.913000", "content": "

that is nice, we have similar approach in our baseline…
\n🌿Herbarium: EDA 🔎 & baseline Flash⚡EfficientNet

", "votes": 1, "replies": [ { "id": 1692814, "author_name": "Sanskar Hasija", "author_url": "", "post_date": "2022-02-16T08:54:08.377000", "content": "

Thanks @jirkaborovec
\nI will check out your notebook.

", "votes": 1, "replies": [] } ] }, { "id": 1692742, "author_name": "Vadim Irtlach", "author_url": "", "post_date": "2022-02-16T08:00:23.583000", "content": "

Hello, @odins0n! Good work! By the way, you also can use my functions from my notebook to create a data frame with all metadata.

", "votes": 2, "replies": [ { "id": 1692813, "author_name": "Sanskar Hasija", "author_url": "", "post_date": "2022-02-16T08:53:31.137000", "content": "

Thanks, @vad13irt.
\nI will check out your notebook

", "votes": 1, "replies": [] } ] } ], "raw_markdown_by_id": { "1691923": "Hello Everyone! \nI have created two Pandas dataframes for train and test data from the .json files provided. The dataframes contains directories and categories for the images which will come in handy while creating generators or tf Datasets.\nLink to Dataset - https://www.kaggle.com/odins0n/herbarium-2022-pandas\n\nThe train data has 4 columns : `Image_id, Directory, Genus_id, Category `\nThe test dataframe has 2 columns : `Image_id, Directory `\n\nScript for creating dataframes - https://www.kaggle.com/odins0n/json-pandas-herbarium-2022/\n\n(If you have any feedback, please let me know in the comments 🙂)", "1703112": "Great idea @odins0n \nThanks a lot 👏", "1701211": "thank you for sharing!", "1692715": "that is nice, we have similar approach in our baseline...\n[🌿Herbarium: EDA 🔎 & baseline Flash⚡EfficientNet](https://www.kaggle.com/jirkaborovec/herbarium-eda-baseline-flash-efficientnet)", "1692742": "Hello, @odins0n! Good work! By the way, you also can use my functions from [my notebook](https://www.kaggle.com/vad13irt/herbarium-2022-fast-exploratory-data-analysis/notebook) to create a data frame with all metadata. " } }