cjm-kaggle-utils
================
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
## Install
``` sh
pip install cjm_kaggle_utils
```
## How to use
### save_kaggle_creds
``` python
from cjm_kaggle_utils.core import save_kaggle_creds
```
``` python
username = "name"
key = "12345"
save_kaggle_creds(username, key, overwrite=False)
```
Credentials already present. Set `overwrite=True` to replace them.
### dl_kaggle
``` python
from cjm_kaggle_utils.core import dl_kaggle
from pathlib import Path
```
``` python
# Get the path to the directory where datasets are stored
dataset_dir = Path("./Datasets/")
dataset_dir.mkdir(parents=True, exist_ok=True)
print(f"Dataset Directory: {dataset_dir}")
# Create the path to the data directory
archive_dir = dataset_dir/'../Archive'
archive_dir.mkdir(parents=True, exist_ok=True)
print(f"Archive Directory: {archive_dir}")
```
Dataset Directory: Datasets
Archive Directory: Datasets/../Archive
``` python
# Set the name of the dataset
dataset_name = 'style-image-samples'
# Construct the Kaggle dataset name by combining the username and dataset name
kaggle_dataset = f'innominate817/{dataset_name}'
```
``` python
# Create the path to the zip file that contains the dataset
archive_path = Path(f'{archive_dir}/{dataset_name}.zip')
print(f"Archive Path: {archive_path}")
# Create the path to the directory where the dataset will be extracted
dataset_path = Path(f'{dataset_dir}/{dataset_name}')
print(f"Dataset Path: {dataset_path}")
```
Archive Path: Datasets/../Archive/style-image-samples.zip
Dataset Path: Datasets/style-image-samples
``` python
dl_kaggle(kaggle_dataset, archive_path, dataset_path)
```
Downloading style-image-samples.zip to Datasets/../Archive
100%|██████████████████████████████████████| 27.5M/27.5M [00:00<00:00, 56.0MB/s]
``` python
!ls {dataset_path}
```
images
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"description": "cjm-kaggle-utils\n================\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n## Install\n\n``` sh\npip install cjm_kaggle_utils\n```\n\n## How to use\n\n### save_kaggle_creds\n\n``` python\nfrom cjm_kaggle_utils.core import save_kaggle_creds\n```\n\n``` python\nusername = \"name\"\nkey = \"12345\"\nsave_kaggle_creds(username, key, overwrite=False)\n```\n\n Credentials already present. Set `overwrite=True` to replace them.\n\n### dl_kaggle\n\n``` python\nfrom cjm_kaggle_utils.core import dl_kaggle\nfrom pathlib import Path\n```\n\n``` python\n# Get the path to the directory where datasets are stored\ndataset_dir = Path(\"./Datasets/\")\ndataset_dir.mkdir(parents=True, exist_ok=True)\nprint(f\"Dataset Directory: {dataset_dir}\")\n\n# Create the path to the data directory\narchive_dir = dataset_dir/'../Archive'\narchive_dir.mkdir(parents=True, exist_ok=True)\nprint(f\"Archive Directory: {archive_dir}\")\n```\n\n Dataset Directory: Datasets\n Archive Directory: Datasets/../Archive\n\n``` python\n# Set the name of the dataset\ndataset_name = 'style-image-samples'\n\n# Construct the Kaggle dataset name by combining the username and dataset name\nkaggle_dataset = f'innominate817/{dataset_name}'\n```\n\n``` python\n# Create the path to the zip file that contains the dataset\narchive_path = Path(f'{archive_dir}/{dataset_name}.zip')\nprint(f\"Archive Path: {archive_path}\")\n\n# Create the path to the directory where the dataset will be extracted\ndataset_path = Path(f'{dataset_dir}/{dataset_name}')\nprint(f\"Dataset Path: {dataset_path}\")\n```\n\n Archive Path: Datasets/../Archive/style-image-samples.zip\n Dataset Path: Datasets/style-image-samples\n\n``` python\ndl_kaggle(kaggle_dataset, archive_path, dataset_path)\n```\n\n Downloading style-image-samples.zip to Datasets/../Archive\n\n 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 27.5M/27.5M [00:00<00:00, 56.0MB/s]\n\n``` python\n!ls {dataset_path}\n```\n\n images\n",
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