kaggle-api | Official Kaggle API - Official API for https : //www | REST library

# !pip install -q kaggle  #install library to communicate with Kaggle API

# uploaded = files.upload() #upload kaggle.json downloaded form kaggle profile


# !rm  ~/.kaggle/kaggle.json
# # use if new kaggle.json is downloaded and has to be replaced

  

$ cd input
$ kaggle datasets download -d ikarus777/best-artworks-of-all-time
$ unzip best-artworks-of-all-time.zip -d artworks

input
├── artworks
│   ├── artists.csv
│   ├── images
│   │   └── images
│ &n  

kaggle competitions download -c kuzushiji-recognition

pip install --upgrade tqdm opencv-python==3.4.5.20 timm --user
pip install --upgrade git+https://github.com/albu/albumentations --user

$ ls | grep images
>>> test_images
>>> tr  

%cd /kaggle/working/detectron2
import glob
cfg.MODEL.WEIGHTS = os.path.join("/kaggle/working/detectron2/output", "model_final.pth")   # path to the model we trained
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.0001   # set a testing threshold

df['avg'] = (df['total_sqft'].str.extractall(r'(\d+\.*\d*)')
                             .astype(float)
                             .groupby(level=0)
                             .mean())
print (df)
       total_sqft      avg
0          

from sklearn.preprocessing import OneHotEncoder

df = pd.DataFrame({'Pclass': [0, 1, 2], 'SibSp': [3, 1, 0],
                   'Parch': [0, 2, 2], 'Sex': [0, 1, 1]})

ohe = OneHotEncoder()
data = ohe.fit_transform(df[['Pclass', 'SibSp', '