🌀️ Conversations with AI — 398

Next,

image-analysis-and-data-fusion

@git clone blue-fusion cd

@plugins transform blue-fusion

@init
@fusion help

@fusion bright install recreate_env

python3 -m blue_fusion.bright get_url --what github

from datasets import load_dataset

# Specify your cache directory
custom_cache_dir = "/path/to/your/custom/cache"
dataset = load_dataset("Kullervo/BRIGHT", cache_dir=custom_cache_dir)

from datasets import load_dataset
from blue_objects import objects, path

# Load 10% of the training data
small_train_dataset = load_dataset(
  "Kullervo/BRIGHT", 
  split='train[:1%]',
  cache_dir=objects.object_path(objects.unique_object()),
)

🤔

@select dfc25_track2_trainval
code metadata.yaml

dfc25_track2_trainval += metadata.yaml ✅ source ✅ archive blue-fusion ✅

import pandas as pd

# Create an empty DataFrame with specified column names and types explicitly set
df = pd.DataFrame(columns=['FloatCol1', 'FloatCol2', 'StrCol1', 'StrCol2'])
df = df.astype({'FloatCol1': 'float', 'FloatCol2': 'float', 'StrCol1': 'str', 'StrCol2': 'str'})

# Define the data for each row as a dictionary
rows = [
    {'FloatCol1': 1.1, 'FloatCol2': 2.2, 'StrCol1': 'apple', 'StrCol2': 'banana'},
    {'FloatCol1': 3.3, 'FloatCol2': 4.4, 'StrCol1': 'cherry', 'StrCol2': 'date'},
    {'FloatCol1': 5.5, 'FloatCol2': 6.6, 'StrCol1': 'fig', 'StrCol2': 'grape'}
]

# Add rows to DataFrame
for row in rows:
    new_row = pd.DataFrame([row])
    df = pd.concat([df, new_row], ignore_index=True)

print(df)

continues.