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from google import genai | |
from google.genai import types | |
from IPython.display import HTML, Markdown, display | |
from dotenv import load_dotenv | |
import os | |
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") | |
client = genai.Client(api_key=GOOGLE_API_KEY) |
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--to check catagorical and numerical cols | |
numerical_cols, categorical_cols = split_numerical_categorical(df_train) | |
--finding all the non-unique items in catagorical cols | |
for c in categorical_cols: | |
print(c, "n unique:",df_all[c].nunique()) | |
-- Colab to Kaggle | |
!mkdir ~/.kaggle | |
!cp kaggle.json ~/.kaggle/ | |
!chmod 600 ~/.kaggle/kaggle.json |
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stop ollama | |
osascript -e 'tell app "Ollama" to quit' | |
-- start ollama | |
ollama ps |
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https://www.youtube.com/watch?v=xoh_L3KPpks |
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https://www.youtube.com/watch?v=uszt88Z-0Fc | |
paraview | |
https://www.youtube.com/watch?v=BsK_WwoSKDM&t=1s | |
good | |
https://www.idtools.com.au/3d-network-graphs-with-python-and-the-mplot3d-toolkit/ | |
https://networkx.org/documentation/stable/auto_examples/3d_drawing/plot_basic.html | |
AR | |
https://openreview.net/pdf?id=VtYxuiX_UQ | |
https://www.nature.com/articles/s41467-021-22570-w | |
https://github.com/topics/graph-theory?l=html&o=desc&s=forks |
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<!DOCTYPE html>> | |
<html lang="eng"> | |
<head> | |
<meta charset="UTF-8"> | |
<meta http-equiv="X-UA-Compatable" content="IE=edge"> | |
<meta name="'viewport" content = "width=device-width, initial-scale=1.0"> | |
<title>Document</title> | |
</head> | |
<body> | |
Hi! |
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cd to your dir | |
virtualenv .venv | |
mac | |
source .venv/bin/activate | |
win | |
conda deactivate | |
.venv/Scripts/activate | |
pip freeze > requirements.txt | |
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# To activate this environment, use | |
# | |
# $ conda activate py10 | |
# | |
# To deactivate an active environment, use | |
# | |
# $ conda deactivate | |
conda activate py3.9 |
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https://www.kaggle.com/code/ryannolan1/kaggle-housing-youtube-video | |
plt.scatter(x='MSSubClass', y='SalePrice', data=train_df) | |
plt.scatter(x='LotFrontage', y='SalePrice', data=train_df) | |
train_df.query('LotFrontage > 300') | |
stats.zscore(train_df['LotArea']).sort_values().tail(10) | |
train_df.query('OverallCond == 5 & SalePrice > 700000') | |
#1183 | |
values = [598, 955, 935, 1299, 250, 314, 336, 707, 379, 1183, 692, 186, 441, 186, 524, 739, 598, 955, 636, 1062, 1191, 496, 198, 1338] | |
train_df = train_df[train_df.Id.isin(values) == False] |
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from rembg import remove | |
from PIL import Image | |
input_path = 'cl.jpg' | |
output_path = 'output.png' | |
input = Image.open(input_path) | |
output = remove(input) | |
output.save (output_path) |
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