# !pip install kaggle
# https://medium.com/@pumaline/easy-analysis-of-your-data-and-ml-model-using-evidently-ai-830ef0c1c4fd
# https://xgboost.readthedocs.io/en/latest/python/python_api.html

import pandas as pd

df = pd.read_csv('~/datasets/kaggle/kc_house_data.csv')

df.head()

!python --version

Python 3.12.4

from constants import OPENAI_API_KEY

!pip show langchain-openai | grep "Version:"

Version: 0.2.9

import os

os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

from langchain_openai import ChatOpenAI

model = ChatOpenAI(model …

import re
from gliner import GLiNER

# Initialize GLiNER with a pre-trained model
model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1")  # Replace with a location-specific model if available

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/home/rajaraman/miniconda3/envs/ml312/lib/python3.12/site-packages/transformers/convert_slow_tokenizer.py:562: UserWarning …

import numpy as np
import pandas as pd

df = pd.DataFrame({
    'maths' : [80, 89, 90, 20],
    'science' : [40, 50, 90, 100],
    'language' : [20, 30 …

import numpy as np
import pandas as pd

df = pd.read_csv('abc.csv')

df

from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook
from bokeh.models import ColumnDataSource

# Set Bokeh output to notebook
output_notebook()

import pandas as pd

df = pd.read_csv('data1.csv', sep=',', header=None)

df

import numpy as np
import pandas as pd

df = pd.read_csv('data1.csv')

df

!python --version

Python 3.10.5

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Path to your PNG file
image_path = 'deer.jpg'

# Load and display the image
img = mpimg.imread(image_path)
plt.imshow(img)
plt.axis('off')  # Turn off axis
plt.show()

a = 12

dictionary = {
  "one": a
}

a

12

dictionary

{'one': 12}

a += 9

a

21

dictionary

{'one': 12}