import matplotlib.pyplot as plt
import collections
import numpy as np

# Generate random data
data = [tuple(pair)
        for pair in np.random.uniform(5, size=(20, 2))
        for _ in range(np.random.randint(1, 50))]  # Replaced random_integers

# Count occurrences of each point
count = collections.Counter(data)

# Print the count …

!python --version

Python 3.10.5

!pip show xgboost | grep "Version"

Version: 2.1.2

import xgboost as xgb
import pandas as pd
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Load the California housing dataset
data = fetch_california_housing()
X …

!python --version

Python 3.10.5

import xgboost as xgb
import pandas as pd
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import seaborn as sns
import matplotlib.pyplot as plt

# Load the California housing dataset
data = fetch_california_housing()
X …

# https://python.langchain.com/docs/how_to/structured_output/
# https://python.langchain.com/docs/how_to/
# https://platform.openai.com/usage

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

llm = ChatOpenAI(model="gpt-4o-mini")

from typing import Optional …

import numpy as np
import pandas as pd

data = {
    'sam' : ['archery', 'badminton', 'athletics', 'cycling_road', 'canoe_sprint', 'boxing'],
    'medal' : ['gold', 'silver', 'bronze', 'gold', 'gold', 'silver']
}

df = pd.DataFrame(data)

df

import numpy as np
import pandas as pd

data = {
    'sam' : ['archery', 'badminton', 'athletics', 'cycling_road', 'canoe_sprint', 'boxing'],
    'medal' : ['gold', 'silver', 'bronze', 'gold', 'gold', 'silver']
}

df = pd.DataFrame(data)

df

def get_abc():

    return "abc"

get_abc()

'abc'

get_xyz()

'xyz'

def get_xyz():

    return "xyz"

import uuid
import base64

def encode_to_8_characters(guid: str) -> str:
    """
    Encode a GUID into an 8-character string.
    """
    # Convert GUID to bytes
    guid_bytes = uuid.UUID(guid).bytes
    # Encode to base64 and take the first 8 characters
    encoded = base64.urlsafe_b64encode(guid_bytes).decode('utf-8')[:8]
    return encoded

def decode_from_8_characters(encoded: str, original_guid: str) -> str …

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import numpy as np
import pandas as pd

census = pd.read_csv("/Users/rajacsp/datasets …

!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="gpt-4o-mini")

# https://python.langchain.com/docs/how_to/functions/

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import …