import pandas as pd

data = {
    'language' : [80, 67, 90],
    'maths' : [100, 78, 98],
    'science' : [98, 77, 56],
    'programming': [100, 100, 90]
}

index = ['chris', 'kevin', 'peter']

df = pd.DataFrame(data, index = index)

df

import pandas as pd

FILEPATH = "../countries-of-the-world.csv"

df = pd.read_csv(FILEPATH)

df.head(2)

!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/passthrough/

content = """
In the distant future, a group of courageous …

import re

fn = "DC_QnA_bo_v.15.12.3_DE_duplicates.xlsx"
rgx = re.compile('\b_[A-Z]{2}\b')
print(re.findall(rgx, fn))

[]

rgx = re.compile('(?<=_)[A-Z]+(?=_)')
print …

import speech_recognition as sr

def startpy():

    # obtain audio from the microphone
    r = sr.Recognizer()
    d= ''
    while (d!='exit' and d!='quit'):
        with sr.Microphone() as source:
            print("Say something!")
            audio = r.listen(source)

    # recognize speech using Google …

UNDERSCORE = "_"

feature_value_map_for_score = {
  "system_businessname_overlapLevenshtein": 0.5,
  "system_address_overlapLevenshtein": 1.0,
  "system_streetname_overlapLevenshtein": 1.0,
  "system_city_overlapLevenshtein": 1.0,
  "system_zipcode_overlapLevenshtein": 1.0,
  "system_businessname_sorensen": 0.56,
  "system_address_sorensen": 1.0,
  "system_streetname_sorensen": 1.0,
  "system_city_sorensen": 1.0,
  "system_region_sorensen": 1.0,
  "system_houseno_exactMatch": 1.0,
  "system_zipcode_ratcliff": 1.0,
  "system_suiteno_exactMatch": 1.0
}

individual_algo_feature_score_map = {
  "system_businessname": {
    "system_businessname_overlapLevenshtein": 0.5,
    "system_businessname_sorensen": 0.56 …

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 re

content = "Hey, you - what are you doing here!?"

contents = re.findall(r"[\w']+", content)

contents

['Hey', 'you', 'what', 'are', 'you', 'doing', 'here']

for c in contents:
    print(c)

Hey
you
what
are
you …

import tensorflow as tf

import os

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

a = tf.constant([1, 2])
b = tf.constant([3, 4])
c = tf.constant([10, 20])

a

<tf.Tensor 'Const_12:0 …