import requests
import pandas as pd
from io import BytesIO

FILEPATH = 'https://raw.githubusercontent.com/rajacsp/public-dataset/master/zynicide-wine-reviews/winemag-data-130k-v2.csv'

response = requests.get(FILEPATH)

data = response.content

onlinedata = BytesIO(data)

df = pd.read_csv(onlinedata, index_col=0)

df.head(2)

from functools import reduce

def add(a, b): 
    return a + b

add(10, 15)

25

reduce(add, [10, 20, 30, 40])

100

list = [
    1, 2, 3, 4, 5, 6
]

for i, j in enumerate(list):
    print(i, j)

0 1
1 2
2 3
3 4
4 5
5 6

for i, j in enumerate(list):
    if(j % 2 == 0):
        del list[i]

list

[1, 3, 5]

# Short form
new_list = [x for …

content = '15. TFRecord in Tensorflow'

content

'15. TFRecord in Tensorflow'

import re

result = re.sub(r'\d+\.', '', content)

result.strip()

'TFRecord in Tensorflow'

content2 = """1. Cross Entropy Loss Derivation

2. How to split a tensorflow model into two parts?

3. RNN Text Generation with Eager Execution

4. Softmax activation in …

!pip install langchain langchain-openai

Collecting langchain
  Downloading langchain-0.3.26-py3-none-any.whl.metadata (7.8 kB)
Collecting langchain-openai
  Downloading langchain_openai-0.3.26-py3-none-any.whl.metadata (2.3 kB)
Collecting langchain-core<1.0.0,>=0.3.66 (from langchain)
  Downloading langchain_core-0 …

# 1. Import scipy
import scipy
print("SciPy version:", scipy.__version__)

SciPy version: 1.13.1

# 2. Use scipy.constants to get pi
from scipy.constants import pi
print("Value of pi:", pi)

Value of pi: 3.141592653589793

# 3. Speed of light
from scipy.constants import c
print("Speed of light …

!pip install seaborn

Requirement already satisfied: seaborn in c:\users\afia jahan\anaconda3\envs\py312\lib\site-packages (0.13.2)
Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\users\afia jahan\anaconda3\envs\py312\lib\site-packages (from seaborn) (1.26.4)
Requirement already satisfied: pandas …

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

flights = sns.load_dataset("flights")
flights.head()

import nltk
nltk.download('vader_lexicon')

[nltk_data] Downloading package vader_lexicon to C:\Users\Afia
[nltk_data]     Jahan\AppData\Roaming\nltk_data...





True

from nltk.sentiment.vader import SentimentIntensityAnalyzer
sid = SentimentIntensityAnalyzer()

import json
from pprint import pprint
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import numpy as np
import matplotlib.pyplot as plt
import pandas …

import pandas as pd

data = {
    'Month' : [1, 2, 3],
    'Temp' : [7, -18, -20]
}

data

{'Month': [1, 2, 3], 'Temp': [7, -18, -20]}

type(data)

dict

df = pd.DataFrame(data)

df