In this lesson we learn NumPy, Pandas, and Web Scraping in simple English with examples.
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| Educational infographic showing NumPy arrays, Pandas DataFrame operations, filtering, and web scraping using requests and BeautifulSoup in Python. |
🔹 1. NumPy Arrays
NumPy is used for fast numerical calculations using arrays.
import numpy as np array_1 = np.array([1,2,3]) array_2 = np.array([4,5,6]) print(array_1 + array_2)
✔ Output: [5 7 9]
Useful functions:
array = np.array([16,36,9]) print(np.sqrt(array)) # Square root print(np.mean(array)) # Average
🔹 2D Arrays
array = np.array([[4,3,5],[9,5,8]]) print(array.shape) # (2,3) print(array.ndim) # 2 dimensions print(array.size) # total elements print(array.dtype) # data type
✔ Python uses Zero-based indexing.
print(array[1,1]) # Access element
🔹 Filtering in NumPy
Use Boolean Indexing to filter values.
arr = np.array([10,50,40,20,30]) filtered = arr[arr > 25] print(filtered)
🔹 2. Pandas DataFrame
A DataFrame is like an Excel table.
import pandas as pd
data = {
'Name':['Alice','Bob'],
'Age':[25,30]
}
df = pd.DataFrame(data)
print(df)
Access data:
print(df.loc[0,'Age']) # label based print(df.iloc[1,0]) # index based
Useful properties:
print(df.shape) print(df.size) print(len(df)) print(df.columns)
🔹 Add New Column
df['City'] = ['NY','LA']
🔹 Filtering
filtered_df = df[df['Age'] > 25] print(filtered_df)
🔹 Aggregate Functions
print(df['Age'].sum()) print(df['Age'].mean())
🔹 Read CSV
df = pd.read_csv('file.csv')
print(df)
🔹 3. Web Scraping
Web Scraping means extracting data from websites.
🔹 GET Request
import requests
response = requests.get("https://api.github.com/events")
print(response.status_code)
🔹 POST Request
data = {'username':'user','password':'pass'}
response = requests.post("https://httpbin.org/post", data=data)
🔹 URL Parameters
params = {'userId':1}
response = requests.get(url, params=params)
🔹 4. BeautifulSoup
Used to extract data from HTML.
from bs4 import BeautifulSoup html = "Hello
" soup = BeautifulSoup(html,'html.parser') print(soup.find('h1').text)
🔹 5. API Example (Weather)
def fetch_weather(latitude, longitude):
url = "https://api.open-meteo.com/v1/forecast"
params = {"latitude":latitude,"longitude":longitude,"current":"temperature_2m"}
res = requests.get(url, params=params)
return res.json()
weather = fetch_weather(6.7,79.9)
print(weather)
🔹 6. Debugging
Use pdb or breakpoint() to debug.
import pdb pdb.set_trace() # OR breakpoint()
🎯 These tools are very important for Data Science, Machine Learning, and Web Development.