📊 "Reading CSV Files with Python & Pandas!"
#TechWithHos #PythonTips #DataScience*

🚀 *"Data is the new oil — and Python is the drill."*

In this snippet, we explore how to read and extract data from a CSV file using both Python’s built-in csv module and the powerful Pandas library.

import csv
'''
with open("/Users/kvava/OneDrive/Desktop/002 weather-data.csv") as data_s:
    h = data_s.readlines()
    print(h)

with open("/Users/kvava/OneDrive/Desktop/002 weather-data.csv") as data_s:
    data = csv.reader(data_s)
    tempratures = []

    for row in data:
        if row[1] != "temp":
            temp = int(row[1])
            tempratures.append(temp)
    print(tempratures)
    '''
import pandas

data = pandas.read_csv("/Users/kvava/OneDrive/Desktop/002 weather-data.csv")
print(data["temp"])

🔍 What this code does:

1. The first part (commented out) uses the csv module to manually read temperature values from a CSV file.
2. The second part uses Pandas to read the same file more efficiently — and directly accesses the "temp" column.

📌 Why use Pandas?
Pandas is a powerful Python library for data manipulation and analysis. It makes working with structured data super easy — whether it’s filtering rows, summarizing columns, or transforming tables.

🔥 With Pandas, a few lines of code can replace dozens!
Perfect for data science, machine learning, or just wrangling messy spreadsheets.



💡 Follow @TechWithHos for more Python & tech tips like this!
\#Python #Pandas #CodeSnippet #CSV #LearnToCode #ProgrammingTips

🚀 Python List Comprehension – Clean, Powerful, and Pythonic! 🐍

List comprehensions make your code shorter, faster, and more readable. Whether you're transforming, filtering, or generating data—this tool is a must-know for any Pythonista!

Here’s a quick showcase of how elegant Python can be 👇

# Add 1 to each item in a list
new_numbers = [item + 1 for item in numbers]

# Turn a string into a list of characters
name = "Angela"
new_list = [letter for letter in name]

# Square numbers from 1 to 4
new_double = [d * d for d in range(1, 5)]

# Double the numbers from 1 to 4
new_one = [s + s for s in range(1, 5)]

# Filter names with exactly 4 letters
names = ["Alex", "beth", "caroline", "Dave", "elias", "freddie"]
short_names = [sh for sh in names if len(sh) == 4]

# Capitalize names longer than 5 letters
capitalized = [ca.upper() for ca in names if len(ca) > 5]

⚡️ Why use list comprehensions?
✅ More readable than loops
✅ Great for data transformations
✅ Ideal for filtering and mapping
✅ Just look cooler 😎

🧠 Try tweaking the conditions and expressions—it's one of the fastest ways to learn Python.

\#Python #ListComprehension #CodeSnippet #PythonTips