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Python: Built-in Functions vs. For & If Loops – 5 Programs Explained

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Python’s built-in functions make coding fast and efficient. But understanding how they work under the hood is crucial to mastering Python. This post shows five Python tasks, each implemented in two ways: Using built-in functions Using for loops and if statements ✅ 1. Sum of a List ✅ Using Built-in Function: numbers = [ 10 , 20 , 30 , 40 ] total = sum (numbers) print ( "Sum:" , total) 🔁 Using For Loop: numbers = [ 10 , 20 , 30 , 40 ] total = 0 for num in numbers: total += num print ( "Sum:" , total) ✅ 2. Find Maximum Value ✅ Using Built-in Function: values = [ 3 , 18 , 7 , 24 , 11 ] maximum = max (values) print ( "Max:" , maximum) 🔁 Using For and If: values = [ 3 , 18 , 7 , 24 , 11 ] maximum = values[ 0 ] for val in values: if val > maximum: maximum = val print ( "Max:" , maximum) ✅ 3. Count Vowels in a String ✅ Using Built-ins: text = "hello world" vowel_count = sum ( 1 for ch in text if ch i...
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A Beginner's Guide to Pandas Project for Immediate Practice

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Pandas is a powerful data manipulation and analysis library in Python that provides a wide range of functions and tools to work with structured data. Whether you are a data scientist, analyst, or just a curious learner, Pandas can help you efficiently handle and analyze data. 


Simple project for practice


In this blog post, we will walk through a step-by-step guide on how to start a Pandas project from scratch. By following these steps, you will be able to import data, explore and manipulate it, perform calculations and transformations, and save the results for further analysis. So let's dive into the world of Pandas and get started with your own project!


Simple Pandas project

Import the necessary libraries:


import pandas as pd

import numpy as np


Read data from a file into a Pandas DataFrame:


df = pd.read_csv('/path/to/file.csv')

Explore and manipulate the data:


View the first few rows of the DataFrame:


print(df.head())


Access specific columns or rows in the DataFrame:


print(df['column_name'])

print(df.iloc[row_index])


Iterate through the DataFrame rows:


for index, row in df.iterrows():

    print(index, row)


Sort the DataFrame by one or more columns:


df_sorted = df.sort_values(['column1', 'column2'], ascending=[True, False])


Perform calculations and transformations on the data:


df['new_column'] = df['column1'] + df['column2']


Save the manipulated data to a new file:

df.to_csv('/path/to/new_file.csv', index=False)

Remember to adjust the file paths and column names based on your project requirements. These steps provide a basic starting point for a Pandas project and can be expanded upon depending on the specific task or analysis you're working on.


Data sources for CSV files

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