Python

CSV or Comma-Separated Values files are commonly used for storing data in tabular structure. CSV files are used for wide variety of data such as storing sales records, user data, or any other structured dataset. Reading these files programmatically allows developers to automate data processing, integrate it in applications, or analyze it effectively. Python is a very versatile ,and also beginner-friendly language, and is perfect for such tasks.

Introduction to Pandas and the CSV Library

Pandas is a powerful Python library designed for data manipulation and analysis. It offers a simple way to handle tabular data, making tasks like reading, filtering, and transforming datasets much easier. While Python’s built-in CSV module can read and write CSV files, Pandas provides more functionality and is better suited for working with structured data.

Read CSV Files in Python Using Pandas

What We Will Implement

We will demonstrate how to:

1. Read a CSV file using Pandas.
2. Display the content in a structured format.
3. Perform basic operations like filtering or summarizing the data.

Development Environment

You can use any Python IDE like PyCharm, VS Code, or Jupyter Notebook when working with Python

Structure of the CSV File

Here’s the content of our sample CSV file, named employees.csv:

Name,Department,Salary
Alice,Engineering,75000
Bob,Marketing,50000
Charlie,HR,45000
David,Engineering,80000
Emma,Marketing,52000

This file contains a list of employees with their respective departments and salaries.

Code Example

Below is the Python code to read the employees.csv file using Pandas:

import pandas as pd

# Step 1: First Load the CSV file
file_path = 'employees.csv'
data = pd.read_csv(file_path)

# Step 2: Then Display the content of the CSV file
print("\nFull Dataset:")
print(data)

# Step 3: Apply Filter to filter employees by department
engineering_team = data[data['Department'] == 'Engineering']
print("\nEngineering Team:")
print(engineering_team)

# Step 4: Finally Calculate the average salary
average_salary = data['Salary'].mean()
print(f"\nAverage Salary: ${average_salary:.2f}")

Main Steps in the Code

1. Load the CSV: Use pd.read_csv() to read the file into a Pandas DataFrame.  This function reads the content of a CSV file and converts it into a Pandas DataFrame, which is a powerful tabular data structure in Python.
2. Display Data: Print the contents for a quick overview.
3. Filter Data: Use conditional indexing to extract specific rows.This data frame syntax is used for filtering rows based on a condition. Here, it selects rows where the ‘Department’ column equals ‘Engineering’.
4. Summarize Data: Perform computations like calculating averages.The mean() function calculates the average of numeric values in the ‘Salary’ column.

Changes in above code for your requirement

• Different File Formats: If your file uses a delimiter other than a comma (e.g., tabs or semicolons), you can specify it using the sep parameter in read_csv().
• Large Files: For very large files, consider reading in chunks using the chunksize parameter.
• Missing Data: Handle missing values using options like fillna() or dropna().
• Additional Processing: Customize operations based on specific requirements, such as grouping data or performing advanced calculations.

Conclusion

Pandas makes working with CSV files in Python straightforward and efficient, even for beginners. With minimal code, we can perform powerful data manipulations, making it a useful tool when working with structured data.

ashish

codecompiled.com

Python’s versatility and extensive library ecosystem make it a favorite among developers worldwide. Whether you’re a beginner or an experienced coder, mastering certain libraries can significantly boost your productivity and expand your capabilities. Here are Python Top 3 Python Libraries which are Simple to Learn and In High Demand

1. Requests

The requests library simplifies the process of making HTTP requests in Python. It offers a more straightforward and human-friendly approach compared to Python’s built-in urllib library. With requests, you can effortlessly send HTTP requests like GET, POST, PUT, DELETE, and more. Its intuitive API design makes it ideal for interacting with web APIs and fetching data from web servers. Many popular web services and APIs, such as those provided by Google, Twitter, and GitHub, recommend or require the use of requests due to its reliability and ease of use.

2. Pandas

Pandas is a powerful library for data manipulation and analysis in Python. It provides high-performance, easy-to-use data structures and data analysis tools, making it essential for tasks involving structured data. With Pandas, you can easily read and write data from various file formats (e.g., CSV, Excel), manipulate data with functions like grouping, filtering, and merging, and perform complex operations such as time series analysis and data visualization. Its popularity in data science, finance, and academic research stems from its ability to handle large datasets efficiently and its seamless integration with other libraries such as Matplotlib and Scikit-learn.

3. Beautiful Soup

Beautiful Soup is a library used for parsing HTML and XML documents and extracting data from them in a hierarchical and readable manner. It simplifies web scraping tasks by providing Pythonic idioms for iterating, searching, and modifying the parse tree. Whether you need to extract information from a webpage for analysis or automate data collection tasks, Beautiful Soup streamlines the process by handling common HTML parsing challenges such as tag nesting and malformed documents. Its versatility and ease of integration with requests make it a preferred choice for developers needing to extract structured data from the web.

In conclusion, mastering these three Python libraries—requests, Pandas, and Beautiful Soup—will equip you with essential tools for web development, data analysis, and automation tasks. Their simplicity, combined with their high demand across various industries, makes them invaluable additions to any Python developer’s toolkit. Whether you’re a beginner aiming to enhance your skills or an experienced professional looking to streamline your workflow, these libraries offer robust solutions to common programming challenges.

ashish

First declare the main text
text=”abcd” #this is main text
Noe declare the pattern you want to search in the main text
patt=”cd” #this is pattern to search for in the main text

Now find the difference in length of main text and pattern

#first calcualte the difference between main text and pattern

diff=len(text)-len(patt)
print(diff)


#now loop over this number and keep on comparing each character in the text with each character in the pattern

for i in range(0,diff):
j=0
#keep on incrementing j till both characters match
while j<len(patt) and text[i+j]==patt[j]:
j+=1;
if(j==len(patt)):
print(i)

print('not found')


ashish

Conditional statements allow to execute logic based on a logic.If is used for defining conditional statements in Python.

It is defined using the keywords:

• if
• elif
• else

Following is a sample if statement

if True:
 print('this will print')
elif False:
 print('this will print')
else:
 print('this will not print')

notice that we have used indentation for nesting the blocks in the if and elif blocks

we normally use conditional expressions using operators in the if statement for comparison as:

x=1
if x==1:
 print('this will print')
elif  x==2:  
 print('this will print')
else:
 print('this will not print')

Commonly used Python conditional operators are:

• ==  equality
• != not equal
• > greater than
• < less than
• >= greater than equal to
• <= less than equal to

there are logical operators

• and
• or
• not

ashish