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Step-by-Step Guide to Reading Different Files in Python

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 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
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2 Top Skills You Need For E-commerce

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The following skills are must for every data analytics engineer to be successful in e-commerce companies. Many software engineers are struggling to get this information. I am giving here both technical and general skills.

skills e-commerce

1# Technical Skills for Analytics Career

What skills they need to be successful in their analytics career. The skills required to get an entry into the Analytics job are here for your reference.

I have told in my previous posts that there are many branches in analytics. You need to apply domesticated techniques to extract actionable knowledge.

2# General Skills

I have selected the following mindset and skills that you need to get a job in data analytics. These are proven skills. Set a clear goal to acquire these skills.
  1. Strong interpersonal, oral and written communication and presentation skills; 
  2. Ability to communicate complex findings and ideas in plain language 
  3. Being able to work in teams towards a shared goal; 
  4. Ability to change direction quickly based on data analysis; 
  5. Enjoying discovering and solving problems; 
  6. Proactively seeking clarification of requirements and direction; take responsibility when needed; 
  7. Being able to work in a stressful situation when insights in (new) data sets are required quickly.

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