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In this Python Data Analysis tutorial, we explore the versatile techniques for reading and processing CSV files using Python's powerful libraries. We begin by leveraging the csv package to parse CSV files using both the standard reader and DictReader for optimized column access. Then, we delve into the pandas library for its robust data frame manipulation capabilities, enhancing our data analysis toolkit. Additionally, I'll guide you through handling various delimiter types, including reading tab-separated values (TSV) for diverse data parsing needs. Whether it's managing headers, row offsets, or different data types, this tutorial will equip you with the necessary skills to efficiently work with CSV data, a staple format in data storage representing "Comma-Separated Values," and a foundational aspect of data science in Python.

CSV stands for "Comma-Separated Values." It is a common format for storing tabular information.

## Timeline
00:00 - Intro
00:18 - What is a CSV file?
01:06 - Read in Python using csv.reader
02:24 - Skipping the CSV header
03:33 - Read in Python using DictReader
04:14 - Change Separator or Delimiter
05:12 - Read CSV in Python by column using Pandas

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