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Daryl Taylor에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Daryl Taylor 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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CSE704L17 - Mastering Data Manipulation in Python

7:54
 
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저장한 시리즈 ("피드 비활성화" status)

When? This feed was archived on February 10, 2025 12:10 (5M ago). Last successful fetch was on October 14, 2024 06:04 (9M ago)

Why? 피드 비활성화 status. 잠시 서버에 문제가 발생해 팟캐스트를 불러오지 못합니다.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 444544475 series 3603581
Daryl Taylor에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Daryl Taylor 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

In this episode, Eugene Uwiragiye delves into essential Python concepts for working with data frames and handling complex operations in data analysis. From understanding the differences between rows and columns to applying custom functions across datasets, Eugene breaks down topics that are critical for anyone working with data in Python. Whether you’re just starting or looking to sharpen your skills, this episode provides practical insights into mastering data manipulation.

Key Topics Covered:

  • Understanding Indexing and Slicing in Pandas: Learn how to effectively slice rows and columns using .iloc[], and the importance of index positions when handling large datasets.
  • Applying Functions to Data Frames: Eugene explains the use of apply() and map() functions to manipulate and transform data frames. He also highlights how custom functions can be applied to specific columns or rows.
  • Common Pitfalls in Data Handling: Insights into avoiding common errors when working with Pandas data frames, such as misinterpreting axis arguments and incorrectly setting index positions.
  • Maximizing Efficiency with Lambda Functions: Discover how using lambda functions and mapping techniques can simplify code and improve data processing performance.
  • Best Practices for Re-indexing and Sorting Data: Eugene shares tips on how to efficiently re-index and sort data, ensuring smooth data operations for analysis.

Memorable Quotes:

  • "You must understand the difference between rows and columns in slicing. A simple mistake here can change the entire outcome of your dataset."
  • "The apply() function is your best friend when it comes to performing operations across your data frame."

Resources Mentioned:

Next Episode:

Join us next week as we dive deeper into advanced data visualization techniques using Python's Matplotlib and Seaborn libraries.

  continue reading

20 에피소드

Artwork
icon공유
 

저장한 시리즈 ("피드 비활성화" status)

When? This feed was archived on February 10, 2025 12:10 (5M ago). Last successful fetch was on October 14, 2024 06:04 (9M ago)

Why? 피드 비활성화 status. 잠시 서버에 문제가 발생해 팟캐스트를 불러오지 못합니다.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 444544475 series 3603581
Daryl Taylor에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Daryl Taylor 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

In this episode, Eugene Uwiragiye delves into essential Python concepts for working with data frames and handling complex operations in data analysis. From understanding the differences between rows and columns to applying custom functions across datasets, Eugene breaks down topics that are critical for anyone working with data in Python. Whether you’re just starting or looking to sharpen your skills, this episode provides practical insights into mastering data manipulation.

Key Topics Covered:

  • Understanding Indexing and Slicing in Pandas: Learn how to effectively slice rows and columns using .iloc[], and the importance of index positions when handling large datasets.
  • Applying Functions to Data Frames: Eugene explains the use of apply() and map() functions to manipulate and transform data frames. He also highlights how custom functions can be applied to specific columns or rows.
  • Common Pitfalls in Data Handling: Insights into avoiding common errors when working with Pandas data frames, such as misinterpreting axis arguments and incorrectly setting index positions.
  • Maximizing Efficiency with Lambda Functions: Discover how using lambda functions and mapping techniques can simplify code and improve data processing performance.
  • Best Practices for Re-indexing and Sorting Data: Eugene shares tips on how to efficiently re-index and sort data, ensuring smooth data operations for analysis.

Memorable Quotes:

  • "You must understand the difference between rows and columns in slicing. A simple mistake here can change the entire outcome of your dataset."
  • "The apply() function is your best friend when it comes to performing operations across your data frame."

Resources Mentioned:

Next Episode:

Join us next week as we dive deeper into advanced data visualization techniques using Python's Matplotlib and Seaborn libraries.

  continue reading

20 에피소드

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