<transcript><audio_0>so this talk is the top 15 python tips</transcript><caption><image_1>The image is a screenshot of a video call between two people. On the left side of the screen, there is a man with a beard wearing a black t-shirt and headphones. He appears to be speaking into a microphone. He is looking directly at the camera with a serious expression on his face. 

On the right side, there are two people, a man and a woman, who appear to be engaged in a conversation. The woman is wearing a blue shirt and glasses and has shoulder-length dark hair. She is also wearing a headset and has a microphone in front of her.

The background of the image is blurred, but it seems to be a room with a window and a curtain. The sky is blue and there are clouds in the background. There is a logo on the top right corner of the screenshot that reads "Nestlé".</caption><caption><image_2>The image is a screenshot of a presentation slide with a blue background. On the right side of the slide, there is a photo of a woman with long dark hair and glasses. She is smiling and looking directly at the camera. The slide has a title "Top 15 Python Tips for Data Cleaning/Understanding" written in white text on a black background. Below the title, there are two bonus tips written in smaller white text. The title reads "With two bonuses tips!" and the subtitle reads "By: Hui Xiang Chen". At the bottom of the image, the website "europython.online 2020" is written in green text.</caption><transcript><audio_3>so yeah so good luck you're ready to go thank you yes so hi everyone um thanks for tuning in so like um for today for today's talk um i'll be talking quite a bit uh about like what i do at</transcript><transcript><audio_4>work like 70 to 80 percent of my time because like you know as a data scientist as someone who works on data science project like 70 to 80 of our time is spent on data cleaning and data understanding</transcript><transcript><audio_5>but in a way like um i feel like this topic is kind of um underrated like a lot of people don't talk about it and mainly i guess it's because like it's boring like you don't see any fantastic um product from this process itself</transcript><transcript><audio_6>but i feel like it's something that you know um all of us whenever we are learning how to work with data how to understand and analyze data um these are the few commands that i feel like are really</transcript><transcript><audio_7>useful for me and i hope like um at the end of this talk uh you'll find this all these comments and also tasks that i'm going through useful for you yeah so this uh you can think of it as like a consolidation</transcript> so this talk is the top 15 python tips so yeah so good luck you're ready to go thank you yes so hi everyone um thanks for tuning in so like um for today for today's talk um i'll be talking quite a bit uh about like what i do at work like 70 to 80 percent of my time because like you know as a data scientist as someone who works on data science project like 70 to 80 of our time is spent on data cleaning and data understanding but in a way like um i feel like this topic is kind of um underrated like a lot of people don't talk about it and mainly i guess it's because like it's boring like you don't see any fantastic um product from this process itself but i feel like it's something that you know um all of us whenever we are learning how to work with data how to understand and analyze data um these are the few commands that i feel like are really useful for me and i hope like um at the end of this talk uh you'll find this all these comments and also tasks that i'm going through useful for you yeah so this uh you can think of it as like a consolidation
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