Patreon:
https://www.patreon.com/kfsoftDoing data science with python:
1) Pandas Time API
2) Plot Stock charts
- Moving average, candlestick, etc.
Files:
https://github.com/learn10kYear/learn-pandas/tree/master/lab200:00 PART 1 Intro - Time - 1) python datetime 2) numpy datetime64 3) pandas timestamp
04:00 Time API: python datetime (easy to use, but slow)
12:28 Time API: numpy datetime64 (fast, vectorized computation)
18:20 Time API: pandas timestamp (python datetime + numpy datetime64 - fast and easy to use)
22:18 Time API: timestamp + TimedeltaIndex
25:55 Time Index: 1) DatetimeIndex 2) PeriodIndex 3) TimedeltaIndex
27:24 Time Index: Create DatetimeIndex(1) - pd.DatetimeIndex()
29:10 Time Index: Create DatetimeIndex(2) - pd.to_datetime()
30:54 Time Index: Use DatetimeIndex as index of a series
32:05 Time Index: filtering time index rows
33:43 Time Index: Convert a DatetimeIndex to a periodInde - datetimeIndex.to_period('D')
35:45 Time Index: TimedeltaIndex conversion & creation: pd.TimedeltaIndex()
40:39 Time Index: Range functions 1) pd.date_range() 2) pd.period_range() 3) pd.timedelta_range()
42:00 Time Index: pd.date_range() - DatetimeIndex
43:56 Time Index: pd.period_range() - PeriodIndex
44:55 Time Index: pd.timedelta_range() - TimedeltaIndex
46:33 Time Index: Time index as series index
47:21 PART1 Summary: 3 forms of time in Pandas, and their time index
01:04:17 PART 1 conclusion
01:05:32 PART 2 Intro: Charts - 1)read stock data 2) moving average 3) bollinger bands 4) candlestick
01:06:23 Charts: install packages (pandas_datareader / seaborn / mplfinance)
01:06:43 Charts: data source (yahoo / quandl)
01:09:31 Charts: stock dataframe
01:13:36 Charts: resampling - groupby a time range & do aggregate functions
01:19:41 Charts: plt.subplots(row, col)
01:24:41 Charts: asfreq('X') - return a Dataframe
01:28:56 Charts: shift(int)
01:31:47 Charts: moving average, rolling(window=size).mean()
01:35:39 Charts: bbands - moving average +/- std*n
01:42:31 Charts: candlestick chart - mplfinance.plot()
01:44:59 Summary & conclusion
Python入門:第1課 - PyCharm + Data Types
https://youtu.be/s9toTBXQSPEPython入門:第2課 - Python containers (1): List, Tuple
https://youtu.be/7hm0zHgEGZ4Python入門:第3課 - Python containers (2): Dictionary & Set
https://youtu.be/7Jvfd6qFLzUPython入門:第4課 - If-Else, Looping, Try-except
https://youtu.be/sXdh5L5rcX0Python入門:第5課 - Function + File
https://youtu.be/rk8kU3no5NoPython入門:第6課 - Class and Object
https://youtu.be/HPb0Lg3FQfMPython入門:第7課 - URL, JSON, Sqlite
https://youtu.be/93lOZTxJtrsPython入門:第8課 - 用Flask進行Web開發
https://youtu.be/Z4CR3rwVkGcPython入門:第9課 - Flask + DB ORM
https://youtu.be/ZQoBdEH1zowPython入門:第10課 - Flask補充1
https://youtu.be/AC23QWvFNWIPython入門:第11課 - Flask補充2
https://youtu.be/-PkZ8sGhm-UPython入門:Apache 安裝Flask app - mod_wsgi
https://youtu.be/E6dqWawzc14Python入門:第12課 - GUI (Tkinter) + PyInstaller 打包EXE
https://youtu.be/_-LKQvmG8Uc1Python入門:第13課 - More loops - Iterable Vs Iterator
https://youtu.be/xKPK6CRnBT4Python入門:第14課 - More loops - Generator
https://youtu.be/sl3seUetRkAPython初級:第15課 - Web Scraping 靜態網頁抓取
https://youtu.be/_LRfuctPLdsPython初級:第16課 - Web Scraping 動態網頁抓取
https://youtu.be/lXwgSweHf5QPython初級:第17課 - Pygame貪食蛇遊戲
https://youtu.be/kaDEcF5LTWUPython初級:第18課 - if __name__ == '__main__' 入口點
https://youtu.be/ihDNLQOQrSkPython初級:第19課 - Type hints 類型提示
https://youtu.be/Z_AF3K-BMBsPython初級:第20課 - Decorator 裝飾器
https://youtu.be/mAyJI-proksPython初級:第21課 - Multi-threading 多線程
https://youtu.be/mHp7bfDZOSUPython初級:第22課 - Multi-processing 多進程
https://youtu.be/yFdGhaxW_5oPython初級:Django 入門 1 - Model + Admin Site
https://youtu.be/en6NXFI6CsQPython初級:Django 入門 2 - Template + View
https://youtu.be/en6NXFI6CsQPython初級:Django 入門 3 - 部署
https://youtu.be/GfMiJvbYk2kPython初級:openpyxl - 讀寫 MS Excel 文件
https://youtu.be/tjcJV2fur5gPython初級:python-docx - 讀寫 MS Word 文件
https://youtu.be/PEKWb5R3sSUPython入門 - 數據科學 - Jupyter Lab & Notebook 安裝+入門教程
https://youtu.be/niWD8kxgpH0Python入門 - 數據科學 - Anaconda + PyCharm 安裝
https://youtu.be/H4ihRvtdY7MPython初級 - 數據科學 - Numpy入門
https://youtu.be/t7ygnafk760Python初級 - 數據科學 - Pandas入門
https://youtu.be/ZYjhM7J9eFQPython初級 - 數據科學 - Pandas入門 (第二版 更新column部分)
https://youtu.be/w76oa7YzvkYPython初級 - 數據科學 - Pandas時間 + 圖表
https://youtu.be/jrd8shHEVFQPython初級 - 數據科學 - Pandas類別 + 樣式
https://youtu.be/4ntwbAWnKbgDatabase初級:SQL入門
https://youtu.be/OtM74u3Fbw0Database初級:JOIN連接
https://youtu.be/tpDvgr7qHswDatabase初級:MongoDB入門
https://youtu.be/XTqW3oOt3PsPython初級 - 機器學習 - Scikit-learn 入門
https://youtu.be/3m8Bb01uNNEPython初級 - 機器學習 - Scikit-learn - Regression 回歸
https://youtu.be/QyYZT8o-f3UPython初級 - 機器學習 - Scikit-learn - Classification 分類
https://youtu.be/JKn0OoHSoRoPython初級 - 機器學習 - Scikit-learn - Clustering 聚類+降維
https://youtu.be/UgXyK-k-CgMPython入門 - 技巧篇 - Debug 偵錯 / 除錯 / 調試
https://youtu.be/1uGdbaVGRBEPython入門 - 工具篇 - PyCharm 10個必學功能
https://youtu.be/A8La270tpHIhttps://kfsoft.infoAbout the Site 🌐
This site provides links to random videos hosted at YouTube, with the emphasis on random. 🎥
Origins of the Idea 🌱
The original idea for this site stemmed from the need to benchmark the popularity of a video against the general population of YouTube videos. 🧠
Challenges Faced 🤔
Obtaining a large sample of videos was crucial for accurate ranking, but YouTube lacks a direct method to gather random video IDs.
Even searching for random strings on YouTube doesn't yield truly random results, complicating the process further. 🔍
Creating Truly Random Links 🛠️
The YouTube API offers additional functions enabling the discovery of more random videos. Through inventive techniques and a touch of space-time manipulation, we've achieved a process yielding nearly 100% random links to YouTube videos.
About YouTube 📺
YouTube, an American video-sharing website based in San Bruno, California, offers a diverse range of user-generated and corporate media content. 🌟
Content and Users 🎵
Users can upload, view, rate, share, and comment on videos, with content spanning video clips, music videos, live streams, and more.
While most content is uploaded by individuals, media corporations like CBS and the BBC also contribute. Unregistered users can watch videos, while registered users enjoy additional privileges such as uploading unlimited videos and adding comments.
Monetization and Impact 🤑
YouTube and creators earn revenue through Google AdSense, with most videos free to view. Premium channels and subscription services like YouTube Music and YouTube Premium offer ad-free streaming.
As of February 2017, over 400 hours of content were uploaded to YouTube every minute, with the site ranking as the second-most popular globally. By May 2019, this figure exceeded 500 hours per minute. 📈
List of ours generators⚡
Random YouTube Videos Generator
Random Film and Animation Video Generator
Random Autos and Vehicles Video Generator
Random Music Video Generator
Random Pets and Animals Video Generator
Random Sports Video Generator
Random Travel and Events Video Generator
Random Gaming Video Generator
Random People and Blogs Video Generator
Random Comedy Video Generator
Random Entertainment Video Generator
Random News and Politics Video Generator
Random Howto and Style Video Generator
Random Education Video Generator
Random Science and Technology Video Generator
Random Nonprofits and Activism Video Generator