Python Pandas Data Science Tutorial (Read CSV/Excel, add/delete columns, Filter, Groupby, Slice) скачать в хорошем качестве

Python Pandas Data Science Tutorial (Read CSV/Excel, add/delete columns, Filter, Groupby, Slice)

In this Python Pandas tutorial we will cover basics of dataframe. DataFrame is a main object of pandas. It is the go-to library for data analysis and data science. The effective data analysis requires the ability to extract, clean, reshape, index, slice and summarize data. If you are familiar with data warehousing term ETL, extract, transform and load we can perform all these steps with Pandas. I will cover all of these topics with examples in this session. Data & code used in this Tutorial: https://github.com/hnawaz007/pythonda... Python Pandas Documentation: https://pandas.pydata.org/pandas-docs... Set up Python and Jupyter Environment:    • Install Jupyter Notebook on Windows | Pyth...   Let me know if you have any questions! If you enjoyed this video, be sure to like and make sure to subscribe to not miss any future videos! Thanks for watching friends! Happy coding! :) --------------------------------------------- Follow me on social media! Subscribe:    / haqnawaz   Github: https://github.com/hnawaz007 Instagram:   / bi_insights_inc   LinkedIn:   / haq-nawaz   --------------------------------------------- #DataScience #Pandas #PythonPandas #Python #PythonTutorial Topics covered in this video: 0:00 - Intro to Pandas 2:49 - Jupyter Notebook UI 4:15 - Install Pandas 5:17 - Loading the data into Pandas DataFrame 6:21 - Examine Data (Check, Columns, Cells, Headers, Rename columns) 12:29 - Handle missing value and data conversion 14:11 - Getting rows based on a specific condition 15:35 - Data Stats (Unique, Sum, Count) 16:55 - Slice Dataframe (Loc) 22:01 - Iterate Over Dataframe rows 24:36 - Adding a calculated column 27:38 - Dataframe iLoc 33:03 - Adding a column 33:51 - Deleting a column 34:24 - Grouping and summarizing data 35:02 - Top Ten Product report 35:30 - Transaction count by category 35:54 - Create pivot tables 36:32 - Saving our Data (CSV, Excel)