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

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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.
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! :)
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#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)
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!
---------------------------------------------
#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)
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