Tutorials¶
This is a guide to many pandas tutorials, geared mainly for new users.
pandas Cookbook¶
The goal of this cookbook (byJulia Evans) is togive you some concrete examples for getting started with pandas. Theseare examples with real-world data, and all the bugs and weirdness thatthat entails.
Here are links to the v0.1 release. For an up-to-date table of contents, see thepandas-cookbook GitHubrepository. To run the examples in this tutorial, you’ll need toclone the GitHub repository and get IPython Notebook running.SeeHow to use this cookbook.
- A quick tour of the IPython Notebook:Shows off IPython’s awesome tab completion and magic functions.
- Chapter 1:Reading your data into pandas is pretty much the easiest thing. Evenwhen the encoding is wrong!
- Chapter 2:It’s not totally obvious how to select data from a pandas dataframe.Here we explain the basics (how to take slices and get columns)
- Chapter 3:Here we get into serious slicing and dicing and learn how to filterdataframes in complicated ways, really fast.
- Chapter 4:Groupby/aggregate is seriously my favorite thing about pandasand I use it all the time. You should probably read this.
- Chapter 5:Here you get to find out if it’s cold in Montreal in the winter(spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes.
- Chapter 6:Strings with pandas are great. It has all these vectorized stringoperations and they’re the best. We will turn a bunch of stringscontaining “Snow” into vectors of numbers in a trice.
- Chapter 7:Cleaning up messy data is never a joy, but with pandas it’s easier.
- Chapter 8:Parsing Unix timestamps is confusing at first but it turns outto be really easy.
Lessons for New pandas Users¶
For more resources, please visit the mainrepository.
- 01 - Lesson:- Importing libraries- Creating data sets- Creating data frames- Reading from CSV- Exporting to CSV- Finding maximums- Plotting data
- 02 - Lesson:- Reading from TXT- Exporting to TXT- Selecting top/bottom records- Descriptive statistics- Grouping/sorting data
- 03 - Lesson:- Creating functions- Reading from EXCEL- Exporting to EXCEL- Outliers- Lambda functions- Slice and dice data
- 04 - Lesson:- Adding/deleting columns- Index operations
- 05 - Lesson:- Stack/Unstack/Transpose functions
- 06 - Lesson:- GroupBy function
- 07 - Lesson:- Ways to calculate outliers
- 08 - Lesson:- Read from Microsoft SQL databases
- 09 - Lesson:- Export to CSV/EXCEL/TXT
- 10 - Lesson:- Converting between different kinds of formats
- 11 - Lesson:- Combining data from various sources
Practical data analysis with Python¶
Thisguide is a comprehensive introduction to the data analysis process using the Python data ecosystem and an interesting open dataset.There are four sections covering selected topics as follows:
Excel charts with pandas, vincent and xlsxwriter¶