997692 1.357252 • Add reorder_levels method to Series and DataFrame (PR534) • Add dict-like get function to DataFrame and Panel (PR521) • Add DataFrame.iterrows method for efficiently iterating through DataFrame for broadcasting values across a level (GH542, PR552, others) • Add attribute-based item access to Panel and add IPython completion (PR563) • Add logy option to Series.plot for log-scaling on sorting of the group keys for potential speedups (GH595) • Can pass MaskedArray to Series constructor (PR563) • Add Panel item access via attributes and IPython completion (GH554) • Implement DataFrame.lookup

Release 0.7.2 • Add reorder_levels method to Series and DataFrame (PR534) • Add dict-like get function to DataFrame and Panel (PR521) • Add DataFrame.iterrows method for efficiently iterating through DataFrame for broadcasting values across a level (GH542, PR552, others) • Add attribute-based item access to Panel and add IPython completion (PR563) • Add logy option to Series.plot for log-scaling on sorting of the group keys for potential speedups (GH595) • Can pass MaskedArray to Series constructor (PR563) • Add Panel item access via attributes and IPython completion (GH554) • Implement DataFrame.lookup

759328 1.369669 • Add reorder_levels method to Series and DataFrame (PR534) • Add dict-like get function to DataFrame and Panel (PR521) • Add DataFrame.iterrows method for efficiently iterating through DataFrame for broadcasting values across a level (GH542, PR552, others) • Add attribute-based item access to Panel and add IPython completion (PR563) • Add logy option to Series.plot for log-scaling on powerful Python data analysis toolkit, Release 0.7.3 • Can pass MaskedArray to Series constructor (PR563) • Add Panel item access via attributes and IPython completion (GH554) • Implement DataFrame.lookup

Performance improvements • Improve memory usage of DataFrame.describe (do not copy data unnecessarily) (PR #425) • Optimize scalar value lookups in the general case by 25% or more in Series and DataFrame you would like to see implemented. 2. Fork the repo, Implement the functionality yourself and open a PR on Github. 3. Write a method that performs the operation you are interested in and Monkey-patch the level (GH542, GH552, others) • Add attribute-based item access to Panel and add IPython completion (PR GH554) • Add logy option to Series.plot for log-scaling on the Y axis • Add index, header, and justify

Performance improvements • Improve memory usage of DataFrame.describe (do not copy data unnecessarily) (PR #425) • Optimize scalar value lookups in the general case by 25% or more in Series and DataFrame you would like to see implemented. 2. Fork the repo, Implement the functionality yourself and open a PR on Github. 101 pandas: powerful Python data analysis toolkit, Release 0.13.1 3. Write a method that seconds. 29.1.3 Where to start? There are a number of issues listed under Docs and Good as first PR where you could start out. Or maybe you have an idea of you own, by using pandas, looking for something

Performance improvements • Improve memory usage of DataFrame.describe (do not copy data unnecessarily) (PR #425) • Optimize scalar value lookups in the general case by 25% or more in Series and DataFrame you would like to see implemented. 2. Fork the repo, Implement the functionality yourself and open a PR on Github. 129 pandas: powerful Python data analysis toolkit, Release 0.14.0 3. Write a method that seconds. 29.1.3 Where to start? There are a number of issues listed under Docs and Good as first PR where you could start out. Or maybe you have an idea of you own, by using pandas, looking for something

Performance improvements • Improve memory usage of DataFrame.describe (do not copy data unnecessarily) (PR #425) • Optimize scalar value lookups in the general case by 25% or more in Series and DataFrame you would like to see implemented. 2. Fork the repo, Implement the functionality yourself and open a PR on Github. 3. Write a method that performs the operation you are interested in and Monkey-patch the seconds. 33.1.3 Where to start? There are a number of issues listed under Docs and Good as first PR where you could start out. Or maybe you have an idea of you own, by using pandas, looking for something

Performance improvements • Improve memory usage of DataFrame.describe (do not copy data unnecessarily) (PR #425) • Optimize scalar value lookups in the general case by 25% or more in Series and DataFrame you would like to see implemented. 2. Fork the repo, Implement the functionality yourself and open a PR on Github. 3. Write a method that performs the operation you are interested in and Monkey-patch the seconds. 33.1.3 Where to start? There are a number of issues listed under Docs and Good as first PR where you could start out. Or maybe you have an idea of you own, by using pandas, looking for something

Performance improvements • Improve memory usage of DataFrame.describe (do not copy data unnecessarily) (PR #425) • Optimize scalar value lookups in the general case by 25% or more in Series and DataFrame your changes to pandas 3.6.1 Committing your code Keep style fixes to a separate commit to make your PR more readable. Once you’ve made changes, you can see them by typing: git status If you’ve created 2 Premier Bank Denver CO 34112 3 Edgebrook Bank Chicago IL 57772 4 Doral BankEn Espanol San Juan PR 32102 5 Capitol City Bank & Trust Company Atlanta GA 33938 6 Highland Community Bank Chicago IL 20290

feature branch with changes in master after you created the branch, check the section on updating a PR. 4.1.4 Contributing to the documentation Contributing to the documentation benefits everyone who doctest will be a blocker for merging a PR. Check the examples section in the docstring guide for some tips and tricks to get the doctests passing. When doing a PR with a docstring update, it is good to upstream/master --name-only -- "*.py"') do flake8 %i This will get all the files being changed by the PR (and ending with .py), and run flake8 on them, one after the other. Note that these commands can be