Noa Tamir

PyData Berlin are excited to bring you this open source workshop dedicated to contributing to pandas. This tutorial is 3 hours. We will have a break and continue with the same group of people. pandas is a data wrangling platform for Python widely adopted in the scientific computing community. In this session, you will be guided on how you can make your own contributions to the project, no prior experience contributing required! Not only will this teach you new skills and boost your CV, you'll also likely get a nice adrenaline rush when your contribution is accepted! If you don’t finish your contribution during the event, we hope you will continue to work on it after the tutorial. pandas offers regular new contributor meetings and has a slack space to provide ongoing support for new contributors. For more details, see our contributor community page: http://pandas.pydata.org/docs/dev/development/community.html .

PyData Berlin are excited to bring you this open source workshop dedicated to contributing to pandas. This tutorial is 3 hours. We will have a break and continue with the same group of people. pandas is a data wrangling platform for Python widely adopted in the scientific computing community. In this session, you will be guided on how you can make your own contributions to the project, no prior experience contributing required! Not only will this teach you new skills and boost your CV, you'll also likely get a nice adrenaline rush when your contribution is accepted! If you don’t finish your contribution during the event, we hope you will continue to work on it after the workshop. pandas offers regular new contributor meetings and has a slack space to provide ongoing support for new contributors. For more details, see our contributor community page: http://pandas.pydata.org/docs/dev/development/community.html . ❓Any other requirements ❓ 1. Bring your own laptop 2. Have Github account: https://github.com 3. Have git installed: https://git-scm.com/book/en/v2/Getting-Started-Installing-Gitf • Format for the session: First 15 minutes : an introduction - what you can contribute, how to contribute, and how to set up your development environment or use gitpod; The rest : "office hours", during which you'll be mentored through setting up a development environment and making a contribution to pandas. • Preparation (optional) For those who are more keen on using the workshop to work on their contribution to pandas, you may want to start setting up your development environment in advance. This w​​ay, by the time you arrive you are ready to get started on picking issues, and starting to contribute. Please be aware that it could take longer to set up a development on ​​a computer running a Windows operating system compared to MacOS or Unix. We will guide you through the steps, and they are useful to learn for many open source projects. We also offer a development environment on gitpod. It can take some minutes to load, but provides you an instant and fresh development environment for each new task directly from your browser, using VScode. Documentation is in the works and will be provided before the workshop. To get the most out of the session, it's encouraged (but not required) that you have a look at the contributing guide beforehand: https://pandas.pydata.org/pandas-docs/dev/development/contributing.html. Particularly, the development environment instructions: https://pandas.pydata.org/docs/dev/development/contributing_environment.html • Audience level Everyone is welcome to attend this session! If you've never contributed to open source software before, then you will learn how to, and if you have experience contributing, then you can either help mentor other attendees or you can work on more challenging contributions. It is useful to have some pandas, git, and python and experience. If you don't have much experience with them, you might expect to spend time "learning by doing".

Other people: Patrick Hoefler

PyData Berlin are excited to bring you this open source workshop dedicated to contributing to pandas. This tutorial is 3 hours. We will have a break and continue with the same group of people. pandas is a data wrangling platform for Python widely adopted in the scientific computing community. In this session, you will be guided on how you can make your own contributions to the project, no prior experience contributing required! Not only will this teach you new skills and boost your CV, you'll also likely get a nice adrenaline rush when your contribution is accepted! If you don’t finish your contribution during the event, we hope you will continue to work on it after the tutorial. pandas offers regular new contributor meetings and has a slack space to provide ongoing support for new contributors. For more details, see our contributor community page: http://pandas.pydata.org/docs/dev/development/community.html .

PyData Berlin are excited to bring you this open source workshop dedicated to contributing to pandas. This tutorial is 3 hours. We will have a break and continue with the same group of people. pandas is a data wrangling platform for Python widely adopted in the scientific computing community. In this session, you will be guided on how you can make your own contributions to the project, no prior experience contributing required! Not only will this teach you new skills and boost your CV, you'll also likely get a nice adrenaline rush when your contribution is accepted! If you don’t finish your contribution during the event, we hope you will continue to work on it after the workshop. pandas offers regular new contributor meetings and has a slack space to provide ongoing support for new contributors. For more details, see our contributor community page: http://pandas.pydata.org/docs/dev/development/community.html . ❓Any other requirements ❓ 1. Bring your own laptop 2. Have Github account: https://github.com 3. Have git installed: https://git-scm.com/book/en/v2/Getting-Started-Installing-Gitf • Format for the session: First 15 minutes : an introduction - what you can contribute, how to contribute, and how to set up your development environment or use gitpod; The rest : "office hours", during which you'll be mentored through setting up a development environment and making a contribution to pandas. • Preparation (optional) For those who are more keen on using the workshop to work on their contribution to pandas, you may want to start setting up your development environment in advance. This w​​ay, by the time you arrive you are ready to get started on picking issues, and starting to contribute. Please be aware that it could take longer to set up a development on ​​a computer running a Windows operating system compared to MacOS or Unix. We will guide you through the steps, and they are useful to learn for many open source projects. We also offer a development environment on gitpod. It can take some minutes to load, but provides you an instant and fresh development environment for each new task directly from your browser, using VScode. Documentation is in the works and will be provided before the workshop. To get the most out of the session, it's encouraged (but not required) that you have a look at the contributing guide beforehand: https://pandas.pydata.org/pandas-docs/dev/development/contributing.html. Particularly, the development environment instructions: https://pandas.pydata.org/docs/dev/development/contributing_environment.html • Audience level Everyone is welcome to attend this session! If you've never contributed to open source software before, then you will learn how to, and if you have experience contributing, then you can either help mentor other attendees or you can work on more challenging contributions. It is useful to have some pandas, git, and python and experience. If you don't have much experience with them, you might expect to spend time "learning by doing".

Other people: Patrick Hoefler

The title “Data Scientist” has been in use for 15 years now. We have been attending PyData conferences for over 10 years as well. The hype around data science and AI seems higher than ever before. But How are we managing?

Most of our conferences are about practical applications, methodologies, and platforms. In this talk, I want to focus on contemporary data science management. Including: - Our patterns and antipatterns. - The challenges we are facing as individual contributors, teams, managers, and leaders. - How the data science function has matured. - The unique aspects of Data Science compared to management in general, and software engineering in particular. If you are a data scientist, or work with some of us, you might be interested to learn about what makes us tick, what makes us great colleagues, and yes, even what makes us challenging to work with 😉.

This is part of the maintainers track. Most of us have been hearing about Diversity Equity and Inclusion (DEI) for some years now, and even had access to many resources by now. Our projects have codes of conduct, and some have been doing sprints and mentorships. But how much has fundamentally changed? Let’s meet for an honest conversation about the challenges of DEI actions, and culture change. How do we achieve long-term impact? What are low-hanging fruit? We can share hard-to-ask questions, effective tools, experiences that shaped our approach, and see if we can all nudge each other forward a little. Inclusion happens at the community level, also when we want to address DEI itself. So, we will need to create a safe space for hard questions and leave judgment at the door. Thanks to our grant to advance an inclusive culture in the scientific Python ecosystem, we have created the contributor experience lead role. We have been working with NumPy, SciPy, Matplotlib, and pandas to learn how to integrate this new role to a project, and how to introduce contributor hospitality techniques. We are working on creating widely available resources, and we would benefit from hearing from maintainers from the wider community.

Most of us have been hearing about Diversity Equity and Inclusion (DEI) for some years now, and even had access to many resources by now. Our projects have codes of conduct, and some have been doing sprints and mentorships. But how much has fundamentally changed? Let’s meet for an honest conversation about the challenges of DEI actions, and culture change. How do we achieve long-term impact? What are low-hanging fruit? We can share hard-to-ask questions, effective tools, experiences that shaped our approach, and see if we can all nudge each other forward a little. Inclusion happens at the community level, also when we want to address DEI itself. So, we will need to create a safe space for hard questions and leave judgment at the door. Thanks to our CZI grant to advance an inclusive culture in the scientific Python ecosystem, we have created the contributor experience lead role. We have been working with NumPy, SciPy, Matplotlib, and pandas to learn how to integrate this new role to a project, and how to introduce contributor hospitality techniques. We are working on creating widely available resources, and we would benefit from hearing from maintainers from the wider community.

In my current work as a contributor experience lead, I am supporting and growing Matplotlib’s and Pandas’ communities by organizing events, meetings, and proactive engagement with a focus on equity and inclusion of historically marginalized groups. In my talk I’ll give an introduction to this new role, the grant that supports it, and some of the work done so far… I will share takeaways for maintainers, and contributors; from simple changes that can be implemented relatively easily, to bigger topics, which one might want to learn more about, and slowly yet proactively, facilitate changes to tweak the contributor experience for a project.

In my current work as a contributor experience lead, I am supporting and growing Matplotlib’s and Pandas’ communities by organizing events, meetings, and proactive engagement with a focus on equity and inclusion of historically marginalized groups. In my talk I’ll give an introduction to this new role, the grant that supports it, and some of the work done so far… I will share takeaways for maintainers, and contributors; from simple changes that can be implemented relatively easily, to bigger topics, which one might want to learn more about, and slowly yet proactively, facilitate changes to tweak the contributor experience for a project.