Python 2.4 support will require either monetary development support or someone contributing to the project to maintain compatibility. 2.2 Binary installers Available on PyPI 2.3 Dependencies • NumPy: should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development ideas. A complete list can be found on Github. 4.5 Development Team pandas is a part of the PyData project. The PyData Development Team is a collection of developers focused on the improvement of Python’s

Python 2.4 support will require either monetary development support or someone contributing to the project to maintain compatibility. 2.2 Binary installers Available on PyPI 2.3 Dependencies • NumPy: should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development ideas. A complete list can be found on Github. 4.5 Development Team pandas is a part of the PyData project. The PyData Development Team is a collection of developers focused on the improvement of Python’s

Python 2.4 support will require either monetary development support or someone contributing to the project to maintain compatibility. 2.2 Binary installers Available on PyPI 2.3 Dependencies • NumPy: should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development ideas. A complete list can be found on Github. 4.5 Development Team pandas is a part of the PyData project. The PyData Development Team is a collection of developers focused on the improvement of Python’s

Your Google BigQuery Project ID # To find this, see your dashboard: # https://code.google.com/apis/console/b/0/?noredirect projectid = xxxxxxxxx; df = gbq.read_gbq(query, project_id = projectid) # Use and 2.5 support will require either monetary development support or someone contributing to the project to restore compatibility. 2.2 Binary installers 2.2.1 All platforms Stable installers available should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development

Your Google BigQuery Project ID # To find this, see your dashboard: # https://code.google.com/apis/console/b/0/?noredirect projectid = xxxxxxxxx; df = gbq.read_gbq(query, project_id = projectid) # Use should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development ideas. A complete list can be found on Github. 4.5 Development Team pandas is a part of the PyData project. The PyData Development Team is a collection of developers focused on the improvement of Python’s

Python 2.4 support will require either monetary development support or someone contributing to the project to maintain compatibility. 2.2 Binary installers 2.2.1 All platforms Stable installers available should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development ideas. A complete list can be found on Github. 4.5 Development Team pandas is a part of the PyData project. The PyData Development Team is a collection of developers focused on the improvement of Python’s

deprecated and will be removed in a future version. Similar functionaility can be accessed thru the rpy2 project (GH9602) • Adding DatetimeIndex/PeriodIndex to another DatetimeIndex/PeriodIndex is being depre- Your Google BigQuery Project ID # To find this, see your dashboard: # https://code.google.com/apis/console/b/0/?noredirect projectid = xxxxxxxxx; df = gbq.read_gbq(query, project_id = projectid) # Use free GitHub account. We use Git for version control to allow many people to work together on the project. Some great resources for learning git: 230 Chapter 3. Contributing to pandas pandas: powerful

Your Google BigQuery Project ID # To find this, see your dashboard: # https://code.google.com/apis/console/b/0/?noredirect projectid = xxxxxxxxx; df = gbq.read_gbq(query, project_id = projectid) # Use should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development ideas. A complete list can be found on Github. 4.5 Development Team pandas is a part of the PyData project. The PyData Development Team is a collection of developers focused on the improvement of Python’s

Your Google BigQuery Project ID # To find this, see your dashboard: # https://code.google.com/apis/console/b/0/?noredirect projectid = xxxxxxxxx; df = gbq.read_gbq(query, project_id = projectid) # Use should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development ideas. A complete list can be found on Github. 4.5 Development Team pandas is a part of the PyData project. The PyData Development Team is a collection of developers focused on the improvement of Python’s

that pandas has become a sponsored project of the (NUMFocus organization). This will help ensure the success of development of pandas as a world-class open-source project. This is a minor bug-fix release deprecated and will be removed in a future version. Similar functionaility can be accessed thru the rpy2 project (GH9602) • Adding DatetimeIndex/PeriodIndex to another DatetimeIndex/PeriodIndex is being depre- Google BigQuery Project ID # To find this, see your dashboard: # https://console.developers.google.com/iam-admin/projects?authuser=0 projectid = xxxxxxxxx; df = gbq.read_gbq(query, project_id = projectid)