potentially heterogeneously-typed columns 3 Panel General 3D labeled, also size-mutable array 4.1.1 Why more than 1 data structure? The best way to think about the pandas data structures is as flexible solution (similar to R) performant enough to be used in pandas. 9.1 Missing data basics 9.1.1 When / why does data become missing? Some might quibble over our usage of missing. By “missing” we simply mean this is an issue in practice. Some explanation for the motivation here in the next section. 17.1.4 Why not make NumPy like R? Many people have suggested that NumPy should simply emulate the NA support

potentially heterogeneously-typed columns 3 Panel General 3D labeled, also size-mutable array 4.1.1 Why more than 1 data structure? The best way to think about the pandas data structures is as flexible solution (similar to R) performant enough to be used in pandas. 9.1 Missing data basics 9.1.1 When / why does data become missing? Some might quibble over our usage of missing. By “missing” we simply mean this is an issue in practice. Some explanation for the motivation here in the next section. 17.1.4 Why not make NumPy like R? Many people have suggested that NumPy should simply emulate the NA support

potentially heterogeneously-typed columns 3 Panel General 3D labeled, also size-mutable array 4.1.1 Why more than 1 data structure? The best way to think about the pandas data structures is as flexible solution (similar to R) performant enough to be used in pandas. 9.1 Missing data basics 9.1.1 When / why does data become missing? Some might quibble over our usage of missing. By “missing” we simply mean this is an issue in practice. Some explanation for the motivation here in the next section. 17.1.4 Why not make NumPy like R? Many people have suggested that NumPy should simply emulate the NA support

BeautifulSoup4 and html5lib and lxml • Only lxml, although see HTML Table Parsing for reasons as to why you should probably not take this approach. Warning: • if you install BeautifulSoup4 you must install General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure? The best way to think about the pandas data structures is as flexible object array will always be an ExtensionArray. The exact details of what an ExtensionArray is and why pandas uses them is a bit beyond the scope of this introduction. See dtypes for more. 3.3. Essential

a glance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 5.1.1 Why more than 1 data structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 5 versus a copy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 568 13.21.1 Why does assignment fail when using chained indexing? . . . . . . . . . . . . . . . . . . . 569 13.21.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625 16.1.1 When / why does data become missing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625 16.1.2 Values

a glance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 5.1.1 Why more than 1 data structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 5 versus a copy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 570 13.21.1 Why does assignment fail when using chained indexing? . . . . . . . . . . . . . . . . . . . 571 13.21.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627 16.1.1 When / why does data become missing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627 16.1.2 Values

a glance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 4.1.1 Why more than 1 data structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 4 versus a copy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632 12.22.1 Why does assignment fail when using chained indexing? . . . . . . . . . . . . . . . . . . . 633 12.22.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697 15.1.1 When / why does data become missing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697 15.1.2 Values

a glance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 4.1.1 Why more than 1 data structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 4 versus a copy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 630 12.22.1 Why does assignment fail when using chained indexing? . . . . . . . . . . . . . . . . . . . 631 12.22.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 15.1.1 When / why does data become missing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 15.1.2 Values

BeautifulSoup4 and html5lib and lxml – Only lxml, although see HTML reading gotchas for reasons as to why you should probably not take this approach. Warning: – if you install BeautifulSoup4 you must install import show_versions >>> show_versions() and in 0.13.1 onwards: >>> pd.show_versions() 3. Explain why the current behavior is wrong/not desired and what you expect instead. The issue will then show up existing code, so don’t break it if at all possible. If you think breakage is required clearly state why as part of the Pull Request. Also, be careful when changing method signatures and add deprecation warnings

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 4.1.1 Why more than one data structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 4 versus a copy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 662 12.22.1 Why does assignment fail when using chained indexing? . . . . . . . . . . . . . . . . . . . 663 12.22.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 727 15.1.1 When / why does data become missing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 727 15.1.2 Values