(see below for an overview). It is recommended to first upgrade to pandas 0.25 and to ensure your code is working without warnings, before upgrading to pandas 1.0. 1.1 New Deprecation Policy Starting way to select just text while excluding non-text, but still object-dtype columns. 3. When reading code, the contents of an object dtype array is less clear than string. In [9]: pd.Series(['abc', None using a Series or DataFrame with sparse values instead. See Migrating for help with migrating existing code. Matplotlib unit registration Previously, pandas would register converters with matplotlib as a

ValueError( 349 "On level {level}, code value ({code})" --> 350 " < -1".format(level=i, code=level_codes.min()) 351 ) 352 if not level.is_unique: ValueError: On level 0, code value (-2) < -1 1.2.3 Groupby evaluated the supplied function consistently twice on the first group to infer if it is safe to use a fast code path. Particularly for functions with side effects, this was an undesired behavior and may have led MultiIndex (GH24813) • Restored performance of DatetimeIndex.__iter__() by re-enabling specialized code path (GH26702) • Improved performance when building MultiIndex with at least one CategoricalIndex

ValueError( 349 "On level {level}, code value ({code})" --> 350 " < -1".format(level=i, code=level_codes.min()) 351 ) 352 if not level.is_unique: ValueError: On level 0, code value (-2) < -1 1.2.3 Groupby evaluated the supplied function consistently twice on the first group to infer if it is safe to use a fast code path. Particularly for functions with side effects, this was an undesired behavior and may have led MultiIndex (GH24813) • Restored performance of DatetimeIndex.__iter__() by re-enabling specialized code path (GH26702) • Improved performance when building MultiIndex with at least one CategoricalIndex

enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2956 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2957 4.1.4 Contributing . . . . . . . . . 2984 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2985 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2998 4.4.10 Documenting your code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2999 4.5 pandas maintenance

values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494 2.8.10 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494 2.8 features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719 2.16.11 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 2.17 enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2421 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2421 4.1.4 Contributing

values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494 2.8.10 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494 2.8 features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719 2.16.11 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 2.17 enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2421 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2421 4.1.4 Contributing

dtype: int64 On their own, a PandasArray isn’t a very useful object. But if you need write low-level code that works generically for any ExtensionArray, PandasArray satisfies that need. Notice that by default the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that everything is working (and that • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus on

values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 568 2.8.10 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 2.8 features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 802 iv 2.18.11 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 814 2.19 enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2692 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2693 4.1.4 Contributing

values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 2.8.10 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 570 2.8 features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803 iv 2.18.11 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815 2.19 enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2692 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2693 4.1.4 Contributing

values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 2.8.10 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544 2.8 features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 771 iv 2.18.11 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 782 2.19 enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2614 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615 4.1.4 Contributing