index/columns keywords . . . . . . . . . . . . . . . . . . . 10 1.2.1.5 rename, reindex now also accept axis keyword . . . . . . . . . . . . . . . . . 10 1.2.1.6 CategoricalDtype for specifying categoricals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 5 10 Minutes to pandas 427 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selecting columns based on dtype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 10 Working with Text Data 585 10.1 Splitting and Replacing Strings . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.1.1 agg API for DataFrame/Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.1.2 dtype keyword for data IO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 5 10 Minutes to pandas 399 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selecting columns based on dtype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553 10 Working with Text Data 557 10.1 Splitting and Replacing Strings . . . . . . . . . . . . . . . . .

. . . 10 1.2.1.3 .to_datetime() has gained an origin parameter . . . . . . . . . . . . . . . 10 1.2.1.4 Groupby Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 5 10 Minutes to pandas 397 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selecting columns based on dtype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 10 Working with Text Data 555 10.1 Splitting and Replacing Strings . . . . . . . . . . . . . . . . .

Out[9]: 0 abc 1 2 def Length: 3, dtype: string You can use the alias "string" as well. In [10]: s = pd.Series(['abc', None, 'def'], dtype="string") In [11]: s Out[11]: 0 abc 1 2 def Length: supplying the axis keyword argument. In [31]: df.rename({0: 1}) Out[31]: (continues on next page) 10 Chapter 1. What’s new in 1.0.0 (January 29, 2020) pandas: powerful Python data analysis toolkit, Release in a future version, the public classes are available in the top-level namespace (GH19711) • pandas.json_normalize() is now exposed in the top-level namespace. Usage of json_normalize as pandas.io.json

Optional Integer NA Support • New APIs for accessing the array backing a Series or Index • A new top-level method for creating arrays • Store Interval and Period data in a Series or DataFrame • Support page) 1 2 1 a 2 NaN 3 b [3 rows x 3 columns] In [10]: df.dtypes \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[10]: ˓→ A Int64 B int64 C object Length: 3, dtype: Dtypes and Attributes and Underlying Data for more. 1.1.3 pandas.array: a new top-level method for creating arrays A new top-level method array() has been added for creating 1-dimensional arrays (GH22860)

2 Package overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.4.3 Getting started tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 2 User Guide 113 2.1 10 minutes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954 3.2.2 Top-level missing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 986 3.2.3 Top-level conversions . . . . . . . . . . . . .

2 Package overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.4.3 Getting started tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 2 User Guide 113 2.1 10 minutes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954 3.2.2 Top-level missing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 986 3.2.3 Top-level conversions . . . . . . . . . . . . .

the vertical screen estate. Therefore, a new option display.min_rows is introduced with a default of 10 which determines the number of rows showed in the truncated repr: • For small Series or DataFrames larger Series of DataFrame with a length above max_rows, only min_rows number of rows is shown (default: 10, i.e. the first and last 5 rows). This dual option allows to still see the full content of relatively normalization (GH23843): The repr now looks like this: In [9]: from pandas.io.json import json_normalize In [10]: data = [{ ....: 'CreatedBy': {'Name': 'User001'}, ....: 'Lookup': {'TextField': 'Some text', (continues

the vertical screen estate. Therefore, a new option display.min_rows is introduced with a default of 10 which determines the number of rows showed in the truncated repr: • For small Series or DataFrames larger Series of DataFrame with a length above max_rows, only min_rows number of rows is shown (default: 10, i.e. the first and last 5 rows). This dual option allows to still see the full content of relatively normalization (GH23843): The repr now looks like this: In [9]: from pandas.io.json import json_normalize In [10]: data = [{ ....: 'CreatedBy': {'Name': 'User001'}, ....: 'Lookup': {'TextField': 'Some text',

Package overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.4.3 10 minutes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 939 3.2.2 Top-level missing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 970 3.2.3 Top-level conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 975 3.2.4 Top-level dealing with datetimelike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 977 3.2.5 Top-level dealing with intervals . . . . . . . .