may wish to take an object and reindex its axes to be labeled the same as another object. While the syntax for this is straightforward albeit verbose, it is a common enough operation that the reindex_like() a dict of like-indexed Series objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations: In [61]: df['one'] Out[61]: a 1.0 b 2.0 c 3.0 d NaN Name: A B 0 2 4 1 2 4 2 2 4 Indexing / selection The basics of indexing are as follows: Operation Syntax Result Select column df[col] Series Select row by label df.loc[label] Series Select row by integer

Business Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 1.7.1.2 .groupby(..) syntax with window and resample operations . . . . . . . . . . . 93 1.7.1.3 Method chaininng improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611 12.15.1 MultiIndex query() Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614 12.15.2 query() Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 12.15.3 query() Python versus pandas Syntax Comparison . . . . . . . . . . . . . . . . . . . . . 616 12.15.4 The in and not in operators . .

Business Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 1.6.1.2 .groupby(..) syntax with window and resample operations . . . . . . . . . . . 91 1.6.1.3 Method chaininng improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 12.15.1 MultiIndex query() Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612 12.15.2 query() Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 12.15.3 query() Python versus pandas Syntax Comparison . . . . . . . . . . . . . . . . . . . . . 614 12.15.4 The in and not in operators . .

Business Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 1.9.1.2 .groupby(..) syntax with window and resample operations . . . . . . . . . . . 122 1.9.1.3 Method chaininng improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642 12.16.1 MultiIndex query() Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644 12.16.2 query() Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 646 12.16.3 query() Python versus pandas Syntax Comparison . . . . . . . . . . . . . . . . . . . . . 646 12.16.4 The in and not in operators . .

a new index type Float64Index, and other Indexing enhancements • HDFStore has a new string based syntax for query specification • support for new methods of interpolation • updated timedelta operations been added that allows you to select elements of a DataFrame using a natural query syntax nearly identical to Python syntax. For example, 98 Chapter 1. What’s New pandas: powerful Python data analysis toolkit a dict of like-indexed Series objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations: 8.2. DataFrame 253 pandas: powerful Python data analysis toolkit

a new index type Float64Index, and other Indexing enhancements • HDFStore has a new string based syntax for query specification • support for new methods of interpolation • updated timedelta operations been added that allows you to select elements of a DataFrame using a natural query syntax nearly identical to Python syntax. For example, 92 Chapter 1. What’s New pandas: powerful Python data analysis toolkit a dict of like-indexed Series objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations: 8.2. DataFrame 245 pandas: powerful Python data analysis toolkit

Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 .groupby(..) syntax with window and resample operations . . . . . . . . . . . . . . . . 44 i Method chaininng improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 548 13.14.1 MultiIndex query() Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 13.14.2 query() Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552 13.14.3 query() Python versus pandas Syntax Comparison . . . . . . . . . . . . . . . . . . . . . 553 13.14.4 The in and not in operators . .

Hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 .groupby(..) syntax with window and resample operations . . . . . . . . . . . . . . . . 45 Method chaininng improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 550 13.14.1 MultiIndex query() Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553 13.14.2 query() Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554 13.14.3 query() Python versus pandas Syntax Comparison . . . . . . . . . . . . . . . . . . . . . 555 13.14.4 The in and not in operators . .

a new index type Float64Index, and other Indexing enhancements • HDFStore has a new string based syntax for query specification • support for new methods of interpolation • updated timedelta operations been added that allows you to select elements of a DataFrame using a natural query syntax nearly identical to Python syntax. For example, In [115]: n = 20 In [116]: df = DataFrame(np.random.randint(n, size=(n the prompt will change to indicate you are in the new development environment. Note: The above syntax is for windows environments. To work on macosx/linux, use: source activate pandas_dev To view your

a new index type Float64Index, and other Indexing enhancements • HDFStore has a new string based syntax for query specification • support for new methods of interpolation • updated timedelta operations been added that allows you to select elements of a DataFrame using a natural query syntax nearly identical to Python syntax. For example, In [115]: n = 20 In [116]: df = DataFrame(np.random.randint(n, size=(n a dict of like-indexed Series objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations: In [54]: df[’one’] Out[54]: a 1 b 2 c 3 d NaN Name: one, dtype: