. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 13.19 Set / Reset Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . dimensional objects • Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align 0 (October 9, 2015) 11 pandas: powerful Python data analysis toolkit, Release 0.17.0 In [38]: pd.set_option('display.unicode.east_asian_width', True) In [39]: df; For further details, see here Other

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Index + / - no longer used for set operations . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Index.difference and .symmetric_difference returns Index . . . . . . . . . . . . . . . . . . . . . . . . . 29 MultiIndex constructors, groupby and set_index preserve categorical dtypes . . . . 30 read_csv will progressively enumerate chunks . . . . metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565 13.19.2 Set operations on Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566 13

. . . . . . . . . . . . . . . . . . . . . . . . . . . 74 1.6.2.8 Index + / - no longer used for set operations . . . . . . . . . . . . . . . . . . . . . 76 1.6.2.9 Index.difference and .symmetric_difference returns Index . . . . . . . . . . . . . . . . . . . . 77 1.6.2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes 77 1.6.2.12 read_csv will progressively enumerate chunks . . . metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627 12.20.2 Set operations on Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628 12

. . . . . . . . . . . . . . . . . . . . . . . . . . . 103 1.8.2.8 Index + / - no longer used for set operations . . . . . . . . . . . . . . . . . . . . . 105 1.8.2.9 Index.difference and .symmetric_difference returns Index . . . . . . . . . . . . . . . . . . . . 106 1.8.2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes106 1.8.2.12 read_csv will progressively enumerate chunks . . . metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 12.20.2 Set operations on Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 12

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Index + / - no longer used for set operations . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Index.difference and .symmetric_difference returns Index . . . . . . . . . . . . . . . . . . . . . . . . . 28 MultiIndex constructors, groupby and set_index preserve categorical dtypes . . . . 28 read_csv will progressively enumerate chunks . . . . metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 13.19.2 Set operations on Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564 13

objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 2.5.22 Set / reset index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 counts...) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine user guide Straight to tutorial... pandas has great support for time series and has an extensive set of tools for working with dates, times, and time- indexed data. 4 Chapter 1. Getting started pandas:

objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458 2.5.22 Set / reset index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 counts...) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine user guide Straight to tutorial... pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data. 4 Chapter 1. Getting started pandas:

objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458 2.5.22 Set / reset index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 counts...) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine user guide Straight to tutorial... pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data. 4 Chapter 1. Getting started pandas:

objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 2.5.22 Set / reset index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464 counts...) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine user guide Straight to tutorial... pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data. 4 Chapter 1. Getting started pandas:

objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 2.5.22 Set / reset index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464 counts...) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine user guide Straight to tutorial... pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data. 4 Chapter 1. Getting started pandas: