Contain a Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . 566 10.5 Creating Indicator Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 567 10.6 Method Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 806 18.8 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807 18.9 Factorizing values DataFrame.eval method (Experimental) . . . . . . . . . . . . . . . . . . . . . . . 1115 26.3.4 Local Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1118 26.3.5 pandas

Contain a Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . 564 10.5 Creating Indicator Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565 10.6 Method Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 802 18.8 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803 18.9 Factorizing values DataFrame.eval method (Experimental) . . . . . . . . . . . . . . . . . . . . . . . 1113 26.3.4 Local Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1116 26.3.5 pandas

explicitly sorted before merging. Different types of joins are accomplished using the in= dummy variables to track whether a match was found in one or both input frames. proc sort data=df1; by key; run; dtype: float64 GroupBy Aggregation SASs PROC SUMMARY can be used to group by one or more key variables and compute aggregations on numeric columns. proc summary data=tips nway; class sex smoker; var dtype: float64 GroupBy Aggregation Statas collapse can be used to group by one or more key variables and compute aggregations on numeric columns. collapse (sum) total_bill tip, by(sex smoker) pandas

Contain a Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . 594 10.5 Creating Indicator Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 10.6 Method Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 836 18.8 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837 18.9 Factorizing values DataFrame.eval method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1152 26.3.4 Local Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154 26.3.5 pandas

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 18.7 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 18.8 Factorizing values Added ability to export Categorical data to Stata (GH8633). See here for limitations of categorical variables exported to Stata data files. • Added flag order_categoricals to StataReader and read_stata to order im- ported categorical data (GH8836). See here for more information on importing categorical variables from Stata data files. • Added ability to export Categorical data to to/from HDF5 (GH7621). Queries

Contain a Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 11.5 Creating Indicator Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 11.6 Method Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722 19.8 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722 19.9 Factorizing values DataFrame.eval method (Experimental) . . . . . . . . . . . . . . . . . . . . . . . 1018 27.3.4 Local Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1020 27.3.5 pandas

Contain a Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 11.5 Creating Indicator Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506 x 11.6 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 19.8 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 19.9 Factorizing values DataFrame.eval method (Experimental) . . . . . . . . . . . . . . . . . . . . . . . 1016 27.3.4 Local Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1018 27.3.5 pandas

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 19.7 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 19.8 Factorizing values Added ability to export Categorical data to Stata (GH8633). See here for limitations of categorical variables exported to Stata data files. • Added flag order_categoricals to StataReader and read_stata to order im- ported categorical data (GH8836). See here for more information on importing categorical variables from Stata data files. • Added ability to export Categorical data to to/from HDF5 (GH7621). Queries

explicitly sorted before merging. Different types of joins are accomplished using the in= dummy variables to track whether a match was found in one or both input frames. 190 Chapter 3. Getting started dtype: float64 GroupBy Aggregation SAS’s PROC SUMMARY can be used to group by one or more key variables and compute aggregations on numeric columns. proc summary data=tips nway; class sex smoker; var dtype: float64 GroupBy Aggregation Stata’s collapse can be used to group by one or more key variables and compute aggregations on numeric columns. collapse (sum) total_bill tip, by(sex smoker) pandas

explicitly sorted before merging. Different types of joins are accomplished using the in= dummy variables to track whether a match was found in one or both input frames. 190 Chapter 3. Getting started dtype: float64 GroupBy Aggregation SAS’s PROC SUMMARY can be used to group by one or more key variables and compute aggregations on numeric columns. proc summary data=tips nway; class sex smoker; var dtype: float64 GroupBy Aggregation Stata’s collapse can be used to group by one or more key variables and compute aggregations on numeric columns. collapse (sum) total_bill tip, by(sex smoker) pandas