operations on differently-indexed SparseSeries objects to use the integer-based (dense) alignment logic which is faster with a larger number of blocks (GH205) • Wrote faster Cython data alignment / merging locations where no data for that label existed • If specified, fill data for missing labels using logic (highly relevant to working with time series data) Here is a simple example: In [82]: s = Series(randn(5) data, but you may have an application on non-time series data where this sort of “interpolation” logic is the correct thing to do. More sophisticated interpolation of missing values would be an obvious

index is a DatetimeIndex with a fixed-offset timezone (GH2683) • Corrected businessday subtraction logic when the offset is more than 5 bdays and the starting date is on a weekend (GH2680) • Fixed C file DateRange class • New PeriodIndex and Period classes for representing time spans and performing calendar logic, in- cluding the 12 fiscal quarterly frequencies . This is a partial port of operations on differently-indexed SparseSeries objects to use the integer-based (dense) alignment logic which is faster with a larger number of blocks (GH205) • Wrote faster Cython data alignment / merging

data tutorial to filter rows of a table using a conditional expression. If you need more advanced logic, you can use arbitrary Python code via apply(). I want to rename the data columns to the corresponding More options on table concatenation (row and column wise) and how concat can be used to define the logic (union or intersection) of the indexes on the other axes is provided at the section on object concatenation 75 Male No Sat Dinner 2 243 16.78 3.00 Female No Thur Dinner 2 [176 rows x 7 columns] If/then logic Let’s say we want to make a bucket column with values of low and high, based on whether the total_bill

various facilities for easily combining together Series and DataFrame objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations. locations where no data for that label existed • If specified, fill data for missing labels using logic (highly relevant to working with time series data) Here is a simple example: In [196]: s = pd.Series(np Dinner 2 4 22.59 3.61 Female No Sun Dinner 4 5 23.29 4.71 Male No Sun Dinner 4 If/then logic In SAS, if/then logic can be used to create new columns. data tips; set tips; format bucket $4.; if total_bill

data tutorial to filter rows of a table using a conditional expression. If you need more advanced logic, you can use arbitrary Python code via apply(). I want to rename the data columns to the corresponding More options on table concatenation (row and column wise) and how concat can be used to define the logic (union or intersection) of the indexes on the other axes is provided at the section on object concatenation 75 Male No Sat Dinner 2 243 16.78 3.00 Female No Thur Dinner 2 [176 rows x 7 columns] If/then logic Let’s say we want to make a bucket column with values of low and high, based on whether the total_bill

data tutorial to filter rows of a table using a conditional expression. If you need more advanced logic, you can use arbitrary Python code via apply(). I want to rename the data columns to the corresponding More options on table concatenation (row and column wise) and how concat can be used to define the logic (union or intersection) of the indexes on the other axes is provided at the section on object concatenation 75 Male No Sat Dinner 2 243 16.78 3.00 Female No Thur Dinner 2 [176 rows x 7 columns] If/then logic Let’s say we want to make a bucket column with values of low and high, based on whether the total_bill

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759 17.1.1 Set logic on the other axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 762 17.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1174 xxiii 33.3.3 If/Then Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1174 33.3.4 Date g, and h are functions that take and return DataFrames f(g(h(df), arg1=1), arg2=2, arg3=3) The logic flows from inside out, and function names are separated from their keyword arguments. This can be

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755 17.1.1 Set logic on the other axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 758 17.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1172 33.3.3 If/Then Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1172 33.3.4 Date g, and h are functions that take and return DataFrames f(g(h(df), arg1=1), arg2=2, arg3=3) The logic flows from inside out, and function names are separated from their keyword arguments. This can be

NA > 1 Out[7]: For logical operations, pd.NA follows the rules of the three-valued logic (or Kleene logic). For example: In [8]: pd.NA | True Out[8]: True For more, see NA section in the user guide various facilities for easily combining together Series and DataFrame objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations. locations where no data for that label existed • If specified, fill data for missing labels using logic (highly relevant to working with time series data) Here is a simple example: In [201]: s = pd.Series(np

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 789 17.1.1 Set logic on the other axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792 17.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1210 33.3.3 If/Then Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1210 33.3.4 Date g, and h are functions that take and return DataFrames f(g(h(df), arg1=1), arg2=2, arg3=3) The logic flows from inside out, and function names are separated from their keyword arguments. This can be