typo

Author: JohnMount

Examples/Introduction/data_algebra_Introduction.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "[The `data_algebra`](https://github.com/WinVector/data_algebra) is a data wrangling system designed to express complex data manipulation as a series of simple data transforms. This is in the spirit of `R`'s `base::transform()`, `dplyr`'s `dplyr::mutate()`, or [`rquery`](https://github.com/WinVector/rquery)'s `rquery::extend()` and uses a method chaining notation. The operators themselves follow the selections in Codd's relational algebra, with the addition of the traditional `SQL` \"window functions.\"  More on the background and context of `data_algebra` can be found [here](https://github.com/WinVector/data_algebra/).\n",
     "\n",
-    "The the `Python`/`data_algebra` version of this introduction is [here](https://github.com/WinVector/data_algebra/blob/master/Examples/Introduction/data_algebra_Introduction.md), and the`R`/`rquery` version of this introduction is [here](https://github.com/WinVector/rquery/blob/master/Examples/Introduction/rquery_Introduction.md).\n",
+    "The `Python`/`data_algebra` version of this introduction is [here](https://github.com/WinVector/data_algebra/blob/master/Examples/Introduction/data_algebra_Introduction.md), and the`R`/`rquery` version of this introduction is [here](https://github.com/WinVector/rquery/blob/master/Examples/Introduction/rquery_Introduction.md).\n",
     "\n",
     "In transform formulations data manipulation is written as transformations that produce new `DataFrame`s, instead of as alterations of a primary data structure (as is the case with `data.table`).  Transform system *can* use more space and time than in-place methods. However, in our opinion, transform systems have a number of pedagogical advantages.\n",
     "\n",

Examples/Introduction/data_algebra_Introduction.md

@@ -3,7 +3,7 @@
 
 [The `data_algebra`](https://github.com/WinVector/data_algebra) is a data wrangling system designed to express complex data manipulation as a series of simple data transforms. This is in the spirit of `R`'s `base::transform()`, `dplyr`'s `dplyr::mutate()`, or [`rquery`](https://github.com/WinVector/rquery)'s `rquery::extend()` and uses a method chaining notation. The operators themselves follow the selections in Codd's relational algebra, with the addition of the traditional `SQL` "window functions."  More on the background and context of `data_algebra` can be found [here](https://github.com/WinVector/data_algebra/).
 
-The the `Python`/`data_algebra` version of this introduction is [here](https://github.com/WinVector/data_algebra/blob/master/Examples/Introduction/data_algebra_Introduction.md), and the`R`/`rquery` version of this introduction is [here](https://github.com/WinVector/rquery/blob/master/Examples/Introduction/rquery_Introduction.md).
+The `Python`/`data_algebra` version of this introduction is [here](https://github.com/WinVector/data_algebra/blob/master/Examples/Introduction/data_algebra_Introduction.md), and the`R`/`rquery` version of this introduction is [here](https://github.com/WinVector/rquery/blob/master/Examples/Introduction/rquery_Introduction.md).
 
 In transform formulations data manipulation is written as transformations that produce new `DataFrame`s, instead of as alterations of a primary data structure (as is the case with `data.table`).  Transform system *can* use more space and time than in-place methods. However, in our opinion, transform systems have a number of pedagogical advantages.