Fix typos (#387)

pola-rs · Aug 29, 2023 · b120e72 · b120e72
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diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -58,7 +58,7 @@ Do not hesitate to [open a new issue](https://github.com/pola-rs/polars-book/iss
 
 ### Content formatting
 
-The `Python` code is checked and linted using [`black`](https://github.com/psf/black). The recommended way is to run the black before commiting:
+The `Python` code is checked and linted using [`black`](https://github.com/psf/black). The recommended way is to run the black before committing:
 
 ```shell
 $ black .

diff --git a/docs/getting-started/series-dataframes.md b/docs/getting-started/series-dataframes.md
@@ -39,7 +39,7 @@ There are a number of methods related to string operations in the `StringNamespa
 
 ##### Datetime
 
-Similar to strings, there is a seperate namespace for datetime related operations in the `DateLikeNameSpace`. These only work on `Series`with DataTypes related to dates.
+Similar to strings, there is a separate namespace for datetime related operations in the `DateLikeNameSpace`. These only work on `Series`with DataTypes related to dates.
 
 {{code_block('getting-started/series-dataframes','dt',['day'])}}
 

diff --git a/docs/user-guide/concepts/contexts.md b/docs/user-guide/concepts/contexts.md
@@ -41,7 +41,7 @@ Similarly to the `select` statement there is the `with_columns` statement which
 
 ## Filter
 
-In the `filter` context you filter the existing dataframe based on arbritary expression which evaluates to the `Boolean` data type.
+In the `filter` context you filter the existing dataframe based on arbitrary expression which evaluates to the `Boolean` data type.
 
 {{code_block('user-guide/concepts/contexts','filter',['filter'])}}
 

diff --git a/docs/user-guide/expressions/lists.md b/docs/user-guide/expressions/lists.md
@@ -1,6 +1,6 @@
 # Lists and Arrays
 
-`Polars` has first-class support for `List` columns: that is, columns where each row is a list of homogenous elements, of varying lengths. `Polars` also has an `Array` datatype, which is analogous to `numpy`'s `ndarray` objects, where the length is identical across rows.
+`Polars` has first-class support for `List` columns: that is, columns where each row is a list of homogeneous elements, of varying lengths. `Polars` also has an `Array` datatype, which is analogous to `numpy`'s `ndarray` objects, where the length is identical across rows.
 
 Note: this is different from Python's `list` object, where the elements can be of any type. Polars can store these within columns, but as a generic `Object` datatype that doesn't have the special list manipulation features that we're about to discuss.
 

diff --git a/docs/user-guide/expressions/operators.md b/docs/user-guide/expressions/operators.md
@@ -1,6 +1,6 @@
 # Basic Operators
 
-This section describes how to use basic operators (e.g. addition, substraction) in conjunction with Expressions. We will provide various examples using different themes in the context of the following dataframe.
+This section describes how to use basic operators (e.g. addition, subtraction) in conjunction with Expressions. We will provide various examples using different themes in the context of the following dataframe.
 
 !!! note Operator Overloading
 

diff --git a/docs/user-guide/io/database.md b/docs/user-guide/io/database.md
@@ -19,7 +19,7 @@ Polars doesn't manage connections and data transfer from databases by itself. In
 
 ConnectorX is the default engine and [supports numerous databases](https://github.com/sfu-db/connector-x#sources) including Postgres, Mysql, SQL Server and Redshift. ConnectorX is written in Rust and stores data in Arrow format to allow for zero-copy to Polars.
 
-To read from one of the supported databases with `ConnectorX` you need to activate the additional dependancy `ConnectorX` when installing Polars or install it manually with
+To read from one of the supported databases with `ConnectorX` you need to activate the additional dependency `ConnectorX` when installing Polars or install it manually with
 
 ```shell
 $ pip install connectorx

diff --git a/docs/user-guide/transformations/joins.md b/docs/user-guide/transformations/joins.md
@@ -95,7 +95,7 @@ The `inner`, `left`, `outer` and `cross` join strategies are standard amongst da
 
 ### Semi join
 
-The `semi` join retuns all rows from the left frame in which the join key is also present in the right frame. Consider the following scenario: a car rental company has a `DataFrame` showing the cars that it owns with each car having a unique `id`.
+The `semi` join returns all rows from the left frame in which the join key is also present in the right frame. Consider the following scenario: a car rental company has a `DataFrame` showing the cars that it owns with each car having a unique `id`.
 
 {{code_block('user-guide/transformations/joins','df5',['DataFrame'])}}