docs: Small fixes/clarifications in user guide

docs/source/src/python/user-guide/misc/arrow_pycapsule.py

@@ -13,7 +13,7 @@
 # --8<-- [end:to_polars]
 
 # --8<-- [start:to_arrow_series]
-arrow_chunked_array = pa.array(df["foo"])
+arrow_chunked_array = pa.chunked_array(df["foo"])
 print(arrow_chunked_array)
 # --8<-- [end:to_arrow_series]
 

docs/source/user-guide/transformations/joins.md

@@ -269,10 +269,10 @@ The broker has another dataframe called `df_quotes` showing prices it has quoted
 --8<-- "python/user-guide/transformations/joins.py:df_quotes"
 ```
 
-You want to produce a dataframe showing for each trade the most recent quote provided _before_ the
-trade. You do this with `join_asof` (using the default `strategy = "backward"`). To avoid joining
-between trades on one stock with a quote on another you must specify an exact preliminary join on
-the stock column with `by="stock"`.
+You want to produce a dataframe showing for each trade the most recent quote provided _on or before_
+the time of the trade. You do this with `join_asof` (using the default `strategy = "backward"`). To
+avoid joining between trades on one stock with a quote on another you must specify an exact
+preliminary join on the stock column with `by="stock"`.
 
 {{code_block('user-guide/transformations/joins','asof', [], ['join_asof'], ['join_asof_by'])}}
 

docs/source/user-guide/transformations/pivot.md

@@ -3,11 +3,13 @@
 Pivot a column in a `DataFrame` and perform one of the following aggregations:
 
 - first
+- last
 - sum
 - min
 - max
 - mean
 - median
+- len
 
 The pivot operation consists of a group by one, or multiple columns (these will be the new y-axis),
 the column that will be pivoted (this will be the new x-axis) and an aggregation.

docs/source/user-guide/transformations/time-series/filter.md

@@ -16,8 +16,7 @@ In the following example we use a time series of Apple stock prices.
 
 ## Filtering by single dates
 
-We can filter by a single date by casting the desired date string to a `Date` object in a filter
-expression:
+We can filter by a single date using an equality comparison in a filter expression:
 
 {{code_block('user-guide/transformations/time-series/filter','filter',['filter'])}}
 

docs/source/user-guide/transformations/time-series/parsing.md

@@ -44,8 +44,7 @@ calling the string `str.to_date` method and passing the format of the date strin
 
 ## Extracting date features from a date column
 
-You can extract data features such as the year or day from a date column using the `.dt` namespace
-on a date column:
+You can extract data features such as the year or day from a date column using the `.dt` namespace:
 
 {{code_block('user-guide/transformations/time-series/parsing','extract',['dt.year'])}}