Update documentation

README.md

@@ -36,7 +36,7 @@ from having to lock the GIL when running those operations.
 Its query engine, DataFusion, is written in [Rust](https://www.rust-lang.org/), which makes strong assumptions
 about thread safety and lack of memory leaks.
 
-There is also experimental support for executing SQL against other DataFrame libraries, such as Polars, Pandas, and any 
+There is also experimental support for executing SQL against other DataFrame libraries, such as Polars, Pandas, and any
 drop-in replacements for Pandas.
 
 Technically, zero-copy is achieved via the [c data interface](https://arrow.apache.org/docs/format/CDataInterface.html).
@@ -70,17 +70,11 @@ df = ctx.sql("select passenger_count, count(*) "
              "group by passenger_count "
              "order by passenger_count")
 
-# collect as list of pyarrow.RecordBatch
-results = df.collect()
-
-# get first batch
-batch = results[0]
-
 # convert to Pandas
-df = batch.to_pandas()
+pandas_df = df.to_pandas()
 
 # create a chart
-fig = df.plot(kind="bar", title="Trip Count by Number of Passengers").get_figure()
+fig = pandas_df.plot(kind="bar", title="Trip Count by Number of Passengers").get_figure()
 fig.savefig('chart.png')
 ```
 

examples/sql-to-pandas.py

@@ -33,17 +33,11 @@
     "order by passenger_count"
 )
 
-# collect as list of pyarrow.RecordBatch
-results = df.collect()
-
-# get first batch
-batch = results[0]
-
 # convert to Pandas
-df = batch.to_pandas()
+pandas_df = df.to_pandas()
 
 # create a chart
-fig = df.plot(
+fig = pandas_df.plot(
     kind="bar", title="Trip Count by Number of Passengers"
 ).get_figure()
 fig.savefig("chart.png")

src/dataframe.rs

@@ -313,8 +313,8 @@ impl PyDataFrame {
         Ok(())
     }
 
-    // Convert to pandas dataframe with pyarrow
-    // Collect the batches, pass to Arrow Table & then convert to Pandas DataFrame
+    /// Convert to pandas dataframe with pyarrow
+    /// Collect the batches, pass to Arrow Table & then convert to Pandas DataFrame
     fn to_pandas(&self, py: Python) -> PyResult<PyObject> {
         let batches = self.collect(py);