diff --git a/README.md b/README.md
index 8327533355..2e236de94d 100644
--- a/README.md
+++ b/README.md
@@ -93,25 +93,43 @@ This example loads NYC taxi trip records and taxi zone information stored as .CS
 #### Load NYC taxi trips and taxi zones data from CSV Files Stored on AWS S3
 
 ```python
-taxidf = sedona.read.format('csv').option("header","true").option("delimiter", ",").load("s3a://your-directory/data/nyc-taxi-data.csv")
-taxidf = taxidf.selectExpr('ST_Point(CAST(Start_Lon AS Decimal(24,20)), CAST(Start_Lat AS Decimal(24,20))) AS pickup', 'Trip_Pickup_DateTime', 'Payment_Type', 'Fare_Amt')
+taxidf = (
+    sedona.read.format("csv")
+    .option("header", "true")
+    .option("delimiter", ",")
+    .load("s3a://your-directory/data/nyc-taxi-data.csv")
+)
+taxidf = taxidf.selectExpr(
+    "ST_Point(CAST(Start_Lon AS Decimal(24,20)), CAST(Start_Lat AS Decimal(24,20))) AS pickup",
+    "Trip_Pickup_DateTime",
+    "Payment_Type",
+    "Fare_Amt",
+)
 ```
 
 ```python
-zoneDf = sedona.read.format('csv').option("delimiter", ",").load("s3a://your-directory/data/TIGER2018_ZCTA5.csv")
-zoneDf = zoneDf.selectExpr('ST_GeomFromWKT(_c0) as zone', '_c1 as zipcode')
+zoneDf = (
+    sedona.read.format("csv")
+    .option("delimiter", ",")
+    .load("s3a://your-directory/data/TIGER2018_ZCTA5.csv")
+)
+zoneDf = zoneDf.selectExpr("ST_GeomFromWKT(_c0) as zone", "_c1 as zipcode")
 ```
 
 #### Spatial SQL query to only return Taxi trips in Manhattan
 
 ```python
-taxidf_mhtn = taxidf.where('ST_Contains(ST_PolygonFromEnvelope(-74.01,40.73,-73.93,40.79), pickup)')
+taxidf_mhtn = taxidf.where(
+    "ST_Contains(ST_PolygonFromEnvelope(-74.01,40.73,-73.93,40.79), pickup)"
+)
 ```
 
 #### Spatial Join between Taxi Dataframe and Zone Dataframe to Find taxis in each zone
 
 ```python
-taxiVsZone = sedona.sql('SELECT zone, zipcode, pickup, Fare_Amt FROM zoneDf, taxiDf WHERE ST_Contains(zone, pickup)')
+taxiVsZone = sedona.sql(
+    "SELECT zone, zipcode, pickup, Fare_Amt FROM zoneDf, taxiDf WHERE ST_Contains(zone, pickup)"
+)
 ```
 
 #### Show a map of the loaded Spatial Dataframes using GeoPandas
@@ -120,14 +138,14 @@ taxiVsZone = sedona.sql('SELECT zone, zipcode, pickup, Fare_Amt FROM zoneDf, tax
 zoneGpd = gpd.GeoDataFrame(zoneDf.toPandas(), geometry="zone")
 taxiGpd = gpd.GeoDataFrame(taxidf.toPandas(), geometry="pickup")
 
-zone = zoneGpd.plot(color='yellow', edgecolor='black', zorder=1)
-zone.set_xlabel('Longitude (degrees)')
-zone.set_ylabel('Latitude (degrees)')
+zone = zoneGpd.plot(color="yellow", edgecolor="black", zorder=1)
+zone.set_xlabel("Longitude (degrees)")
+zone.set_ylabel("Latitude (degrees)")
 
 zone.set_xlim(-74.1, -73.8)
 zone.set_ylim(40.65, 40.9)
 
-taxi = taxiGpd.plot(ax=zone, alpha=0.01, color='red', zorder=3)
+taxi = taxiGpd.plot(ax=zone, alpha=0.01, color="red", zorder=3)
 ```
 
 ## Docker image
diff --git a/docs/api/sql/Raster-visualizer.md b/docs/api/sql/Raster-visualizer.md
index 1ff207a5e5..f0b83cb165 100644
--- a/docs/api/sql/Raster-visualizer.md
+++ b/docs/api/sql/Raster-visualizer.md
@@ -78,9 +78,14 @@ Example:
 
 ```python
 from sedona.raster_utils.SedonaUtils import SedonaUtils
+
 # Or from sedona.spark import *
 
-df = sedona.read.format('binaryFile').load(DATA_DIR + 'raster.tiff').selectExpr("RS_FromGeoTiff(content) as raster")
+df = (
+    sedona.read.format("binaryFile")
+    .load(DATA_DIR + "raster.tiff")
+    .selectExpr("RS_FromGeoTiff(content) as raster")
+)
 htmlDF = df.selectExpr("RS_AsImage(raster, 500) as raster_image")
 SedonaUtils.display_image(htmlDF)
 ```
diff --git a/docs/api/sql/Spider.md b/docs/api/sql/Spider.md
index f3870d5fc0..5dfa6569fe 100644
--- a/docs/api/sql/Spider.md
+++ b/docs/api/sql/Spider.md
@@ -24,9 +24,18 @@ Sedona offers a spatial data generator called Spider. It is a data source that g
 Once you have your [`SedonaContext` object created](../Overview#quick-start), you can create a DataFrame with the `spider` data source.
 
 ```python
-df_random_points = sedona.read.format("spider").load(n=1000, distribution='uniform')
-df_random_boxes = sedona.read.format("spider").load(n=1000, distribution='gaussian', geometryType='box', maxWidth=0.05, maxHeight=0.05)
-df_random_polygons = sedona.read.format("spider").load(n=1000, distribution='bit', geometryType='polygon', minSegment=3, maxSegment=5, maxSize=0.1)
+df_random_points = sedona.read.format("spider").load(n=1000, distribution="uniform")
+df_random_boxes = sedona.read.format("spider").load(
+    n=1000, distribution="gaussian", geometryType="box", maxWidth=0.05, maxHeight=0.05
+)
+df_random_polygons = sedona.read.format("spider").load(
+    n=1000,
+    distribution="bit",
+    geometryType="polygon",
+    minSegment=3,
+    maxSegment=5,
+    maxSize=0.1,
+)
 ```
 
 Now we have three DataFrames with random spatial data. We can show the first three rows of the `df_random_points` DataFrame to verify the data is generated correctly.
@@ -57,22 +66,24 @@ import matplotlib.pyplot as plt
 import geopandas as gpd
 
 # Convert DataFrames to GeoDataFrames
-gdf_random_points = gpd.GeoDataFrame(df_random_points.toPandas(), geometry='geometry')
-gdf_random_boxes = gpd.GeoDataFrame(df_random_boxes.toPandas(), geometry='geometry')
-gdf_random_polygons = gpd.GeoDataFrame(df_random_polygons.toPandas(), geometry='geometry')
+gdf_random_points = gpd.GeoDataFrame(df_random_points.toPandas(), geometry="geometry")
+gdf_random_boxes = gpd.GeoDataFrame(df_random_boxes.toPandas(), geometry="geometry")
+gdf_random_polygons = gpd.GeoDataFrame(
+    df_random_polygons.toPandas(), geometry="geometry"
+)
 
 # Create a figure and a set of subplots
 fig, axes = plt.subplots(1, 3, figsize=(15, 5))
 
 # Plot each GeoDataFrame on a different subplot
-gdf_random_points.plot(ax=axes[0], color='blue', markersize=5)
-axes[0].set_title('Random Points')
+gdf_random_points.plot(ax=axes[0], color="blue", markersize=5)
+axes[0].set_title("Random Points")
 
-gdf_random_boxes.boundary.plot(ax=axes[1], color='red')
-axes[1].set_title('Random Boxes')
+gdf_random_boxes.boundary.plot(ax=axes[1], color="red")
+axes[1].set_title("Random Boxes")
 
-gdf_random_polygons.boundary.plot(ax=axes[2], color='green')
-axes[2].set_title('Random Polygons')
+gdf_random_polygons.boundary.plot(ax=axes[2], color="green")
+axes[2].set_title("Random Polygons")
 
 # Adjust the layout
 plt.tight_layout()
@@ -122,8 +133,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=300, distribution='uniform', geometryType='box', maxWidth=0.05, maxHeight=0.05)
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').boundary.plot()
+
+df = sedona.read.format("spider").load(
+    n=300, distribution="uniform", geometryType="box", maxWidth=0.05, maxHeight=0.05
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").boundary.plot()
 ```
 
 ![Uniform Distribution](../../image/spider/spider-uniform.png)
@@ -145,8 +159,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=300, distribution='gaussian', geometryType='polygon', maxSize=0.05)
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').boundary.plot()
+
+df = sedona.read.format("spider").load(
+    n=300, distribution="gaussian", geometryType="polygon", maxSize=0.05
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").boundary.plot()
 ```
 
 ![Gaussian Distribution](../../image/spider/spider-gaussian.png)
@@ -170,8 +187,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=300, distribution='bit', geometryType='point', probability=0.2, digits=10)
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').plot(markersize=1)
+
+df = sedona.read.format("spider").load(
+    n=300, distribution="bit", geometryType="point", probability=0.2, digits=10
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").plot(markersize=1)
 ```
 
 ![Bit Distribution](../../image/spider/spider-bit.png)
@@ -195,8 +215,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=300, distribution='diagonal', geometryType='point', percentage=0.5, buffer=0.5)
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').plot(markersize=1)
+
+df = sedona.read.format("spider").load(
+    n=300, distribution="diagonal", geometryType="point", percentage=0.5, buffer=0.5
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").plot(markersize=1)
 ```
 
 ![Diagonal Distribution](../../image/spider/spider-diagonal.png)
@@ -218,8 +241,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=2000, distribution='sierpinski', geometryType='point')
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').plot(markersize=1)
+
+df = sedona.read.format("spider").load(
+    n=2000, distribution="sierpinski", geometryType="point"
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").plot(markersize=1)
 ```
 
 ![Sierpinski Distribution](../../image/spider/spider-sierpinski.png)
@@ -237,8 +263,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=300, distribution='parcel', dither=0.5, splitRange=0.5)
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').boundary.plot()
+
+df = sedona.read.format("spider").load(
+    n=300, distribution="parcel", dither=0.5, splitRange=0.5
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").boundary.plot()
 ```
 
 ![Parcel Distribution](../../image/spider/spider-parcel.png)
@@ -274,8 +303,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df_random_points = sedona.read.format("spider").load(n=1000, distribution='uniform', translateX=0.5, translateY=0.5, scaleX=2, scaleY=2)
-gpd.GeoDataFrame(df_random_points.toPandas(), geometry='geometry').plot(markersize=1)
+
+df_random_points = sedona.read.format("spider").load(
+    n=1000, distribution="uniform", translateX=0.5, translateY=0.5, scaleX=2, scaleY=2
+)
+gpd.GeoDataFrame(df_random_points.toPandas(), geometry="geometry").plot(markersize=1)
 ```
 
 The data is now in the region `[0.5, 2.5] x [0.5, 2.5]`.
diff --git a/docs/api/sql/Stac.md b/docs/api/sql/Stac.md
index 26ebd082b7..bf91d4c965 100644
--- a/docs/api/sql/Stac.md
+++ b/docs/api/sql/Stac.md
@@ -34,7 +34,9 @@ df.show()
 You can load a STAC collection from a s3 collection file object:
 
 ```python
-df = sedona.read.format("stac").load("s3a://example.com/stac_bucket/stac_collection.json")
+df = sedona.read.format("stac").load(
+    "s3a://example.com/stac_bucket/stac_collection.json"
+)
 df.printSchema()
 df.show()
 ```
@@ -42,7 +44,9 @@ df.show()
 You can also load a STAC collection from an HTTP/HTTPS endpoint:
 
 ```python
-df = sedona.read.format("stac").load("https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a")
+df = sedona.read.format("stac").load(
+    "https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a"
+)
 df.printSchema()
 df.show()
 ```
@@ -225,9 +229,7 @@ client = Client.open("https://planetarycomputer.microsoft.com/api/stac/v1")
 
 ```python
 items = client.search(
-    collection_id="aster-l1t",
-    datetime="2020",
-    return_dataframe=False
+    collection_id="aster-l1t", datetime="2020", return_dataframe=False
 )
 ```
 
@@ -235,10 +237,7 @@ items = client.search(
 
 ```python
 items = client.search(
-    collection_id="aster-l1t",
-    datetime="2020-05",
-    return_dataframe=False,
-    max_items=5
+    collection_id="aster-l1t", datetime="2020-05", return_dataframe=False, max_items=5
 )
 ```
 
@@ -250,22 +249,15 @@ items = client.search(
     ids=["AST_L1T_00312272006020322_20150518201805"],
     bbox=[-180.0, -90.0, 180.0, 90.0],
     datetime=["2006-01-01T00:00:00Z", "2007-01-01T00:00:00Z"],
-    return_dataframe=False
+    return_dataframe=False,
 )
 ```
 
 ### Search Multiple Items with Multiple Bounding Boxes
 
 ```python
-bbox_list = [
-    [-180.0, -90.0, 180.0, 90.0],
-    [-100.0, -50.0, 100.0, 50.0]
-]
-items = client.search(
-    collection_id="aster-l1t",
-    bbox=bbox_list,
-    return_dataframe=False
-)
+bbox_list = [[-180.0, -90.0, 180.0, 90.0], [-100.0, -50.0, 100.0, 50.0]]
+items = client.search(collection_id="aster-l1t", bbox=bbox_list, return_dataframe=False)
 ```
 
 ### Search Items and Get DataFrame as Return with Multiple Intervals
@@ -273,12 +265,10 @@ items = client.search(
 ```python
 interval_list = [
     ["2020-01-01T00:00:00Z", "2020-06-01T00:00:00Z"],
-    ["2020-07-01T00:00:00Z", "2021-01-01T00:00:00Z"]
+    ["2020-07-01T00:00:00Z", "2021-01-01T00:00:00Z"],
 ]
 df = client.search(
-    collection_id="aster-l1t",
-    datetime=interval_list,
-    return_dataframe=True
+    collection_id="aster-l1t", datetime=interval_list, return_dataframe=True
 )
 df.show()
 ```
@@ -288,9 +278,7 @@ df.show()
 ```python
 # Save items in DataFrame to GeoParquet with both bounding boxes and intervals
 client.get_collection("aster-l1t").save_to_geoparquet(
-    output_path="/path/to/output",
-    bbox=bbox_list,
-    datetime="2020-05"
+    output_path="/path/to/output", bbox=bbox_list, datetime="2020-05"
 )
 ```
 
diff --git a/docs/setup/azure-synapse-analytics.md b/docs/setup/azure-synapse-analytics.md
index 1317dc3aeb..1fb268b9f5 100644
--- a/docs/setup/azure-synapse-analytics.md
+++ b/docs/setup/azure-synapse-analytics.md
@@ -75,14 +75,23 @@ Start your notebook with:
 ```python
 from sedona.spark import SedonaContext
 
-config = SedonaContext.builder() \
-    .config('spark.jars.packages',
-            'org.apache.sedona:sedona-spark-shaded-3.4_2.12-1.6.1,'
-            'org.datasyslab:geotools-wrapper-1.6.1-28.2') \
-    .config("spark.serializer","org.apache.spark.serializer.KryoSerializer") \
-    .config("spark.kryo.registrator", "org.apache.sedona.core.serde.SedonaKryoRegistrator") \
-    .config("spark.sql.extensions", "org.apache.sedona.viz.sql.SedonaVizExtensions,org.apache.sedona.sql.SedonaSqlExtensions") \
+config = (
+    SedonaContext.builder()
+    .config(
+        "spark.jars.packages",
+        "org.apache.sedona:sedona-spark-shaded-3.4_2.12-1.6.1,"
+        "org.datasyslab:geotools-wrapper-1.6.1-28.2",
+    )
+    .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+    .config(
+        "spark.kryo.registrator", "org.apache.sedona.core.serde.SedonaKryoRegistrator"
+    )
+    .config(
+        "spark.sql.extensions",
+        "org.apache.sedona.viz.sql.SedonaVizExtensions,org.apache.sedona.sql.SedonaSqlExtensions",
+    )
     .getOrCreate()
+)
 
 sedona = SedonaContext.create(config)
 ```
diff --git a/docs/setup/databricks.md b/docs/setup/databricks.md
index 434a43f012..0a9e7cda9e 100644
--- a/docs/setup/databricks.md
+++ b/docs/setup/databricks.md
@@ -56,6 +56,7 @@ SedonaSQLRegistrator.registerAll(spark)
 
 ```python
 from sedona.register.geo_registrator import SedonaRegistrator
+
 SedonaRegistrator.registerAll(spark)
 ```
 
diff --git a/docs/setup/install-python.md b/docs/setup/install-python.md
index c2f66e4624..5d65f82ba7 100644
--- a/docs/setup/install-python.md
+++ b/docs/setup/install-python.md
@@ -64,12 +64,20 @@ You can get it using one of the following methods:
 
 ```python
 from sedona.spark import *
-config = SedonaContext.builder(). \
-    config('spark.jars.packages',
-           'org.apache.sedona:sedona-spark-3.3_2.12:{{ sedona.current_version }},'
-           'org.datasyslab:geotools-wrapper:{{ sedona.current_geotools }}'). \
-    config('spark.jars.repositories', 'https://artifacts.unidata.ucar.edu/repository/unidata-all'). \
-    getOrCreate()
+
+config = (
+    SedonaContext.builder()
+    .config(
+        "spark.jars.packages",
+        "org.apache.sedona:sedona-spark-3.3_2.12:{{ sedona.current_version }},"
+        "org.datasyslab:geotools-wrapper:{{ sedona.current_geotools }}",
+    )
+    .config(
+        "spark.jars.repositories",
+        "https://artifacts.unidata.ucar.edu/repository/unidata-all",
+    )
+    .getOrCreate()
+)
 sedona = SedonaContext.create(config)
 ```
 
@@ -81,15 +89,18 @@ SedonaRegistrator is deprecated in Sedona 1.4.1 and later versions. Please use t
 from pyspark.sql import SparkSession
 from sedona.register import SedonaRegistrator
 from sedona.utils import SedonaKryoRegistrator, KryoSerializer
-spark = SparkSession. \
-    builder. \
-    appName('appName'). \
-    config("spark.serializer", KryoSerializer.getName). \
-    config("spark.kryo.registrator", SedonaKryoRegistrator.getName). \
-    config('spark.jars.packages',
-           'org.apache.sedona:sedona-spark-shaded-3.3_2.12:{{ sedona.current_version }},'
-           'org.datasyslab:geotools-wrapper:{{ sedona.current_geotools }}'). \
-    getOrCreate()
+
+spark = (
+    SparkSession.builder.appName("appName")
+    .config("spark.serializer", KryoSerializer.getName)
+    .config("spark.kryo.registrator", SedonaKryoRegistrator.getName)
+    .config(
+        "spark.jars.packages",
+        "org.apache.sedona:sedona-spark-shaded-3.3_2.12:{{ sedona.current_version }},"
+        "org.datasyslab:geotools-wrapper:{{ sedona.current_geotools }}",
+    )
+    .getOrCreate()
+)
 SedonaRegistrator.registerAll(spark)
 ```
 
diff --git a/docs/setup/release-notes.md b/docs/setup/release-notes.md
index 2931e1ce61..7a5666846a 100644
--- a/docs/setup/release-notes.md
+++ b/docs/setup/release-notes.md
@@ -392,8 +392,10 @@ Sedona 1.6.0 is compiled against Spark 3.3 / Spark 3.4 / Spark 3.5, Flink 1.19,
 ```python
 from pyspark.sql.types import DoubleType
 
+
 def mean_udf(raster):
-	return float(raster.as_numpy().mean())
+    return float(raster.as_numpy().mean())
+
 
 sedona.udf.register("mean_udf", mean_udf, DoubleType())
 df_raster.withColumn("mean", expr("mean_udf(rast)")).show()
@@ -1064,11 +1066,15 @@ Sedona 1.4.1 is compiled against Spark 3.3 / Spark 3.4 / Flink 1.12, Java 8.
         ```python
         from sedona.spark import *
 
-        config = SedonaContext.builder().\
-           config('spark.jars.packages',
-               'org.apache.sedona:sedona-spark-shaded-3.3_2.12:1.4.1,'
-               'org.datasyslab:geotools-wrapper:1.4.0-28.2'). \
-           getOrCreate()
+        config = (
+            SedonaContext.builder()
+            .config(
+                "spark.jars.packages",
+                "org.apache.sedona:sedona-spark-shaded-3.3_2.12:1.4.1,"
+                "org.datasyslab:geotools-wrapper:1.4.0-28.2",
+            )
+            .getOrCreate()
+        )
         sedona = SedonaContext.create(config)
         sedona.sql("SELECT ST_GeomFromWKT(XXX) FROM")
         ```
diff --git a/docs/tutorial/concepts/clustering-algorithms.md b/docs/tutorial/concepts/clustering-algorithms.md
index 830b0667ee..79cb72cd8f 100644
--- a/docs/tutorial/concepts/clustering-algorithms.md
+++ b/docs/tutorial/concepts/clustering-algorithms.md
@@ -50,21 +50,24 @@ Let’s create a Spark DataFrame with this data and then run the clustering with
 
 ```python
 df = (
-    sedona.createDataFrame([
-        (1, 8.0, 2.0),
-        (2, 2.6, 4.0),
-        (3, 2.5, 4.0),
-        (4, 8.5, 2.5),
-        (5, 2.8, 4.3),
-        (6, 12.8, 4.5),
-        (7, 2.5, 4.2),
-        (8, 8.2, 2.5),
-        (9, 8.0, 3.0),
-        (10, 1.0, 5.0),
-        (11, 8.0, 2.5),
-        (12, 5.0, 6.0),
-        (13, 4.0, 3.0),
-    ], ["id", "x", "y"])
+    sedona.createDataFrame(
+        [
+            (1, 8.0, 2.0),
+            (2, 2.6, 4.0),
+            (3, 2.5, 4.0),
+            (4, 8.5, 2.5),
+            (5, 2.8, 4.3),
+            (6, 12.8, 4.5),
+            (7, 2.5, 4.2),
+            (8, 8.2, 2.5),
+            (9, 8.0, 3.0),
+            (10, 1.0, 5.0),
+            (11, 8.0, 2.5),
+            (12, 5.0, 6.0),
+            (13, 4.0, 3.0),
+        ],
+        ["id", "x", "y"],
+    )
 ).withColumn("point", ST_Point(col("x"), col("y")))
 ```
 
diff --git a/docs/tutorial/raster.md b/docs/tutorial/raster.md
index 1ac0908718..641b441428 100644
--- a/docs/tutorial/raster.md
+++ b/docs/tutorial/raster.md
@@ -279,7 +279,12 @@ For multiple raster data files use the following code to load the data [from pat
 
 === "Python"
     ```python
-    rawDf = sedona.read.format("binaryFile").option("recursiveFileLookup", "true").option("pathGlobFilter", "*.tif*").load(path_to_raster_data_folder)
+    rawDf = (
+        sedona.read.format("binaryFile")
+        .option("recursiveFileLookup", "true")
+        .option("pathGlobFilter", "*.tif*")
+        .load(path_to_raster_data_folder)
+    )
     rawDf.createOrReplaceTempView("rawdf")
     rawDf.show()
     ```
@@ -608,7 +613,11 @@ Sedona allows collecting Dataframes with raster columns and working with them lo
 The raster objects are represented as `SedonaRaster` objects in Python, which can be used to perform raster operations.
 
 ```python
-df_raster = sedona.read.format("binaryFile").load("/path/to/raster.tif").selectExpr("RS_FromGeoTiff(content) as rast")
+df_raster = (
+    sedona.read.format("binaryFile")
+    .load("/path/to/raster.tif")
+    .selectExpr("RS_FromGeoTiff(content) as rast")
+)
 rows = df_raster.collect()
 raster = rows[0].rast
 raster  # <sedona.raster.sedona_raster.InDbSedonaRaster at 0x1618fb1f0>
@@ -617,18 +626,18 @@ raster  # <sedona.raster.sedona_raster.InDbSedonaRaster at 0x1618fb1f0>
 You can retrieve the metadata of the raster by accessing the properties of the `SedonaRaster` object.
 
 ```python
-raster.width        # width of the raster
-raster.height       # height of the raster
-raster.affine_trans # affine transformation matrix
-raster.crs_wkt      # coordinate reference system as WKT
+raster.width  # width of the raster
+raster.height  # height of the raster
+raster.affine_trans  # affine transformation matrix
+raster.crs_wkt  # coordinate reference system as WKT
 ```
 
 You can get a numpy array containing the band data of the raster using the `as_numpy` or `as_numpy_masked` method. The
 band data is organized in CHW order.
 
 ```python
-raster.as_numpy()        # numpy array of the raster
-raster.as_numpy_masked() # numpy array with nodata values masked as nan
+raster.as_numpy()  # numpy array of the raster
+raster.as_numpy_masked()  # numpy array with nodata values masked as nan
 ```
 
 If you want to work with the raster data using `rasterio`, you can retrieve a `rasterio.DatasetReader` object using the
@@ -640,7 +649,7 @@ If you want to work with the raster data using `rasterio`, you can retrieve a `r
 ```python
 ds = raster.as_rasterio()  # rasterio.DatasetReader object
 # Work with the raster using rasterio
-band1 = ds.read(1)         # read the first band
+band1 = ds.read(1)  # read the first band
 ```
 
 ## Writing Python UDF to work with raster data
@@ -651,9 +660,11 @@ return any Spark data type as output. This is an example of a Python UDF that ca
 ```python
 from pyspark.sql.types import DoubleType
 
+
 def mean_udf(raster):
     return float(raster.as_numpy().mean())
 
+
 sedona.udf.register("mean_udf", mean_udf, DoubleType())
 df_raster.withColumn("mean", expr("mean_udf(rast)")).show()
 ```
@@ -674,13 +685,17 @@ objects yet. However, you can write a UDF that returns the band data as an array
 from pyspark.sql.types import ArrayType, DoubleType
 import numpy as np
 
+
 def mask_udf(raster):
-    band1 = raster.as_numpy()[0,:,:]
+    band1 = raster.as_numpy()[0, :, :]
     mask = (band1 < 1400).astype(np.float64)
     return mask.flatten().tolist()
 
+
 sedona.udf.register("mask_udf", band_udf, ArrayType(DoubleType()))
-df_raster.withColumn("mask", expr("mask_udf(rast)")).withColumn("mask_rast", expr("RS_MakeRaster(rast, 'I', mask)")).show()
+df_raster.withColumn("mask", expr("mask_udf(rast)")).withColumn(
+    "mask_rast", expr("RS_MakeRaster(rast, 'I', mask)")
+).show()
 ```
 
 ```