Readme

README.md

@@ -2,7 +2,7 @@
 
 ### Why Dataframe
 
-Dataframe removes the complexity of handling `SQL parsing`, `SQL rewriting`, `Binding`, `SQL Query Planner` etc. Once
+Dataframe removes the complexity of handling `SQL parsing`, `SQL rewriting`, `Binder/SQL Query Planner` etc. Once
 the dataframe is mature, we can easily integrate it with an SQL engine.
 
 ### Features
@@ -11,7 +11,7 @@ the dataframe is mature, we can easily integrate it with an SQL engine.
 - Support `Parquet` reading with schema inference
 - `Rule Based` Optimizer
 - `AggFunc`: Sum
-- `BooleanBinaryExpr`: Eq
+- `BooleanBinaryExpr`: Lt
 
 ### Example
 

cmd/simple/README.md

@@ -0,0 +1,34 @@
+### Data Generation
+
+```python
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+# Creating a DataFrame with specified data
+df = pd.DataFrame({
+    'c1': [100, 100, 100, 200,200, 300],
+    'c2': [101, 201, 301, 401, 501, 601],
+    'c3': [102, 202, 302, 402, 502, 602]
+}, dtype='int64') 
+
+# Convert the DataFrame to a PyArrow Table
+table = pa.Table.from_pandas(df)
+
+# Save the table as a Parquet file
+pq.write_table(table, 'c1_c2_c3_int64.parquet')
+```
+### Data Reading
+
+```python
+import pyarrow.parquet as pq
+
+# Read the Parquet file
+table_read = pq.read_table('sample_int32.parquet')
+
+# Convert to a pandas DataFrame
+df_read = table_read.to_pandas()
+
+# Display the DataFrame
+print(df_read.head())
+```