fix issues with number precision when showing values in sql table

apps/api/src/python/query/bigquery.ts

@@ -180,6 +180,13 @@ def _briefer_make_bq_query():
                 df = pd.concat(chunks, ignore_index=True)
                 convert_columns(df, columns_by_type)
                 initial_rows = json.loads(df.head(250).to_json(orient='records', date_format="iso"))
+
+                # convert all values to string to make sure we preserve the python values
+                # when displaying this data in the browser
+                for row in initial_rows:
+                    for key in row:
+                        row[key] = str(row[key])
+
                 if columns is None:
                     columns = get_columns(df)
 
@@ -209,6 +216,12 @@ def _briefer_make_bq_query():
         if len(initial_rows) < 250:
             initial_rows = json.loads(df.head(250).to_json(orient='records', date_format="iso"))
 
+            # convert all values to string to make sure we preserve the python values
+            # when displaying this data in the browser
+            for row in initial_rows:
+                for key in row:
+                    row[key] = str(row[key])
+
         columns = get_columns(df)
         result = {
             "type": "success",

apps/api/src/python/query/duckdb.ts

@@ -52,6 +52,13 @@ def _briefer_make_duckdb_query():
 
         df = query.df()
         rows = json.loads(df.head(250).to_json(orient='records', date_format='iso'))
+
+        # convert all values to string to make sure we preserve the python values
+        # when displaying this data in the browser
+        for row in rows:
+            for key in row:
+                row[key] = str(row[key])
+
         columns = [{"name": col, "type": dtype.name} for col, dtype in df.dtypes.items()]
         for col in columns:
             dtype = df[col["name"]].dtype

apps/api/src/python/query/index.ts

@@ -337,26 +337,33 @@ export async function readDataframePage(
   const code = `import json
 
 if not ("${dataframeName}" in globals()):
-  import pandas as pd
-  try:
-    ${dataframeName} = pd.read_parquet("/home/jupyteruser/.briefer/query-${queryId}.parquet.gzip")
-  except:
-    print(json.dumps({"type": "not-found"}))
+    import pandas as pd
+    try:
+      ${dataframeName} = pd.read_parquet("/home/jupyteruser/.briefer/query-${queryId}.parquet.gzip")
+    except:
+      print(json.dumps({"type": "not-found"}))
 
 if "${dataframeName}" in globals():
-  start = ${page * pageSize}
-  end = (${page} + 1) * ${pageSize}
-  rows = ${dataframeName}.iloc[start:end].to_json(
-    orient="records", date_format="iso"
-  )
-  columns = [{"name": col, "type": dtype.name} for col, dtype in ${dataframeName}.dtypes.items()]
-  result = {
-    "type": "success",
-    "rows": json.loads(rows),
-    "count": len(${dataframeName}),
-    "columns": columns
-  }
-  print(json.dumps(result))`
+    start = ${page * pageSize}
+    end = (${page} + 1) * ${pageSize}
+    rows = json.loads(${dataframeName}.iloc[start:end].to_json(
+      orient="records", date_format="iso"
+    ))
+
+    # convert all values to string to make sure we preserve the python values
+    # when displaying this data in the browser
+    for row in rows:
+        for key in row:
+            row[key] = str(row[key])
+
+    columns = [{"name": col, "type": dtype.name} for col, dtype in ${dataframeName}.dtypes.items()]
+    result = {
+      "type": "success",
+      "rows": rows,
+      "count": len(${dataframeName}),
+      "columns": columns
+    }
+    print(json.dumps(result))`
 
   let result: RunQueryResult | null = null
   let error: Error | null = null

apps/api/src/python/query/sqlalchemy.ts

@@ -159,6 +159,13 @@ def briefer_make_sqlalchemy_query():
                 df = convert_df(pd.concat([df, chunk], ignore_index=True))
                 if rows is None:
                     rows = json.loads(df.head(250).to_json(orient='records', date_format="iso"))
+
+                    # convert all values to string to make sure we preserve the python values
+                    # when displaying this data in the browser
+                    for row in rows:
+                        for key in row:
+                            row[key] = str(row[key])
+
                 if columns is None:
                     columns = [{"name": col, "type": dtype.name} for col, dtype in chunk.dtypes.items()]