[Python] Pandas df to Apache iceberg table

I'm trying to insert into a Iceberg table, but I'm getting issues due to the data types mismatching. I've pasted a part of my code. The error message I'm getting is : "errorMessage": "Schema change detected: {'new_columns': {}, 'modified_columns': {'low': 'decimal(13,10)', 'close': 'decimal(13,10)', 'open': 'decimal(13,10).'.....

import json
import awswrangler as wr
import pandas as pd
from decimal import Decimal, getcontext

getcontext().prec = 30
df['open'] = df['open'].apply(lambda x: Decimal(x).quantize(Decimal('0.0000000000')))
df['high'] = df['high'].apply(lambda x: Decimal(x).quantize(Decimal('0.0000000000')))
df['low'] = df['low'].apply(lambda x: Decimal(x).quantize(Decimal('0.0000000000')))
df['close'] = df['close'].apply(lambda x: Decimal(x).quantize(Decimal('0.0000000000')))
df['adjustedclose'] = df['adjustedclose'].apply(lambda x: Decimal(x).quantize(Decimal('0.0000000000')))

#print(df.columns)
print(df.dtypes)

#print(df.head(10))

wr.athena.to_iceberg(
df=df,
database='market_test',
table='price',
table_location='s3://blah',
temp_path='s3://blah-temp',
data_source = 'AwsDataCatalog'
)


Error message

{
"errorMessage": "Schema change detected: {'new_columns': {}, 'modified_columns': {'open': 'decimal(13,10)', 'high': 'decimal(13,10)', 'adjustedclose': 'decimal(13,10)', 'close': 'decimal(13,10)', 'low': 'decimal(13,10)', 'volume': 'bigint'}, 'missing_columns': {}}",
"errorType": "InvalidArgumentValue",
"requestId": "[]",
"stackTrace": [
" File \"/var/task/lambda_function.py\", line 40, in lambda_handler\n wr.athena.to_iceberg(\n",
" File \"/opt/python/awswrangler/_config.py\", line 715, in wrapper\n return function(**args)\n",
" File \"/opt/python/awswrangler/_utils.py\", line 178, in inner\n return func(*args, **kwargs)\n",
" File \"/opt/python/awswrangler/athena/_write_iceberg.py\", line 434, in to_iceberg\n raise exceptions.InvalidArgumentValue(f\"Schema change detected: {schema_differences}\")\n"
]
}```

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