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feat: example to append pandas df to SystemLink Data Frame Service #196

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Merged
santhoshramaraj merged 3 commits into from
Feb 2, 2026
Merged

feat: example to append pandas df to SystemLink Data Frame Service #196

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9e63a18
feat: add example to append pandas df to DFS
santhoshramaraj Feb 2, 2026
dd444d6
refactor: use the constructor method to covert Pandas to PyArrow
santhoshramaraj Feb 2, 2026
7a004d4
refactor: PR comments
santhoshramaraj Feb 2, 2026
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44 changes: 38 additions & 6 deletions examples/dataframe/create_write_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,10 @@
import pyarrow as pa # type: ignore
except Exception:
pa = None
try:
import pandas as pd # type: ignore
except Exception:
pd = None
from nisystemlink.clients.core import HttpConfiguration
from nisystemlink.clients.dataframe import DataFrameClient
from nisystemlink.clients.dataframe.models import (
Expand Down Expand Up @@ -40,6 +44,11 @@
)
)


print(f"Created table with ID: {table_id}")

print("Appending data to table...")

# Append via explicit AppendTableDataRequest (JSON)
frame_request = DataFrame(
data=[[str(i), str(random.random()), datetime.now().isoformat()] for i in range(3)]
Expand All @@ -55,6 +64,7 @@
client.append_table_data(table_id, frame_direct)

if pa is not None:
print("Appending data to table via Arrow RecordBatches...")
# Append via single RecordBatch (Arrow)
batch_single = pa.record_batch(
[
Expand All @@ -79,9 +89,31 @@
]
client.append_table_data(table_id, batch_list)

# Mark end_of_data for the table
# Supply `None` and `end_of_data=True`
client.append_table_data(table_id, None, end_of_data=True)
else:
# If pyarrow not installed, flush via JSON path
client.append_table_data(table_id, None, end_of_data=True)
if pa is not None and pd is not None:
print("Appending data to table via Pandas DataFrame...")
# Append via DataFrame (Pandas)
df = pd.DataFrame(
{
"ix": [11, 12, 13],
"Float_Column": [0.6, 0.7, 0.8],
"Timestamp_Column": [datetime.now() for _ in range(3)],
}
)

# Optional - coerce df types to the dataframe table schema
df = df.astype(
{
"ix": "Int32",
"Float_Column": "float32",
"Timestamp_Column": "datetime64[ns]",
}
)

# convert Pandas DataFrame to Arrow RecordBatch and append
record_batch = pa.RecordBatch.from_pandas(df)
client.append_table_data(table_id, record_batch)

# Mark end_of_data for the table
# Supply `None` and `end_of_data=True`
print("Finished appending data.")
client.append_table_data(table_id, None, end_of_data=True)