Add client.dataframe namespace for pandas DataFrame CRUD operations by zhaodongwang-msft · Pull Request #98 · microsoft/PowerPlatform-DataverseClient-Python

zhaodongwang-msft · 2026-02-11T18:32:10Z

Summary

Adds a client.dataframe namespace with pandas DataFrame/Series wrappers for all CRUD operations, plus two advanced example scripts, and a minor SDK enhancement for table metadata. Users can now query, create, update, and delete Dataverse records using DataFrame-native inputs and outputs -- no manual dict conversion required.

Quick Example

import pandas as pd
from azure.identity import InteractiveBrowserCredential
from PowerPlatform.Dataverse.client import DataverseClient

credential = InteractiveBrowserCredential()

with DataverseClient("https://yourorg.crm.dynamics.com", credential) as client:

    # Query records as a DataFrame (all pages consolidated automatically)
    df = client.dataframe.get("account", select=["name", "telephone1"], top=5)

    # Create records from a DataFrame (returns Series of GUIDs)
    new_records = pd.DataFrame([
        {"name": "Acme Corp", "telephone1": "555-9000"},
        {"name": "Globex Inc", "telephone1": "555-9001"},
    ])
    new_records["accountid"] = client.dataframe.create("account", new_records)

    # Update records (NaN/None skipped by default; use clear_nulls=True to clear fields)
    new_records["telephone1"] = ["555-1111", "555-2222"]
    client.dataframe.update("account", new_records[["accountid", "telephone1"]], id_column="accountid")

    # Delete records
    client.dataframe.delete("account", new_records["accountid"])

Changes

DataFrame CRUD (`client.dataframe` namespace)

File	Description
`src/.../operations/dataframe.py`	`DataFrameOperations` class: `get()`, `create()`, `update()`, `delete()`
`src/.../utils/_pandas.py`	`dataframe_to_records()` helper -- normalizes NumPy, datetime, NaN/None
`client.py`	Added `self.dataframe = DataFrameOperations(self)`
`pyproject.toml`	Added `pandas>=2.0.0` required dependency
`README.md`	DataFrame usage examples
`operations/__init__.py`	Cleanup (`__all__ = []`)

SDK Enhancement: TableInfo primary column metadata (fixes #148)

File	Description
`src/.../data/_odata.py`	`_get_entity_by_table_schema_name()` and `_get_table_info()` now select `PrimaryNameAttribute` and `PrimaryIdAttribute` from EntityDefinitions
`src/.../models/table_info.py`	`TableInfo` includes `primary_name_attribute` and `primary_id_attribute` fields
`tests/unit/models/test_table_info.py`	Tests for new fields in `from_dict`, `from_api_response`, and legacy key access

Advanced Examples

File	Description
`examples/advanced/dataframe_operations.py`	DataFrame CRUD walkthrough
`examples/advanced/prodev_quick_start.py`	Pro-dev: 4-table system with relationships, DataFrame CRUD, query/analyze. Uses `result.primary_name_attribute` from `tables.create()`
`examples/advanced/datascience_risk_assessment.py`	Data science: 5-step risk pipeline with 3 LLM provider options (Azure AI Inference, OpenAI, GitHub Copilot SDK), matplotlib charts

Test Files

File	Tests
`test_dataframe_operations.py`	44
`test_client_dataframe.py`	26
`test_pandas_helpers.py`	33
`test_table_info.py`	+1 (primary fields)

API Design

Method	Input	Output	Underlying API
`get(table, ...)`	OData params	`pd.DataFrame`	`records.get()`
`get(table, record_id=...)`	GUID	1-row `pd.DataFrame`	`records.get()`
`create(table, df)`	`pd.DataFrame`	`pd.Series` of GUIDs	`CreateMultiple`
`update(table, df, id_column)`	`pd.DataFrame`	`None`	`UpdateMultiple`
`delete(table, ids)`	`pd.Series`	`Optional[str]`	`BulkDelete`

Design Decisions

clear_nulls: Default False skips NaN (field unchanged). True sends null to clear.
Type normalization: np.int64/float64/bool_/ndarray, datetime/date/np.datetime64, pd.Timestamp -- all auto-converted.
ID validation: Strip whitespace, report DataFrame index labels in errors.
pandas required: Core dependency by team decision.

Test Results

396 passed, 8 warnings (pre-existing deprecation), 4 subtests passed

Check	Result
Full test suite	396 pass, 0 fail
mypy	0 errors
black / isort	Clean
E2E prodev	PASS (4 tables, 3 relationships, 13 records, full CRUD cycle)
E2E datascience	PASS (7 accounts, 3 cases, 8 opportunities, risk scoring, charts)
PR review threads	54/54 resolved

Issues Addressed

Fixes tables.create() does not expose primary column logical name, causing CRUD payload confusion #148: tables.create() now exposes primary_name_attribute via Dataverse metadata
Followup Add QueryBuilder.to_dataframe() for fluent DataFrame queries #147: QueryBuilder.to_dataframe() tracked for future work

Copilot

Pull request overview

Adds pandas DataFrame/Series wrappers to the Dataverse Python SDK so callers can perform CRUD operations using DataFrame-native inputs/outputs, plus accompanying docs, examples, and tests.

Changes:

Added DataverseClient DataFrame CRUD wrapper methods: get_dataframe, create_dataframe, update_dataframe, delete_dataframe.
Added unit tests and end-to-end example demonstrating DataFrame CRUD workflows.
Updated docs/README and added pandas as a dependency.

Reviewed changes

Copilot reviewed 7 out of 8 changed files in this pull request and generated 8 comments.

Show a summary per file

File	Description
`src/PowerPlatform/Dataverse/client.py`	Implements DataFrame CRUD wrapper methods on `DataverseClient`.
`tests/unit/test_client_dataframe.py`	Adds unit coverage for DataFrame CRUD wrappers.
`examples/advanced/dataframe_operations.py`	Adds a walkthrough script showing DataFrame CRUD usage.
`pyproject.toml`	Adds `pandas` to project dependencies.
`README.md`	Documents DataFrame CRUD usage examples.
`src/PowerPlatform/Dataverse/claude_skill/dataverse-sdk-use/SKILL.md`	Documents DataFrame CRUD usage in the packaged skill doc.
`.claude/skills/dataverse-sdk-use/SKILL.md`	Documents DataFrame CRUD usage in the repo-local skill doc.
`.gitignore`	Ignores additional Claude local markdown files.

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src/PowerPlatform/Dataverse/client.py

pyproject.toml

examples/advanced/dataframe_operations.py

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src/PowerPlatform/Dataverse/utils/_pandas.py

src/PowerPlatform/Dataverse/client.py

README.md

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src/PowerPlatform/Dataverse/client.py

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src/PowerPlatform/Dataverse/utils/_pandas.py

src/PowerPlatform/Dataverse/client.py

src/PowerPlatform/Dataverse/utils/_pandas.py

tests/unit/test_client_dataframe.py

src/PowerPlatform/Dataverse/utils/_pandas.py

src/PowerPlatform/Dataverse/client.py

examples/advanced/dataframe_operations.py

Copilot · 2026-02-12T02:36:58Z

@zhaodongwang-msft I've opened a new pull request, #99, to work on those changes. Once the pull request is ready, I'll request review from you.

Copilot · 2026-02-12T02:37:10Z

@zhaodongwang-msft I've opened a new pull request, #100, to work on those changes. Once the pull request is ready, I'll request review from you.

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src/PowerPlatform/Dataverse/utils/_pandas.py

tests/unit/test_client_dataframe.py

src/PowerPlatform/Dataverse/client.py

src/PowerPlatform/Dataverse/utils/_pandas.py

src/PowerPlatform/Dataverse/client.py

tpellissier-msft · 2026-02-17T21:35:12Z

We'll want to track 2 followups from this that are dependent on other refactor PRs so we can't do quite yet:

Moving the new client methods to a client.dataframe namespace
adding QueryBuilder.to_dataframe to connect the dataframe scenario to the fluent syntax / querybuilder changes.

…lete input validation, export DataFrameOperations (#145) Addresses four unresolved review comments from PR #98 against the `client.dataframe` namespace: a crash on array-valued cells, silent NumPy serialization failures, missing ID validation in `update()` and `delete()`, and missing exports/tests. ## `utils/_pandas.py` - **Fix `pd.notna()` crash on array-like cells**: Guard with `pd.api.types.is_scalar(v)` before calling `pd.notna()`; non-scalar values (lists, dicts, numpy arrays) pass through directly. Previously raised `ValueError: The truth value of an array is ambiguous`. - **Normalize NumPy scalar types**: New `_normalize_scalar(v)` helper converts `np.integer` → `int`, `np.floating` → `float`, `np.bool_` → `bool`, `pd.Timestamp` → ISO string. DataFrames with integer columns produce `np.int64` by default, which `json.dumps()` cannot serialize. ```python # Before: would crash or produce non-serializable values df = pd.DataFrame([{"tags": ["a", "b"]}, {"count": np.int64(5)}]) dataframe_to_records(df) # ValueError / TypeError at serialization time # After: safe [{"tags": ["a", "b"]}, {"count": 5}] ``` ## `operations/dataframe.py` - **`update()` — validate `id_column` values**: After extracting IDs, raises `ValueError` listing offending row indices if any value is not a non-empty string (catches `NaN`, `None`, numeric IDs). - **`update()` — validate non-empty change columns**: Raises `ValueError` if the DataFrame contains only the `id_column` and no fields to update. - **`delete()` — validate `ids` Series**: Returns `None` immediately for an empty Series; raises `ValueError` listing offending indices for any non-string or blank value. ## `operations/__init__.py` - Exports `DataFrameOperations` so consumers can use it for type annotations. ## Tests - `tests/unit/test_pandas_helpers.py` — 11 isolated tests for `dataframe_to_records()` covering NaN handling, NumPy type normalization, Timestamp conversion, list/dict passthrough, and empty input. - `tests/unit/test_dataframe_operations.py` — 35 tests covering the full `DataFrameOperations` namespace, including all new validation paths.  <details> <summary>Original prompt</summary> ## Context This PR addresses unresolved review comments from PR #98 ("add dataframe methods") and adds comprehensive test coverage for the DataFrame operations namespace (`client.dataframe`). The base branch is `users/zhaodongwang/dataFrameExtensionClaude` which contains the current state of the DataFrame operations code from PR #98. ## Files to modify ### 1. `src/PowerPlatform/Dataverse/utils/_pandas.py` Current code at the HEAD of the PR branch (`8838bb69533dd8830bac8724c44696771a6704e7`): ```python # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. """Internal pandas helpers""" from __future__ import annotations from typing import Any, Dict, List import pandas as pd def dataframe_to_records(df: pd.DataFrame, na_as_null: bool = False) -> List[Dict[str, Any]]: """Convert a DataFrame to a list of dicts, converting Timestamps to ISO strings. :param df: Input DataFrame. :param na_as_null: When False (default), missing values are omitted from each dict. When True, missing values are included as None (sends null to Dataverse, clearing the field). """ records = [] for row in df.to_dict(orient="records"): clean = {} for k, v in row.items(): if pd.notna(v): clean[k] = v.isoformat() if isinstance(v, pd.Timestamp) else v elif na_as_null: clean[k] = None records.append(clean) return records ``` **Required changes:** #### Fix A: `pd.notna()` crash on array-like values (unresolved comment #98 (comment)) `pd.notna(v)` raises `ValueError: The truth value of an array is ambiguous` when a cell contains a list, dict, numpy array, etc. Fix by guarding with `pd.api.types.is_scalar(v)`: ```python for k, v in row.items(): if pd.api.types.is_scalar(v): if pd.notna(v): clean[k] = _normalize_scalar(v) elif na_as_null: clean[k] = None else: clean[k] = v # pass through lists, dicts, etc. ``` #### Fix B: NumPy scalar types not normalized (acknowledged but deferred by author in #98 (comment)) NumPy scalars (`np.int64`, `np.float64`, `np.bool_`) are NOT JSON-serializable by default `json.dumps()`. DataFrames with integer columns produce `np.int64` values. Add a helper function `_normalize_scalar(v)` that: - Converts `pd.Timestamp` to `.isoformat()` - Converts `numpy.integer` types to Python `int` - Converts `numpy.floating` types to Python `float` - Converts `numpy.bool_` to Python `bool` - Passes everything else through Use `import numpy as np` and `isinstance` checks. ### 2. `src/PowerPlatform/Dataverse/operations/dataframe.py` Current code at the HEAD of the PR branch: ```python # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. """DataFrame CRUD operations namespace for the Dataverse SDK.""" from __future__ import annotations from typing import List, Optional, TYPE_CHECKING import pandas as pd from ..utils._pandas import dataframe_to_records if TYPE_CHECKING: from ..client import DataverseClient __all__ = ["DataFrameOperations"] class DataFrameOperations: """Namespace for pandas DataFrame CRUD operations. ... """ def __init__(self, client: DataverseClient) -> None: self._client = client def get(self, table, record_id=None, select=None, filter=None, orderby=None, top=None, expand=None, page_size=None) -> pd.DataFrame: # ... (current code) pass def create(self, table, records) -> pd.Series: # ... (current code with empty DataFrame check and ID count validation) pass def update(self, table, changes, id_column, clear_nulls=False) -> None: if not isinstance(changes, pd.DataFrame): raise TypeError("changes must be a pandas DataFrame") if id_column not in changes.columns: raise ValueError(f"id_column '{id_column}' not found in DataFrame columns") ids = changes[id_column].tolist() change_columns = [column for column in changes.columns if column != id_column] change_list = dataframe_to_records(changes[change_columns], na_as_null=clear_nulls) if len(ids) == 1: self._client.records.update(table, ids[0], change_list[0]) else: self._client.records.update(table, ids, change_list) def delete(self, table, ids, use_bulk_delete=True) -> Optional[str]: if not isinstance(ids, pd.Series): raise TypeError("ids must be a pandas Series") id_list = ids.tolist() if len(id_list) == 1: return self._client.records.delete(table, id_list[0]) else: return self._client.records.delete(table, id_list, use_bulk_delete=use_bulk_delete) ``` **Required changes:** #### Fix C: Validate `id_column` values in... </details>  *This pull request was created from Copilot chat.* >  --- ✨ Let Copilot coding agent [set things up for you](https://github.com/microsoft/PowerPlatform-DataverseClient-Python/issues/new?title=✨+Set+up+Copilot+instructions&body=Configure%20instructions%20for%20this%20repository%20as%20documented%20in%20%5BBest%20practices%20for%20Copilot%20coding%20agent%20in%20your%20repository%5D%28https://gh.io/copilot-coding-agent-tips%29%2E%0A%0A%3COnboard%20this%20repo%3E&assignees=copilot) — coding agent works faster and does higher quality work when set up for your repo. --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: saurabhrb <32964911+saurabhrb@users.noreply.github.com>

Adds a client.dataframe namespace with pandas DataFrame/Series wrappers for all CRUD operations (get, create, update, delete). Includes dataframe_to_records helper for NumPy/datetime type normalization, comprehensive test suite (103 tests across 3 files), basic CRUD example, and documentation updates. Key features: - get(): consolidates all pages into single DataFrame - create(): returns Series of GUIDs aligned with input index - update(): clear_nulls parameter controls NaN/None handling - delete(): supports bulk and sequential deletion - Type normalization: np.int64/float64/bool_/ndarray, datetime types, Timestamps - ID validation with whitespace stripping and index label error reporting - pandas>=2.0.0 added as required dependency

…ssment Pro-dev quick start (prodev_quick_start.py): - Create 4 custom tables with columns and relationships - Populate via DataFrame CRUD, query, analyze, update, delete, cleanup - Uses result.primary_name_attribute for correct column naming - Includes pandas vs dict/list rationale Data science risk assessment (datascience_risk_assessment.py): - 5-step pipeline: Extract -> Analyze -> LLM Summarize -> Write-back -> Report - Statistical risk scoring with pandas/numpy (groupby, merge, percentile) - 3 LLM provider options: Azure AI Inference, OpenAI, GitHub Copilot SDK - Template-based fallback when no LLM configured - LLM interaction logging (prompts, responses, timing) - Optional matplotlib visualization with chart export - CSV data exports to script-relative output folder

SDK changes: - _get_entity_by_table_schema_name selects PrimaryNameAttribute and PrimaryIdAttribute from EntityDefinitions - _create_table and _get_table_info return primary_name_attribute - TableInfo model includes primary_name_attribute and primary_id_attribute fields with legacy dict-key access - Tests for from_dict, from_api_response, and legacy key access This allows tables.create() and tables.get() callers to discover the actual primary column logical name instead of guessing from the prefix.

…rty names) @odata.bind keys are case-sensitive and the SDK preserves their casing (does not lowercase keys containing @OData.). The navigation property logical names must be lowercase to match Dataverse's schema. Changed: build @odata.bind keys using .lower() on the lookup field name (e.g., 'new_demoprojectf523b5_customerid@odata.bind' instead of 'new_DemoProjectf523b5_CustomerId@odata.bind').

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src/PowerPlatform/Dataverse/operations/dataframe.py

tests/unit/test_client_dataframe.py

examples/advanced/datascience_risk_assessment.py

…x Copilot SDK package name - get(): replaced per-page DataFrame concat with rows.extend() + from_records() for better performance on large paginated results - create(): detect and raise ValueError for rows with all NaN/None values (empty dicts) before sending to the API - Standardized Copilot SDK package name to 'github-copilot-sdk' everywhere

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src/PowerPlatform/Dataverse/operations/__init__.py

examples/advanced/datascience_risk_assessment.py

examples/advanced/prodev_quick_start.py

…y select comment - __init__.py: use typing.List[str] instead of list[str] for broader compat - datascience: precompute weighted_value column before groupby (faster, cleaner) - prodev: clarify that SDK auto-lowercases select values

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examples/advanced/prodev_quick_start.py

src/PowerPlatform/Dataverse/operations/dataframe.py

examples/advanced/datascience_risk_assessment.py

src/PowerPlatform/Dataverse/operations/dataframe.py

…LM logs by default, fix string concat - prodev: use primary_id_attribute from tables.create() instead of guessing the ID column name (e.g., TABLE_TASKid) - get(): return empty DataFrame with select columns (not columnless) for consistent downstream access on empty results - LLM log: redact prompts/responses by default (include_prompts=False) to avoid logging sensitive data; metadata (timing, provider) always logged - Fix implicit string concatenation in error messages

… cleanup - test_get_no_results_with_select_preserves_columns: verifies empty result with select returns DataFrame with expected column names - test_create_all_nan_rows_raises: verifies all-NaN rows raise ValueError - prodev: cleanup now prompts user (Y/n) before deleting demo tables, matching the pattern in alternate_keys_upsert.py

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Confirm line length compliance for dataframe methods #99

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Fix line length violations in example files #100

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Community Contribution Intake — Triage #1 (2026-03-13) #144

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abelmilash-msft pushed a commit that referenced this pull request Mar 16, 2026

Address PR #98 review: single DF return, rename param, add create guards

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Fix array-like pd.notna crash, normalize NumPy scalars, add update/delete input validation, export DataFrameOperations #145

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Add comprehensive unit tests for DataFrame operations and _normalize_scalar #146

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Summary

Quick Example

Changes

DataFrame CRUD (client.dataframe namespace)

SDK Enhancement: TableInfo primary column metadata (fixes #148)

Advanced Examples

Test Files

API Design

Design Decisions

Test Results

Issues Addressed

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tpellissier-msft commented Feb 17, 2026

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zhaodongwang-msft commented Feb 11, 2026 •

edited by saurabhrb

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DataFrame CRUD (`client.dataframe` namespace)