diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 381ce013d..f134759d9 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -313,6 +313,51 @@ jobs:
           name: coverage-report
           path: coverage.xml
 
+  # run unit tests against older pandas versions, since the test job installs only the newest pandas in the supported range
+  test-pandas-versions:
+    needs: [pre-commit]
+    timeout-minutes: 60
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          # pandas < 2.2.2 is not compatible with numpy 2, so those entries pin numpy 1.26.4 (the last 1.x release)
+          # pandas 1.5 is the lower bound in setup.cfg and installs only on Python 3.11
+          - python: '3.11'
+            pandas: 'pandas==1.5.3'
+            numpy: 'numpy==1.26.4'
+          # pandas 2.1 is the first version with DataFrame.map and the first to support Python 3.12
+          - python: '3.12'
+            pandas: 'pandas==2.1.4'
+            numpy: 'numpy==1.26.4'
+          # pandas 2.2.3 is the first version to support Python 3.13
+          - python: '3.13'
+            pandas: 'pandas==2.2.3'
+            numpy: 'numpy==2.5.4'
+
+    runs-on: ubuntu-22.04
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python }}
+
+      - name: Install py-dependencies
+        shell: bash
+        run: |
+          python -m pip install --upgrade pip
+
+          pip install -e ".[boto3,pandas,pysftp,tests,curator]" "${{ matrix.pandas }}" "${{ matrix.numpy }}"
+
+      - name: run-unit-tests
+        shell: bash
+        run: |
+          python -c "import numpy, pandas; print(f'pandas {pandas.__version__}, numpy {numpy.__version__}')"
+          pytest -sv tests/unit
+
   sonarcloud:
     needs: [test]
     if: ${{ always() && !cancelled()}}
@@ -350,7 +395,7 @@ jobs:
 
   # on a GitHub release, build the pip package and upload it as a GitHub release asset
   package:
-    needs: [test, pre-commit]
+    needs: [test, test-pandas-versions, pre-commit]
 
     runs-on: ubuntu-22.04
 
diff --git a/CLAUDE.md b/CLAUDE.md
index 3155b0df9..a19452b19 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -9,6 +9,7 @@ Synapse Python Client — official Python SDK and CLI for Synapse (synapse.org),
 - Python 3.11–3.14 (`setup.cfg`: `python_requires = >=3.11, <3.15`)
 - HTTP: httpx (async), requests (sync/legacy)
 - Models: stdlib dataclasses (NOT Pydantic)
+- pandas (optional): `pandas>=1.5,<4.0` (`setup.cfg`, 4 places)
 - Tests: pytest 9.0.3, pytest-asyncio, pytest-socket, pytest-xdist
 - Docs: MkDocs with Material theme, mkdocstrings
 - Linting: ruff, black (line-length 88), isort (profile=black), bandit
@@ -107,6 +108,12 @@ pandas, boto3, pysftp, and other optional extras must never be imported at the t
 
 For type annotations referencing pandas types, use `DATA_FRAME_TYPE` and `SERIES_TYPE` from `core/typing_utils.py` rather than `pd.DataFrame` or `pd.Series`. These aliases resolve correctly at type-check time without requiring pandas at runtime.
 
+### pandas version compatibility
+Code must work on every pandas version from 1.5 to 3.x. Use one code path that works on all supported versions. Use a feature check only when no such code path exists. Known pandas 3 traps:
+- Text columns have the `str` dtype, not `object`. Use `pd.api.types.is_string_dtype()` instead of comparing to `object`.
+- `DataFrame.applymap` is replaced by `DataFrame.map`, but `DataFrame.map` is not in pandas < 2.1. Use `df.apply(lambda col: col.map(func))` instead.
+- `iterrows()` gives a `str`-dtype row when all values in the row are strings or missing, and `None` becomes `NaN`. Check each value, not only the column dtype.
+
 ## Constraints
 
 - Do not use Pydantic for models — the codebase uses stdlib dataclasses with custom serialization. Mixing would break the `@async_to_sync` decorator and `fill_from_dict()` pattern.
@@ -126,6 +133,7 @@ For type annotations referencing pandas types, use `DATA_FRAME_TYPE` and `SERIES
 - Integration fixtures create per-worker Synapse projects; use `schedule_for_cleanup()` for teardown
 - Auth env vars: `SYNAPSE_AUTH_TOKEN` (bearer token), `SYNAPSE_PROFILE` (config file profile, default: `"default"`), `SYNAPSE_TOKEN_AWS_SSM_PARAMETER_NAME` (AWS SSM path)
 - CI runs integration tests only on Python 3.11 and 3.14 (oldest + newest) to limit Synapse server load
+- CI job `test-pandas-versions` (`.github/workflows/build.yml`) runs the unit tests on older pandas versions: 1.5.3 on Python 3.11, 2.1.4 on Python 3.12, 2.2.3 on Python 3.13 (ubuntu only). The `package` job needs it. pandas < 2.2.2 requires `numpy<2`, so each matrix entry pins its own numpy version (1.26.4, 1.26.4, 2.5.4).
 
 ## Maintenance
 
diff --git a/Pipfile.lock b/Pipfile.lock
index b38f178bc..5164fb2fb 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -455,118 +455,120 @@
         },
         "opentelemetry-api": {
             "hashes": [
-                "sha256:711ede81773c8025c2c03dac0450bc89f3d30aea6eabcc815c570d4e35a963f7",
-                "sha256:80e068aba7cd56c8b58512d6a36f8d25cb1dfaa0c0a4cc1c938ccf9f362d9cb3"
+                "sha256:aa38ed19bcc084ba42782a73255b3582283eced7ad6dddbd6695189e69adfb75",
+                "sha256:b31553efa588ae44bc306f863c785c5333a9ecc091248c6ee68b4b6c87fdedfb"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==1.45.0"
+            "version": "==1.45.1"
         },
         "opentelemetry-exporter-http-transport": {
             "extras": [
+                "requests",
+                "requests",
                 "urllib3"
             ],
             "hashes": [
-                "sha256:2c229b6593eaa22c86d9b8a15843dc23b406dbda00fb138339189aab07923b4e",
-                "sha256:c82689928a11505a2a0a26a04afe0ce06d270d3db984bc16c1a80ffa902db00e"
+                "sha256:2f95404bdee7f9d2d529c7de56c7bd86d014d774d8fbf137810e0167f8a492bf",
+                "sha256:443080203bf52586ce0b2ad901e8951c61833eab1aa539ae6f1f16fe9e8e7952"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==0.66b0"
+            "version": "==0.66b1"
         },
         "opentelemetry-exporter-otlp-common": {
             "hashes": [
-                "sha256:35d24c867310f4713a9738b0202b6bade77955418fccb9e2f7eea01b76263916",
-                "sha256:362268ec6aa705e183776ff938539df1e8ce45bc5509d242538b1d40c26fe6a6"
+                "sha256:00ff8592c3a7cb729ff3fdc7ffa12372c243bdf2163e80c180994d0c7bd83ee9",
+                "sha256:6b1403487a2185ac1feb45fd5546fdf8630ce71c36bcefaadf51e2130e9e23f9"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==0.66b0"
+            "version": "==0.66b1"
         },
         "opentelemetry-exporter-otlp-proto-common": {
             "hashes": [
-                "sha256:36495115a0c6a7aa946cfda9d59b6ed4e917b6ab0f75cdaf66bc1b176ec1be1f",
-                "sha256:7e1410ae6d3ed301f7a74bec96467d519d3f37e52f9d95b309ae125e5a1af863"
+                "sha256:2e4adcc3a67bcf57804fc49514f0ef64974ca7590aa3491da389852b4a0628f6",
+                "sha256:2f446183ae7047b036226f1d846c41a834b0e8755ad13b51a51dd38952eb466c"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==1.45.0"
+            "version": "==1.45.1"
         },
         "opentelemetry-exporter-otlp-proto-http": {
             "hashes": [
-                "sha256:2f35496d96809f946f41b8805e6b93aec6c9b71b5fd759b75b6af4c084d992ae",
-                "sha256:74385f99266dafdefff41f3da4b76f6dba79d237a9d4487e6ff1ee653c74a0a3"
+                "sha256:24a97cf3753c7fb52fad44a696e452ff371686339e2acf3309e2eda3d0230700",
+                "sha256:45c218405ce3fd879596924b1874bf9a8f6880206d61065c5a912c8e5c297fb7"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==1.45.0"
+            "version": "==1.45.1"
         },
         "opentelemetry-instrumentation": {
             "hashes": [
-                "sha256:0b32e2fcfec1e4bd48511a3b77ce32bfb1c16dcd5479396b21f9086a4b06c0c9",
-                "sha256:316715ed7b33a12530c0ae3e8d4cdf26d70e1aad358d7ba256e5dea2d4ceed71"
+                "sha256:4c4aa14dc9a24a02325a9d4c42c4d0208dbb1374c2b1b8fe6c9392d59f3e1008",
+                "sha256:e79a510f7d87c72d95e964ddb42193a0d9a75668c027d980eab032ea1322a5ce"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==0.66b0"
+            "version": "==0.66b1"
         },
         "opentelemetry-instrumentation-httpx": {
             "hashes": [
-                "sha256:2735d1868749ca04ce47a3802b0b809e3a00c05a65140e05bac5700e74410539",
-                "sha256:c89297975f54d67692d3c29e9e33a1bdac47710031cc3ed0d83a0c9765336981"
+                "sha256:0342a4002c6dbc6c4bf22cc7e698f50f5c8b77f63325c6f40c94ab87e016bf4d",
+                "sha256:5865a72c68098c85955a271ab8744b480a36e3ee492d35b8cadb93c7c4dbb618"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==0.66b0"
+            "version": "==0.66b1"
         },
         "opentelemetry-instrumentation-requests": {
             "hashes": [
-                "sha256:053dde08fbc1d223058e33d477cf81e2bdcbe7af3893787283339b2b557d4b7a",
-                "sha256:2f9df08bf99f5da4bcfda70735492b4364bfcc5bd3f48232658e936f97ee1a22"
+                "sha256:28578f72e68e3a5be3226c618ac9570ed360ef1dd22d5d6e0721c6905a4ccc67",
+                "sha256:7ba17d984a2bd876b88bf0aafcca27b5e3ff4c1821e0fe07641e380a3cf3a3a2"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==0.66b0"
+            "version": "==0.66b1"
         },
         "opentelemetry-instrumentation-threading": {
             "hashes": [
-                "sha256:4aaf38b96b47a04319b8d96bee4f1daa04e0d6c697525595746aeb61618c002d",
-                "sha256:4c1c9adf42b83b6f44ca4bb99799e066ccfb1869c2029bc73d862c40a8ef932e"
+                "sha256:2c217359439d09ca4a4be524db368c05a3cc7f218b85e8086f22664db0a03538",
+                "sha256:94039c60603060d26524e31aed1181a5d153e43f58462d110dbeb4830687d7d7"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==0.66b0"
+            "version": "==0.66b1"
         },
         "opentelemetry-instrumentation-urllib": {
             "hashes": [
-                "sha256:3eb6d2b9b28bb51188d77092a58eac2ab04852d244acb51333bdf42b0e933c68",
-                "sha256:a919d8014dcf0065ad42ce5c78dd158c76c7342929f451d726462f7ffa68e367"
+                "sha256:6f679d785ebcb04b791a889d8a9c0e8edf897a85ab80a7a12b0139c461b14627",
+                "sha256:f00daa25375fbdf88b7e1a425ef54c704674d8bef639c834de93df9b3f88d7f4"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==0.66b0"
+            "version": "==0.66b1"
         },
         "opentelemetry-proto": {
             "hashes": [
-                "sha256:96ee414f24bc3f61ea8e17dc56b4348d4049d73db3eb17c6b3edf75b5b403300",
-                "sha256:9731566359d7b8e1ee1e149d8e4ad6865bab965e657ad3387ec2784d16927666"
+                "sha256:79e0fb95e4616691a469439238aa9224d75779b3e108e895d1aa125ab29ca77c",
+                "sha256:f38e2a8413053c180cd3d2637fbb279673ec2f6a6e09c995aafa2f452c52b46e"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==1.45.0"
+            "version": "==1.45.1"
         },
         "opentelemetry-sdk": {
             "hashes": [
-                "sha256:20caa5130505e386c67c3da1c76e446c842698ced54c76c6148679539aa97972",
-                "sha256:5dc634c946546f61b757c5b1781f9fd1a10e96ffaf7357c797e508fe9c57e75e"
+                "sha256:63d24a6ca645019a631e6a51999c73e93adcac1196ca640b8ae78a7cc4762bf3",
+                "sha256:c604c11dc429810812348989115fa44bd558772a3d7442afc43d024f2c250ca4"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==1.45.0"
+            "version": "==1.45.1"
         },
         "opentelemetry-semantic-conventions": {
             "hashes": [
-                "sha256:175b19dd98c4473f4f43a2b1df59186fd7b4a48cd3f77cbe03438b6d6fda230a",
-                "sha256:97a77dce484c54861e7eeff7651fd8a806dd3c30e501dc316730215ec36890e6"
+                "sha256:497ca63bf383723411e8eaf60c8779e9877633c936bb641080adab59d0eb6ec8",
+                "sha256:d4cddeb4315490b35213f55e2bdc9ac54bb1e4d318927475bed62b35545e581b"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==0.66b0"
+            "version": "==0.66b1"
         },
         "opentelemetry-util-http": {
             "hashes": [
-                "sha256:7f5e581e94f023b30995f700e41ce319cec82bd542ff1c1c7c9b56d6b10bb687",
-                "sha256:a2d53859630abe8e5abcc4f867c5a809bb95873033573b1a67f6ef43b57a8627"
+                "sha256:047dea1a628031f857a5a32261dc0e955bc162d39993ed1cffb8f2cff5ba8a62",
+                "sha256:8f443d7abcaf29c4a07b373bbd31b5b39132c0ed3c27d015a59dc0323d5b1c58"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==0.66b0"
+            "version": "==0.66b1"
         },
         "packaging": {
             "hashes": [
@@ -619,11 +621,11 @@
         },
         "pycparser": {
             "hashes": [
-                "sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29",
-                "sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992"
+                "sha256:51d5a8ba2be0bbe440b99d2112604c95bbbc3c2748a64260186c541e1729cd80",
+                "sha256:d875f09c3507d00e1aba0eecc6dcadc1352f30fff09dc6bff2f1c2935e97c2bc"
             ],
             "markers": "implementation_name != 'PyPy'",
-            "version": "==3.0"
+            "version": "==3.11"
         },
         "requests": {
             "hashes": [
@@ -820,15 +822,16 @@
         },
         "backrefs": {
             "hashes": [
-                "sha256:4a627b817fd2dce43b79ab48da63613340509381cd8ce0897078a0bce79a2ab8",
-                "sha256:601ce68ca12385dbda06ce264406b4c4210cf5b79fd0fd627592365c92f29a88",
-                "sha256:87f0fae8c5f207fe9f4b2887efc71d42f4900ac78faa1af08d675ef303692dc5",
-                "sha256:9ec96efa080938be92323e8e730e57718c9c88eb15ad70bbef4e1766df591408",
-                "sha256:b556cd7d36c3a3a2f256b89590b176b8eddfb73bcfaee3a3ddd84ea66d21ce50",
-                "sha256:f0c35cf0102ba6b6070c12a492be3c1c1d3f5839529784b9a9565d6d04569a01"
+                "sha256:11f61846e446fa8637af2f5c073f1083aa58dbb7460b019fd51689f564e44f7e",
+                "sha256:1e857c32e91b8fe46ddb359ac21021a37b090fa4239566a61e9856123e334041",
+                "sha256:438bd38dc7fa0cded52786a1351a2dad9efc2cda95d047284c41d916a22312a8",
+                "sha256:9c8fe3e2eaae4301a9ce787341d986e5ba25a887d89da6de0de5d58eca26a949",
+                "sha256:b472c76082769f86a97ffccfd1f2599b2b706610c6154955664fffe67e909035",
+                "sha256:db577dbae1c61187534d35fc9bfbd0c01158fff6239a66fecf7590e5b17879a9",
+                "sha256:f86d9312df3ebae1241758af80809a319cafa1d8f32189edfb242c9aa55573b6"
             ],
             "markers": "python_version >= '3.10'",
-            "version": "==8.0"
+            "version": "==8.1"
         },
         "bcrypt": {
             "hashes": [
@@ -901,52 +904,52 @@
         },
         "black": {
             "hashes": [
-                "sha256:0e48b87e03bf109288e55cfceadcfa15ff5470aca2851a851950ed2926f450d7",
-                "sha256:1037d5ac7b7b310b2632ad867ec8d0e4c4819dcdb0b820f63135da746a24e418",
-                "sha256:1ef92b76f7733f282fd096ea406200b5a286c42947412b0eaff3a74e3616cefe",
-                "sha256:1f7ea64ebfa01b50f693508fc39f875e264446d3b097088f84f203b9d09618a0",
-                "sha256:22f2cd76d069cc54c71f10360744ba8983fbb616903b4304a85b734915c8e1b4",
-                "sha256:2b36cf2ddf5566e205f6535f782a62194a184d33e175b64ae8c40b1737522be3",
-                "sha256:30d3c14661f2792e9142cce3eeeb1cbc175b3eb5f733be0c8eeb99651e52b0c3",
-                "sha256:32d5ea7f6c8bdfa6e648326ebca1f02b0764e2a029edc6f8dce2627e19d468c3",
-                "sha256:3915f256e75a2d7cf88d8953d37f780455dc586cc72dee059c528fe77f581217",
-                "sha256:4ad6fa01f941920f54f2bbb35f3df7673428a0ef98a0b0840c2eaef3b110efa8",
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-                "sha256:5119fa92ae61f786e8c3662fd60aece1d0a2dd5cca5d0c79417a95e7a4272a59",
-                "sha256:577f21094ea469ef92ec1adaf2c9441a226d2144d01a5be2fa823cecf6543e50",
-                "sha256:58b4bd92cf88aacf83d88479c8f9caee044b1ec55f2451a337354a7ea2590a22",
-                "sha256:5c34b25da232ead53a6f335b76dbea124f4d152ad568b9080d6f944bc2b34b52",
-                "sha256:87ed5c6f450580a2f6790bc7cbfb016dfc73bc750249762268a3695361315eef",
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-                "sha256:96ae2c733b2aabdd9986e2c5df628ff3473676cd1c5faded1ff496cf6d74083c",
-                "sha256:9942db8888e06943c5dde66ca0037dcff82a2a4ec1ad0ada9e0d2ee9d9823893",
-                "sha256:9d98d4137277c75dfb898ec8d846c4fd68ba1e9cf77f95e2865c203dc18f4c3d",
-                "sha256:a1dca32d9f1784af512a13410ec204c6f7f0aa9797a111c42e1c03449821c264",
-                "sha256:dd321f668053961824bcc1be1cc1df748b2d7e4fa28086b08331e577b0100a73",
-                "sha256:e1a26503279b6b310669fb0b219c39e4820b77e8189fe80f522bb511f247db0a",
-                "sha256:e88976690a64b0af98312ca958415849cb42423423c5f2ee74af4b49a97a2168",
-                "sha256:ea8d16dc41655aa113cd64665e7219446cd7e4ff2248d7178eaa905190c86b18",
-                "sha256:ecb3e624844c798144e9bd986954e0adc81d8911a1f30f375e1252fe26e8c294",
-                "sha256:ed1a20af114c301a0269bf01163d51dbef72737fd65f850001e7cbe7f3c7abae"
+                "sha256:03c0ddd93bb392e71209903a691767eb366fe1a76deb9509ccbaae9e1f14bb52",
+                "sha256:0ce08b367307b0fd91c9dd1d4084e62b05b3055475f951f0f34a46b6e2393b64",
+                "sha256:182f6c32be38074b16d378498c498b32cb51928178ee611485344972c35ec9c6",
+                "sha256:1935b32f5326028019856e18cb42b4da63db23765dc84464cec723e0de478a9b",
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@@ -1475,14 +1478,6 @@
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@@ -1815,11 +1872,11 @@
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         "paramiko": {
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@@ -2149,11 +2204,11 @@
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@@ -2222,11 +2277,11 @@
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@@ -2246,11 +2301,11 @@
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@@ -2382,11 +2437,11 @@
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         "python-discovery": {
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@@ -2436,13 +2491,6 @@
             "markers": "python_version >= '3.8'",
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@@ -2556,125 +2604,137 @@
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+                "sha256:761fdae6728ceb99ab182fad2f0cc1e262f610834dc891aea1d1a2a2e634776f",
+                "sha256:7664419f27db41d4f1c43a78dccda7dd6e8ef2428df3ee01d0c2a07a6b071297",
+                "sha256:76d3af9732d2dab69f28179b40ba2d87e2f1d5824b4a694780aa787d685e8f36",
+                "sha256:78326f4cb4427a56ba4996c0762b63be45f06b85f086526420d2b3a66e40f84d",
+                "sha256:7868b85224291c6cb6759f9b5adb9745f486d226f62b16a614dd5a2a5ab2b35b",
+                "sha256:815d26356930846a40c7bc1366e7b1b0320ab8a063e66c11298a208bed0fd237",
+                "sha256:8171b44a054e5c67fd748ada04187f1250bf35b95f85e52ab64bcf3331a923bb",
+                "sha256:821b2755db9194409254012f429c56643416fb96ef9be090be82ec8826b7f477",
+                "sha256:837c6b305e26fe0f75b15c92cf3b2ba29e0ae19dc40b1c557b026cb426347d0c",
+                "sha256:839dde845559254f34885267c6878f60d61d5205180226d976fe488d45fa128e",
+                "sha256:84a6ecc0c940169190d2c23bd969debd48c94dbc855acd60188a68d71d421608",
+                "sha256:8601470267d938bcb7f3ab1a336100af51a4fd5b6ed030ef52461bb3ef5e7e07",
+                "sha256:88b5268892fde430d5531f95bc560b6efbbd67c929662c586afd729a96e7461c",
+                "sha256:8aa5dda18d39b6143eb24809d158f9252c88f402749b6f1b62a506cc7d96cc35",
+                "sha256:926bdd3e3b5998ddf70cc64bc8cf57209571f9044542913afb673799fec77dd0",
+                "sha256:96beca19ec79de272e8668585380ff9092c47077c1d7a1e098e00bbd921f4785",
+                "sha256:9a0460d43603d1fd9ef59c30278531e15d78581721ddb538fa560aa7817ea4ad",
+                "sha256:a03d57b86d2a51d0a66c92177e2be154ad015f357791d306e714569999cdb4cc",
+                "sha256:a36b70596407634ca82d4b989a3729074a008537a0522e4c8046a67c729103e9",
+                "sha256:a3a52a3ba86436ab3aef510fbe21512abc2ddd1993005dfe50514bd2284ef025",
+                "sha256:a3dbc5ed9514908d5046107d7b1346bde71eea61de6e0e4919c19354f97e769f",
+                "sha256:a431156bb41865fc14cd5d79bb9d7bbed83110b0159e34e62ae30951f96c0009",
+                "sha256:a575404ebc9cf2e91edd32eaf570ec1430eb900d4f56724ba7dd4bc1fc9c176d",
+                "sha256:a5cf77eb04f20b720be95265a3e00eb2a14814074255cc27069c551b2db53118",
+                "sha256:a8763f20692da7df39b0afdd1ba3042b004c50a45994f76c2d9a25641f7673db",
+                "sha256:ab4b2fda7c2b542f7f9d886cc6a838c5079d2b76f72e6081411faba11adde2c9",
+                "sha256:addeda51556dac7c1a2f14cda62db8b621cd12afba3091d03a96c72932387eab",
+                "sha256:b242c27c8f836305a4a72df9cdd564386ac57b807bd252a063223331c9316b37",
+                "sha256:b4f062343e7ad3fa94f2c66e5ae667dee47ee74dd41a9057c4fbe163236a123d",
+                "sha256:b5b8b0753718d258fd454283fbd57e14545d3b40583fa672e27cb4f987626bcc",
+                "sha256:be3e47e2d91aa3942ff9bf4077a505226005abfc39b6f7554a91c1b9393986b9",
+                "sha256:befc2d6a953e563f8a7bfd87a42c22ebf8a3e980dcb7b6a4d17b70b0e914e8a3",
+                "sha256:bf35d0568abda97233239ce32896d3ad53fccc537832c104e30c94aa5fb93569",
+                "sha256:c933c6678c6f116ff8af47a4c6db0868b8ace74af0343016c0ef00f00272ea69",
+                "sha256:c9d1aca01f49170fdcf5c92761b1fafe97f554b721ca4570c5949fff778f0d4b",
+                "sha256:cdeaa99ce822dca76cfb1b993e9120c5ea212f2eb66d48950ad63c349668a018",
+                "sha256:ce4d4f52e2a4324396caddbd45a97d8d7be5f42edd25d2355282a9c34f9b2f7f",
+                "sha256:d1028417bb44037eb3069c1009bd7b7277212876cda22fbe565b0bca9fab6d2c",
+                "sha256:d151e148117294133bf8af7eeace085e7e87432db15ab6adf640330298a47f6f",
+                "sha256:d7841166b7fa64c9c56404617ae4341448847482d45933b13135d26c130519e5",
+                "sha256:d7fca4eb6df565e2a928f1c7dad92d27db8f9df0f449e76423ed5d7e713ed445",
+                "sha256:d95a354e02393eada6d7351184671aced9d4cce109dabf927cb7aa99624352a1",
+                "sha256:d9edf30457d74eebfd76b045535e36f1cd89062566a128a0db2145ca042d787e",
+                "sha256:dbc2673f9223d420c91145599b3ba45a8a50c207d1976908e5fb5ddb0c9b9429",
+                "sha256:e01b3c878c8641913e688edd1b3f08658c6783d29cf6b826bd3c0d1ae7a1ffaa",
+                "sha256:e21c1429e205828ea886a2293a4a2c8e01f4c25d9893ca330e97a6cf73f52e7b",
+                "sha256:e43d4a1f673e8a1cbd8533e809e02b4bf9d4f2280269bb640436556312121250",
+                "sha256:e6d198bad4e49dd6732fbd636e2fc5c082f45c8cad0b4acb756b00c82c76072e",
+                "sha256:e6ea1cda8d8c688278430e4268a42f5e5da3bdd74578dfadc0820c3f1766ce83",
+                "sha256:ea394a937f17a54c51239348bdbe2e3518124c8d4a8951ba04a311d3095bd18f",
+                "sha256:eac2f5dbafd585dfe31f86a23ebf0d3ba480a9d49ebc87947267b5608d4ea0cd",
+                "sha256:eb61be926bb81567c1f48bdc8aa22b9855048dc2efd53871f9f7e6e9a5632346",
+                "sha256:eba5d173f7d5708b22a93815017a4611873ed54db9f268077c0dd1ed99cfc858",
+                "sha256:ec450527cbf485e13c8d3602a54f428ab0432fdade0ede75efd74b735421c871",
+                "sha256:eef6a03b0b6d08d0835ccfa8ec8d1bc70525e3801387567137b50c557695e6da",
+                "sha256:ef0d8c843e2827d6c120ab4687e9423fb1d893db1df27b7c1506615bcb9734a0",
+                "sha256:ef6b65b03247c54692ad4fd9ee97cb772781927db72e3cb05e70b3db6d1ff14f",
+                "sha256:f3d6ed6a98cfd19155996605474982cc470d7601746a6439078f1a5a3fa8b050",
+                "sha256:fce4b85234a0cbad67bf8e6e1201ee815d172c9aebad75f25645bc4d834f8e31",
+                "sha256:fda1d96e542c37b6c804547dbf489c129fe7c97183a76a5ec275909ba1a063df",
+                "sha256:fdcd198979b4ecffcc1beba366a7fbcf4eb41243691a82fe52ceb0b902f09c12",
+                "sha256:fe5ad0664ec772b02c45859041aa17655709cced7a31005817fbbbd988c25567"
             ],
             "markers": "python_version >= '3.11'",
-            "version": "==2026.6.3"
+            "version": "==2026.9.1"
         },
         "s3transfer": {
             "hashes": [
@@ -2735,14 +2795,6 @@
             ],
             "version": "==0.9.0"
         },
-        "tzdata": {
-            "hashes": [
-                "sha256:c2169a8b0a7a5e9674da5a135ccdfb2b3e671b333ed9fed17b41f73c34476e81",
-                "sha256:f1b8bd365d8d210c55353f4d7f8d6d8561c0ba50d704b700d195a9424bba0d79"
-            ],
-            "markers": "python_version >= '2'",
-            "version": "==2026.4"
-        },
         "urllib3": {
             "hashes": [
                 "sha256:0cf3cae568d36aa9576b28dfb35f11328f1cb974ca7647d9475ebb86c75ac6e3",
@@ -2753,11 +2805,11 @@
         },
         "virtualenv": {
             "hashes": [
-                "sha256:2d1a394ef089220b4606408b184433e49417a6e5590a09c7b8a67fb1e013af56",
-                "sha256:d7f167214b3c4f69df5386677dae5091dbc9c400fc46080187ae28064c9d453d"
+                "sha256:3769219a308c5d2f093e7729621ad12a6346b5767a7ec4338414e2a0fb0c526b",
+                "sha256:5f427d56f39eb7e7447d641dd4b7ab1e9c92793f498f35f26da2e4972c5546ae"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==21.14.4"
+            "version": "==21.14.7"
         },
         "watchdog": {
             "hashes": [
diff --git a/setup.cfg b/setup.cfg
index 88203ce89..3e9085626 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -73,7 +73,7 @@ tests_require =
     func-timeout~=4.3
     pytest-cov~=4.1.0
     pytest-html~=4.1.0
-    pandas>=1.5,<3.0
+    pandas>=1.5,<4.0
 
 [options.extras_require]
 dev =
@@ -90,7 +90,7 @@ dev =
     black
     pre-commit
     filelock>=3.20.3
-    pandas>=1.5,<3.0
+    pandas>=1.5,<4.0
 
 tests =
     pytest~=9.0.3
@@ -103,15 +103,14 @@ tests =
     func-timeout~=4.3
     pytest-cov~=4.1.0
     pytest-html~=4.1.0
-    pandas>=1.5,<3.0
+    pandas>=1.5,<4.0
     jsonschema>=4.23.0
 
 pandas =
-    pandas>=1.5,<3.0
+    pandas>=1.5,<4.0
 
 curator =
     %(pandas)s
-    pandarallel>=1.6.4
     inflection>=0.5.1
     networkx>=2.2.8
     dataclasses-json>=0.6.1
diff --git a/synapseclient/extensions/curator/CLAUDE.md b/synapseclient/extensions/curator/CLAUDE.md
index c7402f2ae..f37a97c5d 100644
--- a/synapseclient/extensions/curator/CLAUDE.md
+++ b/synapseclient/extensions/curator/CLAUDE.md
@@ -1,4 +1,4 @@
-<!-- Last reviewed: 2026-03 -->
+<!-- Last reviewed: 2026-10 -->
 
 ## Project
 
@@ -6,7 +6,7 @@ Schema curation tools for data modeling — JSON Schema generation from CSV/JSON
 
 ## Stack
 
-Optional dependencies (gated by `[curator]` extras): pandas, pandarallel, networkx, rdflib, inflection, dataclasses-json.
+Optional dependencies (gated by `[curator]` extras): pandas, networkx, rdflib, inflection, dataclasses-json.
 
 ## Conventions
 
diff --git a/synapseclient/extensions/curator/schema_generation.py b/synapseclient/extensions/curator/schema_generation.py
index 720141aad..b92c28be0 100644
--- a/synapseclient/extensions/curator/schema_generation.py
+++ b/synapseclient/extensions/curator/schema_generation.py
@@ -43,7 +43,6 @@ def check_curator_imports() -> None:
     try:
         import inflection  # noqa: F401
         import networkx  # noqa: F401
-        import pandarallel  # noqa: F401
         import pandas  # noqa: F401
         import rdflib  # noqa: F401
     except ImportError as exception:
@@ -334,27 +333,18 @@ def find_and_convert_ints(
 
     """
 
-    from pandarallel import pandarallel
     from pandas import DataFrame
     from pandas.api.types import is_integer
 
-    large_manifest_cutoff_size = 1000
     # Find integers stored as strings and replace with entries of type np.int64
-    if (
-        dataframe.size < large_manifest_cutoff_size
-    ):  # If small manifest, iterate as normal for improved performance
-        ints = dataframe.map(  # type: ignore
-            lambda cell: convert_ints(cell), na_action="ignore"
-        ).fillna(False)
-
-    else:  # parallelize iterations for large manifests
-        pandarallel.initialize(verbose=1)
-        ints = dataframe.parallel_applymap(  # type: ignore
-            lambda cell: convert_ints(cell), na_action="ignore"
-        ).fillna(False)
+    # Series.map is used per column because DataFrame.map needs pandas 2.1 or
+    # later, and DataFrame.applymap was removed in pandas 3
+    ints = dataframe.apply(
+        lambda col: col.map(lambda cell: convert_ints(cell), na_action="ignore")
+    ).fillna(False)
 
     # Identify cells converted to integers
-    is_int = ints.map(is_integer)  # type: ignore
+    is_int = ints.apply(lambda col: col.map(is_integer))
 
     assert isinstance(ints, DataFrame)
     assert isinstance(is_int, DataFrame)
@@ -396,7 +386,7 @@ def convert_floats(dataframe: DATA_FRAME_TYPE) -> DATA_FRAME_TYPE:
         float_df[col] = to_numeric(float_df[col], errors="coerce").astype("object")
 
         # replace values that couldn't be converted to float with the original str values
-        float_df[col].fillna(dataframe[col][float_df[col].isna()], inplace=True)
+        float_df[col] = float_df[col].fillna(dataframe[col][float_df[col].isna()])
 
     return float_df
 
@@ -459,7 +449,6 @@ def load_df(
     """
     Universal function to load CSVs and return DataFrames
     Parses string entries to convert as appropriate to type int, float, and pandas timestamp
-    Pandarallel is used for type inference for large manifests to improve performance
 
     Args:
         file_path (str): path of csv to open
diff --git a/synapseclient/models/mixins/table_components.py b/synapseclient/models/mixins/table_components.py
index e23af514c..6735af713 100644
--- a/synapseclient/models/mixins/table_components.py
+++ b/synapseclient/models/mixins/table_components.py
@@ -230,7 +230,9 @@ def _reformat_special_values(obj):
         return x
 
     for col in df.columns:
-        df[col] = df[col].apply(_serialize_json_value)
+        # cast to object first so missing values do not turn an Int64 column into
+        # Float64 (pandas 3), which would write 1 as 1.0
+        df[col] = df[col].astype(object).map(_serialize_json_value)
         # restore the original values of the column especially for the int64 and float64 columns since apply function changes the dtype
         df[col] = df[col].convert_dtypes()
         df[col] = df[col].replace({pd.NA: None}).astype(object)
@@ -539,23 +541,30 @@ def _rowset_to_pandas_df(
 
     import pandas as pd
 
-    def construct_rownames(query_result_bundle, offset=0):
+    def construct_rownames(rowset, offset=0):
         try:
             return (
-                row_labels_from_rows(
-                    query_result_bundle.query_result.query_results.rows
-                )
+                row_labels_from_rows(rowset.rows)
                 if row_id_and_version_in_index
                 else None
             )
         except KeyError:
             # if we don't have row id and version, just number the rows
             # python3 cast range to list for safety
-            return list(range(offset, offset + len(rowset["rows"])))
+            return list(range(offset, offset + len(rowset.rows)))
+
+    def append_page(column_name, data, index=None, verify_integrity=False):
+        # add the rows from the next page to the end of the column's series
+        series[column_name] = pd.concat(
+            [series[column_name], pd.Series(name=column_name, data=data, index=index)],
+            verify_integrity=verify_integrity,
+        )
 
     # first page of rows
     offset = 0
-    rownames = construct_rownames(query_result_bundle, offset)
+    rownames = construct_rownames(
+        query_result_bundle.query_result.query_results, offset
+    )
     query_result = query_result_bundle.query_result
     rowset = query_result.query_results
 
@@ -608,38 +617,25 @@ def construct_rownames(query_result_bundle, offset=0):
         offset += len(rowset.rows)
 
         if not row_id_and_version_in_index:
-            # TODO: Look into why this isn't being assigned
-            series["ROW_ID"].append(
-                pd.Series(name="ROW_ID", data=[row.id for row in rowset.rows])
-            )
-            series["ROW_VERSION"].append(
-                pd.Series(
-                    name="ROW_VERSION",
-                    data=[row.version_number for row in rowset.rows],
-                )
-            )
+            append_page("ROW_ID", [row.row_id for row in rowset.rows])
+            append_page("ROW_VERSION", [row.version_number for row in rowset.rows])
             if append_etag:
-                series["ROW_ETAG"] = pd.Series(
-                    name="ROW_ETAG",
-                    data=[row.etag for row in rowset.rows],
-                )
+                append_page("ROW_ETAG", [row.etag for row in rowset.rows])
 
         for i, header in enumerate(rowset.headers):
-            column_name = header.name
-            series[column_name] = pd.concat(
-                [
-                    series[column_name],
-                    pd.Series(
-                        name=column_name,
-                        data=[row.values[i] for row in rowset.rows],
-                        index=rownames,
-                    ),
-                ],
+            append_page(
+                header.name,
+                [row.values[i] for row in rowset.rows],
+                index=rownames,
                 # can't verify integrity when indices are just numbers instead of 'rowid_rowversion'
                 verify_integrity=row_id_and_version_in_index,
             )
 
-    return pd.DataFrame(data=series)
+    df = pd.DataFrame(data=series)
+    if not row_id_and_version_in_index:
+        # each page numbers its rows from 0, so renumber the rows across all pages
+        df = df.reset_index(drop=True)
+    return df
 
 
 @dataclass
diff --git a/synapseclient/models/services/manifest.py b/synapseclient/models/services/manifest.py
index e0b9dbcc6..9f5ddd408 100644
--- a/synapseclient/models/services/manifest.py
+++ b/synapseclient/models/services/manifest.py
@@ -535,6 +535,8 @@ def _apply_synapse_store_defaults(df: "DataFrame") -> None:
     """
     if "synapseStore" not in df.columns:
         df["synapseStore"] = None
+    # A blank column is read as float64, which cannot hold bool values
+    df["synapseStore"] = df["synapseStore"].astype(object)
     df.loc[df["path"].apply(is_url), "synapseStore"] = False
     df.loc[df["synapseStore"].isnull(), "synapseStore"] = True
     df["synapseStore"] = df["synapseStore"].astype(bool)
@@ -650,6 +652,10 @@ async def _sort_and_fix_provenance(syn: Synapse, df: DataFrame) -> DataFrame:
             path, a URL, nor a valid Synapse ID.
     """
     df = df.set_index("path").copy()
+    # Lists are written into these cells, which a str dtype column cannot hold
+    for col in ("used", "executed"):
+        if col in df.columns:
+            df[col] = df[col].astype(object)
 
     results = await asyncio.gather(
         *[_resolve_row(str(path), row, df, syn) for path, row in df.iterrows()]
diff --git a/synapseclient/table.py b/synapseclient/table.py
index f9bc30d7f..8b2ca0234 100644
--- a/synapseclient/table.py
+++ b/synapseclient/table.py
@@ -430,8 +430,11 @@ def _convert_df_date_cols_to_datetime(
         raise ValueError(
             "Cannot convert epoch time to integer. Please make sure that the date columns that you specified contain valid epoch time value"
         )
-    df[date_columns] = df[date_columns].apply(
-        lambda x: pd.to_datetime(x, unit="ms", utc=True)
+    # pandas 3 returns datetime64[ms, UTC] for unit="ms", so force ns resolution
+    df[date_columns] = (
+        df[date_columns]
+        .apply(lambda x: pd.to_datetime(x, unit="ms", utc=True))
+        .astype("datetime64[ns, UTC]")
     )
     return df
 
@@ -2167,22 +2170,33 @@ def construct_rownames(rowset, offset=0):
             offset += len(self.rowset["rows"])
 
             if not rowIdAndVersionInIndex:
-                # TODO: Look into why this isn't being assigned
-                series["ROW_ID"].append(
-                    pd.Series(
-                        name="ROW_ID", data=[row["id"] for row in self.rowset["rows"]]
-                    )
+                series["ROW_ID"] = pd.concat(
+                    [
+                        series["ROW_ID"],
+                        pd.Series(
+                            name="ROW_ID",
+                            data=[row["rowId"] for row in self.rowset["rows"]],
+                        ),
+                    ]
                 )
-                series["ROW_VERSION"].append(
-                    pd.Series(
-                        name="ROW_VERSION",
-                        data=[row["version"] for row in self.rowset["rows"]],
-                    )
+                series["ROW_VERSION"] = pd.concat(
+                    [
+                        series["ROW_VERSION"],
+                        pd.Series(
+                            name="ROW_VERSION",
+                            data=[row["versionNumber"] for row in self.rowset["rows"]],
+                        ),
+                    ]
                 )
                 if append_etag:
-                    series["ROW_ETAG"] = pd.Series(
-                        name="ROW_ETAG",
-                        data=[row.get("etag") for row in self.rowset["rows"]],
+                    series["ROW_ETAG"] = pd.concat(
+                        [
+                            series["ROW_ETAG"],
+                            pd.Series(
+                                name="ROW_ETAG",
+                                data=[row.get("etag") for row in self.rowset["rows"]],
+                            ),
+                        ]
                     )
 
             for i, header in enumerate(self.rowset["headers"]):
@@ -2200,7 +2214,11 @@ def construct_rownames(rowset, offset=0):
                     verify_integrity=rowIdAndVersionInIndex,
                 )
 
-        return pd.DataFrame(data=series)
+        df = pd.DataFrame(data=series)
+        if not rowIdAndVersionInIndex:
+            # each page numbers its rows from 0, so renumber the rows across all pages
+            df = df.reset_index(drop=True)
+        return df
 
     def asRowSet(self):
         # Note that as of stack 60, an empty query will omit the headers field
diff --git a/synapseutils/describe_functions.py b/synapseutils/describe_functions.py
index fe1b7f57b..93a1ff845 100644
--- a/synapseutils/describe_functions.py
+++ b/synapseutils/describe_functions.py
@@ -71,7 +71,12 @@ def _describe_wrapper(df: DATA_FRAME_TYPE, syn: Synapse) -> dict[str, dict[str,
                 stats[column]["dtype"] = df[column].dtype.name
             else:
                 stats[column]["mode"] = df[column].mode()[0]
-                stats[column]["dtype"] = df[column].dtype.name
+                # pandas 3 reports "str" for text columns; keep "object"
+                stats[column]["dtype"] = (
+                    "object"
+                    if pd.api.types.is_string_dtype(df[column].dtype)
+                    else df[column].dtype.name
+                )
 
         except TypeError:
             syn.logger.info(f"Invalid column type: {column}")
diff --git a/synapseutils/sync.py b/synapseutils/sync.py
index 71cdcc9f1..2ea0d02ee 100644
--- a/synapseutils/sync.py
+++ b/synapseutils/sync.py
@@ -5,6 +5,7 @@
 import csv
 import datetime
 import io
+import math
 import os
 import re
 import sys
@@ -982,6 +983,10 @@ def _write_manifest_data(filename: str, keys: List[str], data: List[dict]) -> No
 
 def _sortAndFixProvenance(syn, df):
     df = df.set_index("path")
+    # Lists are written into these cells, which a str dtype column cannot hold
+    for col in PROVENANCE_FIELDS:
+        if col in df.columns:
+            df[col] = df[col].astype(object)
     uploadOrder = {}
 
     def _checkProvenace(item, path):
@@ -1106,6 +1111,8 @@ async def readManifestFile_async(syn: Synapse, manifestFile: str) -> DATA_FRAME_
     df = pd.read_csv(manifestFile, sep="\t")
     if "synapseStore" not in df:
         df = df.assign(synapseStore=None)
+    # A blank column is read as float64, which cannot hold bool values
+    df["synapseStore"] = df["synapseStore"].astype(object)
     df.loc[df["path"].apply(is_url), "synapseStore"] = (
         False  # override synapseStore values to False when path is a url
     )
@@ -1537,8 +1544,8 @@ async def _manifest_upload(
 
         item = _SyncUploadItem(
             file,
-            row["used"] if "used" in row else [],
-            row["executed"] if "executed" in row else [],
+            _missing_to_empty_list(row["used"]) if "used" in row else [],
+            _missing_to_empty_list(row["executed"]) if "executed" in row else [],
             activity_name=row["activityName"] if "activityName" in row else None,
             activity_description=(
                 row["activityDescription"] if "activityDescription" in row else None
@@ -1552,6 +1559,20 @@ async def _manifest_upload(
     return True
 
 
+def _missing_to_empty_list(
+    value: Union[Iterable[str], float, None],
+) -> Iterable[str]:
+    """Return an empty list if value is None or NaN, otherwise return value unchanged.
+
+    With pandas 3, iterrows gives a str dtype row when every cell is a string or
+    missing, and the missing cells are NaN, not None. An empty list keeps the
+    used and executed values iterable for the upload.
+    """
+    if value is None or (isinstance(value, float) and math.isnan(value)):
+        return []
+    return value
+
+
 def _check_file_name(df, syn: Synapse) -> None:
     compiled = re.compile(r"^[`\w \-\+\.\(\)]{1,256}$")
     for _, row in df.iterrows():
diff --git a/tests/integration/synapseclient/deprecated/test_tables.py b/tests/integration/synapseclient/deprecated/test_tables.py
index 9e81b9a15..d2bfeb665 100644
--- a/tests/integration/synapseclient/deprecated/test_tables.py
+++ b/tests/integration/synapseclient/deprecated/test_tables.py
@@ -402,11 +402,17 @@ def test_tables_pandas(syn, project, schedule_for_cleanup):
 
     # SYNPY-717
     # This is a check for windows
+    # The client returns DATE columns as datetime64[ns, UTC]. pandas 2 and later can
+    # give other resolutions, so force ns resolution on the expected column
     if os.name == "nt":
-        df["datetime64"] = pd.to_datetime(df["datetime64"], utc=True)
+        df["datetime64"] = pd.to_datetime(df["datetime64"], utc=True).astype(
+            "datetime64[ns, UTC]"
+        )
     else:
         df["datetime64"] = df["datetime64"].astype(int)
-        df["datetime64"] = pd.to_datetime(df["datetime64"], unit="ms", utc=True)
+        df["datetime64"] = pd.to_datetime(df["datetime64"], unit="ms", utc=True).astype(
+            "datetime64[ns, UTC]"
+        )
 
     # df2 == df gives Dataframe of boolean values; first .all() gives a Series object of ANDed booleans of each column;
     # second .all() gives a bool that is ANDed value of that Series
diff --git a/tests/integration/synapseclient/models/async/test_entityview_async.py b/tests/integration/synapseclient/models/async/test_entityview_async.py
index cdb3df376..ea0a23661 100644
--- a/tests/integration/synapseclient/models/async/test_entityview_async.py
+++ b/tests/integration/synapseclient/models/async/test_entityview_async.py
@@ -416,15 +416,19 @@ def csv_wrapper(*args, **kwargs):
                 check_names=True,
                 check_dtype=False,
             )
+            # dtype=object keeps None for missing values, matching the query output,
+            # on all pandas versions
             pd.testing.assert_series_equal(
                 query_results["integer_column"],
-                pd.Series(test_data["integer_column"], name="integer_column"),
+                pd.Series(
+                    test_data["integer_column"], name="integer_column", dtype=object
+                ),
                 check_names=True,
                 check_dtype=False,
             )
             pd.testing.assert_series_equal(
                 query_results["float_column"],
-                pd.Series(test_data["float_column"], name="float_column"),
+                pd.Series(test_data["float_column"], name="float_column", dtype=object),
                 check_names=True,
                 check_dtype=False,
             )
diff --git a/tests/integration/synapseclient/models/async/test_table_async.py b/tests/integration/synapseclient/models/async/test_table_async.py
index 600a65410..77d5f0633 100644
--- a/tests/integration/synapseclient/models/async/test_table_async.py
+++ b/tests/integration/synapseclient/models/async/test_table_async.py
@@ -1010,6 +1010,8 @@ async def test_store_rows_from_df_with_datetime_columns(
         # The DST column expectations are hardcoded to prove each value converts using the
         # UTC offset in effect on its own date (12:00-08:00 -> 20:00 UTC and
         # 12:00-07:00 -> 19:00 UTC), independent of the machine running the test
+        # dtype=object keeps None for missing values, matching the query output, on all
+        # pandas versions
         expected_results = pd.DataFrame(
             {
                 "column_string": ["value1", "value2", "value3"],
@@ -1022,7 +1024,8 @@ async def test_store_rows_from_df_with_datetime_columns(
                     utils.to_unix_epoch_time(date) if date else None
                     for date in naive_dates
                 ],
-            }
+            },
+            dtype=object,
         )
         pd.testing.assert_frame_equal(results, expected_results, check_dtype=False)
 
@@ -1114,6 +1117,8 @@ async def test_store_rows_from_csv_with_date_columns(
             include_row_id_and_row_version=False,
         )
 
+        # dtype=object keeps None for missing values, matching the query output, on all
+        # pandas versions
         expected_results = pd.DataFrame(
             {
                 "column_string": ["value1", "value2", "value3"],
@@ -1122,7 +1127,8 @@ async def test_store_rows_from_csv_with_date_columns(
                     None,
                     utils.to_unix_epoch_time(datetime(2024, 2, 20)),
                 ],
-            }
+            },
+            dtype=object,
         )
         pd.testing.assert_frame_equal(results, expected_results, check_dtype=False)
 
@@ -1170,6 +1176,8 @@ async def test_store_rows_from_csv_with_tz_aware_date_columns(
             include_row_id_and_row_version=False,
         )
 
+        # dtype=object keeps None for missing values, matching the query output, on all
+        # pandas versions
         expected_results = pd.DataFrame(
             {
                 "column_string": ["value1", "value2", "value3"],
@@ -1178,7 +1186,8 @@ async def test_store_rows_from_csv_with_tz_aware_date_columns(
                     None,
                     1708459200000,
                 ],  # date columns are converted to epoch ms
-            }
+            },
+            dtype=object,
         )
         pd.testing.assert_frame_equal(results, expected_results, check_dtype=False)
 
diff --git a/tests/unit/synapseclient/deprecated/unit_test_tables.py b/tests/unit/synapseclient/deprecated/unit_test_tables.py
index 6f3250eda..36b354c7b 100644
--- a/tests/unit/synapseclient/deprecated/unit_test_tables.py
+++ b/tests/unit/synapseclient/deprecated/unit_test_tables.py
@@ -1524,6 +1524,96 @@ def test_rowset_asDataFrame__with_ROW_ETAG_column(syn: Synapse) -> None:
         assert expected_indicies == dataframe.index.values.tolist()
 
 
+def _multi_page_query_results() -> tuple[dict, list[dict]]:
+    """Return a first page and 2 next pages of a table query, with 5 rows in total."""
+
+    def make_rowset(start: int, count: int) -> dict:
+        return {
+            "concreteType": "org.sagebionetworks.repo.model.table.RowSet",
+            "etag": "DEFAULT",
+            "tableId": "syn11363411",
+            "headers": [
+                {"id": "61770", "columnType": "STRING", "name": "annotationColumn1"},
+            ],
+            "rows": [
+                {
+                    "values": [f"value{row_id}"],
+                    "etag": f"etag{row_id}",
+                    "rowId": row_id,
+                    "versionNumber": 1,
+                }
+                for row_id in range(start, start + count)
+            ],
+        }
+
+    query_result = {
+        "concreteType": "org.sagebionetworks.repo.model.table.QueryResultBundle",
+        "maxRowsPerPage": 6990,
+        "selectColumns": [
+            {"id": "61770", "columnType": "STRING", "name": "annotationColumn1"},
+        ],
+        "queryCount": 5,
+        "queryResult": {
+            "concreteType": "org.sagebionetworks.repo.model.table.QueryResult",
+            "nextPageToken": "token1",
+            "queryResults": make_rowset(0, 2),
+        },
+    }
+    next_pages = [
+        {
+            "concreteType": "org.sagebionetworks.repo.model.table.QueryResult",
+            "nextPageToken": "token2",
+            "queryResults": make_rowset(2, 2),
+        },
+        {
+            "concreteType": "org.sagebionetworks.repo.model.table.QueryResult",
+            "queryResults": make_rowset(4, 1),
+        },
+    ]
+    return query_result, next_pages
+
+
+def test_rowset_asDataFrame__multiple_pages_row_id_and_version_not_in_index(
+    syn: Synapse,
+) -> None:
+    # GIVEN a query result with 3 pages
+    query_result, next_pages = _multi_page_query_results()
+
+    with (
+        patch.object(syn, "_queryTable", return_value=query_result),
+        patch.object(syn, "_queryTableNext", side_effect=next_pages),
+    ):
+        # WHEN I convert the result with row ID and version as columns
+        table = syn.tableQuery("select something from syn123", resultsAs="rowset")
+        dataframe = table.asDataFrame(rowIdAndVersionInIndex=False)
+
+    # THEN the DataFrame has one row for each row in all pages
+    assert 5 == len(dataframe)
+    assert [0, 1, 2, 3, 4] == list(dataframe["ROW_ID"])
+    assert [1, 1, 1, 1, 1] == list(dataframe["ROW_VERSION"])
+    assert [f"etag{i}" for i in range(5)] == list(dataframe["ROW_ETAG"])
+    assert [f"value{i}" for i in range(5)] == list(dataframe["annotationColumn1"])
+
+
+def test_rowset_asDataFrame__multiple_pages_row_id_and_version_not_in_index_has_unique_index(
+    syn: Synapse,
+) -> None:
+    # GIVEN a query result with 3 pages
+    query_result, next_pages = _multi_page_query_results()
+
+    with (
+        patch.object(syn, "_queryTable", return_value=query_result),
+        patch.object(syn, "_queryTableNext", side_effect=next_pages),
+    ):
+        # WHEN I convert the result with row ID and version as columns
+        table = syn.tableQuery("select something from syn123", resultsAs="rowset")
+        dataframe = table.asDataFrame(rowIdAndVersionInIndex=False)
+
+    # THEN the index is unique and numbers the rows across all pages
+    assert dataframe.index.is_unique
+    assert [0, 1, 2, 3, 4] == dataframe.index.tolist()
+
+
 def test_RowSetTable_len() -> None:
     schema = Schema(
         parentId="syn123", id="syn456", columns=[Column(name="column_name", id="123")]
diff --git a/tests/unit/synapseclient/extensions/unit_test_curator.py b/tests/unit/synapseclient/extensions/unit_test_curator.py
index fbce627f9..8fc4d1e6d 100644
--- a/tests/unit/synapseclient/extensions/unit_test_curator.py
+++ b/tests/unit/synapseclient/extensions/unit_test_curator.py
@@ -16,6 +16,7 @@
 from typing import Any, Callable
 from unittest.mock import Mock, mock_open, patch
 
+import numpy as np
 import pandas as pd
 import pytest
 
@@ -45,6 +46,8 @@
     project_id_from_entity_id,
 )
 from synapseclient.extensions.curator.schema_generation import (
+    convert_floats,
+    find_and_convert_ints,
     generate_jsonld,
     generate_jsonschema,
 )
@@ -1967,6 +1970,215 @@ def test_error_names_every_duplicate_once(self):
             validate_column_order_list(["patientId", "patientId", "patientId"])
 
 
+class TestFindAndConvertInts:
+    """Test cases for find_and_convert_ints in curator.schema_generation."""
+
+    def test_converts_digit_strings_to_ints(self) -> None:
+        """Strings of digits become numpy int64 values and are marked as ints."""
+        # GIVEN a dataframe where every cell is a string of digits
+        df = pd.DataFrame({"a": ["1", "22"], "b": ["0", "333"]})
+
+        # WHEN the integers are found and converted
+        ints, is_int = find_and_convert_ints(df)
+
+        # THEN every cell is converted to an int64 and marked as an int
+        assert ints.to_dict("list") == {"a": [1, 22], "b": [0, 333]}
+        assert all(isinstance(value, np.int64) for value in ints.to_numpy().flatten())
+        assert is_int.to_numpy().all()
+
+    @pytest.mark.parametrize(
+        "value",
+        ["abc", "1.5", "-1", "1e3", " 1", "", 5, 1.0],
+        ids=[
+            "text",
+            "float string",
+            "negative string",
+            "scientific notation",
+            "leading space",
+            "empty string",
+            "int",
+            "float",
+        ],
+    )
+    def test_does_not_convert_other_values(self, value: any) -> None:
+        """Values that are not strings of digits become False."""
+        # GIVEN a dataframe with a value that is not a string of digits
+        df = pd.DataFrame({"a": [value]}, dtype=object)
+
+        # WHEN the integers are found and converted
+        ints, is_int = find_and_convert_ints(df)
+
+        # THEN the value is replaced with False and is not marked as an int
+        # (pandas can give a numpy bool when the whole column is False)
+        assert isinstance(ints.loc[0, "a"], (bool, np.bool_))
+        assert not ints.loc[0, "a"]
+        assert not is_int.loc[0, "a"]
+
+    def test_missing_values_become_false(self) -> None:
+        """Missing values become False and are not marked as ints."""
+        # GIVEN a dataframe with missing values
+        df = pd.DataFrame({"a": ["x", None, np.nan]}, dtype=object)
+
+        # WHEN the integers are found and converted
+        ints, is_int = find_and_convert_ints(df)
+
+        # THEN the missing values are replaced with False
+        assert ints["a"].tolist() == [False, False, False]
+        assert is_int["a"].tolist() == [False, False, False]
+
+    def test_mixed_columns(self) -> None:
+        """Each cell is checked on its own, so a column can be partly converted."""
+        # GIVEN a dataframe that mixes digit strings, text, and floats
+        df = pd.DataFrame(
+            {
+                "id": ["1", "2", "3"],
+                "name": ["x", "10", "y"],
+                "score": ["1.5", "2", "abc"],
+            }
+        )
+
+        # WHEN the integers are found and converted
+        ints, is_int = find_and_convert_ints(df)
+
+        # THEN only the digit strings are converted and marked as ints
+        assert ints.to_dict("list") == {
+            "id": [1, 2, 3],
+            "name": [False, 10, False],
+            "score": [False, 2, False],
+        }
+        assert is_int.to_dict("list") == {
+            "id": [True, True, True],
+            "name": [False, True, False],
+            "score": [False, True, False],
+        }
+
+    def test_returns_dataframes_with_same_shape_and_labels(self) -> None:
+        """Both results keep the index and columns of the input dataframe."""
+        # GIVEN a dataframe with a custom index
+        df = pd.DataFrame({"a": ["1", "x"], "b": ["y", "2"]}, index=["r1", "r2"])
+
+        # WHEN the integers are found and converted
+        ints, is_int = find_and_convert_ints(df)
+
+        # THEN both results are dataframes with the same labels as the input
+        for result in (ints, is_int):
+            assert isinstance(result, pd.DataFrame)
+            assert result.index.tolist() == ["r1", "r2"]
+            assert result.columns.tolist() == ["a", "b"]
+
+    def test_empty_dataframe(self) -> None:
+        """An empty dataframe gives empty results."""
+        # GIVEN an empty dataframe
+        df = pd.DataFrame({"a": []}, dtype=object)
+
+        # WHEN the integers are found and converted
+        ints, is_int = find_and_convert_ints(df)
+
+        # THEN both results are empty
+        assert ints.empty
+        assert is_int.empty
+
+
+class TestConvertFloats:
+    """Test cases for convert_floats in curator.schema_generation."""
+
+    @pytest.mark.parametrize(
+        "value, expected",
+        [
+            ("1.5", 1.5),
+            ("-0.5", -0.5),
+            ("1e3", 1000.0),
+            (" 3", 3.0),
+        ],
+        ids=["float", "negative", "scientific notation", "leading space"],
+    )
+    def test_converts_numeric_strings_to_floats(
+        self, value: str, expected: float
+    ) -> None:
+        """Strings that represent numbers become floats."""
+        # GIVEN a dataframe with a numeric string and a text value
+        df = pd.DataFrame({"a": [value, "abc"]}, dtype=object)
+
+        # WHEN the floats are converted
+        float_df = convert_floats(df)
+
+        # THEN the numeric string is converted to a float
+        assert isinstance(float_df.loc[0, "a"], float)
+        assert float_df.loc[0, "a"] == expected
+
+    @pytest.mark.parametrize(
+        "value",
+        ["abc", "", "nan"],
+        ids=["text", "empty string", "nan string"],
+    )
+    def test_keeps_non_numeric_strings(self, value: str) -> None:
+        """Strings that are not numbers keep their original value."""
+        # GIVEN a dataframe with a numeric string and a non-numeric string
+        df = pd.DataFrame({"a": ["1.5", value]}, dtype=object)
+
+        # WHEN the floats are converted
+        float_df = convert_floats(df)
+
+        # THEN the non-numeric string is not changed
+        assert float_df.loc[1, "a"] == value
+        assert isinstance(float_df.loc[1, "a"], str)
+
+    def test_missing_values_stay_missing(self) -> None:
+        """Missing values are not converted."""
+        # GIVEN a dataframe with a missing value
+        df = pd.DataFrame({"a": ["1.5", None]}, dtype=object)
+
+        # WHEN the floats are converted
+        float_df = convert_floats(df)
+
+        # THEN the missing value is still missing
+        # (pandas 2 gives NaN and pandas 3 gives None)
+        assert float_df.loc[0, "a"] == 1.5
+        assert pd.isna(float_df.loc[1, "a"])
+
+    def test_mixed_columns(self) -> None:
+        """Each cell is checked on its own, so a column can be partly converted."""
+        # GIVEN a dataframe that mixes numeric strings and text
+        df = pd.DataFrame(
+            {"a": ["1", "x"], "b": ["y", "2.5"]},
+            index=["r1", "r2"],
+        )
+
+        # WHEN the floats are converted
+        float_df = convert_floats(df)
+
+        # THEN only the numeric strings are converted
+        assert float_df.to_dict("list") == {"a": [1.0, "x"], "b": ["y", 2.5]}
+        # AND the columns are object dtype, with the same labels as the input
+        assert all(dtype == object for dtype in float_df.dtypes)
+        assert float_df.index.tolist() == ["r1", "r2"]
+        assert float_df.columns.tolist() == ["a", "b"]
+
+    def test_does_not_change_input(self) -> None:
+        """The input dataframe is not changed."""
+        # GIVEN a dataframe with numeric strings
+        df = pd.DataFrame({"a": ["1.5", "x"]})
+        original = df.copy()
+
+        # WHEN the floats are converted
+        convert_floats(df)
+
+        # THEN the input dataframe is not changed
+        pd.testing.assert_frame_equal(df, original)
+
+    def test_empty_dataframe(self) -> None:
+        """An empty dataframe gives an empty result."""
+        # GIVEN an empty dataframe
+        df = pd.DataFrame({"a": []}, dtype=object)
+
+        # WHEN the floats are converted
+        float_df = convert_floats(df)
+
+        # THEN the result is empty
+        assert float_df.empty
+        assert float_df.columns.tolist() == ["a"]
+
+
 class TestResolveColumnOrderList:
     """Test cases for resolve_column_order_list in curator.utils."""
 
diff --git a/tests/unit/synapseclient/mixins/unit_test_table_components.py b/tests/unit/synapseclient/mixins/unit_test_table_components.py
index a14a18bb3..714c42bb2 100644
--- a/tests/unit/synapseclient/mixins/unit_test_table_components.py
+++ b/tests/unit/synapseclient/mixins/unit_test_table_components.py
@@ -3,7 +3,7 @@
 import tempfile
 from collections import OrderedDict
 from dataclasses import dataclass, field
-from datetime import date, datetime
+from datetime import date, datetime, timedelta
 from io import BytesIO
 from typing import Any, Dict, List, Optional
 from unittest.mock import AsyncMock, MagicMock, patch
@@ -61,6 +61,7 @@
     _query_table_csv,
     _query_table_next_page,
     _query_table_row_set,
+    _rowset_to_pandas_df,
     _upsert_rows_async,
     _validate_primary_keys,
     convert_dtypes_to_json_serializable,
@@ -4931,6 +4932,129 @@ async def test_query_table_next_page_basic_functionality(
             assert result.select_columns[0].id == "12345"
 
 
+class TestRowsetToPandasDf:
+    """Test suite for the _rowset_to_pandas_df function with more than one page."""
+
+    @pytest.fixture(autouse=True, scope="function")
+    def init_syn(self, syn: Synapse) -> None:
+        self.syn = syn
+
+    def _make_bundle(
+        self, start: int, count: int, next_page_token: Optional[QueryNextPageToken]
+    ) -> QueryResultBundle:
+        """Make a QueryResultBundle with count rows, starting at row ID start."""
+        rows = [
+            Row(
+                row_id=row_id,
+                version_number=1,
+                etag=f"etag{row_id}",
+                values=[f"value{row_id}", str(row_id * 10)],
+            )
+            for row_id in range(start, start + count)
+        ]
+        rowset = RowSet(
+            table_id="syn123",
+            headers=[
+                SelectColumn(name="col1", column_type=ColumnType.STRING),
+                SelectColumn(name="col2", column_type=ColumnType.INTEGER),
+            ],
+            rows=rows,
+        )
+        return QueryResultBundle(
+            query_result=QueryResult(
+                query_results=rowset, next_page_token=next_page_token
+            )
+        )
+
+    def test_multiple_pages_row_id_and_version_not_in_index(self) -> None:
+        # GIVEN a query result with 3 pages
+        first_page = self._make_bundle(0, 2, QueryNextPageToken(token="token1"))
+        next_pages = [
+            self._make_bundle(2, 2, QueryNextPageToken(token="token2")),
+            self._make_bundle(4, 1, None),
+        ]
+
+        with patch(
+            "synapseclient.models.mixins.table_components._query_table_next_page",
+            side_effect=next_pages,
+        ) as mock_next_page:
+            # WHEN I convert the result with row ID and version as columns
+            df = _rowset_to_pandas_df(
+                query_result_bundle=first_page,
+                synapse_client=self.syn,
+                row_id_and_version_in_index=False,
+            )
+
+        # THEN all pages are fetched
+        assert mock_next_page.call_count == 2
+
+        # AND the DataFrame has one row for each row in all pages
+        assert len(df) == 5
+        assert list(df["ROW_ID"]) == [0, 1, 2, 3, 4]
+        assert list(df["ROW_VERSION"]) == [1, 1, 1, 1, 1]
+        assert list(df["ROW_ETAG"]) == [f"etag{i}" for i in range(5)]
+        assert list(df["col1"]) == [f"value{i}" for i in range(5)]
+        assert list(df["col2"]) == [str(i * 10) for i in range(5)]
+
+    def test_multiple_pages_row_id_and_version_not_in_index_has_unique_index(
+        self,
+    ) -> None:
+        # GIVEN a query result with 3 pages
+        first_page = self._make_bundle(0, 2, QueryNextPageToken(token="token1"))
+        next_pages = [
+            self._make_bundle(2, 2, QueryNextPageToken(token="token2")),
+            self._make_bundle(4, 1, None),
+        ]
+
+        with patch(
+            "synapseclient.models.mixins.table_components._query_table_next_page",
+            side_effect=next_pages,
+        ):
+            # WHEN I convert the result with row ID and version as columns
+            df = _rowset_to_pandas_df(
+                query_result_bundle=first_page,
+                synapse_client=self.syn,
+                row_id_and_version_in_index=False,
+            )
+
+        # THEN the index is unique and numbers the rows across all pages
+        assert df.index.is_unique
+        assert df.index.tolist() == [0, 1, 2, 3, 4]
+
+    def test_multiple_pages_row_id_and_version_in_index(self) -> None:
+        # GIVEN a query result with 3 pages
+        first_page = self._make_bundle(0, 2, QueryNextPageToken(token="token1"))
+        next_pages = [
+            self._make_bundle(2, 2, QueryNextPageToken(token="token2")),
+            self._make_bundle(4, 1, None),
+        ]
+
+        with patch(
+            "synapseclient.models.mixins.table_components._query_table_next_page",
+            side_effect=next_pages,
+        ) as mock_next_page:
+            # WHEN I convert the result with row ID and version in the index
+            df = _rowset_to_pandas_df(
+                query_result_bundle=first_page,
+                synapse_client=self.syn,
+                row_id_and_version_in_index=True,
+            )
+
+        # THEN all pages are fetched
+        assert mock_next_page.call_count == 2
+
+        # AND the index has the row ID, version and etag of each row in all pages
+        assert df.index.tolist() == [f"{i}_1_etag{i}" for i in range(5)]
+
+        # AND the DataFrame has one row for each row in all pages
+        assert len(df) == 5
+        assert "ROW_ID" not in df.columns
+        assert "ROW_VERSION" not in df.columns
+        assert "ROW_ETAG" not in df.columns
+        assert list(df["col1"]) == [f"value{i}" for i in range(5)]
+        assert list(df["col2"]) == [str(i * 10) for i in range(5)]
+
+
 class TestCsvToPandasDf:
     """Test suite for csv_to_pandas_df function focusing on date and list columns."""
 
@@ -4972,7 +5096,7 @@ def test_csv_to_pandas_df_with_date_columns(self, csv_with_date_columns):
 
         expected_dates = pd.to_datetime(
             [1609459200000, 1609545600000, 1609632000000], unit="ms", utc=True
-        )
+        ).astype("datetime64[ns, UTC]")
         # THEN assert the create_date column is equal to the expected dates
         pd.testing.assert_series_equal(
             df["created_date"], pd.Series(expected_dates), check_names=False
@@ -5049,7 +5173,7 @@ def test_csv_to_pandas_df_with_date_and_list_columns(
                 "name": ["Alice", "Bob", "Charlie"],
                 "created_date": pd.to_datetime(
                     [1609459200000, 1609545600000, 1609632000000], unit="ms", utc=True
-                ),
+                ).astype("datetime64[ns, UTC]"),
                 "number": [42, 10, 99],
                 "bool": [True, False, True],
                 "string": ["hello", "world", "test"],
@@ -5134,7 +5258,7 @@ def test_csv_to_pandas_df_all_list_types_with_na_in_items_and_date_columns(
                 "string": ["hello", "world", "test"],
                 "created_date": pd.to_datetime(
                     [1609459200000, 1609545600000, 1609632000000], unit="ms", utc=True
-                ),
+                ).astype("datetime64[ns, UTC]"),
                 "number_list": [
                     [1, None, 3],
                     [None, 5],
@@ -5207,7 +5331,7 @@ def test_csv_pandas_df_with_row_id_and_version_etag_in_index(
                 "string": ["hello", "world", "test"],
                 "created_date": pd.to_datetime(
                     [1609459200000, 1609545600000, 1609632000000], unit="ms", utc=True
-                ),
+                ).astype("datetime64[ns, UTC]"),
                 "number_list": [[1, None, 3], [], [6, None, None, 9]],
                 "bool_list": [[True, None, False], [None, True], [None, None, False]],
                 "string_list": [["tag1", "", "tag3"], ["", "tag2"], ["", "tag4", ""]],
@@ -5327,6 +5451,57 @@ def test_convert_na_and_columns_to_object(self):
         assert list(result["int_col"]) == [1, 2, None, 4]
         assert list(result["float_col"]) == [1.1, 2.2, None, 4.4]
 
+    @pytest.mark.parametrize(
+        "values, expected_types",
+        [
+            # nullable Int64 with missing values, as made by convert_dtypes()
+            (pd.array([1, 2, pd.NA, 4], dtype="Int64"), [int, int, type(None), int]),
+            # a list of ints with None, as given by a user
+            (pd.Series([1, None, 3]).convert_dtypes(), [int, type(None), int]),
+            # int64 with no missing values
+            (pd.Series([1, 2, 3]), [int, int, int]),
+            # Float64 with missing values stays float
+            (pd.array([1.5, pd.NA, 3.0], dtype="Float64"), [float, type(None), float]),
+        ],
+        ids=["Int64_with_NA", "list_with_None", "int64", "Float64_with_NA"],
+    )
+    def test_number_columns_keep_python_type(self, values, expected_types):
+        """Test that each value keeps its int or float type. The == check alone
+        does not catch an int that changed to a float, because 1.0 == 1."""
+        # GIVEN a dataframe with a number column
+        df = pd.DataFrame({"col": values})
+
+        # WHEN convert_dtypes_to_json_serializable is called
+        result = convert_dtypes_to_json_serializable(df)
+
+        # THEN each value should have the expected Python type
+        assert [type(value) for value in result["col"]] == expected_types
+
+    def test_int_column_with_na_is_written_to_csv_as_int(self):
+        """Test that an int column with missing values is written to CSV without a
+        decimal part. Synapse rejects "1.0" for an INTEGER column and
+        "1609502400000.0" for a DATE column."""
+        # GIVEN a dataframe with INTEGER and DATE (epoch ms) values and a missing value
+        df = pd.DataFrame(
+            {
+                "int_col": [1, None, 3],
+                "date_col": [1609502400000, None, 1708459200000],
+            }
+        ).convert_dtypes()
+
+        # WHEN convert_dtypes_to_json_serializable is called and the result is
+        # written to CSV with the same arguments as the upload
+        result = convert_dtypes_to_json_serializable(df)
+        csv_text = result.to_csv(index=False, float_format="%.12g")
+
+        # THEN the values should be written as integers
+        assert csv_text.splitlines() == [
+            "int_col,date_col",
+            "1,1609502400000",
+            ",",
+            "3,1708459200000",
+        ]
+
     def test_row_columns_remain_int(self):
         """Test that ROW_ID, ROW_VERSION, and ROW_ID.1 columns remain as int while other columns become object"""
         # GIVEN a dataframe with special columns (ROW_ID, ROW_VERSION, ROW_ID.1) and a regular column
@@ -7090,7 +7265,15 @@ def test_uniform_utc_offsets_parsed_as_tz_aware(self):
                 ]
             }
         ).convert_dtypes()
-        pd.testing.assert_frame_equal(result, expected_result)
+        # pandas 1.5 gives a pytz.FixedOffset timezone and pandas 2+ gives a
+        # datetime.timezone, so check the offset and compare the values in UTC
+        assert result["date_col"].dt.tz.utcoffset(None) == timedelta(hours=-8)
+        pd.testing.assert_frame_equal(
+            result.assign(date_col=result["date_col"].dt.tz_convert("UTC")),
+            expected_result.assign(
+                date_col=expected_result["date_col"].dt.tz_convert("UTC")
+            ),
+        )
         mock_logger.info.assert_not_called()
 
     def test_mixed_utc_offsets_normalized_to_utc(self):
diff --git a/tests/unit/synapseclient/models/async/unit_test_manifest_async.py b/tests/unit/synapseclient/models/async/unit_test_manifest_async.py
index 63577acfd..e8d848da8 100644
--- a/tests/unit/synapseclient/models/async/unit_test_manifest_async.py
+++ b/tests/unit/synapseclient/models/async/unit_test_manifest_async.py
@@ -443,6 +443,27 @@ def test_column_is_bool_dtype(self) -> None:
         _apply_synapse_store_defaults(df)
         assert df["synapseStore"].dtype == bool
 
+    def test_blank_column_read_from_csv(self, tmp_path: Path) -> None:
+        """A synapseStore column with only blank cells is read as float64.
+        Local paths default to True and URL paths are set to False."""
+        # GIVEN a manifest CSV with a local path and a URL path
+        manifest = tmp_path / "manifest.csv"
+        manifest.write_text(
+            "path,parentId,synapseStore\n/a.txt,syn1,\nhttps://example.com/f.csv,syn1,\n"
+        )
+
+        # AND a DataFrame read from the CSV, where the synapseStore column is blank
+        df = pd.read_csv(manifest)
+
+        # WHEN I apply the synapseStore defaults
+        _apply_synapse_store_defaults(df)
+
+        # THEN the local path is True and the URL path is False
+        assert list(df["synapseStore"]) == [True, False]
+
+        # AND the column has the bool dtype
+        assert df["synapseStore"].dtype == bool
+
 
 class TestCheckPathAndNormalize:
     @pytest.fixture(autouse=True)
diff --git a/tests/unit/synapseutils/unit_test_synapseutils_sync.py b/tests/unit/synapseutils/unit_test_synapseutils_sync.py
index 52f5a5d2f..58c60953d 100644
--- a/tests/unit/synapseutils/unit_test_synapseutils_sync.py
+++ b/tests/unit/synapseutils/unit_test_synapseutils_sync.py
@@ -210,6 +210,36 @@ def test_read_manifest_file_synapse_store_values_not_set(syn: Synapse) -> None:
         assert expected_synapseStore == actual_synapseStore
 
 
+def test_read_manifest_file_synapse_store_values_all_blank(syn: Synapse) -> None:
+    # GIVEN a manifest with a local path and a URL path, and no synapseStore values
+    project_id = SYN_123
+    header = "path\tparent\tsynapseStore\n"
+    path1 = os.path.abspath(os.path.expanduser("~/file1.txt"))
+    path2 = SYNAPSE_URL
+    row1 = "%s\t%s\t\n" % (path1, project_id)
+    row2 = "%s\t%s\t\n" % (path2, project_id)
+
+    expected_synapseStore = {
+        str(path1): True,
+        str(path2): False,
+    }
+
+    manifest = StringIO(header + row1 + row2)
+    with (
+        patch.object(syn, "get_async", return_value=Project()),
+        patch.object(os.path, "isfile", return_value=True),
+        patch.object(sync, "_check_size_each_file", return_value=Mock()),
+    ):  # side effect mocks values for: file1.txt
+        # WHEN I read the manifest file
+        manifest_dataframe = synapseutils.sync.readManifestFile(syn, manifest)
+
+        # THEN the local path is True and the URL path is False
+        actual_synapseStore = manifest_dataframe.set_index("path")[
+            "synapseStore"
+        ].to_dict()
+        assert expected_synapseStore == actual_synapseStore
+
+
 def test_read_manifest_file_synapse_store_values_are_set(syn: Synapse) -> None:
     project_id = SYN_123
     header = "path\tparent\tsynapseStore\n"
@@ -1162,8 +1192,9 @@ def mock_uploader_constructor(*args, **kwargs):
         expected_path = data["path"][i]
         expected_parent = data["parent"][i]
         expected_name = data["name"][i]
-        expected_used = data["used"][i]
-        expected_executed = data["executed"][i]
+        # missing provenance values are given to the upload item as empty lists
+        expected_used = data["used"][i] or []
+        expected_executed = data["executed"][i] or []
         expected_annos = expected_anno_data[i]
 
         upload_items.append(upload_item_init.return_value)
@@ -1180,6 +1211,69 @@ def mock_uploader_constructor(*args, **kwargs):
         mock_uploader.upload.assert_called_once_with(upload_items)
 
 
+@patch("os.path.isfile")
+async def test_manifest_upload_missing_provenance_builds_dependency_graph(
+    mock_isfile: MagicMock, syn: Synapse
+) -> None:
+    """Verify that a missing used or executed value in the manifest is treated as
+    no provenance, and does not break the real dependency graph build."""
+    # GIVEN local files that exist
+    mock_isfile.side_effect = lambda path: path.startswith("/tmp")
+
+    # AND a manifest where one executed value is missing
+    df = pd.DataFrame(
+        data={
+            "path": ["/tmp/foo", "/tmp/bar"],
+            "parent": [SYN_123, SYN_123],
+            "used": [[], ["/tmp/foo"]],
+            "executed": [math.nan, []],
+        }
+    )
+
+    with patch.object(
+        _SyncUploader, "_build_tasks_from_dependency_graph", return_value=[]
+    ) as mock_build_tasks:
+        # WHEN I upload the manifest
+        await synapseutils.sync._manifest_upload(syn, df)
+
+    # THEN the dependency graph has no dependencies for the missing value
+    dependency_graph = mock_build_tasks.call_args.kwargs["dependency_graph"]
+    assert dependency_graph.path_to_dependencies == {
+        "/tmp/foo": [],
+        "/tmp/bar": ["/tmp/foo"],
+    }
+
+
+@pytest.mark.parametrize(
+    "value, expected",
+    [
+        (None, []),
+        (math.nan, []),
+        (float("nan"), []),
+        ([], []),
+        (["/tmp/foo", SYN_123], ["/tmp/foo", SYN_123]),
+        ("/tmp/foo", "/tmp/foo"),
+        ("", ""),
+    ],
+    ids=[
+        "none",
+        "math_nan",
+        "float_nan",
+        "empty_list",
+        "list",
+        "str",
+        "empty_str",
+    ],
+)
+def test_missing_to_empty_list(value: Any, expected: Any) -> None:
+    # GIVEN a used or executed value from a manifest row
+    # WHEN I convert the value
+    result = synapseutils.sync._missing_to_empty_list(value)
+
+    # THEN a missing value becomes an empty list, and other values are unchanged
+    assert result == expected
+
+
 class TestSyncUploader:
     @patch("os.path.isfile")
     def test_order_items(self, mock_isfile: MagicMock, syn: Synapse) -> None: