From 96bed021e47376100dcd698de8efd3c10097df91 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Thu, 5 Mar 2026 21:26:51 -0500
Subject: [PATCH 1/2] Switch code formatter from black to ruff format

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 .github/workflows/pull_request.yaml           |   4 +-
 .github/workflows/push.yaml                   |   4 +-
 Makefile                                      |   2 +-
 changelog.d/switch-to-ruff.changed.md         |   1 +
 docs/LA_methodology.ipynb                     |  15 +-
 docs/constituency_methodology.ipynb           |  15 +-
 docs/methodology.ipynb                        | 145 +++++++++---------
 docs/validation/constituencies.ipynb          |  80 ++++++++--
 docs/validation/local_authorities.ipynb       |  77 ++++++++--
 docs/validation/national.ipynb                |   7 +-
 .../datasets/childcare/takeup_rate.py         |   8 +-
 .../datasets/imputations/consumption.py       |   4 +-
 .../targets/sources/_common.py                |   2 +-
 .../microsimulation/test_reform_impacts.py    |  18 +--
 policyengine_uk_data/tests/test_aggregates.py |   6 +-
 .../tests/test_child_limit.py                 |  12 +-
 .../tests/test_low_income_deciles.py          |   4 +-
 .../tests/test_non_negative_incomes.py        |   6 +-
 ...sion_contributions_via_salary_sacrifice.py |  18 +--
 policyengine_uk_data/tests/test_population.py |   6 +-
 .../tests/test_property_purchased.py          |  20 +--
 .../tests/test_salary_sacrifice_headcount.py  |  12 +-
 .../tests/test_scotland_babies.py             |   4 +-
 .../tests/test_scotland_uc_babies.py          |   4 +-
 .../tests/test_target_registry.py             |  12 +-
 .../tests/test_uc_by_children.py              |   4 +-
 pyproject.toml                                |  21 +--
 uv.lock                                       | 108 ++++++-------
 28 files changed, 362 insertions(+), 257 deletions(-)
 create mode 100644 changelog.d/switch-to-ruff.changed.md

diff --git a/.github/workflows/pull_request.yaml b/.github/workflows/pull_request.yaml
index e7be9e6f..0b8e9d64 100644
--- a/.github/workflows/pull_request.yaml
+++ b/.github/workflows/pull_request.yaml
@@ -24,9 +24,9 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install black
+          pip install "ruff>=0.9.0"
       - name: Check formatting
-        run: black . -l 79 --check
+        run: ruff format --check .
   test:
     name: Test
     runs-on: ubuntu-latest
diff --git a/.github/workflows/push.yaml b/.github/workflows/push.yaml
index d4575eb6..5ca27117 100644
--- a/.github/workflows/push.yaml
+++ b/.github/workflows/push.yaml
@@ -23,9 +23,9 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install black
+          pip install "ruff>=0.9.0"
       - name: Check formatting
-        run: black . -l 79 --check
+        run: ruff format --check .
   test:
     name: Build and test
     runs-on: ubuntu-latest
diff --git a/Makefile b/Makefile
index 327895d3..64699a0d 100644
--- a/Makefile
+++ b/Makefile
@@ -1,7 +1,7 @@
 all: data test
 
 format:
-	black . -l 79
+	ruff format .
 
 test:
 	pytest .
diff --git a/changelog.d/switch-to-ruff.changed.md b/changelog.d/switch-to-ruff.changed.md
new file mode 100644
index 00000000..3e176424
--- /dev/null
+++ b/changelog.d/switch-to-ruff.changed.md
@@ -0,0 +1 @@
+Switched code formatter from black to ruff format.
diff --git a/docs/LA_methodology.ipynb b/docs/LA_methodology.ipynb
index 4b81035a..46774f4b 100644
--- a/docs/LA_methodology.ipynb
+++ b/docs/LA_methodology.ipynb
@@ -374,10 +374,13 @@
     "import pandas as pd\n",
     "from itables import init_notebook_mode, show\n",
     "import itables.options as opt\n",
+    "\n",
     "opt.maxBytes = \"1MB\"\n",
     "init_notebook_mode(all_interactive=True)\n",
     "\n",
-    "pd.read_csv(\"../policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/total_income.csv\")"
+    "pd.read_csv(\n",
+    "    \"../policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/total_income.csv\"\n",
+    ")"
    ]
   },
   {
@@ -818,10 +821,13 @@
     "import pandas as pd\n",
     "from itables import init_notebook_mode, show\n",
     "import itables.options as opt\n",
+    "\n",
     "opt.maxBytes = \"1MB\"\n",
     "init_notebook_mode(all_interactive=True)\n",
     "\n",
-    "pd.read_csv(\"../policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/age.csv\")"
+    "pd.read_csv(\n",
+    "    \"../policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/age.csv\"\n",
+    ")"
    ]
   },
   {
@@ -1188,10 +1194,13 @@
     "import pandas as pd\n",
     "from itables import init_notebook_mode, show\n",
     "import itables.options as opt\n",
+    "\n",
     "opt.maxBytes = \"1MB\"\n",
     "init_notebook_mode(all_interactive=True)\n",
     "\n",
-    "pd.read_csv(\"../policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv\")"
+    "pd.read_csv(\n",
+    "    \"../policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv\"\n",
+    ")"
    ]
   },
   {
diff --git a/docs/constituency_methodology.ipynb b/docs/constituency_methodology.ipynb
index 8166105a..9cacc36d 100644
--- a/docs/constituency_methodology.ipynb
+++ b/docs/constituency_methodology.ipynb
@@ -374,10 +374,13 @@
     "import pandas as pd\n",
     "from itables import init_notebook_mode, show\n",
     "import itables.options as opt\n",
+    "\n",
     "opt.maxBytes = \"1MB\"\n",
     "init_notebook_mode(all_interactive=True)\n",
     "\n",
-    "pd.read_csv(\"../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv\")"
+    "pd.read_csv(\n",
+    "    \"../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/total_income.csv\"\n",
+    ")"
    ]
   },
   {
@@ -818,10 +821,13 @@
     "import pandas as pd\n",
     "from itables import init_notebook_mode, show\n",
     "import itables.options as opt\n",
+    "\n",
     "opt.maxBytes = \"1MB\"\n",
     "init_notebook_mode(all_interactive=True)\n",
     "\n",
-    "pd.read_csv(\"../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/age.csv\")"
+    "pd.read_csv(\n",
+    "    \"../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/age.csv\"\n",
+    ")"
    ]
   },
   {
@@ -1302,10 +1308,13 @@
     "import pandas as pd\n",
     "from itables import init_notebook_mode, show\n",
     "import itables.options as opt\n",
+    "\n",
     "opt.maxBytes = \"1MB\"\n",
     "init_notebook_mode(all_interactive=True)\n",
     "\n",
-    "pd.read_csv(\"../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/employment_income.csv\")"
+    "pd.read_csv(\n",
+    "    \"../policyengine_uk_data/datasets/frs/local_areas/constituencies/targets/employment_income.csv\"\n",
+    ")"
    ]
   },
   {
diff --git a/docs/methodology.ipynb b/docs/methodology.ipynb
index 019359af..d580a6d4 100644
--- a/docs/methodology.ipynb
+++ b/docs/methodology.ipynb
@@ -343,10 +343,12 @@
     "import pandas as pd\n",
     "from itables import init_notebook_mode\n",
     "import itables.options as opt\n",
+    "\n",
     "opt.maxBytes = \"1MB\"\n",
     "\n",
     "init_notebook_mode(all_interactive=True)\n",
     "\n",
+    "\n",
     "def get_loss(dataset, reform, time_period):\n",
     "    loss_results = get_loss_results(dataset, time_period, reform)\n",
     "\n",
@@ -362,6 +364,7 @@
     "    loss_results[\"type\"] = loss_results.name.apply(get_type)\n",
     "    return loss_results\n",
     "\n",
+    "\n",
     "reported_benefits = Reform.from_dict(\n",
     "    {\n",
     "        \"gov.contrib.policyengine.disable_simulated_benefits\": True,\n",
@@ -414,10 +417,10 @@
           0.5348574279909283,
           0.33769630678188683,
           0.4724366479253982,
-          0.056382436859752076,
+          0.05638243685975208,
           0.6323673982942971,
           0.9366950828549806,
-          0.11071012537714943,
+          0.11071012537714944,
           0.43224912766430945,
           0.4932054498037529,
           0.4262926636695862,
@@ -428,7 +431,7 @@
           0.2753207784816197,
           0.2723132074858013,
           0.8419023088645935,
-          0.38591469862268324,
+          0.3859146986226832,
           1,
           1,
           0.238984410722884,
@@ -438,7 +441,7 @@
           0.07523280073668749,
           0.6813040233247071,
           1,
-          0.09093881776916021,
+          0.0909388177691602,
           0.7953899467026746,
           0.46345942912091304,
           1,
@@ -482,7 +485,7 @@
           0.056645307043117855,
           0.04613438039833396,
           0.17900131626571955,
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+          0.10437807305849048,
           0.04133579034001572,
           0.01689458524887367,
           0.07304306223253616,
@@ -492,17 +495,17 @@
           0.02570792373305158,
           0.2117702613060218,
           0.06261644764053753,
-          0.11048854967581091,
+          0.11048854967581093,
           0.05986189620431352,
           0.0734813370478334,
           0.14993575315772975,
-          0.09216128695626061,
+          0.0921612869562606,
           0.06654101281860526,
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+          0.030242342070154263,
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+          0.09490778457907528,
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+          0.09147390139813616,
           0.07256124815551596,
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@@ -518,7 +521,7 @@
           0.05198270343522084,
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           0.19421519077802243,
-          0.10227911889430563,
+          0.10227911889430565,
           0.022966625631099497,
           0.04223796065100079,
           0.07170636880139063,
@@ -529,7 +532,7 @@
           0.29116400964006,
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           0.04460071124716505,
-          0.13148586282369953,
+          0.1314858628236995,
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           0.1461505005795304,
           0.09404538078799742,
@@ -537,14 +540,14 @@
           0.07684637616907224,
           0.1739255359932635,
           0.06968768763060447,
-          0.048645930535396516,
+          0.04864593053539651,
           0.04715293440571787,
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+          0.06117956857785887,
           0.14599437481067512,
           0.07423160697853332,
           0.08014078307669949,
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+          0.1872157520559437,
           0.14720678189147138,
           0.021828844225395293,
           0.0625035269825679,
@@ -556,8 +559,8 @@
           0.2078691963062574,
           0.15441863025620098,
           0.10737865871453003,
-          0.09454279295816925,
-          0.09633845930037417,
+          0.09454279295816924,
+          0.09633845930037416,
           0.15611969585317312,
           0.06873017587493258,
           0.0791181851399526,
@@ -567,11 +570,11 @@
           0.06731946150876865,
           0.14050146457241625,
           0.08162092155759072,
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+          0.11667759541165897,
           0.030289924549114444,
           0.06339486495990165,
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+          0.1867882618826091,
           0.13891870679655027,
           0.11545889612594176,
           0.09001345414783932,
@@ -617,14 +620,14 @@
           1,
           1,
           1,
-          0.19149475966528307,
-          0.24673403495070823,
+          0.1914947596652831,
+          0.24673403495070825,
           0.3072794892096273,
           0.379747974710697,
           0.31039305362432584,
           0.10848112754405202,
           0.010619302099698998,
-          0.13888253105203519,
+          0.1388825310520352,
           0.4073900806470958,
           0.2531205286437236,
           0.792085840046357,
@@ -636,7 +639,7 @@
           0.03973531361317136,
           0.2350261871873867,
           0.4310117757696614,
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+          0.24612705047281855,
           0.12926913086657413,
           0.21946877869613568,
           0.5242635218702594,
@@ -646,7 +649,7 @@
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           0.06992062037206717,
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+          0.03895475443017472,
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           0.40331728828616015,
@@ -662,7 +665,7 @@
           0.054489850288550065,
           0.12854107496184566,
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+          0.11321576973743848,
           0.34453613181731424,
           0.1244811487104074,
           0.06072357901250304,
@@ -673,23 +676,23 @@
           0.30226424119880596,
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+          0.10686992109637392,
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+          0.9211680206617688,
           0.22674879162547168,
           0.3174237803524217,
           0.10323686418719458,
@@ -700,28 +703,28 @@
           1,
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           0.8471689821257619,
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           1,
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           0.45696603359696897,
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@@ -741,7 +744,7 @@
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+          0.9107437140452884,
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           1,
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@@ -750,7 +753,7 @@
           0.8684502342014089,
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-          0.43612221537997764,
+          0.4361222153799776,
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@@ -760,10 +763,10 @@
           0.987640762587488,
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@@ -772,7 +775,7 @@
           0.9789237514645788,
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           0.025177568881478127,
@@ -781,14 +784,14 @@
           1,
           0.9926826451871978,
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@@ -797,7 +800,7 @@
           0.257589360854839,
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           0.2932462480888995,
-          0.9040908751204909,
+          0.9040908751204908,
           0.33043668625756617
          ],
          "xaxis": "x",
@@ -1687,8 +1690,9 @@
     "def format_fig(fig):\n",
     "    return fig\n",
     "\n",
+    "\n",
     "loss_results.abs_rel_error = loss_results.abs_rel_error.clip(0, 1)\n",
-    "    \n",
+    "\n",
     "fig = px.histogram(\n",
     "    loss_results,\n",
     "    x=\"abs_rel_error\",\n",
@@ -1704,7 +1708,8 @@
     "    legend_title=\"Category\",\n",
     "    xaxis_title=\"Absolute relative error\",\n",
     "    yaxis_title=\"Number of variables\",\n",
-    "    xaxis_tickformat=\".0%\",)\n",
+    "    xaxis_tickformat=\".0%\",\n",
+    ")\n",
     "format_fig(fig)"
    ]
   },
@@ -1818,7 +1823,7 @@
          "xaxis": "x",
          "y": [
           1033786.8507080078,
-          1849527.0676879883,
+          1849527.0676879885,
           6388252.871826172,
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@@ -1826,7 +1831,7 @@
           1826260.506072998,
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+          204920.73791503903,
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@@ -1875,7 +1880,7 @@
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+          13914.58819580078,
           39777.421630859375,
           5029.7379150390625,
           111758.16729736328,
@@ -1920,18 +1925,18 @@
          "xaxis": "x",
          "y": [
           1.5997536783990056,
-          -0.24673403495070823,
+          -0.24673403495070825,
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+          -0.9599693030243192,
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          ],
          "yaxis": "y"
@@ -1977,7 +1982,7 @@
           505763.6264343262,
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+          13914.58819580078,
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@@ -2022,18 +2027,18 @@
          "xaxis": "x",
          "y": [
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-          0.24673403495070823,
+          0.24673403495070825,
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           0.12854107496184566,
           0.003773473791717633,
           0.03876573963794215,
-          0.014207504312205523,
-          0.14548208834407161,
+          0.014207504312205525,
+          0.1454820883440716,
           0.02516240308487657,
           1.0495005709369527,
           0.9835036985633404,
-          0.9599693030243193,
+          0.9599693030243192,
           0.03891346894228955
          ],
          "yaxis": "y"
@@ -3141,7 +3146,7 @@
           -0.3647842083382897,
           -0.4473604762734464,
           -0.4321594002439528,
-          -0.21622933403097414,
+          -0.21622933403097416,
           -0.2589492388346111,
           -0.2627782706474406,
           -0.34401959095740303,
@@ -3243,7 +3248,7 @@
           0.3647842083382897,
           0.4473604762734464,
           0.4321594002439528,
-          0.21622933403097414,
+          0.21622933403097416,
           0.2589492388346111,
           0.2627782706474406,
           0.34401959095740303,
@@ -5086,13 +5091,13 @@
          ],
          "xaxis": "x",
          "y": [
-          1023954.3214297295,
+          1023954.3214297296,
           1839525.9950885773,
           6387366.710470676,
           5448517.7086930275,
           3420158.3231887817,
           3675436.0024147034,
-          1825305.0654945374,
+          1825305.0654945376,
           995032.6799602509,
           313195.4208712578,
           204920.7386112213,
@@ -5201,7 +5206,7 @@
           1.0495005534866122,
           -0.9835036991585266,
           -0.9599693047283234,
-          -0.038545385074422164
+          -0.03854538507442216
          ],
          "yaxis": "y"
         },
@@ -5303,7 +5308,7 @@
           1.0495005534866122,
           0.9835036991585266,
           0.9599693047283234,
-          0.038545385074422164
+          0.03854538507442216
          ],
          "yaxis": "y"
         }
@@ -6544,7 +6549,7 @@
           1606618.6615459488,
           304110.76789712394,
           217559.91823041387,
-          115103.32235789967,
+          115103.32235789968,
           49768.71192988932,
           21825.629729905864,
           27469371.2800173
@@ -6647,7 +6652,7 @@
           0.11225584232612314,
           0.0883927652091083,
           0.08091431214983678,
-          -0.029357726530223446,
+          -0.02935772653022345,
           -0.18755100767175908,
           0.04168935771146878
          ],
@@ -6749,7 +6754,7 @@
           0.11225584232612314,
           0.0883927652091083,
           0.08091431214983678,
-          0.029357726530223446,
+          0.02935772653022345,
           0.18755100767175908,
           0.04168935771146878
          ],
diff --git a/docs/validation/constituencies.ipynb b/docs/validation/constituencies.ipynb
index 782613f0..0b76f81f 100644
--- a/docs/validation/constituencies.ipynb
+++ b/docs/validation/constituencies.ipynb
@@ -177,35 +177,72 @@
     "\n",
     "weights_file_path = STORAGE_FOLDER / \"parliamentary_constituency_weights.h5\"\n",
     "with h5py.File(weights_file_path, \"r\") as f:\n",
-    "        weights = f[str(2025)][...]\n",
+    "    weights = f[str(2025)][...]\n",
     "\n",
     "constituencies = pd.read_csv(STORAGE_FOLDER / \"constituencies_2024.csv\")\n",
     "\n",
     "baseline = Microsimulation()\n",
     "household_weights = baseline.calculate(\"household_weight\", 2025).values\n",
     "\n",
-    "from policyengine_uk_data.datasets.frs.local_areas.constituencies.loss import create_constituency_target_matrix, create_national_target_matrix\n",
+    "from policyengine_uk_data.datasets.frs.local_areas.constituencies.loss import (\n",
+    "    create_constituency_target_matrix,\n",
+    "    create_national_target_matrix,\n",
+    ")\n",
     "from policyengine_uk_data.datasets import EnhancedFRS_2022_23\n",
-    "constituency_target_matrix, constituency_actuals, _ = create_constituency_target_matrix(EnhancedFRS_2022_23, 2025, None)\n",
-    "national_target_matrix, national_actuals = create_national_target_matrix(EnhancedFRS_2022_23, 2025, None)\n",
+    "\n",
+    "constituency_target_matrix, constituency_actuals, _ = (\n",
+    "    create_constituency_target_matrix(EnhancedFRS_2022_23, 2025, None)\n",
+    ")\n",
+    "national_target_matrix, national_actuals = create_national_target_matrix(\n",
+    "    EnhancedFRS_2022_23, 2025, None\n",
+    ")\n",
     "\n",
     "constituency_wide = weights @ constituency_target_matrix\n",
     "constituency_wide.index = constituencies.code.values\n",
     "constituency_wide[\"name\"] = constituencies.name.values\n",
     "\n",
-    "constituency_results = pd.melt(constituency_wide.reset_index(), id_vars=[\"index\", \"name\"], var_name=\"variable\", value_name=\"value\")\n",
+    "constituency_results = pd.melt(\n",
+    "    constituency_wide.reset_index(),\n",
+    "    id_vars=[\"index\", \"name\"],\n",
+    "    var_name=\"variable\",\n",
+    "    value_name=\"value\",\n",
+    ")\n",
     "\n",
     "constituency_actuals.index = constituencies.code.values\n",
     "constituency_actuals[\"name\"] = constituencies.name.values\n",
-    "constituency_actuals_long = pd.melt(constituency_actuals.reset_index(), id_vars=[\"index\", \"name\"], var_name=\"variable\", value_name=\"value\")\n",
+    "constituency_actuals_long = pd.melt(\n",
+    "    constituency_actuals.reset_index(),\n",
+    "    id_vars=[\"index\", \"name\"],\n",
+    "    var_name=\"variable\",\n",
+    "    value_name=\"value\",\n",
+    ")\n",
     "\n",
-    "constituency_target_validation = pd.merge(constituency_results, constituency_actuals_long, on=[\"index\", \"variable\"], suffixes=(\"_target\", \"_actual\"))\n",
+    "constituency_target_validation = pd.merge(\n",
+    "    constituency_results,\n",
+    "    constituency_actuals_long,\n",
+    "    on=[\"index\", \"variable\"],\n",
+    "    suffixes=(\"_target\", \"_actual\"),\n",
+    ")\n",
     "constituency_target_validation.drop(\"name_actual\", axis=1, inplace=True)\n",
-    "constituency_target_validation.columns = [\"index\", \"name\", \"metric\", \"estimate\", \"target\"]\n",
+    "constituency_target_validation.columns = [\n",
+    "    \"index\",\n",
+    "    \"name\",\n",
+    "    \"metric\",\n",
+    "    \"estimate\",\n",
+    "    \"target\",\n",
+    "]\n",
     "\n",
-    "constituency_target_validation[\"error\"] = constituency_target_validation[\"estimate\"] - constituency_target_validation[\"target\"]\n",
-    "constituency_target_validation[\"abs_error\"] = constituency_target_validation[\"error\"].abs()\n",
-    "constituency_target_validation[\"rel_abs_error\"] = constituency_target_validation[\"abs_error\"] / constituency_target_validation[\"target\"]"
+    "constituency_target_validation[\"error\"] = (\n",
+    "    constituency_target_validation[\"estimate\"]\n",
+    "    - constituency_target_validation[\"target\"]\n",
+    ")\n",
+    "constituency_target_validation[\"abs_error\"] = constituency_target_validation[\n",
+    "    \"error\"\n",
+    "].abs()\n",
+    "constituency_target_validation[\"rel_abs_error\"] = (\n",
+    "    constituency_target_validation[\"abs_error\"]\n",
+    "    / constituency_target_validation[\"target\"]\n",
+    ")"
    ]
   },
   {
@@ -398,12 +435,25 @@
    "outputs": [],
    "source": [
     "national_performance = household_weights @ national_target_matrix\n",
-    "national_target_validation = pd.DataFrame({\"metric\": national_performance.index, \"estimate\": national_performance.values})\n",
+    "national_target_validation = pd.DataFrame(\n",
+    "    {\n",
+    "        \"metric\": national_performance.index,\n",
+    "        \"estimate\": national_performance.values,\n",
+    "    }\n",
+    ")\n",
     "national_target_validation[\"target\"] = national_actuals.values\n",
     "\n",
-    "national_target_validation[\"error\"] = national_target_validation[\"estimate\"] - national_target_validation[\"target\"]\n",
-    "national_target_validation[\"abs_error\"] = national_target_validation[\"error\"].abs()\n",
-    "national_target_validation[\"rel_abs_error\"] = national_target_validation[\"abs_error\"] / national_target_validation[\"target\"]"
+    "national_target_validation[\"error\"] = (\n",
+    "    national_target_validation[\"estimate\"]\n",
+    "    - national_target_validation[\"target\"]\n",
+    ")\n",
+    "national_target_validation[\"abs_error\"] = national_target_validation[\n",
+    "    \"error\"\n",
+    "].abs()\n",
+    "national_target_validation[\"rel_abs_error\"] = (\n",
+    "    national_target_validation[\"abs_error\"]\n",
+    "    / national_target_validation[\"target\"]\n",
+    ")"
    ]
   },
   {
diff --git a/docs/validation/local_authorities.ipynb b/docs/validation/local_authorities.ipynb
index 10998700..37f17d72 100644
--- a/docs/validation/local_authorities.ipynb
+++ b/docs/validation/local_authorities.ipynb
@@ -196,29 +196,65 @@
     "baseline = Microsimulation()\n",
     "household_weights = baseline.calculate(\"household_weight\", 2025).values\n",
     "\n",
-    "from policyengine_uk_data.datasets.frs.local_areas.local_authorities.loss import create_local_authority_target_matrix, create_national_target_matrix\n",
+    "from policyengine_uk_data.datasets.frs.local_areas.local_authorities.loss import (\n",
+    "    create_local_authority_target_matrix,\n",
+    "    create_national_target_matrix,\n",
+    ")\n",
     "from policyengine_uk_data.datasets import EnhancedFRS_2022_23\n",
     "\n",
-    "local_authority_target_matrix, local_authority_actuals, _ = create_local_authority_target_matrix(EnhancedFRS_2022_23, 2025, None)\n",
-    "national_target_matrix, national_actuals = create_national_target_matrix(EnhancedFRS_2022_23, 2025, None)\n",
+    "local_authority_target_matrix, local_authority_actuals, _ = (\n",
+    "    create_local_authority_target_matrix(EnhancedFRS_2022_23, 2025, None)\n",
+    ")\n",
+    "national_target_matrix, national_actuals = create_national_target_matrix(\n",
+    "    EnhancedFRS_2022_23, 2025, None\n",
+    ")\n",
     "\n",
     "local_authority_wide = weights @ local_authority_target_matrix\n",
     "local_authority_wide.index = constituencies_2024.code.values\n",
     "local_authority_wide[\"name\"] = constituencies_2024.name.values\n",
     "\n",
-    "local_authority_results = pd.melt(local_authority_wide.reset_index(), id_vars=[\"index\", \"name\"], var_name=\"variable\", value_name=\"value\")\n",
+    "local_authority_results = pd.melt(\n",
+    "    local_authority_wide.reset_index(),\n",
+    "    id_vars=[\"index\", \"name\"],\n",
+    "    var_name=\"variable\",\n",
+    "    value_name=\"value\",\n",
+    ")\n",
     "\n",
     "local_authority_actuals.index = constituencies_2024.code.values\n",
     "local_authority_actuals[\"name\"] = constituencies_2024.name.values\n",
-    "local_authority_actuals_long = pd.melt(local_authority_actuals.reset_index(), id_vars=[\"index\", \"name\"], var_name=\"variable\", value_name=\"value\")\n",
+    "local_authority_actuals_long = pd.melt(\n",
+    "    local_authority_actuals.reset_index(),\n",
+    "    id_vars=[\"index\", \"name\"],\n",
+    "    var_name=\"variable\",\n",
+    "    value_name=\"value\",\n",
+    ")\n",
     "\n",
-    "local_authority_target_validation = pd.merge(local_authority_results, local_authority_actuals_long, on=[\"index\", \"variable\"], suffixes=(\"_target\", \"_actual\"))\n",
+    "local_authority_target_validation = pd.merge(\n",
+    "    local_authority_results,\n",
+    "    local_authority_actuals_long,\n",
+    "    on=[\"index\", \"variable\"],\n",
+    "    suffixes=(\"_target\", \"_actual\"),\n",
+    ")\n",
     "local_authority_target_validation.drop(\"name_actual\", axis=1, inplace=True)\n",
-    "local_authority_target_validation.columns = [\"index\", \"name\", \"metric\", \"estimate\", \"target\"]\n",
+    "local_authority_target_validation.columns = [\n",
+    "    \"index\",\n",
+    "    \"name\",\n",
+    "    \"metric\",\n",
+    "    \"estimate\",\n",
+    "    \"target\",\n",
+    "]\n",
     "\n",
-    "local_authority_target_validation[\"error\"] = local_authority_target_validation[\"estimate\"] - local_authority_target_validation[\"target\"]\n",
-    "local_authority_target_validation[\"abs_error\"] = local_authority_target_validation[\"error\"].abs()\n",
-    "local_authority_target_validation[\"rel_abs_error\"] = local_authority_target_validation[\"abs_error\"] / local_authority_target_validation[\"target\"]"
+    "local_authority_target_validation[\"error\"] = (\n",
+    "    local_authority_target_validation[\"estimate\"]\n",
+    "    - local_authority_target_validation[\"target\"]\n",
+    ")\n",
+    "local_authority_target_validation[\"abs_error\"] = (\n",
+    "    local_authority_target_validation[\"error\"].abs()\n",
+    ")\n",
+    "local_authority_target_validation[\"rel_abs_error\"] = (\n",
+    "    local_authority_target_validation[\"abs_error\"]\n",
+    "    / local_authority_target_validation[\"target\"]\n",
+    ")"
    ]
   },
   {
@@ -400,12 +436,25 @@
    "outputs": [],
    "source": [
     "national_performance = household_weights @ national_target_matrix\n",
-    "national_target_validation = pd.DataFrame({\"metric\": national_performance.index, \"estimate\": national_performance.values})\n",
+    "national_target_validation = pd.DataFrame(\n",
+    "    {\n",
+    "        \"metric\": national_performance.index,\n",
+    "        \"estimate\": national_performance.values,\n",
+    "    }\n",
+    ")\n",
     "national_target_validation[\"target\"] = national_actuals.values\n",
     "\n",
-    "national_target_validation[\"error\"] = national_target_validation[\"estimate\"] - national_target_validation[\"target\"]\n",
-    "national_target_validation[\"abs_error\"] = national_target_validation[\"error\"].abs()\n",
-    "national_target_validation[\"rel_abs_error\"] = national_target_validation[\"abs_error\"] / national_target_validation[\"target\"]"
+    "national_target_validation[\"error\"] = (\n",
+    "    national_target_validation[\"estimate\"]\n",
+    "    - national_target_validation[\"target\"]\n",
+    ")\n",
+    "national_target_validation[\"abs_error\"] = national_target_validation[\n",
+    "    \"error\"\n",
+    "].abs()\n",
+    "national_target_validation[\"rel_abs_error\"] = (\n",
+    "    national_target_validation[\"abs_error\"]\n",
+    "    / national_target_validation[\"target\"]\n",
+    ")"
    ]
   },
   {
diff --git a/docs/validation/national.ipynb b/docs/validation/national.ipynb
index 3ade912a..ad9b8e3b 100644
--- a/docs/validation/national.ipynb
+++ b/docs/validation/national.ipynb
@@ -170,10 +170,12 @@
     "import pandas as pd\n",
     "from itables import init_notebook_mode\n",
     "import itables.options as opt\n",
+    "\n",
     "opt.maxBytes = \"1MB\"\n",
     "\n",
     "init_notebook_mode(all_interactive=True)\n",
     "\n",
+    "\n",
     "def get_validation():\n",
     "    df = pd.DataFrame()\n",
     "    for dataset in [FRS_2022_23, EnhancedFRS_2022_23]:\n",
@@ -185,6 +187,7 @@
     "    df = df.reset_index(drop=True)\n",
     "    return df\n",
     "\n",
+    "\n",
     "df = get_validation()\n",
     "truth_df = df[df.dataset == df.dataset.unique()[0]].reset_index()\n",
     "truth_df[\"estimate\"] = truth_df[\"target\"]\n",
@@ -617,7 +620,9 @@
     "    on=[\"time_period\", \"name\"],\n",
     "    suffixes=(\"_frs\", \"_efrs\"),\n",
     ")\n",
-    "merged[\"rel_error_change_under_efrs\"] = merged[\"abs_rel_error_efrs\"] - merged[\"abs_rel_error_frs\"]\n",
+    "merged[\"rel_error_change_under_efrs\"] = (\n",
+    "    merged[\"abs_rel_error_efrs\"] - merged[\"abs_rel_error_frs\"]\n",
+    ")\n",
     "# Sort columns\n",
     "merged = merged[\n",
     "    [\n",
diff --git a/policyengine_uk_data/datasets/childcare/takeup_rate.py b/policyengine_uk_data/datasets/childcare/takeup_rate.py
index dcb3ae18..9e5031c3 100644
--- a/policyengine_uk_data/datasets/childcare/takeup_rate.py
+++ b/policyengine_uk_data/datasets/childcare/takeup_rate.py
@@ -161,13 +161,13 @@ def objective(params: list[float]) -> float:
     print("\nSpending (£ billion):")
     for key in targets["spending"]:
         print(
-            f"  {key.upper()}: {spending[key]:.3f} (Target: {targets['spending'][key]:.3f}, Ratio: {spending[key]/targets['spending'][key]:.3f})"
+            f"  {key.upper()}: {spending[key]:.3f} (Target: {targets['spending'][key]:.3f}, Ratio: {spending[key] / targets['spending'][key]:.3f})"
         )
 
     print("\nCaseload (thousands):")
     for key in targets["caseload"]:
         print(
-            f"  {key.upper()}: {caseload[key]:.1f} (Target: {targets['caseload'][key]:.1f}, Ratio: {caseload[key]/targets['caseload'][key]:.3f})"
+            f"  {key.upper()}: {caseload[key]:.1f} (Target: {targets['caseload'][key]:.1f}, Ratio: {caseload[key] / targets['caseload'][key]:.3f})"
         )
 
     return loss
@@ -211,11 +211,11 @@ def objective(params: list[float]) -> float:
     print("\nSpending (£ billion):")
     for key in targets["spending"]:
         print(
-            f"  {key.upper()}: {final_spending[key]:.3f} (Target: {targets['spending'][key]:.3f}, Ratio: {final_spending[key]/targets['spending'][key]:.3f})"
+            f"  {key.upper()}: {final_spending[key]:.3f} (Target: {targets['spending'][key]:.3f}, Ratio: {final_spending[key] / targets['spending'][key]:.3f})"
         )
 
     print("\nCaseload (thousands):")
     for key in targets["caseload"]:
         print(
-            f"  {key.upper()}: {final_caseload[key]:.1f} (Target: {targets['caseload'][key]:.1f}, Ratio: {final_caseload[key]/targets['caseload'][key]:.3f})"
+            f"  {key.upper()}: {final_caseload[key]:.1f} (Target: {targets['caseload'][key]:.1f}, Ratio: {final_caseload[key] / targets['caseload'][key]:.3f})"
         )
diff --git a/policyengine_uk_data/datasets/imputations/consumption.py b/policyengine_uk_data/datasets/imputations/consumption.py
index 34cbdc8b..c726441c 100644
--- a/policyengine_uk_data/datasets/imputations/consumption.py
+++ b/policyengine_uk_data/datasets/imputations/consumption.py
@@ -262,9 +262,7 @@ def uprate_lcfs_table(
     household["petrol_spending"] *= fuel_uprating
     household["diesel_spending"] *= fuel_uprating
 
-    cpi = (
-        system.parameters.gov.economic_assumptions.indices.obr.consumer_price_index
-    )
+    cpi = system.parameters.gov.economic_assumptions.indices.obr.consumer_price_index
     cpi_uprating = cpi(time_period) / cpi(start_period)
 
     for variable in IMPUTATIONS:
diff --git a/policyengine_uk_data/targets/sources/_common.py b/policyengine_uk_data/targets/sources/_common.py
index 69fb6988..110bc819 100644
--- a/policyengine_uk_data/targets/sources/_common.py
+++ b/policyengine_uk_data/targets/sources/_common.py
@@ -9,7 +9,7 @@
 
 HEADERS = {
     "User-Agent": (
-        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)" " AppleWebKit/537.36"
+        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"
     ),
 }
 
diff --git a/policyengine_uk_data/tests/microsimulation/test_reform_impacts.py b/policyengine_uk_data/tests/microsimulation/test_reform_impacts.py
index 19baa19f..3b3f0446 100644
--- a/policyengine_uk_data/tests/microsimulation/test_reform_impacts.py
+++ b/policyengine_uk_data/tests/microsimulation/test_reform_impacts.py
@@ -59,9 +59,9 @@ def test_reform_fiscal_impacts(
     """Test that each reform produces the expected fiscal impact."""
     impact = get_fiscal_impact(baseline, enhanced_frs, reform)
 
-    assert (
-        abs(impact - expected_impact) < tolerance
-    ), f"Impact for {reform_name} is {impact:.1f} billion, expected {expected_impact:.1f} billion"
+    assert abs(impact - expected_impact) < tolerance, (
+        f"Impact for {reform_name} is {impact:.1f} billion, expected {expected_impact:.1f} billion"
+    )
 
 
 def test_config_file_exists():
@@ -77,11 +77,11 @@ def test_all_reforms_have_required_fields():
 
     for i, reform in enumerate(reforms_data):
         for field in required_fields:
-            assert (
-                field in reform
-            ), f"Reform {i} missing required field: {field}"
+            assert field in reform, (
+                f"Reform {i} missing required field: {field}"
+            )
 
-        assert isinstance(
-            reform["parameters"], dict
-        ), f"Reform {i} parameters must be a dictionary"
+        assert isinstance(reform["parameters"], dict), (
+            f"Reform {i} parameters must be a dictionary"
+        )
         assert len(reform["parameters"]) > 0, f"Reform {i} has no parameters"
diff --git a/policyengine_uk_data/tests/test_aggregates.py b/policyengine_uk_data/tests/test_aggregates.py
index 8a76d37f..10a5fb2c 100644
--- a/policyengine_uk_data/tests/test_aggregates.py
+++ b/policyengine_uk_data/tests/test_aggregates.py
@@ -14,6 +14,6 @@ def test_aggregates(baseline, variable: str):
         variable, map_to="household", period=2025
     ).sum()
 
-    assert (
-        abs(estimate / AGGREGATES[variable] - 1) < 0.7
-    ), f"Expected {AGGREGATES[variable]/1e9:.1f} billion for {variable}, got {estimate/1e9:.1f} billion (relative error = {abs(estimate / AGGREGATES[variable] - 1):.1%})."
+    assert abs(estimate / AGGREGATES[variable] - 1) < 0.7, (
+        f"Expected {AGGREGATES[variable] / 1e9:.1f} billion for {variable}, got {estimate / 1e9:.1f} billion (relative error = {abs(estimate / AGGREGATES[variable] - 1):.1%})."
+    )
diff --git a/policyengine_uk_data/tests/test_child_limit.py b/policyengine_uk_data/tests/test_child_limit.py
index e0103982..446ed4c4 100644
--- a/policyengine_uk_data/tests/test_child_limit.py
+++ b/policyengine_uk_data/tests/test_child_limit.py
@@ -35,9 +35,9 @@ def test_child_limit(baseline):
         440e3 * UPRATING_24_25
     )  # Expected number of affected households
 
-    assert (
-        abs(children_affected / child_target - 1) < 0.3
-    ), f"Expected {child_target/1e6:.1f} million affected children, got {children_affected/1e6:.1f} million."
-    assert (
-        abs(households_affected / household_target - 1) < 0.3
-    ), f"Expected {household_target/1e3:.0f} thousand affected households, got {households_affected/1e3:.0f} thousand."
+    assert abs(children_affected / child_target - 1) < 0.3, (
+        f"Expected {child_target / 1e6:.1f} million affected children, got {children_affected / 1e6:.1f} million."
+    )
+    assert abs(households_affected / household_target - 1) < 0.3, (
+        f"Expected {household_target / 1e3:.0f} thousand affected households, got {households_affected / 1e3:.0f} thousand."
+    )
diff --git a/policyengine_uk_data/tests/test_low_income_deciles.py b/policyengine_uk_data/tests/test_low_income_deciles.py
index 189ac071..c093ddf6 100644
--- a/policyengine_uk_data/tests/test_low_income_deciles.py
+++ b/policyengine_uk_data/tests/test_low_income_deciles.py
@@ -42,8 +42,8 @@ def test_first_decile_tax_rate_reasonable(baseline):
         d1_tax_rate = d1_tax / d1_market
         assert d1_tax_rate < 1.75, (
             f"First decile tax rate is {d1_tax_rate:.0%}, which exceeds 175%. "
-            f"Total D1 tax: £{d1_tax/1e9:.1f}bn, "
-            f"Total D1 market income: £{d1_market/1e9:.1f}bn. "
+            f"Total D1 tax: £{d1_tax / 1e9:.1f}bn, "
+            f"Total D1 market income: £{d1_market / 1e9:.1f}bn. "
             "This likely indicates a bug in property_purchased or similar variable."
         )
 
diff --git a/policyengine_uk_data/tests/test_non_negative_incomes.py b/policyengine_uk_data/tests/test_non_negative_incomes.py
index 4d9f671a..633305e8 100644
--- a/policyengine_uk_data/tests/test_non_negative_incomes.py
+++ b/policyengine_uk_data/tests/test_non_negative_incomes.py
@@ -18,6 +18,6 @@ def test_income_non_negative(frs, variable: str):
     """Test that income variables have no negative values."""
     values = frs.person[variable]
     min_value = values.min()
-    assert (
-        min_value >= 0
-    ), f"{variable} has negative values (min = {min_value:.2f})"
+    assert min_value >= 0, (
+        f"{variable} has negative values (min = {min_value:.2f})"
+    )
diff --git a/policyengine_uk_data/tests/test_pension_contributions_via_salary_sacrifice.py b/policyengine_uk_data/tests/test_pension_contributions_via_salary_sacrifice.py
index 57c5edf8..3f5c2ba3 100644
--- a/policyengine_uk_data/tests/test_pension_contributions_via_salary_sacrifice.py
+++ b/policyengine_uk_data/tests/test_pension_contributions_via_salary_sacrifice.py
@@ -5,20 +5,20 @@ def test_pension_contributions_via_salary_sacrifice(baseline):
     )
 
     # Basic validation: all values should be non-negative
-    assert (
-        values >= 0
-    ).all(), "Salary sacrifice pension contributions must be non-negative"
+    assert (values >= 0).all(), (
+        "Salary sacrifice pension contributions must be non-negative"
+    )
 
     # Should have some non-zero values (not everyone uses salary sacrifice, but some do)
     total = values.sum()
-    assert (
-        total > 0
-    ), f"Expected some salary sacrifice contributions, got {total}"
+    assert total > 0, (
+        f"Expected some salary sacrifice contributions, got {total}"
+    )
 
     # Reasonableness check: total should be less than total employment income
     # This is a very loose check just to catch major issues
     employment_income = baseline.calculate("employment_income", period=2025)
     total_employment = employment_income.sum()
-    assert (
-        total < total_employment
-    ), f"Salary sacrifice contributions ({total/1e9:.1f}B) cannot exceed total employment income ({total_employment/1e9:.1f}B)"
+    assert total < total_employment, (
+        f"Salary sacrifice contributions ({total / 1e9:.1f}B) cannot exceed total employment income ({total_employment / 1e9:.1f}B)"
+    )
diff --git a/policyengine_uk_data/tests/test_population.py b/policyengine_uk_data/tests/test_population.py
index 321c377c..43645791 100644
--- a/policyengine_uk_data/tests/test_population.py
+++ b/policyengine_uk_data/tests/test_population.py
@@ -2,6 +2,6 @@ def test_population(baseline):
     population = baseline.calculate("people", 2025).sum() / 1e6
     POPULATION_TARGET = 69.5  # Expected UK population in millions, per ONS 2022-based estimate here: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections/bulletins/nationalpopulationprojections/2022based
     # Tolerance temporarily relaxed to 7% due to calibration inflation issue #217
-    assert (
-        abs(population / POPULATION_TARGET - 1) < 0.07
-    ), f"Expected UK population of {POPULATION_TARGET:.1f} million, got {population:.1f} million."
+    assert abs(population / POPULATION_TARGET - 1) < 0.07, (
+        f"Expected UK population of {POPULATION_TARGET:.1f} million, got {population:.1f} million."
+    )
diff --git a/policyengine_uk_data/tests/test_property_purchased.py b/policyengine_uk_data/tests/test_property_purchased.py
index 709e86bf..0c742180 100644
--- a/policyengine_uk_data/tests/test_property_purchased.py
+++ b/policyengine_uk_data/tests/test_property_purchased.py
@@ -34,9 +34,9 @@ def test_property_purchased_rate(baseline):
     target_rate = PROPERTY_PURCHASE_RATE
     tolerance = 0.02
 
-    assert (
-        abs(actual_rate - target_rate) < tolerance
-    ), f"property_purchased rate {actual_rate:.2%} is not close to target {target_rate:.2%}"
+    assert abs(actual_rate - target_rate) < tolerance, (
+        f"property_purchased rate {actual_rate:.2%} is not close to target {target_rate:.2%}"
+    )
 
 
 def test_property_purchased_not_all_true(baseline):
@@ -47,9 +47,9 @@ def test_property_purchased_not_all_true(baseline):
     n_households = len(property_purchased)
 
     # Should NOT be 100% True (the bug we fixed)
-    assert (
-        true_count < n_households * 0.1
-    ), f"Too many households have property_purchased=True ({true_count}/{n_households})"
+    assert true_count < n_households * 0.1, (
+        f"Too many households have property_purchased=True ({true_count}/{n_households})"
+    )
 
 
 def test_property_purchased_has_some_true(baseline):
@@ -80,13 +80,13 @@ def test_sdlt_total_reasonable(baseline):
     max_sdlt = 50e9  # £50bn maximum (official is ~£14bn)
 
     assert total_sdlt > min_sdlt, (
-        f"Total SDLT £{total_sdlt/1e9:.1f}bn is too low "
-        f"(minimum expected: £{min_sdlt/1e9:.1f}bn)"
+        f"Total SDLT £{total_sdlt / 1e9:.1f}bn is too low "
+        f"(minimum expected: £{min_sdlt / 1e9:.1f}bn)"
     )
 
     assert total_sdlt < max_sdlt, (
-        f"Total SDLT £{total_sdlt/1e9:.1f}bn is unrealistically high "
-        f"(maximum expected: £{max_sdlt/1e9:.1f}bn). "
+        f"Total SDLT £{total_sdlt / 1e9:.1f}bn is unrealistically high "
+        f"(maximum expected: £{max_sdlt / 1e9:.1f}bn). "
         f"Official SDLT is ~£14bn. "
         "This suggests property_purchased may be incorrectly set to True for all households."
     )
diff --git a/policyengine_uk_data/tests/test_salary_sacrifice_headcount.py b/policyengine_uk_data/tests/test_salary_sacrifice_headcount.py
index af0fdfd4..09f3ab25 100644
--- a/policyengine_uk_data/tests/test_salary_sacrifice_headcount.py
+++ b/policyengine_uk_data/tests/test_salary_sacrifice_headcount.py
@@ -23,8 +23,8 @@ def test_salary_sacrifice_total_users(baseline):
     TARGET = 7_700_000
 
     assert abs(total_users / TARGET - 1) < TOLERANCE, (
-        f"Expected ~{TARGET/1e6:.1f}mn SS users, "
-        f"got {total_users/1e6:.1f}mn ({total_users/TARGET*100:.0f}% of target)"
+        f"Expected ~{TARGET / 1e6:.1f}mn SS users, "
+        f"got {total_users / 1e6:.1f}mn ({total_users / TARGET * 100:.0f}% of target)"
     )
 
 
@@ -44,8 +44,8 @@ def test_salary_sacrifice_below_cap_users(baseline):
     TARGET = 4_300_000
 
     assert abs(total_below_cap / TARGET - 1) < TOLERANCE, (
-        f"Expected ~{TARGET/1e6:.1f}mn below-cap SS users, "
-        f"got {total_below_cap/1e6:.1f}mn ({total_below_cap/TARGET*100:.0f}% of target)"
+        f"Expected ~{TARGET / 1e6:.1f}mn below-cap SS users, "
+        f"got {total_below_cap / 1e6:.1f}mn ({total_below_cap / TARGET * 100:.0f}% of target)"
     )
 
 
@@ -65,6 +65,6 @@ def test_salary_sacrifice_above_cap_users(baseline):
     TARGET = 3_300_000
 
     assert abs(total_above_cap / TARGET - 1) < TOLERANCE, (
-        f"Expected ~{TARGET/1e6:.1f}mn above-cap SS users, "
-        f"got {total_above_cap/1e6:.1f}mn ({total_above_cap/TARGET*100:.0f}% of target)"
+        f"Expected ~{TARGET / 1e6:.1f}mn above-cap SS users, "
+        f"got {total_above_cap / 1e6:.1f}mn ({total_above_cap / TARGET * 100:.0f}% of target)"
     )
diff --git a/policyengine_uk_data/tests/test_scotland_babies.py b/policyengine_uk_data/tests/test_scotland_babies.py
index a6674018..b5eba325 100644
--- a/policyengine_uk_data/tests/test_scotland_babies.py
+++ b/policyengine_uk_data/tests/test_scotland_babies.py
@@ -30,6 +30,6 @@ def test_scotland_babies_under_1(baseline):
     TOLERANCE = 0.15  # 15% tolerance
 
     assert abs(total_babies / TARGET - 1) < TOLERANCE, (
-        f"Expected ~{TARGET/1000:.0f}k babies under 1 in Scotland, "
-        f"got {total_babies/1000:.0f}k ({total_babies/TARGET*100:.0f}% of target)"
+        f"Expected ~{TARGET / 1000:.0f}k babies under 1 in Scotland, "
+        f"got {total_babies / 1000:.0f}k ({total_babies / TARGET * 100:.0f}% of target)"
     )
diff --git a/policyengine_uk_data/tests/test_scotland_uc_babies.py b/policyengine_uk_data/tests/test_scotland_uc_babies.py
index 36246b48..85751cca 100644
--- a/policyengine_uk_data/tests/test_scotland_uc_babies.py
+++ b/policyengine_uk_data/tests/test_scotland_uc_babies.py
@@ -44,6 +44,6 @@ def test_scotland_uc_households_child_under_1(baseline):
     TOLERANCE = 0.15  # 15% tolerance
 
     assert abs(total / TARGET - 1) < TOLERANCE, (
-        f"Expected ~{TARGET/1000:.0f}k UC households with child under 1 in Scotland, "
-        f"got {total/1000:.0f}k ({total/TARGET*100:.0f}% of target)"
+        f"Expected ~{TARGET / 1000:.0f}k UC households with child under 1 in Scotland, "
+        f"got {total / 1000:.0f}k ({total / TARGET * 100:.0f}% of target)"
     )
diff --git a/policyengine_uk_data/tests/test_target_registry.py b/policyengine_uk_data/tests/test_target_registry.py
index ccc49e00..c6f78bdd 100644
--- a/policyengine_uk_data/tests/test_target_registry.py
+++ b/policyengine_uk_data/tests/test_target_registry.py
@@ -52,9 +52,9 @@ def test_hmrc_spi_targets_exist():
     targets = get_all_targets(year=2025)
     spi_targets = [t for t in targets if t.source == "hmrc_spi"]
     # 13 bands × 6 income types × 2 (count + amount) = 156 per year
-    assert (
-        len(spi_targets) >= 100
-    ), f"Expected 100+ SPI targets, got {len(spi_targets)}"
+    assert len(spi_targets) >= 100, (
+        f"Expected 100+ SPI targets, got {len(spi_targets)}"
+    )
 
 
 def test_dwp_pip_targets():
@@ -76,9 +76,9 @@ def test_voa_council_tax_targets():
 def test_core_target_count():
     """Total target count should be substantial."""
     targets = get_all_targets(year=2025)
-    assert (
-        len(targets) >= 200
-    ), f"Expected 200+ targets for 2025, got {len(targets)}"
+    assert len(targets) >= 200, (
+        f"Expected 200+ targets for 2025, got {len(targets)}"
+    )
 
 
 def test_two_child_limit_targets():
diff --git a/policyengine_uk_data/tests/test_uc_by_children.py b/policyengine_uk_data/tests/test_uc_by_children.py
index 3878d99b..649e0fb3 100644
--- a/policyengine_uk_data/tests/test_uc_by_children.py
+++ b/policyengine_uk_data/tests/test_uc_by_children.py
@@ -55,6 +55,6 @@ def test_uc_households_by_children(baseline, bucket, target):
     actual = (household_weight * match).sum()
 
     assert abs(actual / target - 1) < TOLERANCE, (
-        f"UC households with {bucket}: expected {target/1e3:.0f}k, "
-        f"got {actual/1e3:.0f}k ({actual/target*100:.0f}% of target)"
+        f"UC households with {bucket}: expected {target / 1e3:.0f}k, "
+        f"got {actual / 1e3:.0f}k ({actual / target * 100:.0f}% of target)"
     )
diff --git a/pyproject.toml b/pyproject.toml
index 72df5b5a..73200a77 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,7 +24,7 @@ dependencies = [
     "policyengine-uk>=2.43.5",
     "microcalibrate>=0.18.0",
     "microimpute>=1.0.1",
-    "black>=25.1.0",
+    "ruff>=0.9.0",
     "rich>=13.0.0",
     "odfpy",
     "pandas",
@@ -35,7 +35,7 @@ dependencies = [
 
 [project.optional-dependencies]
 dev = [
-    "black",
+    "ruff>=0.9.0",
     "pytest",
     "torch",
     "tables",
@@ -70,23 +70,8 @@ filterwarnings = [
     "ignore::PendingDeprecationWarning",
 ]
 
-[tool.black]
+[tool.ruff]
 line-length = 79
-target-version = ['py311']
-include = '\.pyi?$'
-extend-exclude = '''
-/(
-  # directories
-  \.eggs
-  | \.git
-  | \.hg
-  | \.mypy_cache
-  | \.tox
-  | \.venv
-  | build
-  | dist
-)/
-'''
 
 [tool.towncrier]
 package = "policyengine_uk_data"
diff --git a/uv.lock b/uv.lock
index 8c63a74e..9e03803a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -99,31 +99,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/94/fe/3aed5d0be4d404d12d36ab97e2f1791424d9ca39c2f754a6285d59a3b01d/beautifulsoup4-4.14.2-py3-none-any.whl", hash = "sha256:5ef6fa3a8cbece8488d66985560f97ed091e22bbc4e9c2338508a9d5de6d4515", size = 106392, upload-time = "2025-09-29T10:05:43.771Z" },
 ]
 
-[[package]]
-name = "black"
-version = "25.11.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "click" },
-    { name = "mypy-extensions" },
-    { name = "packaging" },
-    { name = "pathspec" },
-    { name = "platformdirs" },
-    { name = "pytokens" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/8c/ad/33adf4708633d047950ff2dfdea2e215d84ac50ef95aff14a614e4b6e9b2/black-25.11.0.tar.gz", hash = "sha256:9a323ac32f5dc75ce7470501b887250be5005a01602e931a15e45593f70f6e08", size = 655669, upload-time = "2025-11-10T01:53:50.558Z" }
-wheels = [
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-    { url = "https://files.pythonhosted.org/packages/ba/4b/0f00bfb3d1f7e05e25bfc7c363f54dc523bb6ba502f98f4ad3acf01ab2e4/black-25.11.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:51c65d7d60bb25429ea2bf0731c32b2a2442eb4bd3b2afcb47830f0b13e58bfd", size = 1607983, upload-time = "2025-11-10T02:02:52.502Z" },
-    { url = "https://files.pythonhosted.org/packages/99/fe/49b0768f8c9ae57eb74cc10a1f87b4c70453551d8ad498959721cc345cb7/black-25.11.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:936c4dd07669269f40b497440159a221ee435e3fddcf668e0c05244a9be71993", size = 1682481, upload-time = "2025-11-10T01:57:12.35Z" },
-    { url = "https://files.pythonhosted.org/packages/55/17/7e10ff1267bfa950cc16f0a411d457cdff79678fbb77a6c73b73a5317904/black-25.11.0-cp313-cp313-win_amd64.whl", hash = "sha256:f42c0ea7f59994490f4dccd64e6b2dd49ac57c7c84f38b8faab50f8759db245c", size = 1363869, upload-time = "2025-11-10T01:58:24.608Z" },
-    { url = "https://files.pythonhosted.org/packages/67/c0/cc865ce594d09e4cd4dfca5e11994ebb51604328489f3ca3ae7bb38a7db5/black-25.11.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:35690a383f22dd3e468c85dc4b915217f87667ad9cce781d7b42678ce63c4170", size = 1771358, upload-time = "2025-11-10T02:03:33.331Z" },
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-    { url = "https://files.pythonhosted.org/packages/ff/4b/5604710d61cdff613584028b4cb4607e56e148801ed9b38ee7970799dab6/black-25.11.0-cp314-cp314-win_amd64.whl", hash = "sha256:aa211411e94fdf86519996b7f5f05e71ba34835d8f0c0f03c00a26271da02664", size = 1382599, upload-time = "2025-11-10T01:57:57.427Z" },
-    { url = "https://files.pythonhosted.org/packages/00/5d/aed32636ed30a6e7f9efd6ad14e2a0b0d687ae7c8c7ec4e4a557174b895c/black-25.11.0-py3-none-any.whl", hash = "sha256:e3f562da087791e96cefcd9dda058380a442ab322a02e222add53736451f604b", size = 204918, upload-time = "2025-11-10T01:53:48.917Z" },
-]
-
 [[package]]
 name = "blosc2"
 version = "3.11.1"
@@ -471,6 +446,7 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/49/e8/58c7f85958bda41dafea50497cbd59738c5c43dbbea5ee83d651234398f4/greenlet-3.2.4-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:1a921e542453fe531144e91e1feedf12e07351b1cf6c9e8a3325ea600a715a31", size = 272814, upload-time = "2025-08-07T13:15:50.011Z" },
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     { name = "torch", marker = "extra == 'dev'" },
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+    { name = "jinja2" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c2/eb/5bf25a34123698d3bbab39c5bc5375f8f8bcbcc5a136964ade66935b8b9d/towncrier-25.8.0.tar.gz", hash = "sha256:eef16d29f831ad57abb3ae32a0565739866219f1ebfbdd297d32894eb9940eb1", size = 76322, upload-time = "2025-08-30T11:41:55.393Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/42/06/8ba22ec32c74ac1be3baa26116e3c28bc0e76a5387476921d20b6fdade11/towncrier-25.8.0-py3-none-any.whl", hash = "sha256:b953d133d98f9aeae9084b56a3563fd2519dfc6ec33f61c9cd2c61ff243fb513", size = 65101, upload-time = "2025-08-30T11:41:53.644Z" },
+]
+
 [[package]]
 name = "tqdm"
 version = "4.67.1"

From cd75d30ea73d7421a1de02788aef7a04f0143101 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Thu, 5 Mar 2026 22:06:18 -0500
Subject: [PATCH 2/2] Use ruff default line-length (88) instead of 79

Remove the explicit line-length = 79 setting from [tool.ruff] in
pyproject.toml and reformat all files with ruff's default of 88.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 .github/bump_version.py                       |   4 +-
 docs/methodology.ipynb                        |  16 +-
 docs/validation/constituencies.ipynb          |  14 +-
 docs/validation/local_authorities.ipynb       |  16 +-
 .../datasets/childcare/takeup_rate.py         |  19 +--
 .../datasets/create_datasets.py               |   4 +-
 policyengine_uk_data/datasets/frs.py          | 148 +++++-------------
 .../datasets/imputations/capital_gains.py     |  16 +-
 .../datasets/imputations/consumption.py       |  22 +--
 .../datasets/imputations/salary_sacrifice.py  |  28 +---
 .../datasets/imputations/services/etb.py      |   8 +-
 .../datasets/imputations/services/nhs.py      |  14 +-
 .../datasets/imputations/services/services.py |   4 +-
 .../boundary_changes/mapping_matrix.py        |   4 +-
 .../local_areas/constituencies/calibrate.py   |  16 +-
 .../local_areas/constituencies/loss.py        |  16 +-
 .../targets/create_employment_incomes.py      |  43 ++---
 .../targets/fill_missing_age_demographics.py  |   4 +-
 .../local_authorities/calibrate.py            |  22 +--
 .../local_areas/local_authorities/loss.py     |  22 +--
 .../targets/create_employment_incomes.py      |  54 ++-----
 policyengine_uk_data/datasets/spi.py          |  13 +-
 policyengine_uk_data/parameters/__init__.py   |   8 +-
 .../targets/build_loss_matrix.py              |  16 +-
 .../targets/compute/benefits.py               |  32 +---
 .../targets/compute/demographics.py           |  10 +-
 .../targets/compute/households.py             |  26 ++-
 .../targets/compute/income.py                 |  20 +--
 policyengine_uk_data/targets/registry.py      |   4 +-
 .../targets/sources/hmrc_salary_sacrifice.py  |  11 +-
 .../targets/sources/hmrc_spi.py               |   8 +-
 .../targets/sources/local_age.py              |  12 +-
 .../targets/sources/local_income.py           |  12 +-
 .../targets/sources/local_la_extras.py        |   4 +-
 policyengine_uk_data/targets/sources/obr.py   |  25 +--
 .../targets/sources/ons_demographics.py       |   8 +-
 .../targets/sources/ons_savings.py            |   8 +-
 .../microsimulation/test_reform_impacts.py    |   4 +-
 .../microsimulation/update_reform_impacts.py  |  12 +-
 policyengine_uk_data/tests/test_aggregates.py |   4 +-
 .../tests/test_child_limit.py                 |  21 +--
 policyengine_uk_data/tests/test_childcare.py  |  24 +--
 .../tests/test_non_negative_incomes.py        |   4 +-
 ...sion_contributions_via_salary_sacrifice.py |   4 +-
 .../tests/test_scotland_uc_babies.py          |   8 +-
 .../tests/test_target_registry.py             |   8 +-
 .../tests/test_uc_by_children.py              |   8 +-
 .../tests/test_vehicle_ownership.py           |   8 +-
 policyengine_uk_data/utils/calibrate.py       |  27 +---
 policyengine_uk_data/utils/data_upload.py     |   8 +-
 policyengine_uk_data/utils/datasets.py        |  20 +--
 policyengine_uk_data/utils/huggingface.py     |   4 +-
 .../utils/incomes_projection.py               |  30 ++--
 policyengine_uk_data/utils/loss.py            |   4 +-
 policyengine_uk_data/utils/progress.py        |  12 +-
 policyengine_uk_data/utils/qrf.py             |   4 +-
 policyengine_uk_data/utils/spi.py             |   8 +-
 policyengine_uk_data/utils/stack.py           |   4 +-
 policyengine_uk_data/utils/uc_data.py         |  12 +-
 pyproject.toml                                |   3 -
 60 files changed, 261 insertions(+), 691 deletions(-)

diff --git a/.github/bump_version.py b/.github/bump_version.py
index bb0fd6dd..779a82e3 100644
--- a/.github/bump_version.py
+++ b/.github/bump_version.py
@@ -19,9 +19,7 @@ def get_current_version(pyproject_path: Path) -> str:
 
 def infer_bump(changelog_dir: Path) -> str:
     fragments = [
-        f
-        for f in changelog_dir.iterdir()
-        if f.is_file() and f.name != ".gitkeep"
+        f for f in changelog_dir.iterdir() if f.is_file() and f.name != ".gitkeep"
     ]
     if not fragments:
         print("No changelog fragments found", file=sys.stderr)
diff --git a/docs/methodology.ipynb b/docs/methodology.ipynb
index d580a6d4..892371e3 100644
--- a/docs/methodology.ipynb
+++ b/docs/methodology.ipynb
@@ -2913,9 +2913,7 @@
     "\n",
     "variable = \"employment_income\"\n",
     "count = True\n",
-    "variable_df = incomes[\n",
-    "    (incomes.variable == variable) & (incomes[\"count\"] == count)\n",
-    "]\n",
+    "variable_df = incomes[(incomes.variable == variable) & (incomes[\"count\"] == count)]\n",
     "\n",
     "fig = px.bar(\n",
     "    variable_df,\n",
@@ -4120,9 +4118,7 @@
    "source": [
     "variable = \"dividend_income\"\n",
     "count = True\n",
-    "variable_df = incomes[\n",
-    "    (incomes.variable == variable) & (incomes[\"count\"] == count)\n",
-    "]\n",
+    "variable_df = incomes[(incomes.variable == variable) & (incomes[\"count\"] == count)]\n",
     "\n",
     "fig = px.bar(\n",
     "    variable_df,\n",
@@ -6179,9 +6175,7 @@
     "\n",
     "variable = \"employment_income\"\n",
     "count = True\n",
-    "variable_df = incomes[\n",
-    "    (incomes.variable == variable) & (incomes[\"count\"] == count)\n",
-    "]\n",
+    "variable_df = incomes[(incomes.variable == variable) & (incomes[\"count\"] == count)]\n",
     "\n",
     "fig = px.bar(\n",
     "    variable_df,\n",
@@ -7627,9 +7621,7 @@
     "\n",
     "variable = \"employment_income\"\n",
     "count = True\n",
-    "variable_df = incomes[\n",
-    "    (incomes.variable == variable) & (incomes[\"count\"] == count)\n",
-    "]\n",
+    "variable_df = incomes[(incomes.variable == variable) & (incomes[\"count\"] == count)]\n",
     "\n",
     "fig = px.bar(\n",
     "    variable_df,\n",
diff --git a/docs/validation/constituencies.ipynb b/docs/validation/constituencies.ipynb
index 0b76f81f..8160e553 100644
--- a/docs/validation/constituencies.ipynb
+++ b/docs/validation/constituencies.ipynb
@@ -190,8 +190,8 @@
     ")\n",
     "from policyengine_uk_data.datasets import EnhancedFRS_2022_23\n",
     "\n",
-    "constituency_target_matrix, constituency_actuals, _ = (\n",
-    "    create_constituency_target_matrix(EnhancedFRS_2022_23, 2025, None)\n",
+    "constituency_target_matrix, constituency_actuals, _ = create_constituency_target_matrix(\n",
+    "    EnhancedFRS_2022_23, 2025, None\n",
     ")\n",
     "national_target_matrix, national_actuals = create_national_target_matrix(\n",
     "    EnhancedFRS_2022_23, 2025, None\n",
@@ -444,15 +444,11 @@
     "national_target_validation[\"target\"] = national_actuals.values\n",
     "\n",
     "national_target_validation[\"error\"] = (\n",
-    "    national_target_validation[\"estimate\"]\n",
-    "    - national_target_validation[\"target\"]\n",
+    "    national_target_validation[\"estimate\"] - national_target_validation[\"target\"]\n",
     ")\n",
-    "national_target_validation[\"abs_error\"] = national_target_validation[\n",
-    "    \"error\"\n",
-    "].abs()\n",
+    "national_target_validation[\"abs_error\"] = national_target_validation[\"error\"].abs()\n",
     "national_target_validation[\"rel_abs_error\"] = (\n",
-    "    national_target_validation[\"abs_error\"]\n",
-    "    / national_target_validation[\"target\"]\n",
+    "    national_target_validation[\"abs_error\"] / national_target_validation[\"target\"]\n",
     ")"
    ]
   },
diff --git a/docs/validation/local_authorities.ipynb b/docs/validation/local_authorities.ipynb
index 37f17d72..7d972bc1 100644
--- a/docs/validation/local_authorities.ipynb
+++ b/docs/validation/local_authorities.ipynb
@@ -248,9 +248,9 @@
     "    local_authority_target_validation[\"estimate\"]\n",
     "    - local_authority_target_validation[\"target\"]\n",
     ")\n",
-    "local_authority_target_validation[\"abs_error\"] = (\n",
-    "    local_authority_target_validation[\"error\"].abs()\n",
-    ")\n",
+    "local_authority_target_validation[\"abs_error\"] = local_authority_target_validation[\n",
+    "    \"error\"\n",
+    "].abs()\n",
     "local_authority_target_validation[\"rel_abs_error\"] = (\n",
     "    local_authority_target_validation[\"abs_error\"]\n",
     "    / local_authority_target_validation[\"target\"]\n",
@@ -445,15 +445,11 @@
     "national_target_validation[\"target\"] = national_actuals.values\n",
     "\n",
     "national_target_validation[\"error\"] = (\n",
-    "    national_target_validation[\"estimate\"]\n",
-    "    - national_target_validation[\"target\"]\n",
+    "    national_target_validation[\"estimate\"] - national_target_validation[\"target\"]\n",
     ")\n",
-    "national_target_validation[\"abs_error\"] = national_target_validation[\n",
-    "    \"error\"\n",
-    "].abs()\n",
+    "national_target_validation[\"abs_error\"] = national_target_validation[\"error\"].abs()\n",
     "national_target_validation[\"rel_abs_error\"] = (\n",
-    "    national_target_validation[\"abs_error\"]\n",
-    "    / national_target_validation[\"target\"]\n",
+    "    national_target_validation[\"abs_error\"] / national_target_validation[\"target\"]\n",
     ")"
    ]
   },
diff --git a/policyengine_uk_data/datasets/childcare/takeup_rate.py b/policyengine_uk_data/datasets/childcare/takeup_rate.py
index 9e5031c3..db643e93 100644
--- a/policyengine_uk_data/datasets/childcare/takeup_rate.py
+++ b/policyengine_uk_data/datasets/childcare/takeup_rate.py
@@ -59,9 +59,7 @@ def simulate_childcare_programs(
     np.random.seed(seed)
 
     # Take-up flags
-    sim.set_input(
-        "would_claim_tfc", 2024, np.random.random(benunit_count) < tfc
-    )
+    sim.set_input("would_claim_tfc", 2024, np.random.random(benunit_count) < tfc)
     sim.set_input(
         "would_claim_extended_childcare",
         2024,
@@ -87,9 +85,7 @@ def simulate_childcare_programs(
     extended_hours_values = np.clip(extended_hours_values, 0, 30)
 
     # Set the maximum extended childcare hours usage variable
-    sim.set_input(
-        "maximum_extended_childcare_hours_usage", 2024, extended_hours_values
-    )
+    sim.set_input("maximum_extended_childcare_hours_usage", 2024, extended_hours_values)
 
     # Calculate outputs
     df = sim.calculate_dataframe(
@@ -114,14 +110,9 @@ def simulate_childcare_programs(
 
     spending = {
         "tfc": sim.calculate("tax_free_childcare", 2024).sum() / 1e9,
-        "extended": sim.calculate("extended_childcare_entitlement", 2024).sum()
-        / 1e9,
-        "targeted": sim.calculate("targeted_childcare_entitlement", 2024).sum()
-        / 1e9,
-        "universal": sim.calculate(
-            "universal_childcare_entitlement", 2024
-        ).sum()
-        / 1e9,
+        "extended": sim.calculate("extended_childcare_entitlement", 2024).sum() / 1e9,
+        "targeted": sim.calculate("targeted_childcare_entitlement", 2024).sum() / 1e9,
+        "universal": sim.calculate("universal_childcare_entitlement", 2024).sum() / 1e9,
     }
 
     caseload = {
diff --git a/policyengine_uk_data/datasets/create_datasets.py b/policyengine_uk_data/datasets/create_datasets.py
index 07f6b362..2396946e 100644
--- a/policyengine_uk_data/datasets/create_datasets.py
+++ b/policyengine_uk_data/datasets/create_datasets.py
@@ -165,9 +165,7 @@ def main():
 
             # Downrate and save
             update_dataset("Downrate to 2023", "processing")
-            frs_calibrated = uprate_dataset(
-                frs_calibrated_constituencies, 2023
-            )
+            frs_calibrated = uprate_dataset(frs_calibrated_constituencies, 2023)
             update_dataset("Downrate to 2023", "completed")
 
             update_dataset("Save final dataset", "processing")
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 37ce70dc..7cc05cd3 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -74,23 +74,17 @@ def create_frs(
         if "benunit" in df.columns:
             # In the tables, benunit is the index of the benefit unit *within* the household.
             df.rename(columns={"benunit": "benunit_id"}, inplace=True)
-            df["benunit_id"] = (
-                df["household_id"] * 1e2 + df["benunit_id"]
-            ).astype(int)
+            df["benunit_id"] = (df["household_id"] * 1e2 + df["benunit_id"]).astype(int)
 
         if "person" in df.columns:
             df.rename(columns={"person": "person_id"}, inplace=True)
-            df["person_id"] = (
-                df["household_id"] * 1e3 + df["person_id"]
-            ).astype(int)
+            df["person_id"] = (df["household_id"] * 1e3 + df["person_id"]).astype(int)
 
         frs[table_name] = df
 
     # Combine adult and child tables for convenience
 
-    frs["person"] = (
-        pd.concat([frs["adult"], frs["child"]]).sort_index().fillna(0)
-    )
+    frs["person"] = pd.concat([frs["adult"], frs["child"]]).sort_index().fillna(0)
 
     person = frs["person"]
     benunit = frs["benunit"]
@@ -165,12 +159,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
         elif (
             typeed2_val in (2, 4)
             or (typeed2_val in (3, 8) and age_val < 11)
-            or (
-                typeed2_val == 0
-                and fted_val == 1
-                and age_val > 5
-                and age_val < 11
-            )
+            or (typeed2_val == 0 and fted_val == 1 and age_val > 5 and age_val < 11)
         ):
             return "PRIMARY"
         # In lower secondary
@@ -191,19 +180,14 @@ def determine_education_level(fted_val, typeed2_val, age_val):
         elif typeed2_val in (7, 8) and age_val >= 19:
             return "POST_SECONDARY"
         # In tertiary
-        elif typeed2_val == 9 or (
-            typeed2_val == 0 and fted_val == 1 and age_val >= 19
-        ):
+        elif typeed2_val == 9 or (typeed2_val == 0 and fted_val == 1 and age_val >= 19):
             return "TERTIARY"
         else:
             return "NOT_IN_EDUCATION"
 
     # Apply the function to determine education level
     pe_person["current_education"] = pd.Series(
-        [
-            determine_education_level(f, t, a)
-            for f, t, a in zip(fted, typeed2, age)
-        ],
+        [determine_education_level(f, t, a) for f, t, a in zip(fted, typeed2, age)],
         index=pe_person.index,
     )
 
@@ -326,9 +310,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     ctannual = household.ctannual[CT_valid]
 
     # Build the table
-    ct_mean = ctannual.groupby(
-        [region, band, single_person], dropna=False
-    ).mean()
+    ct_mean = ctannual.groupby([region, band, single_person], dropna=False).mean()
     ct_mean = ct_mean.replace(-1, ct_mean.mean())
 
     # For every household consult the table to find the imputed
@@ -357,9 +339,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     BANDS = ["A", "B", "C", "D", "E", "F", "G", "H", "I"]
     # Band 1 is the most common
     pe_household["council_tax_band"] = (
-        categorical(household.ctband, 1, range(1, 10), BANDS)
-        .fillna("D")
-        .values
+        categorical(household.ctband, 1, range(1, 10), BANDS).fillna("D").values
     )
     # Domestic rates variables are all weeklyised, unlike Council Tax variables (despite the variable name suggesting otherwise)
     if year < 2021:
@@ -384,9 +364,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
 
     WEEKS_IN_YEAR = 365.25 / 7
 
-    pe_person["employment_income"] = (
-        np.maximum(0, person.inearns) * WEEKS_IN_YEAR
-    )
+    pe_person["employment_income"] = np.maximum(0, person.inearns) * WEEKS_IN_YEAR
 
     pension_payment = sum_to_entity(
         pension.penpay * (pension.penpay > 0),
@@ -400,10 +378,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     )
     pension_deductions_removed = sum_to_entity(
         pension.poamt
-        * (
-            ((pension.poinc == 2) | (pension.penoth == 1))
-            & (pension.poamt > 0)
-        ),
+        * (((pension.poinc == 2) | (pension.penoth == 1)) & (pension.poamt > 0)),
         pension.person_id,
         person.person_id,
     )
@@ -412,9 +387,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
         pension_payment + pension_tax_paid + pension_deductions_removed
     ) * WEEKS_IN_YEAR
 
-    pe_person["self_employment_income"] = (
-        np.maximum(0, person.seincam2) * WEEKS_IN_YEAR
-    )
+    pe_person["self_employment_income"] = np.maximum(0, person.seincam2) * WEEKS_IN_YEAR
 
     INVERTED_BASIC_RATE = 1.25
 
@@ -429,10 +402,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     )
     taxable_savings_interest = (
         sum_to_entity(
-            (
-                account.accint
-                * np.where(account.acctax == 1, INVERTED_BASIC_RATE, 1)
-            )
+            (account.accint * np.where(account.acctax == 1, INVERTED_BASIC_RATE, 1))
             * (account.account.isin((1, 3, 5, 27, 28))),
             account.person_id,
             person.person_id,
@@ -446,10 +416,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     pe_person["dividend_income"] = np.maximum(
         0,
         sum_to_entity(
-            (
-                account.accint
-                * np.where(account.invtax == 1, INVERTED_BASIC_RATE, 1)
-            )
+            (account.accint * np.where(account.invtax == 1, INVERTED_BASIC_RATE, 1))
             * (
                 ((account.account == 6) & (account.invtax == 1))  # GGES
                 | account.account.isin((7, 8))  # Stocks/shares/UITs
@@ -474,16 +441,14 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     pe_person["property_income"] = (
         np.maximum(
             0,
-            is_head * persons_household_property_income
-            + person.cvpay
-            + person.royyr1,
+            is_head * persons_household_property_income + person.cvpay + person.royyr1,
         )
         * WEEKS_IN_YEAR
     )
     maintenance_to_self = np.maximum(
-        pd.Series(
-            np.where(person.mntus1 == 2, person.mntusam1, person.mntamt1)
-        ).fillna(0),
+        pd.Series(np.where(person.mntus1 == 2, person.mntusam1, person.mntamt1)).fillna(
+            0
+        ),
         0,
     )
     maintenance_from_dwp = person.mntamt2
@@ -519,8 +484,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     ]
 
     pe_person["private_transfer_income"] = (
-        sum_from_positive_fields(person, PRIVATE_TRANSFER_INCOME_FIELDS)
-        * WEEKS_IN_YEAR
+        sum_from_positive_fields(person, PRIVATE_TRANSFER_INCOME_FIELDS) * WEEKS_IN_YEAR
     )
 
     pe_person["lump_sum_income"] = person.redamt
@@ -561,9 +525,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
 
     pe_person["jsa_contrib_reported"] = (
         sum_to_entity(
-            benefits.benamt
-            * (benefits.var2.isin((1, 3)))
-            * (benefits.benefit == 14),
+            benefits.benamt * (benefits.var2.isin((1, 3))) * (benefits.benefit == 14),
             benefits.person_id,
             person.person_id,
         )
@@ -571,9 +533,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     )
     pe_person["jsa_income_reported"] = (
         sum_to_entity(
-            benefits.benamt
-            * (benefits.var2.isin((2, 4)))
-            * (benefits.benefit == 14),
+            benefits.benamt * (benefits.var2.isin((2, 4))) * (benefits.benefit == 14),
             benefits.person_id,
             person.person_id,
         )
@@ -581,9 +541,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     )
     pe_person["esa_contrib_reported"] = (
         sum_to_entity(
-            benefits.benamt
-            * (benefits.var2.isin((1, 3)))
-            * (benefits.benefit == 16),
+            benefits.benamt * (benefits.var2.isin((1, 3))) * (benefits.benefit == 16),
             benefits.person_id,
             person.person_id,
         )
@@ -591,9 +549,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     )
     pe_person["esa_income_reported"] = (
         sum_to_entity(
-            benefits.benamt
-            * (benefits.var2.isin((2, 4)))
-            * (benefits.benefit == 16),
+            benefits.benamt * (benefits.var2.isin((2, 4))) * (benefits.benefit == 16),
             benefits.person_id,
             person.person_id,
         )
@@ -647,9 +603,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
 
     pe_person["maintenance_expenses"] = (
         pd.Series(
-            np.where(
-                maintenance.mrus == 2, maintenance.mruamt, maintenance.mramt
-            )
+            np.where(maintenance.mrus == 2, maintenance.mruamt, maintenance.mramt)
         )
         .groupby(maintenance.person_id)
         .sum()
@@ -662,9 +616,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     pe_household["mortgage_interest_repayment"] = (
         household.mortint.fillna(0).values * WEEKS_IN_YEAR
     )
-    mortgage_capital = np.where(
-        mortgage.rmort == 1, mortgage.rmamt, mortgage.borramt
-    )
+    mortgage_capital = np.where(mortgage.rmort == 1, mortgage.rmamt, mortgage.borramt)
     mortgage_capital_repayment = sum_to_entity(
         mortgage_capital / mortgage.mortend,
         mortgage.household_id,
@@ -674,9 +626,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
 
     pe_person["childcare_expenses"] = (
         sum_to_entity(
-            childcare.chamt
-            * (childcare.cost == 1)
-            * (childcare.registrd == 1),
+            childcare.chamt * (childcare.cost == 1) * (childcare.registrd == 1),
             childcare.person_id,
             person.person_id,
         )
@@ -721,10 +671,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     # respondents who were not asked the question (imputation candidates)
     if "salsac_raw" in job.columns:
         salsac_numeric = (
-            job["salsac_raw"]
-            .map({"1": 1, "2": 0, " ": -1})
-            .fillna(-1)
-            .astype(int)
+            job["salsac_raw"].map({"1": 1, "2": 0, " ": -1}).fillna(-1).astype(int)
         )
         # Aggregate to person level: take max (any job with SS = person has SS)
         pe_person["salary_sacrifice_reported"] = np.clip(
@@ -795,17 +742,13 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     from policyengine_uk import Microsimulation
 
     sim = Microsimulation(dataset=dataset)
-    region = sim.populations["benunit"].household(
-        "region", dataset.time_period
-    )
+    region = sim.populations["benunit"].household("region", dataset.time_period)
     lha_category = sim.calculate("LHA_category", year)
 
     brma = np.empty(len(region), dtype=object)
 
     # Sample from a random BRMA in the region, weighted by the number of observations in each BRMA
-    lha_list_of_rents = pd.read_csv(
-        STORAGE_FOLDER / "lha_list_of_rents.csv.gz"
-    )
+    lha_list_of_rents = pd.read_csv(STORAGE_FOLDER / "lha_list_of_rents.csv.gz")
     lha_list_of_rents = lha_list_of_rents.copy()
 
     for possible_region in lha_list_of_rents.region.unique():
@@ -813,9 +756,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
             lor_mask = (lha_list_of_rents.region == possible_region) & (
                 lha_list_of_rents.lha_category == possible_lha_category
             )
-            mask = (region == possible_region) & (
-                lha_category == possible_lha_category
-            )
+            mask = (region == possible_region) & (lha_category == possible_lha_category)
             brma[mask] = lha_list_of_rents[lor_mask].brma.sample(
                 n=len(region[mask]), replace=True
             )
@@ -831,9 +772,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
         }
     )
 
-    df = df.groupby("household_id").brma.aggregate(
-        lambda x: x.sample(n=1).iloc[0]
-    )
+    df = df.groupby("household_id").brma.aggregate(lambda x: x.sample(n=1).iloc[0])
     brmas = df[sim.calculate("household_id")].values
 
     pe_household["brma"] = brmas
@@ -862,8 +801,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     )
     paragraph_4 = (
         pe_person.pip_dl_reported
-        >= benefit.pip.daily_living.enhanced * WEEKS_IN_YEAR
-        - THRESHOLD_SAFETY_GAP
+        >= benefit.pip.daily_living.enhanced * WEEKS_IN_YEAR - THRESHOLD_SAFETY_GAP
     )
     paragraph_5 = pe_person.afcs_reported > 0
     pe_person["is_severely_disabled_for_benefits"] = (
@@ -882,9 +820,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     pension_credit_rate = load_take_up_rate("pension_credit", year)
     universal_credit_rate = load_take_up_rate("universal_credit", year)
     marriage_allowance_rate = load_take_up_rate("marriage_allowance", year)
-    child_benefit_opts_out_rate = load_take_up_rate(
-        "child_benefit_opts_out_rate", year
-    )
+    child_benefit_opts_out_rate = load_take_up_rate("child_benefit_opts_out_rate", year)
     tfc_rate = load_take_up_rate("tax_free_childcare", year)
     extended_childcare_rate = load_take_up_rate("extended_childcare", year)
     universal_childcare_rate = load_take_up_rate("universal_childcare", year)
@@ -911,9 +847,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     pe_benunit["would_claim_uc"] = (
         generator.random(len(pe_benunit)) < universal_credit_rate
     )
-    pe_benunit["would_claim_tfc"] = (
-        generator.random(len(pe_benunit)) < tfc_rate
-    )
+    pe_benunit["would_claim_tfc"] = generator.random(len(pe_benunit)) < tfc_rate
     pe_benunit["would_claim_extended_childcare"] = (
         generator.random(len(pe_benunit)) < extended_childcare_rate
     )
@@ -937,12 +871,8 @@ def determine_education_level(fted_val, typeed2_val, age_val):
 
     # Generate other stochastic variables using rates from parameter files
     tv_ownership_rate = load_parameter("stochastic", "tv_ownership_rate", year)
-    tv_evasion_rate = load_parameter(
-        "stochastic", "tv_licence_evasion_rate", year
-    )
-    first_time_buyer_rate = load_parameter(
-        "stochastic", "first_time_buyer_rate", year
-    )
+    tv_evasion_rate = load_parameter("stochastic", "tv_licence_evasion_rate", year)
+    first_time_buyer_rate = load_parameter("stochastic", "first_time_buyer_rate", year)
 
     # Household-level: TV ownership
     pe_household["household_owns_tv"] = (
@@ -963,9 +893,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     pe_person["higher_earner_tie_break"] = generator.random(len(pe_person))
 
     # Person-level: Private school attendance random draw
-    pe_person["attends_private_school_random_draw"] = generator.random(
-        len(pe_person)
-    )
+    pe_person["attends_private_school_random_draw"] = generator.random(len(pe_person))
 
     # Generate extended childcare hours usage values with mean 15.019 and sd
     # 4.972
@@ -974,9 +902,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     extended_hours_values = np.clip(extended_hours_values, 0, 30)
 
     # Add the maximum extended childcare hours usage
-    pe_benunit["maximum_extended_childcare_hours_usage"] = (
-        extended_hours_values
-    )
+    pe_benunit["maximum_extended_childcare_hours_usage"] = extended_hours_values
 
     # Add marital status at the benefit unit level
 
diff --git a/policyengine_uk_data/datasets/imputations/capital_gains.py b/policyengine_uk_data/datasets/imputations/capital_gains.py
index 7408af5a..65dfb9b8 100644
--- a/policyengine_uk_data/datasets/imputations/capital_gains.py
+++ b/policyengine_uk_data/datasets/imputations/capital_gains.py
@@ -20,9 +20,9 @@
 capital_gains = pd.read_csv(
     STORAGE_FOLDER / "capital_gains_distribution_advani_summers.csv.gz"
 )
-capital_gains["maximum_total_income"] = (
-    capital_gains.minimum_total_income.shift(-1).fillna(np.inf)
-)
+capital_gains["maximum_total_income"] = capital_gains.minimum_total_income.shift(
+    -1
+).fillna(np.inf)
 
 # Silence verbose logging
 logging.getLogger("root").setLevel(logging.WARNING)
@@ -82,9 +82,7 @@ def loss(blend_factor):
                 sim.map_result(ti_in_range, "person", "household", how="sum")
             )
             household_cg_in_income_range_count = torch.tensor(
-                sim.map_result(
-                    cg_in_income_range, "person", "household", how="sum"
-                )
+                sim.map_result(cg_in_income_range, "person", "household", how="sum")
             )
             pred_ti_in_range = (
                 blended_household_weight * household_ti_in_range_count
@@ -116,9 +114,9 @@ def loss(blend_factor):
     new_household_weight[first_half] = (
         blend_factor * original_household_weight[first_half]
     )
-    new_household_weight[~first_half] = (
-        1 - blend_factor
-    ) * original_household_weight[first_half]
+    new_household_weight[~first_half] = (1 - blend_factor) * original_household_weight[
+        first_half
+    ]
 
     # Impute actual capital gains amounts given gains
     new_cg = np.zeros(len(ti))
diff --git a/policyengine_uk_data/datasets/imputations/consumption.py b/policyengine_uk_data/datasets/imputations/consumption.py
index c726441c..639e95d4 100644
--- a/policyengine_uk_data/datasets/imputations/consumption.py
+++ b/policyengine_uk_data/datasets/imputations/consumption.py
@@ -213,16 +213,14 @@ def impute_has_fuel_to_lcfs(household: pd.DataFrame) -> pd.DataFrame:
 
     output_df = model.predict(input_df)
     # Clip to [0, 1] as it's a probability
-    household["has_fuel_consumption"] = output_df[
-        "has_fuel_consumption"
-    ].values.clip(0, 1)
+    household["has_fuel_consumption"] = output_df["has_fuel_consumption"].values.clip(
+        0, 1
+    )
 
     return household
 
 
-def generate_lcfs_table(
-    lcfs_person: pd.DataFrame, lcfs_household: pd.DataFrame
-):
+def generate_lcfs_table(lcfs_person: pd.DataFrame, lcfs_household: pd.DataFrame):
     """
     Generate LCFS training table for consumption imputation.
 
@@ -247,14 +245,10 @@ def generate_lcfs_table(
     # This bridges WAS (has vehicles) to LCFS (has fuel spending)
     household = impute_has_fuel_to_lcfs(household)
 
-    return household[
-        PREDICTOR_VARIABLES + IMPUTATIONS + ["household_weight"]
-    ].dropna()
+    return household[PREDICTOR_VARIABLES + IMPUTATIONS + ["household_weight"]].dropna()
 
 
-def uprate_lcfs_table(
-    household: pd.DataFrame, time_period: str
-) -> pd.DataFrame:
+def uprate_lcfs_table(household: pd.DataFrame, time_period: str) -> pd.DataFrame:
     from policyengine_uk.system import system
 
     start_period = 2021
@@ -298,9 +292,7 @@ def save_imputation_models():
 def create_consumption_model(overwrite_existing: bool = False):
     from policyengine_uk_data.utils.qrf import QRF
 
-    if (
-        STORAGE_FOLDER / "consumption.pkl"
-    ).exists() and not overwrite_existing:
+    if (STORAGE_FOLDER / "consumption.pkl").exists() and not overwrite_existing:
         return QRF(file_path=STORAGE_FOLDER / "consumption.pkl")
     return save_imputation_models()
 
diff --git a/policyengine_uk_data/datasets/imputations/salary_sacrifice.py b/policyengine_uk_data/datasets/imputations/salary_sacrifice.py
index 7769f7a5..ff7cf4e2 100644
--- a/policyengine_uk_data/datasets/imputations/salary_sacrifice.py
+++ b/policyengine_uk_data/datasets/imputations/salary_sacrifice.py
@@ -62,9 +62,7 @@ def save_salary_sacrifice_model():
     employment_income = sim.calculate("employment_income").values
 
     # Get SS amounts and indicator for who was asked
-    ss_amount = (
-        dataset.person.pension_contributions_via_salary_sacrifice.values
-    )
+    ss_amount = dataset.person.pension_contributions_via_salary_sacrifice.values
     if "salary_sacrifice_asked" not in dataset.person.columns:
         raise ValueError(
             "Dataset missing salary_sacrifice_asked field. "
@@ -77,17 +75,13 @@ def save_salary_sacrifice_model():
     training_mask = ss_asked == 1
 
     if training_mask.sum() == 0:
-        raise ValueError(
-            "No training data found - no respondents were asked SALSAC."
-        )
+        raise ValueError("No training data found - no respondents were asked SALSAC.")
 
     train_df = pd.DataFrame(
         {
             "age": age[training_mask],
             "employment_income": employment_income[training_mask],
-            "pension_contributions_via_salary_sacrifice": ss_amount[
-                training_mask
-            ],
+            "pension_contributions_via_salary_sacrifice": ss_amount[training_mask],
         }
     )
 
@@ -141,9 +135,7 @@ def impute_salary_sacrifice(
     # Get variables needed for imputation
     age = sim.calculate("age").values
     employment_income = sim.calculate("employment_income").values
-    current_ss = (
-        dataset.person.pension_contributions_via_salary_sacrifice.values
-    )
+    current_ss = dataset.person.pension_contributions_via_salary_sacrifice.values
 
     # Get indicator for who was asked
     if "salary_sacrifice_asked" not in dataset.person.columns:
@@ -164,9 +156,7 @@ def impute_salary_sacrifice(
     predictions = model.predict(pred_df)
 
     # Get imputed amounts (QRF predicts continuous values)
-    imputed_ss = predictions[
-        "pension_contributions_via_salary_sacrifice"
-    ].values
+    imputed_ss = predictions["pension_contributions_via_salary_sacrifice"].values
 
     # Ensure non-negative
     imputed_ss = np.maximum(0, imputed_ss)
@@ -187,9 +177,7 @@ def impute_salary_sacrifice(
     # 4.3mn below 2k). Donors keep their full employee pension amount
     # so those above 2k become above-cap records and the rest below-cap.
     person_weight = sim.calculate("person_weight").values
-    employee_pension = dataset.person[
-        "employee_pension_contributions"
-    ].values.copy()
+    employee_pension = dataset.person["employee_pension_contributions"].values.copy()
     has_ss = final_ss > 0
 
     # Donor pool: employed pension contributors not already SS users
@@ -207,9 +195,7 @@ def impute_salary_sacrifice(
         if donor_weighted > 0:
             imputation_rate = min(0.5, shortfall / donor_weighted)
             rng = np.random.default_rng(seed=2024)
-            newly_imputed = is_donor & (
-                rng.random(len(final_ss)) < imputation_rate
-            )
+            newly_imputed = is_donor & (rng.random(len(final_ss)) < imputation_rate)
 
             # Move full employee pension to SS so the above/below
             # 2k split reflects the natural pension distribution
diff --git a/policyengine_uk_data/datasets/imputations/services/etb.py b/policyengine_uk_data/datasets/imputations/services/etb.py
index cc9e28fd..31678e42 100644
--- a/policyengine_uk_data/datasets/imputations/services/etb.py
+++ b/policyengine_uk_data/datasets/imputations/services/etb.py
@@ -48,9 +48,7 @@ def create_public_services_model(overwrite_existing: bool = False) -> None:
         overwrite_existing: Whether to overwrite an existing model file.
     """
     # Check if model already exists and we're not overwriting
-    if (
-        STORAGE_FOLDER / "public_services.pkl"
-    ).exists() and not overwrite_existing:
+    if (STORAGE_FOLDER / "public_services.pkl").exists() and not overwrite_existing:
         return
 
     etb_path = STORAGE_FOLDER / "etb_1977_21" / "householdv2_1977-2021.tab"
@@ -161,9 +159,7 @@ def create_efrs_input_dataset(dataset: UKSingleYearDataset) -> pd.DataFrame:
 
     df["count_primary_education"] = education == "PRIMARY"
     df["count_secondary_education"] = education == "LOWER_SECONDARY"
-    df["count_further_education"] = education.isin(
-        ["UPPER_SECONDARY", "TERTIARY"]
-    )
+    df["count_further_education"] = education.isin(["UPPER_SECONDARY", "TERTIARY"])
     df["hbai_household_net_income"] = (
         df["hbai_household_net_income"] / df["household_count_people"]
     )
diff --git a/policyengine_uk_data/datasets/imputations/services/nhs.py b/policyengine_uk_data/datasets/imputations/services/nhs.py
index fa25d7ca..09e73b53 100644
--- a/policyengine_uk_data/datasets/imputations/services/nhs.py
+++ b/policyengine_uk_data/datasets/imputations/services/nhs.py
@@ -66,9 +66,9 @@ def get_age_bounds(age_group: str):
         + nhs[nhs["Lower age"] > 80].groupby(["Gender", "Service"]).sum()
     ).reset_index()
 
-    nhs[nhs["Lower age"] == 80][["Activity Count", "Total Cost"]] = (
-        over_80_values[["Activity Count", "Total Cost"]]
-    )
+    nhs[nhs["Lower age"] == 80][["Activity Count", "Total Cost"]] = over_80_values[
+        ["Activity Count", "Total Cost"]
+    ]
     nhs = nhs[nhs["Lower age"] <= 80]
     nhs[nhs["Lower age"] == 80]["Upper age"] = 120
 
@@ -85,12 +85,8 @@ def get_age_bounds(age_group: str):
         ].household_weight.values.sum()
         nhs.loc[i, "Total people"] = count
 
-    nhs["Per-person average units"] = (
-        nhs["Activity Count"] / nhs["Total people"]
-    )
-    nhs["Per-person average spending"] = (
-        nhs["Total Cost"] / nhs["Total people"]
-    )
+    nhs["Per-person average units"] = nhs["Activity Count"] / nhs["Total people"]
+    nhs["Per-person average spending"] = nhs["Total Cost"] / nhs["Total people"]
     indirect_cost_adjustment_factor = (
         202e9 / nhs["Total Cost"].sum()
     )  # £202 billion 2025/26 budget
diff --git a/policyengine_uk_data/datasets/imputations/services/services.py b/policyengine_uk_data/datasets/imputations/services/services.py
index 22717e07..2b7892f2 100644
--- a/policyengine_uk_data/datasets/imputations/services/services.py
+++ b/policyengine_uk_data/datasets/imputations/services/services.py
@@ -86,8 +86,6 @@ def impute_services(
     ]
 
     for person_imputations in visit_variables + spending_variables:
-        dataset.person[person_imputations] = input_data[
-            person_imputations
-        ].values
+        dataset.person[person_imputations] = input_data[person_imputations].values
 
     return dataset
diff --git a/policyengine_uk_data/datasets/local_areas/constituencies/boundary_changes/mapping_matrix.py b/policyengine_uk_data/datasets/local_areas/constituencies/boundary_changes/mapping_matrix.py
index c493f92f..45713388 100644
--- a/policyengine_uk_data/datasets/local_areas/constituencies/boundary_changes/mapping_matrix.py
+++ b/policyengine_uk_data/datasets/local_areas/constituencies/boundary_changes/mapping_matrix.py
@@ -15,9 +15,7 @@
 
 # 4. Calculate proportion of old constituency's population in each new constituency
 #    First, compute total population of each old constituency using groupby and transform
-total_old_pop = df.groupby("code_2010")["old_population_present"].transform(
-    "sum"
-)
+total_old_pop = df.groupby("code_2010")["old_population_present"].transform("sum")
 #    Then compute the proportion for each row
 df["proportion"] = df["old_population_present"] / total_old_pop
 
diff --git a/policyengine_uk_data/datasets/local_areas/constituencies/calibrate.py b/policyengine_uk_data/datasets/local_areas/constituencies/calibrate.py
index 6ea99677..24aa3c30 100644
--- a/policyengine_uk_data/datasets/local_areas/constituencies/calibrate.py
+++ b/policyengine_uk_data/datasets/local_areas/constituencies/calibrate.py
@@ -73,9 +73,9 @@ def get_performance(weights, m_c, y_c, m_n, y_n, excluded_targets):
         constituency_target_validation["estimate"]
         - constituency_target_validation["target"]
     )
-    constituency_target_validation["abs_error"] = (
-        constituency_target_validation["error"].abs()
-    )
+    constituency_target_validation["abs_error"] = constituency_target_validation[
+        "error"
+    ].abs()
     constituency_target_validation["rel_abs_error"] = (
         constituency_target_validation["abs_error"]
         / constituency_target_validation["target"]
@@ -91,15 +91,11 @@ def get_performance(weights, m_c, y_c, m_n, y_n, excluded_targets):
     national_target_validation["target"] = national_actuals.values
 
     national_target_validation["error"] = (
-        national_target_validation["estimate"]
-        - national_target_validation["target"]
+        national_target_validation["estimate"] - national_target_validation["target"]
     )
-    national_target_validation["abs_error"] = national_target_validation[
-        "error"
-    ].abs()
+    national_target_validation["abs_error"] = national_target_validation["error"].abs()
     national_target_validation["rel_abs_error"] = (
-        national_target_validation["abs_error"]
-        / national_target_validation["target"]
+        national_target_validation["abs_error"] / national_target_validation["target"]
     )
 
     df = pd.concat(
diff --git a/policyengine_uk_data/datasets/local_areas/constituencies/loss.py b/policyengine_uk_data/datasets/local_areas/constituencies/loss.py
index bad839f4..3ea6e12a 100644
--- a/policyengine_uk_data/datasets/local_areas/constituencies/loss.py
+++ b/policyengine_uk_data/datasets/local_areas/constituencies/loss.py
@@ -50,9 +50,7 @@ def create_constituency_target_matrix(
 
     # ── Income targets ─────────────────────────────────────────────
     incomes = get_constituency_income_targets()
-    national_incomes = get_national_income_projections(
-        int(dataset.time_period)
-    )
+    national_incomes = get_national_income_projections(int(dataset.time_period))
 
     for income_variable in INCOME_VARIABLES:
         income_values = sim.calculate(income_variable).values
@@ -107,16 +105,10 @@ def create_constituency_target_matrix(
     children_per_hh = sim.map_result(is_child, "person", "household")
     on_uc_hh = sim.map_result(on_uc, "benunit", "household") > 0
 
-    matrix["uc_hh_0_children"] = (on_uc_hh & (children_per_hh == 0)).astype(
-        float
-    )
+    matrix["uc_hh_0_children"] = (on_uc_hh & (children_per_hh == 0)).astype(float)
     matrix["uc_hh_1_child"] = (on_uc_hh & (children_per_hh == 1)).astype(float)
-    matrix["uc_hh_2_children"] = (on_uc_hh & (children_per_hh == 2)).astype(
-        float
-    )
-    matrix["uc_hh_3plus_children"] = (
-        on_uc_hh & (children_per_hh >= 3)
-    ).astype(float)
+    matrix["uc_hh_2_children"] = (on_uc_hh & (children_per_hh == 2)).astype(float)
+    matrix["uc_hh_3plus_children"] = (on_uc_hh & (children_per_hh >= 3)).astype(float)
 
     uc_by_children = get_constituency_uc_by_children_targets()
     for col in uc_by_children.columns:
diff --git a/policyengine_uk_data/datasets/local_areas/constituencies/targets/create_employment_incomes.py b/policyengine_uk_data/datasets/local_areas/constituencies/targets/create_employment_incomes.py
index a502fa78..a99c8dc9 100644
--- a/policyengine_uk_data/datasets/local_areas/constituencies/targets/create_employment_incomes.py
+++ b/policyengine_uk_data/datasets/local_areas/constituencies/targets/create_employment_incomes.py
@@ -131,33 +131,23 @@ def fill_missing_percentiles(row):
         # If this percentile is missing in the row
         if pd.isna(row[col]):
             # Find the closest lower and upper known percentiles
-            lower = max(
-                [p for p in known_percentiles if p < percentile], default=None
-            )
-            upper = min(
-                [p for p in known_percentiles if p > percentile], default=None
-            )
+            lower = max([p for p in known_percentiles if p < percentile], default=None)
+            upper = min([p for p in known_percentiles if p > percentile], default=None)
 
             # If both lower and upper bounds exist, interpolate
             if lower is not None and upper is not None:
                 # Ratio between the target percentile and the lower bound
-                lower_ratio = (
-                    reference_values[percentile] / reference_values[lower]
-                )
+                lower_ratio = reference_values[percentile] / reference_values[lower]
                 row[col] = row[f"{lower} percentile"] * lower_ratio
 
             # If only the lower bound exists, extrapolate upwards
             elif lower is not None:
-                lower_ratio = (
-                    reference_values[percentile] / reference_values[lower]
-                )
+                lower_ratio = reference_values[percentile] / reference_values[lower]
                 row[col] = row[f"{lower} percentile"] * lower_ratio
 
             # If only the upper bound exists, extrapolate downwards
             elif upper is not None:
-                upper_ratio = (
-                    reference_values[percentile] / reference_values[upper]
-                )
+                upper_ratio = reference_values[percentile] / reference_values[upper]
                 row[col] = row[f"{upper} percentile"] * upper_ratio
 
     return row
@@ -311,9 +301,7 @@ def calculate_band_population(row):
         # Ensure lower_percentile is less than upper_percentile
         if lower_percentile < upper_percentile:
             # Integrate to get proportion in this income band
-            proportion_in_band, _ = quad(
-                spline, lower_percentile, upper_percentile
-            )
+            proportion_in_band, _ = quad(spline, lower_percentile, upper_percentile)
             proportion_in_band = proportion_in_band / spline(
                 filtered_percentiles[-1]
             )  # Normalize by max spline value
@@ -340,9 +328,7 @@ def calculate_band_population(row):
         income_bands, columns=["income_lower_bound", "income_upper_bound"]
     )
     band_df["population_count"] = band_population_counts
-    band_df["parliamentary constituency 2010"] = row[
-        "parliamentary constituency 2010"
-    ]
+    band_df["parliamentary constituency 2010"] = row["parliamentary constituency 2010"]
     band_df["constituency_code"] = row["constituency_code"]
 
     return band_df
@@ -435,9 +421,7 @@ def calculate_band_population(row):
 import numpy as np
 
 
-def find_and_replace_zero_populations(
-    result_df_copy, total_income
-) -> pd.DataFrame:
+def find_and_replace_zero_populations(result_df_copy, total_income) -> pd.DataFrame:
     # Step 1: Find constituencies with all zero populations
     constituencies_with_zero_population = (
         result_df_copy.groupby("constituency_code")
@@ -480,15 +464,12 @@ def find_and_replace_zero_populations(
 
             # Calculate absolute differences
             differences = np.abs(
-                other_constituencies["total_income_count"]
-                - current_total_income
+                other_constituencies["total_income_count"] - current_total_income
             )
 
             # Get the index of the minimum difference
             min_diff_idx = differences.values.argmin()
-            nearest_constituency = other_constituencies.iloc[min_diff_idx][
-                "code"
-            ]
+            nearest_constituency = other_constituencies.iloc[min_diff_idx]["code"]
 
             # Step 3: Copy population and earnings data from nearest constituency
             # For each income band of the zero constituency
@@ -543,9 +524,7 @@ def find_and_replace_zero_populations(
                 ].values[0]
 
         except Exception as e:
-            print(
-                f"Error processing constituency {zero_constituency}: {str(e)}"
-            )
+            print(f"Error processing constituency {zero_constituency}: {str(e)}")
             continue
 
     return result_df
diff --git a/policyengine_uk_data/datasets/local_areas/constituencies/targets/fill_missing_age_demographics.py b/policyengine_uk_data/datasets/local_areas/constituencies/targets/fill_missing_age_demographics.py
index b95d7938..9bacfb71 100644
--- a/policyengine_uk_data/datasets/local_areas/constituencies/targets/fill_missing_age_demographics.py
+++ b/policyengine_uk_data/datasets/local_areas/constituencies/targets/fill_missing_age_demographics.py
@@ -29,9 +29,7 @@
 missing_constituencies = pd.DataFrame(
     {
         "code": missing_constituencies.values,
-        "name": incomes.set_index("code")
-        .loc[missing_constituencies]
-        .name.values,
+        "name": incomes.set_index("code").loc[missing_constituencies].name.values,
     }
 )
 for col in ages.columns[2:]:
diff --git a/policyengine_uk_data/datasets/local_areas/local_authorities/calibrate.py b/policyengine_uk_data/datasets/local_areas/local_authorities/calibrate.py
index 588f2955..746d94e7 100644
--- a/policyengine_uk_data/datasets/local_areas/local_authorities/calibrate.py
+++ b/policyengine_uk_data/datasets/local_areas/local_authorities/calibrate.py
@@ -18,12 +18,8 @@ def calibrate(
 ):
     return calibrate_local_areas(
         dataset=dataset,
-        matrix_fn=lambda ds: create_local_authority_target_matrix(
-            ds, ds.time_period
-        ),
-        national_matrix_fn=lambda ds: create_national_target_matrix(
-            ds, ds.time_period
-        ),
+        matrix_fn=lambda ds: create_local_authority_target_matrix(ds, ds.time_period),
+        national_matrix_fn=lambda ds: create_national_target_matrix(ds, ds.time_period),
         area_count=360,
         weight_file="local_authority_weights.h5",
         excluded_training_targets=excluded_training_targets,
@@ -37,9 +33,7 @@ def calibrate(
 def get_performance(weights, m_c, y_c, m_n, y_n, excluded_targets):
     la_target_matrix, la_actuals = m_c, y_c
     national_target_matrix, national_actuals = m_n, y_n
-    local_authorities = pd.read_csv(
-        STORAGE_FOLDER / "local_authorities_2021.csv"
-    )
+    local_authorities = pd.read_csv(STORAGE_FOLDER / "local_authorities_2021.csv")
     la_wide = weights @ la_target_matrix
     la_wide.index = local_authorities.code.values
     la_wide["name"] = local_authorities.name.values
@@ -93,15 +87,11 @@ def get_performance(weights, m_c, y_c, m_n, y_n, excluded_targets):
     national_target_validation["target"] = national_actuals.values
 
     national_target_validation["error"] = (
-        national_target_validation["estimate"]
-        - national_target_validation["target"]
+        national_target_validation["estimate"] - national_target_validation["target"]
     )
-    national_target_validation["abs_error"] = national_target_validation[
-        "error"
-    ].abs()
+    national_target_validation["abs_error"] = national_target_validation["error"].abs()
     national_target_validation["rel_abs_error"] = (
-        national_target_validation["abs_error"]
-        / national_target_validation["target"]
+        national_target_validation["abs_error"] / national_target_validation["target"]
     )
 
     df = pd.concat(
diff --git a/policyengine_uk_data/datasets/local_areas/local_authorities/loss.py b/policyengine_uk_data/datasets/local_areas/local_authorities/loss.py
index 1b4e113e..446329ba 100644
--- a/policyengine_uk_data/datasets/local_areas/local_authorities/loss.py
+++ b/policyengine_uk_data/datasets/local_areas/local_authorities/loss.py
@@ -60,9 +60,7 @@ def create_local_authority_target_matrix(
 
     # ── Income targets ─────────────────────────────────────────────
     incomes = get_la_income_targets()
-    national_incomes = get_national_income_projections(
-        int(dataset.time_period)
-    )
+    national_incomes = get_national_income_projections(int(dataset.time_period))
 
     for income_variable in INCOME_VARIABLES:
         income_values = sim.calculate(income_variable).values
@@ -127,9 +125,7 @@ def create_local_authority_target_matrix(
     )
 
     hbai_net_income = sim.calculate("equiv_hbai_household_net_income").values
-    hbai_net_income_ahc = sim.calculate(
-        "equiv_hbai_household_net_income_ahc"
-    ).values
+    hbai_net_income_ahc = sim.calculate("equiv_hbai_household_net_income_ahc").values
     housing_costs = hbai_net_income - hbai_net_income_ahc
 
     matrix["ons/equiv_net_income_bhc"] = hbai_net_income
@@ -194,15 +190,11 @@ def create_local_authority_target_matrix(
     )
 
     tenure_type = sim.calculate("tenure_type").values
-    matrix["tenure/owned_outright"] = (tenure_type == "OWNED_OUTRIGHT").astype(
-        float
-    )
-    matrix["tenure/owned_mortgage"] = (
-        tenure_type == "OWNED_WITH_MORTGAGE"
-    ).astype(float)
-    matrix["tenure/private_rent"] = (tenure_type == "RENT_PRIVATELY").astype(
+    matrix["tenure/owned_outright"] = (tenure_type == "OWNED_OUTRIGHT").astype(float)
+    matrix["tenure/owned_mortgage"] = (tenure_type == "OWNED_WITH_MORTGAGE").astype(
         float
     )
+    matrix["tenure/private_rent"] = (tenure_type == "RENT_PRIVATELY").astype(float)
     matrix["tenure/social_rent"] = (
         (tenure_type == "RENT_FROM_COUNCIL") | (tenure_type == "RENT_FROM_HA")
     ).astype(float)
@@ -219,9 +211,7 @@ def create_local_authority_target_matrix(
         ("social_rent", "social_rent_pct"),
     ]:
         targets = tenure_merged[pct_col] / 100 * tenure_merged["households"]
-        national = (
-            original_weights * matrix[f"tenure/{tenure_key}"].values
-        ).sum()
+        national = (original_weights * matrix[f"tenure/{tenure_key}"].values).sum()
         y[f"tenure/{tenure_key}"] = np.where(
             has_tenure, targets.values, national * la_household_share
         )
diff --git a/policyengine_uk_data/datasets/local_areas/local_authorities/targets/create_employment_incomes.py b/policyengine_uk_data/datasets/local_areas/local_authorities/targets/create_employment_incomes.py
index 4e34dd3b..3f789442 100644
--- a/policyengine_uk_data/datasets/local_areas/local_authorities/targets/create_employment_incomes.py
+++ b/policyengine_uk_data/datasets/local_areas/local_authorities/targets/create_employment_incomes.py
@@ -131,33 +131,23 @@ def fill_missing_percentiles(row):
         # If this percentile is missing in the row
         if pd.isna(row[col]):
             # Find the closest lower and upper known percentiles
-            lower = max(
-                [p for p in known_percentiles if p < percentile], default=None
-            )
-            upper = min(
-                [p for p in known_percentiles if p > percentile], default=None
-            )
+            lower = max([p for p in known_percentiles if p < percentile], default=None)
+            upper = min([p for p in known_percentiles if p > percentile], default=None)
 
             # If both lower and upper bounds exist, interpolate
             if lower is not None and upper is not None:
                 # Ratio between the target percentile and the lower bound
-                lower_ratio = (
-                    reference_values[percentile] / reference_values[lower]
-                )
+                lower_ratio = reference_values[percentile] / reference_values[lower]
                 row[col] = row[f"{lower} percentile"] * lower_ratio
 
             # If only the lower bound exists, extrapolate upwards
             elif lower is not None:
-                lower_ratio = (
-                    reference_values[percentile] / reference_values[lower]
-                )
+                lower_ratio = reference_values[percentile] / reference_values[lower]
                 row[col] = row[f"{lower} percentile"] * lower_ratio
 
             # If only the upper bound exists, extrapolate downwards
             elif upper is not None:
-                upper_ratio = (
-                    reference_values[percentile] / reference_values[upper]
-                )
+                upper_ratio = reference_values[percentile] / reference_values[upper]
                 row[col] = row[f"{upper} percentile"] * upper_ratio
 
     return row
@@ -280,9 +270,9 @@ def calculate_band_population(row):
             income_bands, columns=["income_lower_bound", "income_upper_bound"]
         )
         band_df["population_count"] = [0] * len(income_bands)
-        band_df["local authority: district / unitary (as of April 2023)"] = (
-            row["local authority: district / unitary (as of April 2023)"]
-        )
+        band_df["local authority: district / unitary (as of April 2023)"] = row[
+            "local authority: district / unitary (as of April 2023)"
+        ]
         band_df["LA_code"] = row["LA_code"]
         return band_df
 
@@ -311,9 +301,7 @@ def calculate_band_population(row):
         # Ensure lower_percentile is less than upper_percentile
         if lower_percentile < upper_percentile:
             # Integrate to get proportion in this income band
-            proportion_in_band, _ = quad(
-                spline, lower_percentile, upper_percentile
-            )
+            proportion_in_band, _ = quad(spline, lower_percentile, upper_percentile)
             proportion_in_band = proportion_in_band / spline(
                 filtered_percentiles[-1]
             )  # Normalize by max spline value
@@ -435,15 +423,11 @@ def calculate_band_population(row):
 import numpy as np
 
 
-def find_and_replace_zero_populations(
-    result_df_copy, total_income
-) -> pd.DataFrame:
+def find_and_replace_zero_populations(result_df_copy, total_income) -> pd.DataFrame:
     # Step 1: Find local authorities with all zero populations
     LA_with_zero_population = (
         result_df_copy.groupby("LA_code")
-        .filter(lambda group: (group["population_count"] == 0).all())[
-            "LA_code"
-        ]
+        .filter(lambda group: (group["population_count"] == 0).all())["LA_code"]
         .unique()
     )
 
@@ -462,26 +446,18 @@ def find_and_replace_zero_populations(
                 )
                 continue
 
-            current_total_income = current_LA_data[
-                "total_income_count"
-            ].values[0]
+            current_total_income = current_LA_data["total_income_count"].values[0]
 
             # Find the nearest local authority by total_income_count
             # Exclude both the current local authority and other zero population local authorities
-            other_LA = total_income[
-                ~total_income["code"].isin(LA_with_zero_population)
-            ]
+            other_LA = total_income[~total_income["code"].isin(LA_with_zero_population)]
 
             if other_LA.empty:
-                print(
-                    f"Warning: No valid local authorities found to copy from"
-                )
+                print(f"Warning: No valid local authorities found to copy from")
                 continue
 
             # Calculate absolute differences
-            differences = np.abs(
-                other_LA["total_income_count"] - current_total_income
-            )
+            differences = np.abs(other_LA["total_income_count"] - current_total_income)
 
             # Get the index of the minimum difference
             min_diff_idx = differences.values.argmin()
diff --git a/policyengine_uk_data/datasets/spi.py b/policyengine_uk_data/datasets/spi.py
index 253b1455..f0c08626 100644
--- a/policyengine_uk_data/datasets/spi.py
+++ b/policyengine_uk_data/datasets/spi.py
@@ -76,19 +76,14 @@ def create_spi(
     percent_along_age_range = np.random.rand(len(df))
     min_age = np.array([AGE_RANGES[age][0] for age in age_range])
     max_age = np.array([AGE_RANGES[age][1] for age in age_range])
-    person["age"] = (
-        min_age + (max_age - min_age) * percent_along_age_range
-    ).astype(int)
+    person["age"] = (min_age + (max_age - min_age) * percent_along_age_range).astype(
+        int
+    )
 
     person["state_pension_reported"] = df.SRP
     person["other_tax_credits"] = df.TAX_CRED
     person["miscellaneous_income"] = (
-        df.MOTHINC
-        + df.INCPBEN
-        + df.OSSBEN
-        + df.TAXTERM
-        + df.UBISJA
-        + df.OTHERINC
+        df.MOTHINC + df.INCPBEN + df.OSSBEN + df.TAXTERM + df.UBISJA + df.OTHERINC
     )
     person["gift_aid"] = df.GIFTAID + df.GIFTINV
     person["other_investment_income"] = df.OTHERINV
diff --git a/policyengine_uk_data/parameters/__init__.py b/policyengine_uk_data/parameters/__init__.py
index dcf981ba..06f3b558 100644
--- a/policyengine_uk_data/parameters/__init__.py
+++ b/policyengine_uk_data/parameters/__init__.py
@@ -11,9 +11,7 @@
 PARAMETERS_DIR = Path(__file__).parent
 
 
-def load_parameter(
-    category: str, variable_name: str, year: int = 2015
-) -> float:
+def load_parameter(category: str, variable_name: str, year: int = 2015) -> float:
     """Load parameter from YAML files in a specific category.
 
     Args:
@@ -48,9 +46,7 @@ def load_parameter(
             break
 
     if applicable_value is None:
-        raise ValueError(
-            f"No value found for {category}/{variable_name} in {year}"
-        )
+        raise ValueError(f"No value found for {category}/{variable_name} in {year}")
 
     return applicable_value
 
diff --git a/policyengine_uk_data/targets/build_loss_matrix.py b/policyengine_uk_data/targets/build_loss_matrix.py
index 8af391a3..3358a646 100644
--- a/policyengine_uk_data/targets/build_loss_matrix.py
+++ b/policyengine_uk_data/targets/build_loss_matrix.py
@@ -161,9 +161,7 @@ def pe(self, variable: str):
         """Calculate variable mapped to household level."""
         key = ("pe", variable)
         if key not in self._cache:
-            self._cache[key] = self.sim.calculate(
-                variable, map_to="household"
-            ).values
+            self._cache[key] = self.sim.calculate(variable, map_to="household").values
         return self._cache[key]
 
     def pe_person(self, variable: str):
@@ -194,9 +192,7 @@ def household_from_family(self, values):
     @property
     def region(self):
         if "region" not in self._cache:
-            self._cache["region"] = self.sim.calculate(
-                "region", map_to="person"
-            )
+            self._cache["region"] = self.sim.calculate("region", map_to="person")
         return self._cache["region"]
 
     @property
@@ -225,9 +221,7 @@ def counterfactual_sim(self):
         if "counterfactual_sim" not in self._cache:
             from policyengine_uk import Microsimulation
 
-            ss = self.sim.calculate(
-                "pension_contributions_via_salary_sacrifice"
-            )
+            ss = self.sim.calculate("pension_contributions_via_salary_sacrifice")
             emp = self.sim.calculate("employment_income")
             cf_sim = Microsimulation(dataset=self.dataset, reform=self.reform)
             cf_sim.set_input(
@@ -247,9 +241,7 @@ def counterfactual_sim(self):
 # ── Column computation dispatch ──────────────────────────────────────
 
 
-def _compute_column(
-    target: Target, ctx: _SimContext, year: int
-) -> np.ndarray | None:
+def _compute_column(target: Target, ctx: _SimContext, year: int) -> np.ndarray | None:
     """Compute the household-level column for a target.
 
     Dispatches to domain-specific compute modules.
diff --git a/policyengine_uk_data/targets/compute/benefits.py b/policyengine_uk_data/targets/compute/benefits.py
index 140eab68..0a6d291b 100644
--- a/policyengine_uk_data/targets/compute/benefits.py
+++ b/policyengine_uk_data/targets/compute/benefits.py
@@ -17,9 +17,7 @@ def compute_benefit_cap(target, ctx) -> np.ndarray:
         return ctx.sim.calculate(
             "benefit_cap_reduction", map_to="household"
         ).values.astype(float)
-    reduction = ctx.sim.calculate(
-        "benefit_cap_reduction", map_to="household"
-    ).values
+    reduction = ctx.sim.calculate("benefit_cap_reduction", map_to="household").values
     return (reduction > 0).astype(float)
 
 
@@ -29,9 +27,7 @@ def compute_scotland_uc_child(target, ctx) -> np.ndarray:
     on_uc = ctx.household_from_family(uc > 0) > 0
     child_u1 = ctx.pe_person("is_child") & (ctx.age < 1)
     has_child_u1 = ctx.household_from_person(child_u1) > 0
-    return (
-        (ctx.household_region == "SCOTLAND") & on_uc & has_child_u1
-    ).astype(float)
+    return ((ctx.household_region == "SCOTLAND") & on_uc & has_child_u1).astype(float)
 
 
 def compute_uc_by_children(target, ctx) -> np.ndarray:
@@ -74,9 +70,7 @@ def ft_hh(value):
     if ft_str == "single_no_children":
         match = ft_hh("SINGLE") & (children_per_hh == 0)
     elif ft_str == "single_with_children":
-        match = (ft_hh("SINGLE") | ft_hh("LONE_PARENT")) & (
-            children_per_hh > 0
-        )
+        match = (ft_hh("SINGLE") | ft_hh("LONE_PARENT")) & (children_per_hh > 0)
     elif ft_str == "couple_no_children":
         match = ft_hh("COUPLE_NO_CHILDREN")
     elif ft_str == "couple_with_children":
@@ -95,15 +89,11 @@ def compute_uc_payment_dist(target, ctx) -> np.ndarray:
     lower = target.lower_bound
     upper = target.upper_bound
 
-    uc_payments = ctx.sim.calculate(
-        "universal_credit", map_to="benunit"
-    ).values
+    uc_payments = ctx.sim.calculate("universal_credit", map_to="benunit").values
     uc_family_type = ctx.sim.calculate("family_type", map_to="benunit").values
 
     in_band = (
-        (uc_payments >= lower)
-        & (uc_payments < upper)
-        & (uc_family_type == family_type)
+        (uc_payments >= lower) & (uc_payments < upper) & (uc_family_type == family_type)
     )
     return ctx.household_from_family(in_band)
 
@@ -113,9 +103,7 @@ def compute_uc_jobseeker(target, ctx) -> np.ndarray:
     family = ctx.sim.populations["benunit"]
     uc = ctx.sim.calculate("universal_credit")
     on_uc = uc > 0
-    unemployed = family.any(
-        ctx.sim.calculate("employment_status") == "UNEMPLOYED"
-    )
+    unemployed = family.any(ctx.sim.calculate("employment_status") == "UNEMPLOYED")
 
     if "non_jobseekers" in target.name:
         mask = on_uc * ~unemployed
@@ -164,17 +152,13 @@ def compute_two_child_limit(target, ctx) -> np.ndarray | None:
     if name == "dwp/uc/two_child_limit/children_affected":
         return children_in_capped
     if name == "dwp/uc/two_child_limit/children_in_affected_households":
-        total_children = sim.map_result(
-            is_child * child_in_uc, "person", "household"
-        )
+        total_children = sim.map_result(is_child * child_in_uc, "person", "household")
         return total_children * capped_hh
 
     if "_children_households_total_children" in name:
         n = int(name.split("/")[-1].split("_")[0])
         children_count = sim.map_result(is_child, "person", "household")
-        return (capped_hh * (children_count == n) * children_count).astype(
-            float
-        )
+        return (capped_hh * (children_count == n) * children_count).astype(float)
     if "_children_households" in name and "total" not in name:
         n = int(name.split("/")[-1].split("_")[0])
         children_count = sim.map_result(is_child, "person", "household")
diff --git a/policyengine_uk_data/targets/compute/demographics.py b/policyengine_uk_data/targets/compute/demographics.py
index 670b8072..4d0b8092 100644
--- a/policyengine_uk_data/targets/compute/demographics.py
+++ b/policyengine_uk_data/targets/compute/demographics.py
@@ -33,9 +33,7 @@ def compute_regional_age(target, ctx) -> np.ndarray | None:
         return None
 
     person_match = (
-        (ctx.region.values == pe_region)
-        & (ctx.age >= lower)
-        & (ctx.age <= upper)
+        (ctx.region.values == pe_region) & (ctx.age >= lower) & (ctx.age <= upper)
     )
     return ctx.household_from_person(person_match)
 
@@ -73,7 +71,7 @@ def compute_scotland_demographics(target, ctx) -> np.ndarray | None:
     if name == "ons/scotland_households_3plus_children":
         is_child = ctx.pe_person("is_child")
         children_per_hh = ctx.household_from_person(is_child)
-        return (
-            (ctx.household_region == "SCOTLAND") & (children_per_hh >= 3)
-        ).astype(float)
+        return ((ctx.household_region == "SCOTLAND") & (children_per_hh >= 3)).astype(
+            float
+        )
     return None
diff --git a/policyengine_uk_data/targets/compute/households.py b/policyengine_uk_data/targets/compute/households.py
index be7686ac..be51f2d0 100644
--- a/policyengine_uk_data/targets/compute/households.py
+++ b/policyengine_uk_data/targets/compute/households.py
@@ -15,18 +15,18 @@ def ft_hh(value):
         return ctx.household_from_family(ft == value) > 0
 
     if name == "lone_households_under_65":
-        return (
-            ft_hh("SINGLE") & (children_per_hh == 0) & (age_hh_head < 65)
-        ).astype(float)
+        return (ft_hh("SINGLE") & (children_per_hh == 0) & (age_hh_head < 65)).astype(
+            float
+        )
     if name == "lone_households_over_65":
-        return (
-            ft_hh("SINGLE") & (children_per_hh == 0) & (age_hh_head >= 65)
-        ).astype(float)
+        return (ft_hh("SINGLE") & (children_per_hh == 0) & (age_hh_head >= 65)).astype(
+            float
+        )
     if name == "unrelated_adult_households":
         people_per_hh = ctx.household_from_person(np.ones_like(is_child))
-        return (
-            ft_hh("SINGLE") & (children_per_hh == 0) & (people_per_hh > 1)
-        ).astype(float)
+        return (ft_hh("SINGLE") & (children_per_hh == 0) & (people_per_hh > 1)).astype(
+            float
+        )
     if name == "couple_no_children_households":
         return ft_hh("COUPLE_NO_CHILDREN").astype(float)
     if name == "couple_under_3_children_households":
@@ -36,14 +36,10 @@ def ft_hh(value):
             & (children_per_hh <= 2)
         ).astype(float)
     if name == "couple_3_plus_children_households":
-        return (ft_hh("COUPLE_WITH_CHILDREN") & (children_per_hh >= 3)).astype(
-            float
-        )
+        return (ft_hh("COUPLE_WITH_CHILDREN") & (children_per_hh >= 3)).astype(float)
     if name == "couple_non_dependent_children_only_households":
         people_per_hh = ctx.household_from_person(np.ones_like(is_child))
-        return (ft_hh("COUPLE_NO_CHILDREN") & (people_per_hh > 2)).astype(
-            float
-        )
+        return (ft_hh("COUPLE_NO_CHILDREN") & (people_per_hh > 2)).astype(float)
     if name == "lone_parent_dependent_children_households":
         return (ft_hh("LONE_PARENT") & (children_per_hh > 0)).astype(float)
     if name == "lone_parent_non_dependent_children_households":
diff --git a/policyengine_uk_data/targets/compute/income.py b/policyengine_uk_data/targets/compute/income.py
index c2f286c4..1953be0e 100644
--- a/policyengine_uk_data/targets/compute/income.py
+++ b/policyengine_uk_data/targets/compute/income.py
@@ -12,9 +12,7 @@ def compute_income_band(target, ctx) -> np.ndarray:
     upper = target.upper_bound
 
     income_df = ctx.sim.calculate_dataframe(["total_income", variable])
-    in_band = (income_df.total_income >= lower) & (
-        income_df.total_income < upper
-    )
+    in_band = (income_df.total_income >= lower) & (income_df.total_income < upper)
 
     if target.is_count:
         return ctx.household_from_person((income_df[variable] > 0) * in_band)
@@ -39,9 +37,7 @@ def compute_ss_it_relief(target, ctx) -> np.ndarray:
 
     name = target.name
     if "basic" in name:
-        mask = (adj_net_income_cf > basic_thresh) & (
-            adj_net_income_cf <= higher_thresh
-        )
+        mask = (adj_net_income_cf > basic_thresh) & (adj_net_income_cf <= higher_thresh)
     elif "higher" in name:
         mask = (adj_net_income_cf > higher_thresh) & (
             adj_net_income_cf <= additional_thresh
@@ -65,14 +61,10 @@ def compute_ss_ni_relief(target, ctx) -> np.ndarray:
     name = target.name
     if "employee" in name:
         ni_base = ctx.sim.calculate("ni_employee")
-        ni_cf = ctx.counterfactual_sim.calculate(
-            "ni_employee", ctx.time_period
-        )
+        ni_cf = ctx.counterfactual_sim.calculate("ni_employee", ctx.time_period)
     else:
         ni_base = ctx.sim.calculate("ni_employer")
-        ni_cf = ctx.counterfactual_sim.calculate(
-            "ni_employer", ctx.time_period
-        )
+        ni_cf = ctx.counterfactual_sim.calculate("ni_employer", ctx.time_period)
     return ctx.household_from_person(ni_cf - ni_base)
 
 
@@ -90,9 +82,7 @@ def compute_ss_headcount(target, ctx) -> np.ndarray:
         "Variable"
     )
     row = "pension_contributions_via_salary_sacrifice"
-    price_adj = (
-        uprating.loc[row, "2023"] / uprating.loc[row, str(ctx.time_period)]
-    )
+    price_adj = uprating.loc[row, "2023"] / uprating.loc[row, str(ctx.time_period)]
     ss_base = ss * price_adj
 
     name = target.name
diff --git a/policyengine_uk_data/targets/registry.py b/policyengine_uk_data/targets/registry.py
index ebd0af4f..b4c97108 100644
--- a/policyengine_uk_data/targets/registry.py
+++ b/policyengine_uk_data/targets/registry.py
@@ -25,9 +25,7 @@ def discover_source_modules() -> list:
     modules = []
     package_path = Path(sources_pkg.__file__).parent
     for importer, modname, ispkg in pkgutil.iter_modules([str(package_path)]):
-        mod = importlib.import_module(
-            f"policyengine_uk_data.targets.sources.{modname}"
-        )
+        mod = importlib.import_module(f"policyengine_uk_data.targets.sources.{modname}")
         if hasattr(mod, "get_targets"):
             modules.append(mod)
     return modules
diff --git a/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py b/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
index 97ec499a..f9865e6f 100644
--- a/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
+++ b/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
@@ -32,9 +32,7 @@ def get_targets() -> list[Target]:
     targets = []
 
     try:
-        r = requests.get(
-            ref, headers=HEADERS, allow_redirects=True, timeout=30
-        )
+        r = requests.get(ref, headers=HEADERS, allow_redirects=True, timeout=30)
         r.raise_for_status()
         df = pd.read_csv(io.StringIO(r.content.decode("utf-8-sig")))
 
@@ -108,14 +106,11 @@ def get_targets() -> list[Target]:
             )
 
     except Exception as e:
-        logger.error(
-            "Failed to download/parse HMRC salary sacrifice CSV: %s", e
-        )
+        logger.error("Failed to download/parse HMRC salary sacrifice CSV: %s", e)
 
     # Total salary sacrifice contributions (SPP Review 2025: £24bn base)
     _SS_CONTRIBUTIONS = {
-        y: 24e9 * _GROWTH ** max(0, y - _BASE_YEAR)
-        for y in range(_BASE_YEAR, 2030)
+        y: 24e9 * _GROWTH ** max(0, y - _BASE_YEAR) for y in range(_BASE_YEAR, 2030)
     }
     targets.append(
         Target(
diff --git a/policyengine_uk_data/targets/sources/hmrc_spi.py b/policyengine_uk_data/targets/sources/hmrc_spi.py
index de993d49..72d792e9 100644
--- a/policyengine_uk_data/targets/sources/hmrc_spi.py
+++ b/policyengine_uk_data/targets/sources/hmrc_spi.py
@@ -154,9 +154,7 @@ def get_targets() -> list[Target]:
 
         for idx, row in merged.iterrows():
             lower = int(row["lower_bound"])
-            upper = (
-                _BAND_UPPER[idx] if idx < len(_BAND_UPPER) else float("inf")
-            )
+            upper = _BAND_UPPER[idx] if idx < len(_BAND_UPPER) else float("inf")
             band_label = f"{lower:_}_to_{upper:_}"
 
             for variable in INCOME_VARIABLES:
@@ -182,9 +180,7 @@ def get_targets() -> list[Target]:
                 if count_col in row.index and row[count_col] > 0:
                     targets.append(
                         Target(
-                            name=(
-                                f"hmrc/{variable}_count_income_band_{band_label}"
-                            ),
+                            name=(f"hmrc/{variable}_count_income_band_{band_label}"),
                             variable=variable,
                             source="hmrc_spi",
                             unit=Unit.COUNT,
diff --git a/policyengine_uk_data/targets/sources/local_age.py b/policyengine_uk_data/targets/sources/local_age.py
index 0f74bc44..f4879977 100644
--- a/policyengine_uk_data/targets/sources/local_age.py
+++ b/policyengine_uk_data/targets/sources/local_age.py
@@ -17,16 +17,8 @@
 
 logger = logging.getLogger(__name__)
 
-_CONST_DIR = (
-    STORAGE.parent / "datasets" / "local_areas" / "constituencies" / "targets"
-)
-_LA_DIR = (
-    STORAGE.parent
-    / "datasets"
-    / "local_areas"
-    / "local_authorities"
-    / "targets"
-)
+_CONST_DIR = STORAGE.parent / "datasets" / "local_areas" / "constituencies" / "targets"
+_LA_DIR = STORAGE.parent / "datasets" / "local_areas" / "local_authorities" / "targets"
 
 _REF = (
     "https://www.ons.gov.uk/peoplepopulationandcommunity/"
diff --git a/policyengine_uk_data/targets/sources/local_income.py b/policyengine_uk_data/targets/sources/local_income.py
index 695f790c..8fd7adb3 100644
--- a/policyengine_uk_data/targets/sources/local_income.py
+++ b/policyengine_uk_data/targets/sources/local_income.py
@@ -19,16 +19,8 @@
 
 logger = logging.getLogger(__name__)
 
-_CONST_DIR = (
-    STORAGE.parent / "datasets" / "local_areas" / "constituencies" / "targets"
-)
-_LA_DIR = (
-    STORAGE.parent
-    / "datasets"
-    / "local_areas"
-    / "local_authorities"
-    / "targets"
-)
+_CONST_DIR = STORAGE.parent / "datasets" / "local_areas" / "constituencies" / "targets"
+_LA_DIR = STORAGE.parent / "datasets" / "local_areas" / "local_authorities" / "targets"
 
 _REF = (
     "https://www.gov.uk/government/statistics/"
diff --git a/policyengine_uk_data/targets/sources/local_la_extras.py b/policyengine_uk_data/targets/sources/local_la_extras.py
index 76bcf06d..6bc20b6f 100644
--- a/policyengine_uk_data/targets/sources/local_la_extras.py
+++ b/policyengine_uk_data/targets/sources/local_la_extras.py
@@ -28,9 +28,7 @@
     "earningsandworkinghours/datasets/"
     "smallareaincomeestimatesformiddlelayersuperoutputareasenglandandwales"
 )
-_REF_TENURE = (
-    "https://www.gov.uk/government/statistics/english-housing-survey-2023"
-)
+_REF_TENURE = "https://www.gov.uk/government/statistics/english-housing-survey-2023"
 _REF_RENT = (
     "https://www.ons.gov.uk/peoplepopulationandcommunity/housing/datasets/"
     "privaterentalmarketsummarystatisticsinengland"
diff --git a/policyengine_uk_data/targets/sources/obr.py b/policyengine_uk_data/targets/sources/obr.py
index c4f92db3..3268f428 100644
--- a/policyengine_uk_data/targets/sources/obr.py
+++ b/policyengine_uk_data/targets/sources/obr.py
@@ -45,9 +45,7 @@ def _download_workbook(url: str) -> openpyxl.Workbook:
     return openpyxl.load_workbook(io.BytesIO(r.content), data_only=False)
 
 
-def _read_row_values(
-    ws, row_num: int, col_letters: list[str]
-) -> dict[int, float]:
+def _read_row_values(ws, row_num: int, col_letters: list[str]) -> dict[int, float]:
     """Read numeric values from a row, mapped to calendar years."""
     result = {}
     for col in col_letters:
@@ -353,9 +351,7 @@ def read_49(row_num: int) -> dict[int, float]:
         # Find the second UC row (outside cap section)
         for row in range(uc_outside_row + 1, 55):
             cell_val = ws[f"B{row}"].value
-            if cell_val and str(cell_val).strip().startswith(
-                "Universal credit"
-            ):
+            if cell_val and str(cell_val).strip().startswith("Universal credit"):
                 values = read_49(row)
                 if values:
                     targets.append(
@@ -427,27 +423,20 @@ def _parse_tv_licence(wb: openpyxl.Workbook) -> list[Target]:
 _PRIVATE_SCHOOL = {y: 557_000 for y in range(2018, 2032)}
 
 # SPP Review: salary sacrifice NI relief (uprated 3% pa from 2024 base)
-_SS_EMPLOYEE_NI = {
-    y: 1.2e9 * 1.03 ** max(0, y - 2024) for y in range(2024, 2032)
-}
-_SS_EMPLOYER_NI = {
-    y: 2.9e9 * 1.03 ** max(0, y - 2024) for y in range(2024, 2032)
-}
+_SS_EMPLOYEE_NI = {y: 1.2e9 * 1.03 ** max(0, y - 2024) for y in range(2024, 2032)}
+_SS_EMPLOYER_NI = {y: 2.9e9 * 1.03 ** max(0, y - 2024) for y in range(2024, 2032)}
 
 # Salary sacrifice headcount: 7.7m total (3.3m above £2k, 4.3m below)
 # OBR para 1.7: SS population grows 0.9% faster than employees (~2.4%/yr)
 _SS_HEADCOUNT_GROWTH = 1.024
 _SS_TOTAL_USERS = {
-    y: 7_700_000 * _SS_HEADCOUNT_GROWTH ** max(0, y - 2024)
-    for y in range(2024, 2032)
+    y: 7_700_000 * _SS_HEADCOUNT_GROWTH ** max(0, y - 2024) for y in range(2024, 2032)
 }
 _SS_BELOW_CAP_USERS = {
-    y: 4_300_000 * _SS_HEADCOUNT_GROWTH ** max(0, y - 2024)
-    for y in range(2024, 2032)
+    y: 4_300_000 * _SS_HEADCOUNT_GROWTH ** max(0, y - 2024) for y in range(2024, 2032)
 }
 _SS_ABOVE_CAP_USERS = {
-    y: 3_300_000 * _SS_HEADCOUNT_GROWTH ** max(0, y - 2024)
-    for y in range(2024, 2032)
+    y: 3_300_000 * _SS_HEADCOUNT_GROWTH ** max(0, y - 2024) for y in range(2024, 2032)
 }
 
 
diff --git a/policyengine_uk_data/targets/sources/ons_demographics.py b/policyengine_uk_data/targets/sources/ons_demographics.py
index a51adf39..dba77671 100644
--- a/policyengine_uk_data/targets/sources/ons_demographics.py
+++ b/policyengine_uk_data/targets/sources/ons_demographics.py
@@ -78,9 +78,7 @@
 @lru_cache(maxsize=1)
 def _download_uk_projection() -> pd.DataFrame:
     """Download and parse the UK principal population projection."""
-    r = requests.get(
-        _UK_ZIP_URL, headers=HEADERS, allow_redirects=True, timeout=120
-    )
+    r = requests.get(_UK_ZIP_URL, headers=HEADERS, allow_redirects=True, timeout=120)
     r.raise_for_status()
     z = zipfile.ZipFile(io.BytesIO(r.content))
     with z.open("uk/uk_ppp_machine_readable.xlsx") as f:
@@ -182,9 +180,7 @@ def _parse_regional_from_csv() -> list[Target]:
 
     for _, row in demographics.iterrows():
         name = row["name"]
-        if name in _SKIP_NAMES or any(
-            name.startswith(p) for p in _SKIP_PREFIXES
-        ):
+        if name in _SKIP_NAMES or any(name.startswith(p) for p in _SKIP_PREFIXES):
             continue
         values = {}
         for y in _YEARS:
diff --git a/policyengine_uk_data/targets/sources/ons_savings.py b/policyengine_uk_data/targets/sources/ons_savings.py
index 3764f22e..2046a4d3 100644
--- a/policyengine_uk_data/targets/sources/ons_savings.py
+++ b/policyengine_uk_data/targets/sources/ons_savings.py
@@ -18,15 +18,15 @@
 
 logger = logging.getLogger(__name__)
 
-_API_URL = "https://www.ons.gov.uk/economy/grossdomesticproductgdp/timeseries/haxv/ukea/data"
+_API_URL = (
+    "https://www.ons.gov.uk/economy/grossdomesticproductgdp/timeseries/haxv/ukea/data"
+)
 _REF = "https://www.ons.gov.uk/economy/grossdomesticproductgdp/timeseries/haxv/ukea"
 
 
 def get_targets() -> list[Target]:
     try:
-        r = requests.get(
-            _API_URL, headers=HEADERS, allow_redirects=True, timeout=30
-        )
+        r = requests.get(_API_URL, headers=HEADERS, allow_redirects=True, timeout=30)
         r.raise_for_status()
         data = r.json()
 
diff --git a/policyengine_uk_data/tests/microsimulation/test_reform_impacts.py b/policyengine_uk_data/tests/microsimulation/test_reform_impacts.py
index 3b3f0446..1e266ca9 100644
--- a/policyengine_uk_data/tests/microsimulation/test_reform_impacts.py
+++ b/policyengine_uk_data/tests/microsimulation/test_reform_impacts.py
@@ -77,9 +77,7 @@ def test_all_reforms_have_required_fields():
 
     for i, reform in enumerate(reforms_data):
         for field in required_fields:
-            assert field in reform, (
-                f"Reform {i} missing required field: {field}"
-            )
+            assert field in reform, f"Reform {i} missing required field: {field}"
 
         assert isinstance(reform["parameters"], dict), (
             f"Reform {i} parameters must be a dictionary"
diff --git a/policyengine_uk_data/tests/microsimulation/update_reform_impacts.py b/policyengine_uk_data/tests/microsimulation/update_reform_impacts.py
index 56a23c55..e98151b6 100644
--- a/policyengine_uk_data/tests/microsimulation/update_reform_impacts.py
+++ b/policyengine_uk_data/tests/microsimulation/update_reform_impacts.py
@@ -33,9 +33,7 @@ def get_fiscal_impact(reform: dict) -> float:
     return float((reform_revenue - baseline_revenue) / 1e9)
 
 
-def update_impacts(
-    config_path: Path, dry_run: bool = False, verbose: bool = True
-):
+def update_impacts(config_path: Path, dry_run: bool = False, verbose: bool = True):
     """
     Update the expected impacts in the configuration file with current model values.
 
@@ -61,9 +59,7 @@ def update_impacts(
         old_impact = reform["expected_impact"]
         new_impact = round(get_fiscal_impact(reform["parameters"]), 1)
 
-        if (
-            abs(old_impact - new_impact) > 0.01
-        ):  # Only record meaningful changes
+        if abs(old_impact - new_impact) > 0.01:  # Only record meaningful changes
             changes.append(
                 {
                     "name": reform["name"],
@@ -126,9 +122,7 @@ def main():
     parser.add_argument(
         "--config",
         type=Path,
-        default=Path(
-            "policyengine_uk_data/tests/microsimulation/reforms_config.yaml"
-        ),
+        default=Path("policyengine_uk_data/tests/microsimulation/reforms_config.yaml"),
         help="Path to the reforms configuration file (default: reforms_config.yaml)",
     )
     parser.add_argument(
diff --git a/policyengine_uk_data/tests/test_aggregates.py b/policyengine_uk_data/tests/test_aggregates.py
index 10a5fb2c..c8326a20 100644
--- a/policyengine_uk_data/tests/test_aggregates.py
+++ b/policyengine_uk_data/tests/test_aggregates.py
@@ -10,9 +10,7 @@
 
 @pytest.mark.parametrize("variable", AGGREGATES.keys())
 def test_aggregates(baseline, variable: str):
-    estimate = baseline.calculate(
-        variable, map_to="household", period=2025
-    ).sum()
+    estimate = baseline.calculate(variable, map_to="household", period=2025).sum()
 
     assert abs(estimate / AGGREGATES[variable] - 1) < 0.7, (
         f"Expected {AGGREGATES[variable] / 1e9:.1f} billion for {variable}, got {estimate / 1e9:.1f} billion (relative error = {abs(estimate / AGGREGATES[variable] - 1):.1%})."
diff --git a/policyengine_uk_data/tests/test_child_limit.py b/policyengine_uk_data/tests/test_child_limit.py
index 446ed4c4..76c065db 100644
--- a/policyengine_uk_data/tests/test_child_limit.py
+++ b/policyengine_uk_data/tests/test_child_limit.py
@@ -10,30 +10,19 @@ def test_child_limit(baseline):
         > 0
     ) * baseline.calculate("is_child", map_to="person").values
     child_in_uc_household = (
-        baseline.calculate(
-            "universal_credit", map_to="person", period=2025
-        ).values
-        > 0
+        baseline.calculate("universal_credit", map_to="person", period=2025).values > 0
     )
     children_in_capped_households = baseline.map_result(
         child_is_affected * child_in_uc_household, "person", "household"
     )
     capped_households = (children_in_capped_households > 0) * 1.0
-    household_weight = baseline.calculate(
-        "household_weight", period=2025
-    ).values
-    children_affected = (
-        children_in_capped_households * household_weight
-    ).sum()
+    household_weight = baseline.calculate("household_weight", period=2025).values
+    children_affected = (children_in_capped_households * household_weight).sum()
     households_affected = (capped_households * household_weight).sum()
 
     UPRATING_24_25 = 1.12  # https://ifs.org.uk/articles/two-child-limit-poverty-incentives-and-cost, table at the end
-    child_target = (
-        1.6e6 * UPRATING_24_25
-    )  # Expected number of affected children
-    household_target = (
-        440e3 * UPRATING_24_25
-    )  # Expected number of affected households
+    child_target = 1.6e6 * UPRATING_24_25  # Expected number of affected children
+    household_target = 440e3 * UPRATING_24_25  # Expected number of affected households
 
     assert abs(children_affected / child_target - 1) < 0.3, (
         f"Expected {child_target / 1e6:.1f} million affected children, got {children_affected / 1e6:.1f} million."
diff --git a/policyengine_uk_data/tests/test_childcare.py b/policyengine_uk_data/tests/test_childcare.py
index 7e5e16e6..72842946 100644
--- a/policyengine_uk_data/tests/test_childcare.py
+++ b/policyengine_uk_data/tests/test_childcare.py
@@ -41,17 +41,11 @@ def test_childcare(baseline, enhanced_frs):
     # Calculate actual spending values
     spending = {
         "tfc": baseline.calculate("tax_free_childcare", 2024).sum() / 1e9,
-        "extended": baseline.calculate(
-            "extended_childcare_entitlement", 2024
-        ).sum()
+        "extended": baseline.calculate("extended_childcare_entitlement", 2024).sum()
         / 1e9,
-        "targeted": baseline.calculate(
-            "targeted_childcare_entitlement", 2024
-        ).sum()
+        "targeted": baseline.calculate("targeted_childcare_entitlement", 2024).sum()
         / 1e9,
-        "universal": baseline.calculate(
-            "universal_childcare_entitlement", 2024
-        ).sum()
+        "universal": baseline.calculate("universal_childcare_entitlement", 2024).sum()
         / 1e9,
     }
 
@@ -82,14 +76,10 @@ def test_childcare(baseline, enhanced_frs):
     for key, rate in take_up_rates.items():
         print(f"{key.upper():<12} {rate:.3f}")
 
-    print(
-        f"\nEXTENDED HOURS: Mean = {hours_mean:.2f}, Std Dev = {hours_std:.2f}"
-    )
+    print(f"\nEXTENDED HOURS: Mean = {hours_mean:.2f}, Std Dev = {hours_std:.2f}")
 
     print("\nSPENDING (£ billion):")
-    print(
-        f"{'PROGRAM':<12} {'ACTUAL':<10} {'TARGET':<10} {'RATIO':<10} {'PASS?':<10}"
-    )
+    print(f"{'PROGRAM':<12} {'ACTUAL':<10} {'TARGET':<10} {'RATIO':<10} {'PASS?':<10}")
     print("-" * 55)
 
     failed_any = False
@@ -106,9 +96,7 @@ def test_childcare(baseline, enhanced_frs):
             failed_any = True
 
     print("\nCASELOAD (thousands):")
-    print(
-        f"{'PROGRAM':<12} {'ACTUAL':<10} {'TARGET':<10} {'RATIO':<10} {'PASS?':<10}"
-    )
+    print(f"{'PROGRAM':<12} {'ACTUAL':<10} {'TARGET':<10} {'RATIO':<10} {'PASS?':<10}")
     print("-" * 55)
 
     # Test caseload for each program
diff --git a/policyengine_uk_data/tests/test_non_negative_incomes.py b/policyengine_uk_data/tests/test_non_negative_incomes.py
index 633305e8..762d8fb0 100644
--- a/policyengine_uk_data/tests/test_non_negative_incomes.py
+++ b/policyengine_uk_data/tests/test_non_negative_incomes.py
@@ -18,6 +18,4 @@ def test_income_non_negative(frs, variable: str):
     """Test that income variables have no negative values."""
     values = frs.person[variable]
     min_value = values.min()
-    assert min_value >= 0, (
-        f"{variable} has negative values (min = {min_value:.2f})"
-    )
+    assert min_value >= 0, f"{variable} has negative values (min = {min_value:.2f})"
diff --git a/policyengine_uk_data/tests/test_pension_contributions_via_salary_sacrifice.py b/policyengine_uk_data/tests/test_pension_contributions_via_salary_sacrifice.py
index 3f5c2ba3..9ed48abf 100644
--- a/policyengine_uk_data/tests/test_pension_contributions_via_salary_sacrifice.py
+++ b/policyengine_uk_data/tests/test_pension_contributions_via_salary_sacrifice.py
@@ -11,9 +11,7 @@ def test_pension_contributions_via_salary_sacrifice(baseline):
 
     # Should have some non-zero values (not everyone uses salary sacrifice, but some do)
     total = values.sum()
-    assert total > 0, (
-        f"Expected some salary sacrifice contributions, got {total}"
-    )
+    assert total > 0, f"Expected some salary sacrifice contributions, got {total}"
 
     # Reasonableness check: total should be less than total employment income
     # This is a very loose check just to catch major issues
diff --git a/policyengine_uk_data/tests/test_scotland_uc_babies.py b/policyengine_uk_data/tests/test_scotland_uc_babies.py
index 85751cca..14394838 100644
--- a/policyengine_uk_data/tests/test_scotland_uc_babies.py
+++ b/policyengine_uk_data/tests/test_scotland_uc_babies.py
@@ -25,15 +25,11 @@ def test_scotland_uc_households_child_under_1(baseline):
     ).values
 
     # Check if household has child under 1
-    is_child = baseline.calculate(
-        "is_child", map_to="person", period=2025
-    ).values
+    is_child = baseline.calculate("is_child", map_to="person", period=2025).values
     age = baseline.calculate("age", map_to="person", period=2025).values
 
     child_under_1 = is_child & (age < 1)
-    has_child_under_1 = (
-        baseline.map_result(child_under_1, "person", "household") > 0
-    )
+    has_child_under_1 = baseline.map_result(child_under_1, "person", "household") > 0
 
     scotland_uc_child_under_1 = (
         (region.values == "SCOTLAND") & (uc > 0) & has_child_under_1
diff --git a/policyengine_uk_data/tests/test_target_registry.py b/policyengine_uk_data/tests/test_target_registry.py
index c6f78bdd..bc0854c5 100644
--- a/policyengine_uk_data/tests/test_target_registry.py
+++ b/policyengine_uk_data/tests/test_target_registry.py
@@ -52,9 +52,7 @@ def test_hmrc_spi_targets_exist():
     targets = get_all_targets(year=2025)
     spi_targets = [t for t in targets if t.source == "hmrc_spi"]
     # 13 bands × 6 income types × 2 (count + amount) = 156 per year
-    assert len(spi_targets) >= 100, (
-        f"Expected 100+ SPI targets, got {len(spi_targets)}"
-    )
+    assert len(spi_targets) >= 100, f"Expected 100+ SPI targets, got {len(spi_targets)}"
 
 
 def test_dwp_pip_targets():
@@ -76,9 +74,7 @@ def test_voa_council_tax_targets():
 def test_core_target_count():
     """Total target count should be substantial."""
     targets = get_all_targets(year=2025)
-    assert len(targets) >= 200, (
-        f"Expected 200+ targets for 2025, got {len(targets)}"
-    )
+    assert len(targets) >= 200, f"Expected 200+ targets for 2025, got {len(targets)}"
 
 
 def test_two_child_limit_targets():
diff --git a/policyengine_uk_data/tests/test_uc_by_children.py b/policyengine_uk_data/tests/test_uc_by_children.py
index 649e0fb3..8de5d558 100644
--- a/policyengine_uk_data/tests/test_uc_by_children.py
+++ b/policyengine_uk_data/tests/test_uc_by_children.py
@@ -35,9 +35,7 @@ def test_uc_households_by_children(baseline, bucket, target):
     uc = baseline.calculate("universal_credit", period=2025).values
     on_uc = baseline.map_result(uc > 0, "benunit", "household") > 0
 
-    is_child = baseline.calculate(
-        "is_child", map_to="person", period=2025
-    ).values
+    is_child = baseline.calculate("is_child", map_to="person", period=2025).values
     children_per_hh = baseline.map_result(is_child, "person", "household")
 
     if bucket == "0_children":
@@ -49,9 +47,7 @@ def test_uc_households_by_children(baseline, bucket, target):
     else:  # 3plus_children
         match = on_uc & (children_per_hh >= 3)
 
-    household_weight = baseline.calculate(
-        "household_weight", period=2025
-    ).values
+    household_weight = baseline.calculate("household_weight", period=2025).values
     actual = (household_weight * match).sum()
 
     assert abs(actual / target - 1) < TOLERANCE, (
diff --git a/policyengine_uk_data/tests/test_vehicle_ownership.py b/policyengine_uk_data/tests/test_vehicle_ownership.py
index d2fbc73b..6e9f6923 100644
--- a/policyengine_uk_data/tests/test_vehicle_ownership.py
+++ b/policyengine_uk_data/tests/test_vehicle_ownership.py
@@ -9,9 +9,7 @@
 
 def test_vehicle_ownership(baseline):
     """Test that vehicle ownership distribution matches NTS 2024 targets."""
-    num_vehicles = baseline.calculate(
-        "num_vehicles", map_to="household", period=2025
-    )
+    num_vehicles = baseline.calculate("num_vehicles", map_to="household", period=2025)
     weights = baseline.calculate("household_weight", period=2025)
 
     total_hh = weights.sum()
@@ -28,9 +26,7 @@ def test_vehicle_ownership(baseline):
         f"Expected {NTS_ONE_VEHICLE_RATE:.0%} households with one vehicle, "
         f"got {one_vehicle_rate:.0%}"
     )
-    assert (
-        abs(two_plus_rate - NTS_TWO_PLUS_VEHICLE_RATE) < ABSOLUTE_TOLERANCE
-    ), (
+    assert abs(two_plus_rate - NTS_TWO_PLUS_VEHICLE_RATE) < ABSOLUTE_TOLERANCE, (
         f"Expected {NTS_TWO_PLUS_VEHICLE_RATE:.0%} households with two+ vehicles, "
         f"got {two_plus_rate:.0%}"
     )
diff --git a/policyengine_uk_data/utils/calibrate.py b/policyengine_uk_data/utils/calibrate.py
index 6e31402c..c9fc5a92 100644
--- a/policyengine_uk_data/utils/calibrate.py
+++ b/policyengine_uk_data/utils/calibrate.py
@@ -53,13 +53,10 @@ def calibrate_local_areas(
     areas_per_household = r.sum(
         axis=0
     )  # number of areas each household can contribute to
-    areas_per_household = np.maximum(
-        areas_per_household, 1
-    )  # avoid division by zero
+    areas_per_household = np.maximum(areas_per_household, 1)  # avoid division by zero
     original_weights = np.log(
         dataset.household.household_weight.values / areas_per_household
-        + np.random.random(len(dataset.household.household_weight.values))
-        * 0.01
+        + np.random.random(len(dataset.household.household_weight.values)) * 0.01
     )
     weights = torch.tensor(
         np.ones((area_count, len(original_weights))) * original_weights,
@@ -85,9 +82,7 @@ def calibrate_local_areas(
         matrix.values if hasattr(matrix, "values") else matrix,
         dtype=torch.float32,
     )
-    y = torch.tensor(
-        y.values if hasattr(y, "values") else y, dtype=torch.float32
-    )
+    y = torch.tensor(y.values if hasattr(y, "values") else y, dtype=torch.float32)
     matrix_national = torch.tensor(
         m_national.values if hasattr(m_national, "values") else m_national,
         dtype=torch.float32,
@@ -135,9 +130,7 @@ def pct_close(w, t=0.1, local=True, national=True):
 
         if local:
             pred_local = (w.unsqueeze(-1) * metrics.unsqueeze(0)).sum(dim=1)
-            e_local = torch.sum(
-                torch.abs((pred_local / (1 + y) - 1)) < t
-            ).item()
+            e_local = torch.sum(torch.abs((pred_local / (1 + y) - 1)) < t).item()
             c_local = pred_local.shape[0] * pred_local.shape[1]
             numerator += e_local
             denominator += c_local
@@ -183,9 +176,7 @@ def dropout_weights(weights, p):
                 optimizer.step()
 
                 local_close = pct_close(weights_, local=True, national=False)
-                national_close = pct_close(
-                    weights_, local=False, national=True
-                )
+                national_close = pct_close(weights_, local=False, national=True)
 
                 if dropout_targets:
                     validation_loss = loss(weights_, validation=True)
@@ -213,9 +204,7 @@ def dropout_weights(weights, p):
                             excluded_training_targets,
                         )
                         performance_step["epoch"] = epoch
-                        performance_step["loss"] = (
-                            performance_step.rel_abs_error**2
-                        )
+                        performance_step["loss"] = performance_step.rel_abs_error**2
                         performance_step["target_name"] = [
                             f"{area}/{metric}"
                             for area, metric in zip(
@@ -231,9 +220,7 @@ def dropout_weights(weights, p):
                     with h5py.File(STORAGE_FOLDER / weight_file, "w") as f:
                         f.create_dataset(dataset_key, data=final_weights)
 
-                    dataset.household.household_weight = final_weights.sum(
-                        axis=0
-                    )
+                    dataset.household.household_weight = final_weights.sum(axis=0)
     else:
         for epoch in range(epochs):
             optimizer.zero_grad()
diff --git a/policyengine_uk_data/utils/data_upload.py b/policyengine_uk_data/utils/data_upload.py
index 89445ad9..342a02f7 100644
--- a/policyengine_uk_data/utils/data_upload.py
+++ b/policyengine_uk_data/utils/data_upload.py
@@ -89,18 +89,14 @@ def upload_files_to_gcs(
     Upload files to Google Cloud Storage and set metadata with the version.
     """
     credentials, project_id = google.auth.default()
-    storage_client = storage.Client(
-        credentials=credentials, project=project_id
-    )
+    storage_client = storage.Client(credentials=credentials, project=project_id)
     bucket = storage_client.bucket(gcs_bucket_name)
 
     for file_path in files:
         file_path = Path(file_path)
         blob = bucket.blob(file_path.name)
         blob.upload_from_filename(file_path)
-        logging.info(
-            f"Uploaded {file_path.name} to GCS bucket {gcs_bucket_name}."
-        )
+        logging.info(f"Uploaded {file_path.name} to GCS bucket {gcs_bucket_name}.")
 
         # Set metadata
         blob.metadata = {"version": version}
diff --git a/policyengine_uk_data/utils/datasets.py b/policyengine_uk_data/utils/datasets.py
index 82c9e273..2f4cd1b4 100644
--- a/policyengine_uk_data/utils/datasets.py
+++ b/policyengine_uk_data/utils/datasets.py
@@ -10,9 +10,7 @@
 warnings.filterwarnings("ignore")
 
 
-def sum_to_entity(
-    values: pd.Series, foreign_key: pd.Series, primary_key
-) -> np.ndarray:
+def sum_to_entity(values: pd.Series, foreign_key: pd.Series, primary_key) -> np.ndarray:
     """Sums values by joining foreign and primary keys.
 
     Args:
@@ -23,14 +21,10 @@ def sum_to_entity(
     Returns:
         pd.Series: A value for each person.
     """
-    return (
-        values.groupby(foreign_key).sum().reindex(primary_key).fillna(0).values
-    )
+    return values.groupby(foreign_key).sum().reindex(primary_key).fillna(0).values
 
 
-def categorical(
-    values: pd.Series, default: int, left: list, right: list
-) -> pd.Series:
+def categorical(values: pd.Series, default: int, left: list, right: list) -> pd.Series:
     """Maps a categorical input to an output using given left and right arrays.
 
     Args:
@@ -45,9 +39,7 @@ def categorical(
     return values.fillna(default).map({i: j for i, j in zip(left, right)})
 
 
-def sum_from_positive_fields(
-    table: pd.DataFrame, fields: List[str]
-) -> np.array:
+def sum_from_positive_fields(table: pd.DataFrame, fields: List[str]) -> np.array:
     """Sum from fields in table, ignoring negative values.
 
     Args:
@@ -57,9 +49,7 @@ def sum_from_positive_fields(
     Returns:
         np.array
     """
-    return np.where(
-        table[fields].sum(axis=1) > 0, table[fields].sum(axis=1), 0
-    )
+    return np.where(table[fields].sum(axis=1) > 0, table[fields].sum(axis=1), 0)
 
 
 def sum_positive_variables(variables: List[str]) -> np.array:
diff --git a/policyengine_uk_data/utils/huggingface.py b/policyengine_uk_data/utils/huggingface.py
index 1ed8de25..7fc5d9e9 100644
--- a/policyengine_uk_data/utils/huggingface.py
+++ b/policyengine_uk_data/utils/huggingface.py
@@ -2,9 +2,7 @@
 import os
 
 
-def download(
-    repo: str, repo_filename: str, local_folder: str, version: str = None
-):
+def download(repo: str, repo_filename: str, local_folder: str, version: str = None):
     token = os.environ.get(
         "HUGGING_FACE_TOKEN",
     )
diff --git a/policyengine_uk_data/utils/incomes_projection.py b/policyengine_uk_data/utils/incomes_projection.py
index 1f62b1cb..302dea9d 100644
--- a/policyengine_uk_data/utils/incomes_projection.py
+++ b/policyengine_uk_data/utils/incomes_projection.py
@@ -20,9 +20,9 @@
 MAX_YEAR = 2029
 
 for time_period in range(MIN_YEAR, MAX_YEAR + 1):
-    time_period_df = statistics[
-        ["name", "unit", "reference", str(time_period)]
-    ].rename(columns={str(time_period): "value"})
+    time_period_df = statistics[["name", "unit", "reference", str(time_period)]].rename(
+        columns={str(time_period): "value"}
+    )
     time_period_df["time_period"] = time_period
     dfs.append(time_period_df)
 
@@ -49,9 +49,7 @@ def create_target_matrix(
     sim = Microsimulation(dataset=dataset, reform=reform)
     sim.default_calculation_period = time_period
 
-    household_from_person = lambda values: sim.map_result(
-        values, "person", "household"
-    )
+    household_from_person = lambda values: sim.map_result(values, "person", "household")
 
     df = pd.DataFrame()
 
@@ -99,9 +97,7 @@ def create_target_matrix(
             target_values.append(row[variable + "_amount"])
             target_names.append(name_amount)
             name_count = (
-                "hmrc/"
-                + variable
-                + f"_count_income_band_{i}_{lower:_}_to_{upper:_}"
+                "hmrc/" + variable + f"_count_income_band_{i}_{lower:_}_to_{upper:_}"
             )
             df[name_count] = household_from_person(
                 (income_df[variable] > 0) * in_income_band
@@ -184,16 +180,10 @@ def create_income_projections():
         for variable in INCOME_VARIABLES:
             count_values = []
             amount_values = []
-            for i, (lower, upper) in enumerate(
-                zip(lower_bounds, upper_bounds)
-            ):
-                in_band = sim.calculate("total_income", year).between(
-                    lower, upper
-                )
+            for i, (lower, upper) in enumerate(zip(lower_bounds, upper_bounds)):
+                in_band = sim.calculate("total_income", year).between(lower, upper)
                 value = sim.calculate(variable, year)
-                count_in_band_with_nonzero_value = round(
-                    ((value > 0) * in_band).sum()
-                )
+                count_in_band_with_nonzero_value = round(((value > 0) * in_band).sum())
                 amount_in_band = round(value[in_band].sum())
                 count_values.append(count_in_band_with_nonzero_value)
                 amount_values.append(amount_in_band)
@@ -202,9 +192,7 @@ def create_income_projections():
         year_df["year"] = year
         projection_df = pd.concat([projection_df, year_df])
 
-    projection_df.to_csv(
-        STORAGE_FOLDER / "incomes_projection.csv", index=False
-    )
+    projection_df.to_csv(STORAGE_FOLDER / "incomes_projection.csv", index=False)
 
 
 if __name__ == "__main__":
diff --git a/policyengine_uk_data/utils/loss.py b/policyengine_uk_data/utils/loss.py
index 18d30bed..27eb919f 100644
--- a/policyengine_uk_data/utils/loss.py
+++ b/policyengine_uk_data/utils/loss.py
@@ -11,9 +11,7 @@
 )
 
 
-def get_loss_results(
-    dataset, time_period, reform=None, household_weights=None
-):
+def get_loss_results(dataset, time_period, reform=None, household_weights=None):
     """Calculate loss metrics comparing model outputs to targets.
 
     Args:
diff --git a/policyengine_uk_data/utils/progress.py b/policyengine_uk_data/utils/progress.py
index ccb5efcf..e6a70f89 100644
--- a/policyengine_uk_data/utils/progress.py
+++ b/policyengine_uk_data/utils/progress.py
@@ -285,9 +285,7 @@ def update_calibration(
                         description=f"[yellow]●[/yellow] Calibration epoch {iteration}/{iterations} • calculating loss",
                     )
                 else:
-                    loss_text = (
-                        f" • loss: {loss_value:.6f}" if loss_value else ""
-                    )
+                    loss_text = f" • loss: {loss_value:.6f}" if loss_value else ""
                     nested_progress.update_task(
                         calibration_task,
                         description=f"[blue]●[/blue] Calibration epoch {iteration}/{iterations}{loss_text}",
@@ -317,9 +315,7 @@ def update_calibration(
                             description=f"Calibration iteration {iteration}/{iterations} • [yellow]calculating loss[/yellow]",
                         )
                     else:
-                        loss_text = (
-                            f" • loss: {loss_value:.6f}" if loss_value else ""
-                        )
+                        loss_text = f" • loss: {loss_value:.6f}" if loss_value else ""
                         progress.update_task(
                             main_task,
                             description=f"Calibration iteration {iteration}/{iterations}{loss_text}",
@@ -329,9 +325,7 @@ def update_calibration(
                 yield update_calibration
 
     @contextmanager
-    def track_file_processing(
-        self, files: List[str], operation: str = "processing"
-    ):
+    def track_file_processing(self, files: List[str], operation: str = "processing"):
         """Track file processing operations.
 
         Args:
diff --git a/policyengine_uk_data/utils/qrf.py b/policyengine_uk_data/utils/qrf.py
index d99e5a25..05c0ba66 100644
--- a/policyengine_uk_data/utils/qrf.py
+++ b/policyengine_uk_data/utils/qrf.py
@@ -74,6 +74,4 @@ def save(self, file_path: str):
             file_path: Path where model should be saved.
         """
         with open(file_path, "wb") as f:
-            pickle.dump(
-                {"model": self.model, "input_columns": self.input_columns}, f
-            )
+            pickle.dump({"model": self.model, "input_columns": self.input_columns}, f)
diff --git a/policyengine_uk_data/utils/spi.py b/policyengine_uk_data/utils/spi.py
index fae6b451..5e94bcff 100644
--- a/policyengine_uk_data/utils/spi.py
+++ b/policyengine_uk_data/utils/spi.py
@@ -70,12 +70,8 @@ def parse_value(value):
 
 import numpy as np
 
-income["total_income_lower_bound"] = list(income["income_range"][:-1]) + [
-    12_570
-]
-income["total_income_upper_bound"] = (
-    list(income["income_range"][1:-1]) + [np.inf] * 2
-)
+income["total_income_lower_bound"] = list(income["income_range"][:-1]) + [12_570]
+income["total_income_upper_bound"] = list(income["income_range"][1:-1]) + [np.inf] * 2
 # Order the income bound columns first
 income = income[
     [
diff --git a/policyengine_uk_data/utils/stack.py b/policyengine_uk_data/utils/stack.py
index 2ded165c..2fe9df82 100644
--- a/policyengine_uk_data/utils/stack.py
+++ b/policyengine_uk_data/utils/stack.py
@@ -17,8 +17,6 @@ def stack_datasets(
     return UKSingleYearDataset(
         person=pd.concat([data_1.person, data_2.person], ignore_index=True),
         benunit=pd.concat([data_1.benunit, data_2.benunit], ignore_index=True),
-        household=pd.concat(
-            [data_1.household, data_2.household], ignore_index=True
-        ),
+        household=pd.concat([data_1.household, data_2.household], ignore_index=True),
         fiscal_year=data_1.time_period,
     )
diff --git a/policyengine_uk_data/utils/uc_data.py b/policyengine_uk_data/utils/uc_data.py
index 1e2282cb..7bacebc5 100644
--- a/policyengine_uk_data/utils/uc_data.py
+++ b/policyengine_uk_data/utils/uc_data.py
@@ -63,9 +63,7 @@ def parse_band(band):
         "Couple, no children": "COUPLE_NO_CHILDREN",
         "Couple, with children": "COUPLE_WITH_CHILDREN",
     }
-    result_df["family_type"] = result_df["family_type"].map(
-        family_type_mapping
-    )
+    result_df["family_type"] = result_df["family_type"].map(family_type_mapping)
 
     # Reorder columns and drop monthly band
     result_df = result_df[
@@ -111,9 +109,7 @@ def _parse_uc_pc_households():
 
     # Parse NI data
     ni_file_path = storage_path / "dfc-ni-uc-stats-supp-tables-may-2025.ods"
-    df_ni = pd.read_excel(
-        ni_file_path, sheet_name="5b", engine="odf", header=None
-    )
+    df_ni = pd.read_excel(ni_file_path, sheet_name="5b", engine="odf", header=None)
 
     # Get constituency names from row 2, columns 1-18
     ni_constituencies = df_ni.iloc[2, 1:19].tolist()
@@ -179,9 +175,7 @@ def _parse_uc_la_households():
 
     # Parse NI data
     ni_file_path = storage_path / "dfc-ni-uc-stats-supp-tables-may-2025.ods"
-    df_ni = pd.read_excel(
-        ni_file_path, sheet_name="5c", engine="odf", header=None
-    )
+    df_ni = pd.read_excel(ni_file_path, sheet_name="5c", engine="odf", header=None)
 
     # Get LGD names from row 2, columns 1-11
     ni_lgd_names = df_ni.iloc[2, 1:12].tolist()
diff --git a/pyproject.toml b/pyproject.toml
index 73200a77..9e4f1030 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -70,9 +70,6 @@ filterwarnings = [
     "ignore::PendingDeprecationWarning",
 ]
 
-[tool.ruff]
-line-length = 79
-
 [tool.towncrier]
 package = "policyengine_uk_data"
 directory = "changelog.d"