[pre-commit.ci] pre-commit autoupdate

.gitignore

@@ -23,4 +23,3 @@ readme.ipynb
 #docs/site_libs
 #site_libs
 tests/.coverage
-

.pre-commit-config.yaml

@@ -14,27 +14,27 @@ repos:
       - id: check-added-large-files
 
   - repo: https://github.com/python-jsonschema/check-jsonschema
-    rev: 0.28.3
+    rev: 0.29.2
     hooks:
       - id: check-github-workflows
         args: ["--verbose"]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.4
+    rev: v0.6.2
     hooks:
       - id: ruff
         args: ["--fix", "--output-format=full"]
       - id: ruff-format
 
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.8.5
+    rev: 1.8.7
     hooks:
       - id: nbqa-ruff
         args: ["--fix", "--output-format=full"]
         files: ^docs/.*\.ipynb$
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.10.0
+    rev: v1.11.2
     hooks:
       - id: mypy
         args: [--ignore-missing-imports]

benchmarks/run_benchmarks.ipynb

@@ -26,11 +26,12 @@
     "%autoreload 2\n",
     "\n",
     "import time\n",
+    "\n",
     "import pandas as pd\n",
-    "import numpy as np\n",
-    "from pyfixest.estimation import feols, fepois\n",
     "from tqdm import tqdm  # note: tqdm is not a dependency of pyfixest\n",
     "\n",
+    "from pyfixest.estimation import feols, fepois\n",
+    "\n",
     "\n",
     "def run_standard_benchmark(model, fixed_effect):\n",
     "    \"\"\"\n",
@@ -41,7 +42,6 @@
     "    Returns:\n",
     "        A pd.DataFrame with the results.\n",
     "    \"\"\"\n",
-    "\n",
     "    assert model in [\"feols\", \"fepois\"]\n",
     "    assert fixed_effect in [\"dum_1\", \"dum_1+dum_2\", \"dum_1+dum_2+dum_3\"]\n",
     "\n",
@@ -58,7 +58,7 @@
     "    fml = f\"{fml_base} | {fixed_effect}\"\n",
     "\n",
     "    # warmup\n",
-    "    df = pd.read_stata(f\"./data/_STATA/base_s2_r1.dta\")\n",
+    "    df = pd.read_stata(\"./data/_STATA/base_s2_r1.dta\")\n",
     "    feols(fml, data=df)\n",
     "\n",
     "    for size in tqdm(range(1, 6)):\n",
@@ -98,7 +98,6 @@
     "    \"\"\"\n",
     "    Run all the benchmarks.\n",
     "    \"\"\"\n",
-    "\n",
     "    res = pd.DataFrame()\n",
     "    for model in [\"feols\", \"fepois\"]:\n",
     "        for fixef in [\"dum_1\", \"dum_1+dum_2\", \"dum_1+dum_2+dum_3\"]:\n",

benchmarks/visualise_benchmarks.ipynb

@@ -13,8 +13,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
     "import numpy as np\n",
+    "import pandas as pd\n",
     "from lets_plot import *\n",
     "\n",
     "LetsPlot.setup_html(no_js=True)"

docs/compare-fixest-pyfixest.ipynb

@@ -48,7 +48,7 @@
     "%autoreload 2\n",
     "\n",
     "# Get data using pyfixest\n",
-    "data = pf.get_data(model=\"Feols\", N=10_000, seed = 99292)"
+    "data = pf.get_data(model=\"Feols\", N=10_000, seed=99292)"
    ]
   },
   {
@@ -78,11 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fit = pf.feols(\n",
-    "    fml = \"Y ~ X1 + X2 | f1 + f2\",\n",
-    "    data = data,\n",
-    "    vcov = \"iid\"\n",
-    ")"
+    "fit = pf.feols(fml=\"Y ~ X1 + X2 | f1 + f2\", data=data, vcov=\"iid\")"
    ]
   },
   {
@@ -99,10 +95,7 @@
    "outputs": [],
    "source": [
     "fit_weights = pf.feols(\n",
-    "    fml = \"Y ~ X1 + X2 | f1 + f2\",\n",
-    "    data = data,\n",
-    "    weights = \"weights\",\n",
-    "    vcov = \"iid\"\n",
+    "    fml=\"Y ~ X1 + X2 | f1 + f2\", data=data, weights=\"weights\", vcov=\"iid\"\n",
     ")"
    ]
   },
@@ -311,7 +304,7 @@
     }
    ],
    "source": [
-    "pf.etable([fit, fit_weights], digits = 6)"
+    "pf.etable([fit, fit_weights], digits=6)"
    ]
   },
   {
@@ -444,7 +437,7 @@
     }
    ],
    "source": [
-    "pd.DataFrame(fixest.etable(r_fit, r_fit_weights, digits = 6)).T"
+    "pd.DataFrame(fixest.etable(r_fit, r_fit_weights, digits=6)).T"
    ]
   },
   {
@@ -467,15 +460,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fit = pf.feols(\n",
-    "    fml = \"Y ~ X1 + X2 | f1 + f2\", data = data,\n",
-    "    vcov = \"hetero\"\n",
-    ")\n",
+    "fit = pf.feols(fml=\"Y ~ X1 + X2 | f1 + f2\", data=data, vcov=\"hetero\")\n",
     "fit_weights = pf.feols(\n",
-    "    fml = \"Y ~ X1 + X2 | f1 + f2\",\n",
-    "    data = data,\n",
-    "    vcov = \"hetero\",\n",
-    "    weights = \"weights\"\n",
+    "    fml=\"Y ~ X1 + X2 | f1 + f2\", data=data, vcov=\"hetero\", weights=\"weights\"\n",
     ")"
    ]
   },
@@ -661,7 +648,7 @@
     }
    ],
    "source": [
-    "pf.etable([fit, fit_weights], digits = 6)"
+    "pf.etable([fit, fit_weights], digits=6)"
    ]
   },
   {
@@ -794,7 +781,7 @@
     }
    ],
    "source": [
-    "pd.DataFrame(fixest.etable(fit_r, fit_weights_r, digits = 6)).T"
+    "pd.DataFrame(fixest.etable(fit_r, fit_weights_r, digits=6)).T"
    ]
   },
   {
@@ -828,29 +815,22 @@
     }
    ],
    "source": [
-    "fit = pf.feols(\n",
-    "    fml = \"Y ~ X1 + X2 | f1 + f2\",\n",
-    "    data = data,\n",
-    "    vcov = {\"CRV1\": \"f1\"}\n",
-    ")\n",
+    "fit = pf.feols(fml=\"Y ~ X1 + X2 | f1 + f2\", data=data, vcov={\"CRV1\": \"f1\"})\n",
     "fit_weights = pf.feols(\n",
-    "    fml = \"Y ~ X1 + X2 | f1 + f2\",\n",
-    "    data = data,\n",
-    "    vcov = {\"CRV1\":\"f1\"},\n",
-    "    weights = \"weights\"\n",
+    "    fml=\"Y ~ X1 + X2 | f1 + f2\", data=data, vcov={\"CRV1\": \"f1\"}, weights=\"weights\"\n",
     ")\n",
     "\n",
     "fit_r = fixest.feols(\n",
     "    ro.Formula(\"Y ~ X1 + X2 | f1 + f2\"),\n",
     "    data=data,\n",
-    "    vcov= ro.Formula(\"~f1\"),\n",
+    "    vcov=ro.Formula(\"~f1\"),\n",
     "    ssc=fixest.ssc(True, \"none\", True, \"min\", \"min\", False),\n",
     ")\n",
     "fit_r_weights = fixest.feols(\n",
     "    ro.Formula(\"Y ~ X1 + X2 | f1 + f2\"),\n",
     "    data=data,\n",
     "    weights=ro.Formula(\"~weights\"),\n",
-    "    vcov= ro.Formula(\"~f1\"),\n",
+    "    vcov=ro.Formula(\"~f1\"),\n",
     "    ssc=fixest.ssc(True, \"none\", True, \"min\", \"min\", False),\n",
     ")"
    ]
@@ -997,7 +977,7 @@
     }
    ],
    "source": [
-    "pf.etable([fit, fit_weights], digits = 6)"
+    "pf.etable([fit, fit_weights], digits=6)"
    ]
   },
   {
@@ -1130,7 +1110,7 @@
     }
    ],
    "source": [
-    "pd.DataFrame(fixest.etable(fit_r, fit_r_weights, digits = 6)).T"
+    "pd.DataFrame(fixest.etable(fit_r, fit_r_weights, digits=6)).T"
    ]
   },
   {
@@ -1146,7 +1126,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = pf.get_data(model = \"Fepois\")"
+    "data = pf.get_data(model=\"Fepois\")"
    ]
   },
   {
@@ -1168,22 +1148,12 @@
     }
    ],
    "source": [
-    "fit_iid = pf.fepois(\n",
-    "    fml = \"Y ~ X1 + X2 | f1 + f2\",\n",
-    "    data = data,\n",
-    "    vcov = \"iid\",\n",
-    "    iwls_tol = 1e-10\n",
-    ")\n",
+    "fit_iid = pf.fepois(fml=\"Y ~ X1 + X2 | f1 + f2\", data=data, vcov=\"iid\", iwls_tol=1e-10)\n",
     "fit_hetero = pf.fepois(\n",
-    "    fml = \"Y ~ X1 + X2 | f1 + f2\", data = data,\n",
-    "    vcov = \"hetero\",\n",
-    "    iwls_tol = 1e-10\n",
+    "    fml=\"Y ~ X1 + X2 | f1 + f2\", data=data, vcov=\"hetero\", iwls_tol=1e-10\n",
     ")\n",
     "fit_crv = pf.fepois(\n",
-    "    fml = \"Y ~ X1 + X2 | f1 + f2\",\n",
-    "    data = data,\n",
-    "    vcov = {\"CRV1\":\"f1\"},\n",
-    "    iwls_tol = 1e-10\n",
+    "    fml=\"Y ~ X1 + X2 | f1 + f2\", data=data, vcov={\"CRV1\": \"f1\"}, iwls_tol=1e-10\n",
     ")\n",
     "\n",
     "fit_r_iid = fixest.fepois(\n",
@@ -1380,7 +1350,7 @@
     }
    ],
    "source": [
-    "pf.etable([fit_iid, fit_hetero, fit_crv], digits = 6)"
+    "pf.etable([fit_iid, fit_hetero, fit_crv], digits=6)"
    ]
   },
   {
@@ -1549,7 +1519,7 @@
     }
    ],
    "source": [
-    "pd.DataFrame(fixest.etable(fit_r_iid, fit_r_hetero, fit_r_crv, digits = 6)).T"
+    "pd.DataFrame(fixest.etable(fit_r_iid, fit_r_hetero, fit_r_crv, digits=6)).T"
    ]
   }
  ],

docs/difference-in-differences.ipynb

@@ -380,7 +380,6 @@
     }
    ],
    "source": [
-    "\n",
     "# multi-cohort adoption data\n",
     "df_multi_cohort = pd.read_csv(\n",
     "    resources.files(\"pyfixest.did.data\").joinpath(\"df_het.csv\")\n",
@@ -426,11 +425,14 @@
    "source": [
     "panelview(\n",
     "    df_one_cohort,\n",
-    "    unit=\"unit\", time=\"year\", treat=\"treat\",\n",
+    "    unit=\"unit\",\n",
+    "    time=\"year\",\n",
+    "    treat=\"treat\",\n",
     "    collapse_to_cohort=True,\n",
     "    sort_by_timing=True,\n",
-    "    ylab=\"Cohort\", xlab=\"Year\",\n",
-    "    title = \"Treatment Assignment Cohorts\",\n",
+    "    ylab=\"Cohort\",\n",
+    "    xlab=\"Year\",\n",
+    "    title=\"Treatment Assignment Cohorts\",\n",
     ")"
    ]
   },
@@ -463,11 +465,14 @@
    "source": [
     "panelview(\n",
     "    df_multi_cohort,\n",
-    "    unit=\"unit\", time=\"year\", treat=\"treat\",\n",
+    "    unit=\"unit\",\n",
+    "    time=\"year\",\n",
+    "    treat=\"treat\",\n",
     "    collapse_to_cohort=True,\n",
     "    sort_by_timing=True,\n",
-    "    ylab=\"Cohort\", xlab=\"Year\",\n",
-    "    title = \"Treatment Assignment Cohorts\",\n",
+    "    ylab=\"Cohort\",\n",
+    "    xlab=\"Year\",\n",
+    "    title=\"Treatment Assignment Cohorts\",\n",
     ")"
    ]
   },
@@ -509,10 +514,13 @@
    "source": [
     "panelview(\n",
     "    df_multi_cohort,\n",
-    "    unit=\"unit\", time=\"year\", treat=\"treat\",\n",
+    "    unit=\"unit\",\n",
+    "    time=\"year\",\n",
+    "    treat=\"treat\",\n",
     "    sort_by_timing=True,\n",
-    "    ylab=\"Unit\", xlab=\"Year\",\n",
-    "    title = \"Treatment Assignment (all units)\",\n",
+    "    ylab=\"Unit\",\n",
+    "    xlab=\"Year\",\n",
+    "    title=\"Treatment Assignment (all units)\",\n",
     ")"
    ]
   },
@@ -695,7 +703,7 @@
     "    figsize=[1200, 400],\n",
     "    yintercept=0,\n",
     "    xintercept=13.5,\n",
-    "    labels = rename_event_study_coefs(fit_dynamic_twfe._coefnames),\n",
+    "    labels=rename_event_study_coefs(fit_dynamic_twfe._coefnames),\n",
     ")"
    ]
   },
@@ -842,7 +850,7 @@
     "    yintercept=0,\n",
     "    xintercept=13.5,\n",
     "    joint=\"both\",\n",
-    "    labels = rename_event_study_coefs(fit_dynamic_twfe._coefnames),\n",
+    "    labels=rename_event_study_coefs(fit_dynamic_twfe._coefnames),\n",
     ")"
    ]
   },
@@ -1060,7 +1068,7 @@
     "    figsize=figsize,\n",
     "    xintercept=18.5,\n",
     "    yintercept=0,\n",
-    "    labels = rename_event_study_coefs(fit_twfe._coefnames), # rename coefficients\n",
+    "    labels=rename_event_study_coefs(fit_twfe._coefnames),  # rename coefficients\n",
     ").show()"
    ]
   },
@@ -1179,7 +1187,7 @@
     "    figsize=figsize,\n",
     "    xintercept=18.5,\n",
     "    yintercept=0,\n",
-    "    labels = rename_event_study_coefs(fit_twfe._coefnames),\n",
+    "    labels=rename_event_study_coefs(fit_twfe._coefnames),\n",
     ").show()"
    ]
   },
@@ -1422,11 +1430,13 @@
     "    post_window=20,\n",
     "    att=True,\n",
     ")\n",
-    "pd.concat([\n",
-    "    fit_twfe.tidy().assign(estimator=\"TWFE\"),\n",
-    "    fit_did2s.tidy().assign(estimator=\"DID2s\"),\n",
-    "    fit_lpdid.tidy().assign(estimator=\"LPDID\").drop(\"N\", axis=1),\n",
-    "], axis=0\n",
+    "pd.concat(\n",
+    "    [\n",
+    "        fit_twfe.tidy().assign(estimator=\"TWFE\"),\n",
+    "        fit_did2s.tidy().assign(estimator=\"DID2s\"),\n",
+    "        fit_lpdid.tidy().assign(estimator=\"LPDID\").drop(\"N\", axis=1),\n",
+    "    ],\n",
+    "    axis=0,\n",
     ")"
    ]
   }