Fix Linter Errors

.github/workflows/deploy-docs.yml

@@ -19,7 +19,7 @@ concurrency:
 jobs:
   build:
     runs-on: ubuntu-latest
-    
+
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -59,8 +59,8 @@ jobs:
     runs-on: ubuntu-latest
     needs: build
     if: github.ref == 'refs/heads/main'
-    
+
     steps:
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v4
+        uses: actions/deploy-pages@v4

.pre-commit-config.yaml

@@ -39,4 +39,4 @@ repos:
       - id: mypy
         args: [--ignore-missing-imports]
         files: ^pyfixest/
-        additional_dependencies: [numpy>=1.20, pandas-stubs]
+        additional_dependencies: [numpy>=1.20, pandas-stubs]

DTable.ipynb

@@ -40,8 +40,8 @@
    "outputs": [],
    "source": [
     "# Import necessary libraries\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
+    "\n",
     "import maketables as mt\n",
     "\n",
     "# Load sample dataset\n",
@@ -56,11 +56,11 @@
     "    \"tenure\": \"Years of Tenure\",\n",
     "    \"occupation\": \"Occupation\",\n",
     "    \"worker_type\": \"Worker Type\",\n",
-    "    \"education\": \"Education Level\"\n",
+    "    \"education\": \"Education Level\",\n",
     "}\n",
     "\n",
-    "# Set default labels \n",
-    "mt.MTable.DEFAULT_LABELS = labels\n"
+    "# Set default labels\n",
+    "mt.MTable.DEFAULT_LABELS = labels"
    ]
   },
   {
@@ -706,7 +706,7 @@
     "mt.DTable(\n",
     "    df,\n",
     "    vars=[\"wage\", \"logwage\", \"age\", \"tenure\"],\n",
-    "    bycol=[\"worker_type\",\"gender\"],\n",
+    "    bycol=[\"worker_type\", \"gender\"],\n",
     "    stats=[\"count\", \"mean\", \"std\"],\n",
     "    caption=\"Descriptive statistics by worker type and gender\",\n",
     "    stats_labels={\"count\": \"Number of observations\"},\n",
@@ -1407,19 +1407,19 @@
    "source": [
     "# Custom format specifications\n",
     "format_specs = {\n",
-    "    'mean': ',.2f',     # Comma separators, no decimals for means\n",
-    "    'std': '.2f',       # 2 decimal places for standard deviations\n",
-    "    'count': ',.0f',    # Comma separators for counts\n",
-    "    'min': ',.0f',      # No decimals for min/max\n",
-    "    'max': ',.0f'\n",
+    "    \"mean\": \",.2f\",  # Comma separators, no decimals for means\n",
+    "    \"std\": \".2f\",  # 2 decimal places for standard deviations\n",
+    "    \"count\": \",.0f\",  # Comma separators for counts\n",
+    "    \"min\": \",.0f\",  # No decimals for min/max\n",
+    "    \"max\": \",.0f\",\n",
     "}\n",
     "\n",
     "mt.DTable(\n",
     "    df,\n",
-    "    vars=[\"wage\", \"age\",\"tenure\"],\n",
+    "    vars=[\"wage\", \"age\", \"tenure\"],\n",
     "    stats=[\"mean\", \"std\", \"min\", \"max\", \"count\"],\n",
     "    format_spec=format_specs,\n",
-    "    caption=\"Custom formatting example\"\n",
+    "    caption=\"Custom formatting example\",\n",
     ")"
    ]
   }

DocxTables.ipynb

@@ -67,10 +67,10 @@
    "outputs": [],
    "source": [
     "# Import necessary libraries\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
     "import pyfixest as pf\n",
     "import statsmodels.formula.api as smf\n",
+    "\n",
     "import maketables as mt\n",
     "\n",
     "# Load sample dataset\n",
@@ -87,10 +87,10 @@
     "    \"occupation\": \"Occupation\",\n",
     "    \"worker_type\": \"Worker Type\",\n",
     "    \"education\": \"Education Level\",\n",
-    "    \"promoted\": \"Promotion\"\n",
+    "    \"promoted\": \"Promotion\",\n",
     "}\n",
     "\n",
-    "# Set default labels \n",
+    "# Set default labels\n",
     "mt.MTable.DEFAULT_LABELS = labels\n",
     "\n",
     "# Generate a categorical variable for gender from the dummy variable\n",
@@ -120,12 +120,18 @@
    "outputs": [],
    "source": [
     "# Descriptive statistics\n",
-    "tab1 =   mt.DTable(df, vars=[\"wage\", \"age\", \"tenure\"],\n",
-    "                       bycol=[\"worker_type\"], byrow=\"gender\",\n",
-    "                       stats=[\"count\", \"mean\", \"std\"],\n",
-    "                       caption=\"Descriptive statistics by worker type and gender\",\n",
-    "                       format_spec = {'mean': ',.2f', 'std': '.2f',})\n",
-    "\n"
+    "tab1 = mt.DTable(\n",
+    "    df,\n",
+    "    vars=[\"wage\", \"age\", \"tenure\"],\n",
+    "    bycol=[\"worker_type\"],\n",
+    "    byrow=\"gender\",\n",
+    "    stats=[\"count\", \"mean\", \"std\"],\n",
+    "    caption=\"Descriptive statistics by worker type and gender\",\n",
+    "    format_spec={\n",
+    "        \"mean\": \",.2f\",\n",
+    "        \"std\": \".2f\",\n",
+    "    },\n",
+    ")"
    ]
   },
   {
@@ -162,8 +168,12 @@
    "outputs": [],
    "source": [
     "# Fill/update the first table in the document to display the descriptive statistics:\n",
-    "tab1.update_docx(file_name=\"docs/WordOutput.docx\", tab_num=1, \n",
-    "                 show=False, docx_style={\"first_col_width\": \"5cm\"})"
+    "tab1.update_docx(\n",
+    "    file_name=\"docs/WordOutput.docx\",\n",
+    "    tab_num=1,\n",
+    "    show=False,\n",
+    "    docx_style={\"first_col_width\": \"5cm\"},\n",
+    ")"
    ]
   },
   {
@@ -190,8 +200,10 @@
    "source": [
     "# Here we use (py)fixest's stepwise notation to estimate several regressions in one go\n",
     "# And directly generate a regression table with the results\n",
-    "tab2=mt.ETable(pf.feols(\"logwage+wage~ age + female + sw0(age:female)\", data=df),\n",
-    "                caption=\"Wage regressions\")\n",
+    "tab2 = mt.ETable(\n",
+    "    pf.feols(\"logwage+wage~ age + female + sw0(age:female)\", data=df),\n",
+    "    caption=\"Wage regressions\",\n",
+    ")\n",
     "\n",
     "# Fill/update the second table in the document\n",
     "tab2.update_docx(file_name=\"docs/WordOutput.docx\", tab_num=2, show=False)"
@@ -219,17 +231,18 @@
    },
    "outputs": [],
    "source": [
-    "\n",
-    "# Fit your models \n",
+    "# Fit your models\n",
     "est1 = smf.ols(\"promoted ~ tenure + female + worker_type\", data=df).fit()\n",
     "est2 = smf.probit(\"promoted ~ tenure + female + worker_type\", data=df).fit(disp=0)\n",
     "\n",
     "# Make the table\n",
-    "tab3= mt.ETable([est1, est2],\n",
-    "                keep=[\"Intercept\", \"tenure\", \"female\", \"worker_type\"],\n",
-    "                model_stats=[\"N\",\"r2\",\"pseudo_r2\"],\n",
-    "                model_heads=[\"OLS\",\"Probit\"],\n",
-    "                caption=\"Predicting Promotions\")\n",
+    "tab3 = mt.ETable(\n",
+    "    [est1, est2],\n",
+    "    keep=[\"Intercept\", \"tenure\", \"female\", \"worker_type\"],\n",
+    "    model_stats=[\"N\", \"r2\", \"pseudo_r2\"],\n",
+    "    model_heads=[\"OLS\", \"Probit\"],\n",
+    "    caption=\"Predicting Promotions\",\n",
+    ")\n",
     "\n",
     "# Fill/update the third table in the document\n",
     "tab3.update_docx(file_name=\"docs/WordOutput.docx\", tab_num=3, show=False)"
@@ -302,13 +315,15 @@
    "source": [
     "# Example: Change global defaults\n",
     "# Set a different default font and first column width\n",
-    "mt.MTable.DEFAULT_DOCX_STYLE.update({\n",
-    "    \"font_name\": \"Calibri\",\n",
-    "    \"font_size_pt\": 10,\n",
-    "    \"first_col_width\": \"3cm\",\n",
-    "    \"border_top_rule_sz\": 12,  # Thicker top border\n",
-    "    \"caption_align\": \"right\"\n",
-    "})"
+    "mt.MTable.DEFAULT_DOCX_STYLE.update(\n",
+    "    {\n",
+    "        \"font_name\": \"Calibri\",\n",
+    "        \"font_size_pt\": 10,\n",
+    "        \"first_col_width\": \"3cm\",\n",
+    "        \"border_top_rule_sz\": 12,  # Thicker top border\n",
+    "        \"caption_align\": \"right\",\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -343,10 +358,11 @@
     "    \"caption_align\": \"left\",\n",
     "}\n",
     "\n",
-    "# Apply custom style to a specific table, \n",
+    "# Apply custom style to a specific table,\n",
     "# Here we just add the last table again as now fourth table in the document with a different style:\n",
-    "tab3.update_docx(file_name=\"docs/WordOutput.docx\", tab_num=4, show=False, \n",
-    "                 docx_style=custom_style)"
+    "tab3.update_docx(\n",
+    "    file_name=\"docs/WordOutput.docx\", tab_num=4, show=False, docx_style=custom_style\n",
+    ")"
    ]
   }
  ],

ETable.ipynb

@@ -39,10 +39,9 @@
    "outputs": [],
    "source": [
     "# Import necessary libraries\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
     "import pyfixest as pf\n",
-    "import statsmodels.formula.api as smf\n",
+    "\n",
     "import maketables as mt\n",
     "\n",
     "# Load sample dataset\n",
@@ -52,7 +51,7 @@
     "est1 = pf.feols(\"logwage ~ age\", data=df)\n",
     "est2 = pf.feols(\"logwage ~ age + female\", data=df)\n",
     "est3 = pf.feols(\"logwage ~ age + female | worker_type\", data=df)\n",
-    "est4 = pf.feols(\"logwage ~ age + female | worker_type + occupation\", data=df)\n"
+    "est4 = pf.feols(\"logwage ~ age + female | worker_type + occupation\", data=df)"
    ]
   },
   {
@@ -1404,7 +1403,7 @@
     }
    ],
    "source": [
-    "mt.ETable([est1, est2, est3, est4], model_stats=['N','r2','r2_within'])"
+    "mt.ETable([est1, est2, est3, est4], model_stats=[\"N\", \"r2\", \"r2_within\"])"
    ]
   },
   {
@@ -1633,7 +1632,11 @@
     }
    ],
    "source": [
-    "mt.ETable([est1, est2, est3, est4], model_stats=['N','r2'], model_stats_labels={\"N\": \"Number of observations\", \"r2\": \"R squared\"})"
+    "mt.ETable(\n",
+    "    [est1, est2, est3, est4],\n",
+    "    model_stats=[\"N\", \"r2\"],\n",
+    "    model_stats_labels={\"N\": \"Number of observations\", \"r2\": \"R squared\"},\n",
+    ")"
    ]
   },
   {
@@ -1839,7 +1842,7 @@
     }
    ],
    "source": [
-    "mt.ETable([est1, est2, est3, est4], model_stats=['r2','N'], show_fe=False)"
+    "mt.ETable([est1, est2, est3, est4], model_stats=[\"r2\", \"N\"], show_fe=False)"
    ]
   },
   {
@@ -2734,7 +2737,7 @@
     "    \"tenure\": \"Years of Tenure\",\n",
     "    \"occupation\": \"Occupation\",\n",
     "    \"worker_type\": \"Worker Type\",\n",
-    "    \"education\": \"Education Level\"\n",
+    "    \"education\": \"Education Level\",\n",
     "}\n",
     "\n",
     "mt.ETable([est1, est2, est3, est4], labels=labels)"
@@ -2969,7 +2972,10 @@
     "mt.ETable(\n",
     "    [est1, est2, est3, est4],\n",
     "    labels=labels,\n",
-    "    felabels={\"occupation\": \"Occupation Fixed Effects\", \"worker_type\": \"Worker Type Fixed Effects\"},\n",
+    "    felabels={\n",
+    "        \"occupation\": \"Occupation Fixed Effects\",\n",
+    "        \"worker_type\": \"Worker Type Fixed Effects\",\n",
+    "    },\n",
     ")"
    ]
   },
@@ -2995,9 +3001,9 @@
    "outputs": [],
    "source": [
     "# Set default options for all future ETable calls\n",
-    "mt.ETable.DEFAULT_MODEL_STATS = [\"N\",\"r2\",\"r2_within\"]\n",
+    "mt.ETable.DEFAULT_MODEL_STATS = [\"N\", \"r2\", \"r2_within\"]\n",
     "mt.ETable.DEFAULT_COEF_FMT = \"b:.3f \\n (se:.3f)\"\n",
-    "mt.ETable.DEFAULT_SIGNIF_CODE = [0.01, 0.05, 0.1]\n"
+    "mt.ETable.DEFAULT_SIGNIF_CODE = [0.01, 0.05, 0.1]"
    ]
   },
   {
@@ -3020,7 +3026,7 @@
    },
    "outputs": [],
    "source": [
-    "# Note: Default labels are set as class attribute of MTable \n",
+    "# Note: Default labels are set as class attribute of MTable\n",
     "mt.MTable.DEFAULT_LABELS = labels"
    ]
   },
@@ -3478,9 +3484,9 @@
     }
    ],
    "source": [
-    "est5 = pf.feols(\"logwage ~ age + female + education\", data = df)\n",
+    "est5 = pf.feols(\"logwage ~ age + female + education\", data=df)\n",
     "\n",
-    "mt.ETable([est5],  cat_template = \"{variable}: {value}\")"
+    "mt.ETable([est5], cat_template=\"{variable}: {value}\")"
    ]
   },
   {
@@ -3745,10 +3751,10 @@
     }
    ],
    "source": [
-    "est6 = pf.feols(\"logwage ~ age + female + education\", data = df)\n",
-    "est7 = pf.feols(\"logwage ~ age + female + education*age\", data = df)\n",
+    "est6 = pf.feols(\"logwage ~ age + female + education\", data=df)\n",
+    "est7 = pf.feols(\"logwage ~ age + female + education*age\", data=df)\n",
     "\n",
-    "mt.ETable([est6, est7],  cat_template=\"{value}\")"
+    "mt.ETable([est6, est7], cat_template=\"{value}\")"
    ]
   },
   {
@@ -4270,10 +4276,11 @@
     "mod3 = pf.feols(\"logwage ~ age + female | worker_type + occupation\", data=df)\n",
     "mod4 = pf.feols(\"wage ~ age + female | worker_type + occupation\", data=df)\n",
     "\n",
-    "mt.ETable([mod1, mod2, mod3, mod4], \n",
-    "          model_heads=[\"OLS\", \"OLS\", \"Fixed Effects\", \"Fixed Effects\"], \n",
-    "          head_order=\"hd\")\n",
-    "    "
+    "mt.ETable(\n",
+    "    [mod1, mod2, mod3, mod4],\n",
+    "    model_heads=[\"OLS\", \"OLS\", \"Fixed Effects\", \"Fixed Effects\"],\n",
+    "    head_order=\"hd\",\n",
+    ")"
    ]
   },
   {
@@ -4749,7 +4756,12 @@
     "    show_fe=False,\n",
     "    custom_model_stats={\n",
     "        \"Fixed Effects\": [\"No\", \"No\", \"Worker Type\", \"Worker Type & Occupation\"],\n",
-    "        \"Data Type\": [\"Cross-section\", \"Cross-section\", \"Cross-section\", \"Cross-section\"],\n",
+    "        \"Data Type\": [\n",
+    "            \"Cross-section\",\n",
+    "            \"Cross-section\",\n",
+    "            \"Cross-section\",\n",
+    "            \"Cross-section\",\n",
+    "        ],\n",
     "    },\n",
     ")"
    ]

LICENSE

@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.