MAINT: Clean up code

Fix small issues identified by ruff
Improve typing

examples/asset-pricing_examples.ipynb

@@ -412,7 +412,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/asset-pricing_formulas.ipynb

@@ -67,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from linearmodels.asset_pricing import LinearFactorModel, TradedFactorModel\n",
+    "from linearmodels.asset_pricing import LinearFactorModel\n",
     "\n",
     "formula = \"NoDur + Chems + S1V1 + S5V5 + S1M1 + S5M5 ~ MktRF + HML + Mom\"\n",
     "mod = LinearFactorModel.from_formula(formula, data)\n",
@@ -139,7 +139,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/iv_absorbing-regression.ipynb

@@ -151,7 +151,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/iv_advanced-examples.ipynb

@@ -115,8 +115,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from statsmodels.api import OLS, add_constant\n",
-    "\n",
     "data[\"const\"] = 1\n",
     "controls = [\"const\"] + controls"
    ]
@@ -572,7 +570,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "nbsphinx": {
    "allow_errors": true

examples/iv_basic-examples.ipynb

@@ -32,9 +32,11 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from linearmodels.datasets import mroz\n",
+    "import pandas as pd\n",
     "from statsmodels.api import add_constant\n",
     "\n",
+    "from linearmodels.datasets import mroz\n",
+    "\n",
     "print(mroz.DESCR)\n",
     "data = mroz.load()\n",
     "data = data.dropna()\n",
@@ -574,7 +576,6 @@
    "outputs": [],
    "source": [
     "v = IV2SLS(data[endog], data[exog + instr], None, None).fit().resids\n",
-    "import pandas as pd\n",
     "\n",
     "res_direct = IV2SLS(\n",
     "    data[dep], pd.concat([v, data[exog]], axis=1), data[endog], data[instr]\n",
@@ -802,7 +803,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/iv_using-formulas.ipynb

@@ -72,7 +72,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "from linearmodels.datasets import meps\n",
     "from linearmodels.iv import IV2SLS\n",
     "\n",
@@ -192,7 +191,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/panel_data-formats.ipynb

@@ -230,7 +230,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/panel_examples.ipynb

@@ -29,6 +29,7 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
+    "\n",
     "from linearmodels.datasets import wage_panel\n",
     "\n",
     "data = wage_panel.load()\n",
@@ -54,6 +55,7 @@
    "outputs": [],
    "source": [
     "import statsmodels.api as sm\n",
+    "\n",
     "from linearmodels.panel import PooledOLS\n",
     "\n",
     "exog_vars = [\"black\", \"hisp\", \"exper\", \"expersq\", \"married\", \"educ\", \"union\", \"year\"]\n",
@@ -427,7 +429,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/panel_using-formulas.ipynb

@@ -182,7 +182,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/system_examples.ipynb

@@ -39,9 +39,10 @@
    "outputs": [],
    "source": [
     "# Common libraries\n",
-    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import seaborn as sns\n",
     "import statsmodels.api as sm"
    ]
   },
@@ -161,7 +162,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
     "from linearmodels.system import SUR\n",
     "\n",
     "mod = SUR(mod_data)\n",
@@ -186,9 +186,6 @@
     "regions = [k for k in mod_data.keys()]\n",
     "corr = pd.DataFrame(cov / (std @ std.T), columns=regions, index=regions)\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
     "sns.heatmap(corr, vmax=0.8, square=True)\n",
     "plt.show()\n",
     "\n",
@@ -734,6 +731,7 @@
    "outputs": [],
    "source": [
     "import statsmodels.api as sm\n",
+    "\n",
     "from linearmodels.datasets import french\n",
     "\n",
     "data = french.load()\n",
@@ -780,7 +778,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/system_formulas.ipynb

@@ -23,6 +23,7 @@
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
+    "\n",
     "from linearmodels.datasets import fringe\n",
     "\n",
     "data = fringe.load()"
@@ -179,7 +180,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/system_three-stage-ls.ipynb

@@ -75,7 +75,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from linearmodels import IV2SLS, IV3SLS, SUR, IVSystemGMM"
+    "from linearmodels import IV2SLS, IV3SLS, IVSystemGMM"
    ]
   },
   {
@@ -353,7 +353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

linearmodels/iv/absorbing.py

@@ -153,7 +153,7 @@ def lsmr_annihilate(
     return column_stack(resids)
 
 
-def category_product(cats: linearmodels.typing.data.AnyPandas) -> Series:
+def category_product(cats: linearmodels.typing.AnyPandas) -> Series:
     """
     Construct category from all combination of input categories
 
@@ -236,8 +236,8 @@ def category_interaction(
 
 
 def category_continuous_interaction(
-    cat: linearmodels.typing.data.AnyPandas,
-    cont: linearmodels.typing.data.AnyPandas,
+    cat: linearmodels.typing.AnyPandas,
+    cont: linearmodels.typing.AnyPandas,
     precondition: bool = True,
 ) -> sp.csc_matrix:
     """

linearmodels/iv/data.py

@@ -18,9 +18,7 @@
 type_err = "Only ndarrays, DataArrays and Series and DataFrames are supported"
 
 
-def convert_columns(
-    s: pd.Series, drop_first: bool
-) -> linearmodels.typing.data.AnyPandas:
+def convert_columns(s: pd.Series, drop_first: bool) -> linearmodels.typing.AnyPandas:
     if isinstance(s.dtype, pd.CategoricalDtype):
         out = pd.get_dummies(s, drop_first=drop_first)
         # TODO: Remove once pandas typing fixed
@@ -172,7 +170,7 @@ def pandas(self) -> pd.DataFrame:
         return self._pandas
 
     @property
-    def ndarray(self) -> linearmodels.typing.data.NumericArray:
+    def ndarray(self) -> linearmodels.typing.NumericArray:
         """ndarray view of data, always 2d"""
         return self._ndarray
 

linearmodels/panel/data.py

@@ -63,10 +63,10 @@ def __init__(self, df: pandas.DataFrame):
     @classmethod
     def from_array(
         cls,
-        values: linearmodels.typing.data.NumericArray,
-        items: Sequence[linearmodels.typing.data.Label],
-        major_axis: Sequence[linearmodels.typing.data.Label],
-        minor_axis: Sequence[linearmodels.typing.data.Label],
+        values: linearmodels.typing.NumericArray,
+        items: Sequence[linearmodels.typing.Label],
+        major_axis: Sequence[linearmodels.typing.Label],
+        minor_axis: Sequence[linearmodels.typing.Label],
     ) -> _Panel:
         index = list(product(minor_axis, major_axis))
         multi_index = MultiIndex.from_tuples(index)
@@ -103,7 +103,7 @@ def to_frame(self) -> DataFrame:
 
 def convert_columns(
     s: pandas.Series, drop_first: bool
-) -> linearmodels.typing.data.AnyPandas:
+) -> linearmodels.typing.AnyPandas:
     if is_string_dtype(s.dtype) and s.map(lambda v: isinstance(v, str)).all():
         s = s.astype("category")
 
@@ -340,18 +340,18 @@ def nentity(self) -> int:
         return self._n
 
     @property
-    def vars(self) -> list[linearmodels.typing.data.Label]:
+    def vars(self) -> list[linearmodels.typing.Label]:
         """List of variable names"""
         return list(self._frame.columns)
 
     @property
-    def time(self) -> list[linearmodels.typing.data.Label]:
+    def time(self) -> list[linearmodels.typing.Label]:
         """List of time index names"""
         index = self.index
         return list(index.levels[1][index.codes[1]].unique())
 
     @property
-    def entities(self) -> list[linearmodels.typing.data.Label]:
+    def entities(self) -> list[linearmodels.typing.Label]:
         """List of entity index names"""
         index = self.index
         return list(index.levels[0][index.codes[0]].unique())

linearmodels/panel/model.py

@@ -1269,7 +1269,7 @@ def __init__(
         self._singleton_index = None
         self._drop_singletons()
 
-    def _collect_effects(self) -> linearmodels.typing.data.NumericArray:
+    def _collect_effects(self) -> linearmodels.typing.NumericArray:
         if not self._has_effect:
             return np.empty((self.dependent.shape[0], 0))
         effects = []
@@ -1768,8 +1768,8 @@ def _info(self) -> tuple[Series, Series, DataFrame | None]:
 
     @staticmethod
     def _is_effect_nested(
-        effects: linearmodels.typing.data.NumericArray,
-        clusters: linearmodels.typing.data.NumericArray,
+        effects: linearmodels.typing.NumericArray,
+        clusters: linearmodels.typing.NumericArray,
     ) -> bool:
         """Determine whether an effect is nested by the covariance clusters"""
         is_nested = np.zeros(effects.shape[1], dtype=bool)

linearmodels/system/_utility.py

@@ -12,7 +12,7 @@
 
 
 def blocked_column_product(
-    x: linearmodels.typing.data.ArraySequence, s: linearmodels.typing.data.Float64Array
+    x: linearmodels.typing.ArraySequence, s: linearmodels.typing.data.Float64Array
 ) -> linearmodels.typing.data.Float64Array:
     """
     Parameters
@@ -39,7 +39,7 @@ def blocked_column_product(
 
 
 def blocked_diag_product(
-    x: linearmodels.typing.data.ArraySequence, s: linearmodels.typing.data.Float64Array
+    x: linearmodels.typing.ArraySequence, s: linearmodels.typing.data.Float64Array
 ) -> linearmodels.typing.data.Float64Array:
     """
     Parameters
@@ -68,7 +68,7 @@ def blocked_diag_product(
 
 
 def blocked_inner_prod(
-    x: linearmodels.typing.data.ArraySequence, s: linearmodels.typing.data.Float64Array
+    x: linearmodels.typing.ArraySequence, s: linearmodels.typing.data.Float64Array
 ) -> linearmodels.typing.data.Float64Array:
     r"""
     Parameters
@@ -135,8 +135,8 @@ def blocked_inner_prod(
 
 
 def blocked_cross_prod(
-    x: linearmodels.typing.data.ArraySequence,
-    z: linearmodels.typing.data.ArraySequence,
+    x: linearmodels.typing.ArraySequence,
+    z: linearmodels.typing.ArraySequence,
     s: linearmodels.typing.data.Float64Array,
 ) -> linearmodels.typing.data.Float64Array:
     r"""

linearmodels/system/model.py

@@ -2059,9 +2059,9 @@ def fit(
 
     @staticmethod
     def _blocked_gmm(
-        x: linearmodels.typing.data.ArraySequence,
-        y: linearmodels.typing.data.ArraySequence,
-        z: linearmodels.typing.data.ArraySequence,
+        x: linearmodels.typing.ArraySequence,
+        y: linearmodels.typing.ArraySequence,
+        z: linearmodels.typing.ArraySequence,
         *,
         w: linearmodels.typing.data.Float64Array,
         constraints: LinearConstraint | None = None,

requirements-dev.txt

@@ -6,6 +6,7 @@ isort>=5.12
 ipython
 matplotlib
 ruff
+jupyterlab-code-formatter
 flake8
 jupyter
 nbconvert