statsmodels · Aniketsy · Oct 16, 2025 · Oct 23, 2025 · Oct 25, 2025 · Oct 25, 2025
diff --git a/archive/docs/fix_longtable.py b/archive/docs/fix_longtable.py
@@ -1,7 +1,6 @@
 #!/usr/bin/env python
-import sys
 import os
-
+import sys
 
 BUILDDIR = sys.argv[-1]
 read_file_path = os.path.join(BUILDDIR,'latex','statsmodels.tex')

diff --git a/archive/tsa/ex_arma.py b/archive/tsa/ex_arma.py
@@ -5,12 +5,11 @@
 '''
 
 import numpy as np
+
 import statsmodels.api as sm
-from statsmodels.tsa.arima_process import arma_generate_sample
-from statsmodels.tsa.arma_mle import Arma as Arma
-from statsmodels.tsa.arima_process import ARIMA as ARIMA_old
 from statsmodels.sandbox.tsa.garch import Arma as Armamle_old
-
+from statsmodels.tsa.arima_process import ARIMA as ARIMA_old, arma_generate_sample
+from statsmodels.tsa.arma_mle import Arma as Arma
 
 print("\nExample 1")
 ar = [1.0,  -0.6, 0.1]

diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -12,14 +12,14 @@
 # serve to show the default.
 
 import contextlib
-from packaging.version import parse
 import os
 from os.path import dirname, join
 import sys
-from jinja2 import FileSystemLoader, Environment
 
-import yaml
+from jinja2 import Environment, FileSystemLoader
 from numpydoc.xref import DEFAULT_LINKS
+from packaging.version import parse
+import yaml
 
 from statsmodels import __version__
 

diff --git a/docs/source/plots/stl_plot.py b/docs/source/plots/stl_plot.py
@@ -1,5 +1,5 @@
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 from pandas.plotting import register_matplotlib_converters
 
 from statsmodels.datasets import co2

diff --git a/examples/incomplete/arima.py b/examples/incomplete/arima.py
@@ -1,10 +1,12 @@
-from statsmodels.datasets.macrodata import load_pandas
-from statsmodels.tsa.base.datetools import dates_from_range
-from statsmodels.tsa.arima_model import ARIMA
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy import stats
+
 import statsmodels.api as sm
+from statsmodels.datasets.macrodata import load_pandas
+from statsmodels.tsa.arima_model import ARIMA
+from statsmodels.tsa.base.datetools import dates_from_range
+
 plt.interactive(False)
 
 # let's examine an ARIMA model of CPI

diff --git a/examples/incomplete/arma2.py b/examples/incomplete/arma2.py
@@ -3,6 +3,7 @@
 """
 import numpy as np
 import pandas as pd
+
 import statsmodels.api as sm
 
 # Generate some data from an ARMA process

diff --git a/examples/incomplete/dates.py b/examples/incomplete/dates.py
@@ -1,9 +1,10 @@
 """
 Using dates with timeseries models
 """
-import statsmodels.api as sm
 import pandas as pd
 
+import statsmodels.api as sm
+
 # Getting started
 # ---------------
 

diff --git a/examples/incomplete/glsar.py b/examples/incomplete/glsar.py
@@ -11,6 +11,7 @@
 import numpy as np
 import numpy.testing as npt
 from scipy import signal
+
 import statsmodels.api as sm
 from statsmodels.regression.linear_model import GLSAR, yule_walker
 

diff --git a/examples/incomplete/ols_table.py b/examples/incomplete/ols_table.py
@@ -1,10 +1,11 @@
 """Example: statsmodels.OLS
 """
 
-from statsmodels.datasets.longley import load
+import numpy as np
+
 import statsmodels.api as sm
+from statsmodels.datasets.longley import load
 from statsmodels.iolib.table import SimpleTable, default_txt_fmt
-import numpy as np
 
 data = load()
 

diff --git a/examples/incomplete/ols_tftest.py b/examples/incomplete/ols_tftest.py
@@ -14,6 +14,7 @@
 import numpy as np
 import numpy.testing as npt
 from scipy import stats
+
 import statsmodels.api as sm
 
 print('\n\n Example 1: Longley Data, high multicollinearity')

diff --git a/examples/incomplete/wls_extended.py b/examples/incomplete/wls_extended.py
@@ -5,9 +5,10 @@
 at outliers, compares with RLM and a short bootstrap
 
 """
+import matplotlib.pyplot as plt
 import numpy as np
+
 import statsmodels.api as sm
-import matplotlib.pyplot as plt
 
 data = sm.datasets.ccard.load()
 data.exog = sm.add_constant(data.exog, prepend=False)

diff --git a/examples/python/autoregressions.py b/examples/python/autoregressions.py
@@ -59,6 +59,7 @@
 import pandas as pd
 import pandas_datareader as pdr
 import seaborn as sns
+
 from statsmodels.tsa.api import acf, graphics, pacf
 from statsmodels.tsa.ar_model import AutoReg, ar_select_order
 

diff --git a/examples/python/categorical_interaction_plot.py b/examples/python/categorical_interaction_plot.py
@@ -14,9 +14,10 @@
 # it using the interaction_plot function, which internally re-codes the
 # x-factor categories to integers.
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
+
 from statsmodels.graphics.factorplots import interaction_plot
 
 np.random.seed(12345)

diff --git a/examples/python/chi2_fitting.py b/examples/python/chi2_fitting.py
@@ -21,6 +21,7 @@
 
 import numpy as np
 import pandas as pd
+
 import statsmodels.api as sm
 
 # ## Linear models

diff --git a/examples/python/contrasts.py b/examples/python/contrasts.py
@@ -10,6 +10,8 @@
 # # Contrasts Overview
 
 import numpy as np
+import pandas as pd
+
 import statsmodels.api as sm
 
 # This document is based heavily on this excellent resource from UCLA
@@ -41,7 +43,6 @@
 
 # #### Example Data
 
-import pandas as pd
 
 url = "https://stats.idre.ucla.edu/stat/data/hsb2.csv"
 hsb2 = pd.read_table(url, delimiter=",")

diff --git a/examples/python/copula.py b/examples/python/copula.py
@@ -11,8 +11,8 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-import seaborn as sns
 from scipy import stats
+import seaborn as sns
 
 sns.set_style("darkgrid")
 sns.mpl.rc("figure", figsize=(8, 8))
@@ -33,9 +33,11 @@
 # In this case, we are using the Gumbel copula and fix its hyperparameter
 # `theta=2`. We can visualize it's 2-dimensional PDF.
 
-from statsmodels.distributions.copula.api import (CopulaDistribution,
-                                                  GumbelCopula,
-                                                  IndependenceCopula)
+from statsmodels.distributions.copula.api import (
+    CopulaDistribution,
+    GumbelCopula,
+    IndependenceCopula,
+)
 
 copula = GumbelCopula(theta=2)
 _ = copula.plot_pdf()  # returns a matplotlib figure

diff --git a/examples/python/discrete_choice_example.py b/examples/python/discrete_choice_example.py
@@ -17,8 +17,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import statsmodels.api as sm
 from scipy import stats
+
+import statsmodels.api as sm
 from statsmodels.formula.api import logit
 
 print(sm.datasets.fair.SOURCE)

diff --git a/examples/python/discrete_choice_overview.py b/examples/python/discrete_choice_overview.py
@@ -10,6 +10,7 @@
 # # Discrete Choice Models Overview
 
 import numpy as np
+
 import statsmodels.api as sm
 
 # ## Data

diff --git a/examples/python/distributed_estimation.py b/examples/python/distributed_estimation.py
@@ -15,6 +15,7 @@
 
 import numpy as np
 from scipy.stats.distributions import norm
+
 from statsmodels.base.distributed_estimation import DistributedModel
 
 
@@ -62,8 +63,8 @@ def _endog_gen(endog, partitions):
 # Then we run through a slightly more complicated example which uses the
 # GLM model class.
 
-from statsmodels.genmod.generalized_linear_model import GLM
 from statsmodels.genmod.families import Gaussian
+from statsmodels.genmod.generalized_linear_model import GLM
 
 debiased_GLM_mod = DistributedModel(m,
                                     model_class=GLM,
@@ -89,8 +90,8 @@ def _endog_gen(endog, partitions):
 # and naive averaging.
 
 from statsmodels.base.distributed_estimation import (
-    _est_unregularized_naive,
     DistributedResults,
+    _est_unregularized_naive,
 )
 
 naive_OLS_unreg_mod = DistributedModel(

diff --git a/examples/python/ets.py b/examples/python/ets.py
@@ -29,7 +29,6 @@
 # [1] Hyndman, Rob J., and George Athanasopoulos. *Forecasting: principles
 # and practice*, 3rd edition, OTexts, 2019. https://www.otexts.org/fpp3/7
 
-import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 

diff --git a/examples/python/exponential_smoothing.py b/examples/python/exponential_smoothing.py
@@ -22,10 +22,12 @@
 # expedience.
 
 import os
+
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
+
+from statsmodels.tsa.api import ExponentialSmoothing, Holt, SimpleExpSmoothing
 
 data = [
     446.6565,

diff --git a/examples/python/formulas.py b/examples/python/formulas.py
@@ -21,13 +21,14 @@
 # ## Loading modules and functions
 
 import numpy as np  # noqa:F401  needed in namespace for patsy
+
 import statsmodels.api as sm
+from statsmodels.formula.api import ols
 
 # #### Import convention
 
 # You can import explicitly from statsmodels.formula.api
 
-from statsmodels.formula.api import ols
 
 # Alternatively, you can just use the `formula` namespace of the main
 # `statsmodels.api`.

diff --git a/examples/python/gee_nested_simulation.py b/examples/python/gee_nested_simulation.py
@@ -19,6 +19,7 @@
 
 import numpy as np
 import pandas as pd
+
 import statsmodels.api as sm
 
 # Set the number of covariates.

diff --git a/examples/python/gee_score_test_simulation.py b/examples/python/gee_score_test_simulation.py
@@ -24,11 +24,12 @@
 # The level and power of the tests are considered below to assess the
 # performance of the tests.
 
-import pandas as pd
+import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 from scipy.stats.distributions import norm, poisson
+
 import statsmodels.api as sm
-import matplotlib.pyplot as plt
 
 # The function defined in the following cell uses a copula approach to
 # simulate correlated random values that marginally follow a negative

diff --git a/examples/python/generic_mle.py b/examples/python/generic_mle.py
@@ -24,6 +24,7 @@
 
 import numpy as np
 from scipy import stats
+
 import statsmodels.api as sm
 from statsmodels.base.model import GenericLikelihoodModel
 

diff --git a/examples/python/glm.py b/examples/python/glm.py
@@ -9,10 +9,11 @@
 
 # # Generalized Linear Models
 
+from matplotlib import pyplot as plt
 import numpy as np
-import statsmodels.api as sm
 from scipy import stats
-from matplotlib import pyplot as plt
+
+import statsmodels.api as sm
 
 plt.rc("figure", figsize=(16, 8))
 plt.rc("font", size=14)

diff --git a/examples/python/glm_weights.py b/examples/python/glm_weights.py
@@ -11,8 +11,9 @@
 
 import numpy as np
 import pandas as pd
-import statsmodels.formula.api as smf
+
 import statsmodels.api as sm
+import statsmodels.formula.api as smf
 
 # ## Weighted GLM: Poisson response data
 #

diff --git a/examples/python/influence_glm_logit.py b/examples/python/influence_glm_logit.py
@@ -22,11 +22,12 @@
 # deviance and effects on confidence intervals.
 
 import os.path
-import pandas as pd
+
 import matplotlib.pyplot as plt
+import pandas as pd
 
-from statsmodels.genmod.generalized_linear_model import GLM
 from statsmodels.genmod import families
+from statsmodels.genmod.generalized_linear_model import GLM
 
 plt.rc("figure", figsize=(16, 8))
 plt.rc("font", size=14)

diff --git a/examples/python/interactions_anova.py b/examples/python/interactions_anova.py
@@ -15,6 +15,7 @@
 # Download and format data:
 
 from urllib.request import urlopen
+
 import numpy as np
 
 np.set_printoptions(precision=4, suppress=True)
@@ -23,8 +24,9 @@
 
 pd.set_option("display.width", 100)
 import matplotlib.pyplot as plt
+
 from statsmodels.formula.api import ols
-from statsmodels.graphics.api import interaction_plot, abline_plot
+from statsmodels.graphics.api import abline_plot, interaction_plot
 from statsmodels.stats.anova import anova_lm
 
 try:

diff --git a/examples/python/kernel_density.py b/examples/python/kernel_density.py
@@ -34,10 +34,11 @@
 # argument.
 # * The various kernel functions available using the `kernel` argument.
 
+import matplotlib.pyplot as plt
 import numpy as np
 from scipy import stats
+
 import statsmodels.api as sm
-import matplotlib.pyplot as plt
 from statsmodels.distributions.mixture_rvs import mixture_rvs
 
 # ## A univariate example