Merge pull request #101 from sjsrey/3.8

Fixes for pandas deprecation and 3.8 verbosity

.travis.yml

@@ -8,6 +8,7 @@ branches:
 python:
   - 3.6
   - 3.7
+  - 3.8
 
 env:
   - PYSAL_PYPI=true  MPLBACKEND="pdf"

esda/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "2.2.0"
+__version__ = "2.2.1"
 """
 :mod:`esda` --- Exploratory Spatial Data Analysis
 =================================================

esda/moran.py

@@ -179,7 +179,7 @@ def __init__(self, y, w, transformation="r", permutations=PERMUTATIONS,
         # provide .z attribute that is znormalized
         sy = y.std()
         self.z /= sy
-        
+
     def __moments(self):
         self.n = len(self.y)
         y = self.y
@@ -193,7 +193,7 @@ def __moments(self):
         s0 = self.w.s0
         s2 = self.w.s2
         s02 = s0 * s0
-        v_num = n2 * s1 - n * s2 + 3 * s02 
+        v_num = n2 * s1 - n * s2 + 3 * s02
         v_den = (n - 1) * (n + 1) * s02
         self.VI_norm = v_num / v_den - (1.0 / (n - 1)) ** 2
         self.seI_norm = self.VI_norm ** (1 / 2.)
@@ -208,7 +208,7 @@ def __moments(self):
         A = n * ((n2 - 3 * n + 3) * s1 - n * s2 + 3 * s02)
         B = k * ((n2 - n) * s1 - 2 * n * s2 + 6 * s02  )
         VIR = (A - B) / ((n - 1) * (n - 2) * (n - 3 ) * s02) - EI*EI
-        self.VI_rand = VIR 
+        self.VI_rand = VIR
         self.seI_rand = VIR ** (1 / 2.)
 
     def __calc(self, z):
@@ -483,7 +483,7 @@ def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
 
     Examples
     --------
-    
+
     open dbf
 
     >>> import libpysal
@@ -524,7 +524,7 @@ def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
             variables_n = variables
     except ImportError:
         variables_n = variables
-    
+
     results = _Moran_BV_Matrix_array(variables=variables_n, w=w,
                                      permutations=permutations,
                                      varnames=varnames)
@@ -731,15 +731,15 @@ def by_col(cls, df, events, populations, w=None, inplace=False,
 
         if isinstance(adjusted, bool):
             adjusted = [adjusted] * len(events)
-        if swapname is '':
+        if swapname == '':
             swapname = cls.__name__.lower()
 
         rates = [assuncao_rate(df[e], df[pop]) if adj
                  else df[e].astype(float) / df[pop]
                  for e,pop,adj in zip(events, populations, adjusted)]
         names = ['-'.join((e,p)) for e,p in zip(events, populations)]
         out_df = df.copy()
-        rate_df = out_df.from_items(list(zip(names, rates))) #trick to avoid importing pandas
+        rate_df = out_df.from_dict(dict(zip(names, rates))) #trick to avoid importing pandas
         stat_df = _univariate_handler(rate_df, names, w=w, inplace=False,
                                       pvalue = pvalue, outvals = outvals,
                                       swapname=swapname,
@@ -1389,15 +1389,15 @@ def by_col(cls, df, events, populations, w=None, inplace=False,
 
         if isinstance(adjusted, bool):
             adjusted = [adjusted] * len(events)
-        if swapname is '':
+        if swapname == '':
             swapname = cls.__name__.lower()
 
         rates = [assuncao_rate(df[e], df[pop]) if adj
                  else df[e].astype(float) / df[pop]
                  for e,pop,adj in zip(events, populations, adjusted)]
         names = ['-'.join((e,p)) for e,p in zip(events, populations)]
         out_df = df.copy()
-        rate_df = out_df.from_items(list(zip(names, rates))) #trick to avoid importing pandas
+        rate_df = out_df.from_dict(dict(zip(names, rates))) #trick to avoid importing pandas
         _univariate_handler(rate_df, names, w=w, inplace=True,
                                       pvalue = pvalue, outvals = outvals,
                                       swapname=swapname,

esda/smoothing.py

@@ -1206,8 +1206,8 @@ def by_col(cls, df, e,b, w=None, s=None, **kwargs):
             max_len = 0 if len(this_r) > max_len else max_len
             rdf.append((outcol, this_r.tolist()))
         padded = (r[1] + [None] * max_len for r in rdf)
-        rdf = list(zip((r[0] for r in rdf), padded))
-        rdf = pd.DataFrame.from_items(rdf)
+        rdf = dict(zip((r[0] for r in rdf), padded))
+        rdf = pd.DataFrame.from_dict(rdf)
         return rdf
 
 
@@ -1586,7 +1586,7 @@ def by_col(cls, df, e, b, x_grid, y_grid, geom_col='geometry', **kwargs):
             items = [(name, col) for name,col in zip(colnames, [grid[:,0],
                                                                 grid[:,1],
                                                                 r.r])]
-            res.append(pd.DataFrame.from_items(items))
+            res.append(pd.DataFrame.from_dict(dict(items)))
         outdf = pd.concat(res)
         return outdf
 

esda/tabular.py

@@ -74,7 +74,7 @@ def _univariate_handler(df, cols, stat=None, w=None, inplace=True,
     # objects, determine which pvalue types are available, and then grab them
     # all if needed.
 
-    if pvalue is not '':
+    if pvalue != '':
         outvals.append('p_'+pvalue.lower())
     if isinstance(cols, str):
         cols = [cols]
@@ -93,7 +93,7 @@ def column_stat(column):
         outcols = ['_'.join((col, val)) for val in outvals]
         for colname, attname in zip(outcols, outvals):
             df[colname] = stat_obj.__getattribute__(attname)
-    if swapname is not '':
+    if swapname != '':
         df.columns = [_swap_ending(col, swapname) if col.endswith('_statistic') else col
                       for col in df.columns]
 
@@ -148,7 +148,8 @@ def _bivariate_handler(df, x, y=None, w=None, inplace=True, pvalue='sim',
             continue
         _univariate_handler(df, cols=xi, w=w, y=df[yi], inplace=True,
                             pvalue=pvalue, outvals=outvals, swapname='', **kwargs)
-    if real_swapname is not '':
+    if real_swapname != '':
+
         df.columns = [_swap_ending(col, real_swapname)
                       if col.endswith('_statistic')
                       else col for col in df.columns]

esda/tests/test_gamma.py → tests/test_gamma.py

@@ -1,7 +1,7 @@
 import unittest
 import numpy as np
 from libpysal.weights.util import lat2W
-from ..gamma import Gamma
+from esda.gamma import Gamma
 from libpysal.common import pandas
 
 PANDAS_EXTINCT = pandas is None

esda/tests/test_geary.py → tests/test_geary.py

@@ -5,7 +5,7 @@
 from libpysal import examples
 from libpysal.common import pandas
 
-from .. import geary
+from esda import geary
 import numpy as np
 
 PANDAS_EXTINCT = pandas is None

esda/tests/test_getisord.py → tests/test_getisord.py

@@ -1,7 +1,7 @@
 import unittest
 import numpy as np
 
-from .. import getisord
+from esda import getisord
 from libpysal.weights.distance import DistanceBand
 from libpysal.common import pandas
 

esda/tests/test_join_counts.py → tests/test_join_counts.py

@@ -1,7 +1,7 @@
 import unittest
 import numpy as np
 
-from ..join_counts import Join_Counts
+from esda.join_counts import Join_Counts
 from libpysal.weights.util import lat2W
 from libpysal.common import pandas
 

esda/tests/test_mixture_smoothing.py → tests/test_mixture_smoothing.py

@@ -1,7 +1,7 @@
 import unittest
 import numpy as np
 import libpysal
-from .. import mixture_smoothing as m_s
+from esda import mixture_smoothing as m_s
 
 
 class MS_Tester(unittest.TestCase):

esda/tests/test_moran.py → tests/test_moran.py

@@ -1,7 +1,7 @@
 import unittest
 import libpysal
 from libpysal.common import pandas, RTOL, ATOL
-from .. import moran
+from esda import moran
 import numpy as np
 
 
@@ -32,14 +32,14 @@ def test_variance(self):
         mi = moran.Moran(y, w, transformation='B')
         np.testing.assert_allclose(mi.VI_rand, 0.059687500000000004, atol=ATOL, rtol=RTOL)
         np.testing.assert_allclose(mi.VI_norm, 0.053125000000000006, atol=ATOL, rtol=RTOL)
-    
+
     def test_z_consistency(self):
         m1 = moran.Moran(self.y, self.w)
         # m2 = moran.Moran_BV(self.x, self.y, self.w) TODO testing for other.z values
         m3 = moran.Moran_Local(self.y, self.w)
         # m4 = moran.Moran_Local_BV(self.x, self.y, self.w)
         np.testing.assert_allclose(m1.z, m3.z, atol=ATOL, rtol=RTOL)
- 
+
 
     @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
     def test_by_col(self):

esda/tests/test_silhouette.py → tests/test_silhouette.py

@@ -1,5 +1,4 @@
-from .. import silhouettes
-
+from esda import silhouettes
 import geopandas
 import libpysal
 import numpy
@@ -57,7 +56,7 @@ def test_boundary(self):
                                                metric=self.precomputed)
         numpy.testing.assert_allclose(known, test, rtol=RTOL, atol=ATOL)
         with self.assertRaises(AssertionError):
-            silhouettes.boundary_silhouette(self.X, self.groups, self.w, 
+            silhouettes.boundary_silhouette(self.X, self.groups, self.w,
                                             metric=self.precomputed - self.precomputed.mean())
 
     def test_path(self):
@@ -81,11 +80,11 @@ def test_path(self):
                              -0.03874118,
                               0.28623703,
                               0.40062121])
-        test = silhouettes.path_silhouette(self.X, self.groups, self.w, 
+        test = silhouettes.path_silhouette(self.X, self.groups, self.w,
                                             metric=self.altmetric)
         numpy.testing.assert_allclose(known, test, rtol=RTOL, atol=ATOL)
         with self.assertRaises(TypeError):
-            silhouettes.path_silhouette(self.X, self.groups, self.w, 
+            silhouettes.path_silhouette(self.X, self.groups, self.w,
                                          metric=self.precomputed)
         with self.assertRaises(AssertionError):
             silhouettes.path_silhouette(self.X, self.groups, self.w,

esda/tests/test_smaup.py → tests/test_smaup.py

@@ -1,8 +1,8 @@
 import unittest
 import libpysal
 from libpysal.common import pandas, RTOL, ATOL
-from ..smaup import Smaup
-from ..moran import Moran
+from esda.smaup import Smaup
+from esda.moran import Moran
 import numpy as np
 
 
@@ -17,7 +17,7 @@ def setup(self):
         self.rho = Moran(self.y, self.w).I
         self.n = len(self.y)
         self.k = int(self.n/2)
-    
+
     def test_smaup(self):
         self.setup()
         sm = Smaup(self.n, self.k, self.rho)
@@ -29,22 +29,22 @@ def test_smaup(self):
         self.assertAlmostEqual(sm.critical_05, 0.3557221333333333)
         self.assertAlmostEqual(sm.critical_1, 0.3157950666666666)
         self.assertEqual(sm.summary, 'Pseudo p-value > 0.10 (H0 is not rejected)')
-        
+
     def test_sids(self):
         from esda import moran
         w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
         f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
         SIDR = np.array(f.by_col("SIDR74"))
         rho = moran.Moran(SIDR, w, two_tailed=False).I
         n = len(SIDR)
-        k = int(n/2)       
+        k = int(n/2)
         sm = Smaup(n, k, rho)
         np.testing.assert_allclose(sm.smaup,  0.15176796553181948, rtol=RTOL, atol=ATOL)
         self.assertAlmostEqual(sm.critical_01, 0.23404000000000003)
         self.assertAlmostEqual(sm.critical_05, 0.21088)
         self.assertAlmostEqual(sm.critical_1, 0.18239)
         self.assertEqual(sm.summary, 'Pseudo p-value > 0.10 (H0 is not rejected)')
-        
+
     @unittest.skipIf(PANDAS_EXTINCT, 'missing pandas')
     def test_by_col(self):
         from libpysal.io import geotable as pdio

esda/tests/test_smoothing.py → tests/test_smoothing.py

@@ -1,7 +1,7 @@
 import unittest
 import libpysal
 from libpysal.weights.distance import KNN, Kernel
-from .. import smoothing as sm
+from esda import smoothing as sm
 import numpy as np
 from libpysal.common import RTOL, ATOL, pandas
 
@@ -293,7 +293,7 @@ def test_Headbanging_Triples(self):
         self.assertEqual(len(htr.r), len(self.e))
         for i in htr.r:
             self.assertTrue(i is not None)
-    
+
     @unittest.skip('Deprecated')
     def test_Headbanging_Median_Rate(self):
         s_ht = sm.Headbanging_Triples(self.d, self.w, k=5)

esda/tests/test_util.py → tests/test_util.py

@@ -1,7 +1,7 @@
 import unittest
 import libpysal
-from .. import util
-from .. import moran
+from esda import util
+from esda import moran
 import numpy as np
 
 class Fdr_Tester(unittest.TestCase):