Merge pull request statsmodels#5217 from jbrockmendel/npcompat3

MAINT: Remove outdated pandas compat shims

Author: josef-pkt

examples/python/robust_models_1.py

@@ -7,7 +7,6 @@
 import matplotlib.pyplot as plt
 
 import statsmodels.api as sm
-from statsmodels.compat.pandas import sort_values
 
 
 # * An M-estimator minimizes the function 
@@ -233,12 +232,12 @@ def plot_weights(support, weights_func, xlabels, xticks):
 
 
 sidak = ols_model.outlier_test('sidak')
-sort_values(sidak, 'unadj_p', inplace=True)
+sidak.sort_values('unadj_p', inplace=True)
 print(sidak)
 
 
 fdr = ols_model.outlier_test('fdr_bh')
-sort_values(fdr, 'unadj_p', inplace=True)
+fdr.sort_values('unadj_p', inplace=True)
 print(fdr)
 
 
@@ -300,12 +299,12 @@ def plot_weights(support, weights_func, xlabels, xticks):
 
 
 sidak2 = ols_model.outlier_test('sidak')
-sort_values(sidak2, 'unadj_p', inplace=True)
+sidak2.sort_values('unadj_p', inplace=True)
 print(sidak2)
 
 
 fdr2 = ols_model.outlier_test('fdr_bh')
-sort_values(fdr2, 'unadj_p', inplace=True)
+fdr2.sort_values('unadj_p', inplace=True)
 print(fdr2)
 
 

statsmodels/compat/pandas.py

@@ -3,16 +3,11 @@
 from distutils.version import LooseVersion
 
 import pandas
-from pandas import RangeIndex, Float64Index  # noqa:F401
 
 
 version = LooseVersion(pandas.__version__)
 
 
-def sort_values(df, *args, **kwargs):
-    return df.sort_values(*args, **kwargs)
-
-
 try:
     from pandas.api.types import is_numeric_dtype  # noqa:F401
 except ImportError:

statsmodels/graphics/tests/test_mosaicplot.py

@@ -7,7 +7,6 @@
 
 # utilities for the tests
 
-from statsmodels.compat.pandas import sort_values
 from collections import OrderedDict
 from statsmodels.api import datasets
 
@@ -110,7 +109,7 @@ def test_mosaic(close_figures):
     # sort by the marriage quality and give meaningful name
     # [rate_marriage, age, yrs_married, children,
     # religious, educ, occupation, occupation_husb]
-    datas = sort_values(datas, ['rate_marriage', 'religious'])
+    datas = datas.sort_values(['rate_marriage', 'religious'])
 
     num_to_desc = {1: 'awful', 2: 'bad', 3: 'intermediate',
                       4: 'good', 5: 'wonderful'}

statsmodels/imputation/ros.py

@@ -19,8 +19,6 @@
 from scipy import stats
 import pandas
 
-from statsmodels.compat.pandas import sort_values
-
 
 def _ros_sort(df, observations, censorship, warn=False):
     """
@@ -53,8 +51,8 @@ def _ros_sort(df, observations, censorship, warn=False):
     """
 
     # separate uncensored data from censored data
-    censored = sort_values(df[df[censorship]], observations, axis=0)
-    uncensored = sort_values(df[~df[censorship]], observations, axis=0)
+    censored = df[df[censorship]].sort_values(observations, axis=0)
+    uncensored = df[~df[censorship]].sort_values(observations, axis=0)
 
     if censored[observations].max() > uncensored[observations].max():
         censored = censored[censored[observations] <= uncensored[observations].max()]

statsmodels/sandbox/multilinear.py

@@ -11,7 +11,6 @@
     more significant than outside the group.
 """
 
-from statsmodels.compat.pandas import sort_values
 from statsmodels.compat.python import iteritems, string_types
 from patsy import dmatrix
 import pandas as pd
@@ -172,7 +171,7 @@ def multiOLS(model, dataframe, column_list=None, method='fdr_bh',
     # mangle them togheter and sort by complexive p-value
     summary = pd.DataFrame(col_results)
     # order by the p-value: the most useful model first!
-    summary = sort_values(summary.T, [('pvals', '_f_test')])
+    summary = summary.T.sort_values([('pvals', '_f_test')])
     summary.index.name = 'endogenous vars'
     # implementing the pvalue correction method
     smt = stats.multipletests
@@ -317,7 +316,7 @@ def multigroup(pvals, groups, exact=True, keep_all=True, alpha=0.05):
         results['_in_non'][group_name] = res[2][1]
         results['_out_sign'][group_name] = res[2][2]
         results['_out_non'][group_name] = res[2][3]
-    result_df = sort_values(pd.DataFrame(results), 'pvals')
+    result_df = pd.DataFrame(results).sort_values('pvals')
     if not keep_all:
         result_df = result_df[result_df.increase]
     smt = stats.multipletests

statsmodels/tsa/base/tsa_model.py

@@ -1,12 +1,11 @@
-from statsmodels.compat.python import lrange, long
-from statsmodels.compat.pandas import is_numeric_dtype, Float64Index
-
-import datetime
+from statsmodels.compat.python import long
+from statsmodels.compat.pandas import is_numeric_dtype
 
 import warnings
 import numpy as np
 from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,
-                    PeriodIndex, RangeIndex, Timestamp, Series, Index)
+                    PeriodIndex, RangeIndex, Timestamp, Series, Index,
+                    Float64Index)
 from pandas.tseries.frequencies import to_offset
 
 from statsmodels.base import data