Merge pull request #100 from spotify/Release-3.0.3

Release 3.0.3

HISTORY.rst

@@ -2,6 +2,11 @@
 History
 =======
 
+3.0.3 (2023-10-12)
+------------------
+* Relaxing version requirements for scipy and pandas to allow versions 2.x
+
+
 3.0.2 (2023-08-08)
 ------------------
 * Added docstring to Experiment

README.md

@@ -2,7 +2,7 @@ Spotify Confidence
 ========
 
 ![Status](https://img.shields.io/badge/Status-Beta-blue.svg)
-![Latest release](https://img.shields.io/badge/release-3.0.2-green.svg "Latest release: 3.0.2")
+![Latest release](https://img.shields.io/badge/release-3.0.3-green.svg "Latest release: 3.0.3")
 ![Python](https://img.shields.io/badge/Python-3.7-blue.svg "Python")
 ![Python](https://img.shields.io/badge/Python-3.8-blue.svg "Python")
 ![Python](https://img.shields.io/badge/Python-3.9-blue.svg "Python")

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = spotify-confidence
-version = 3.0.2
+version = 3.0.3
 author = Per Sillren
 author_email = [email protected]
 description = Package for calculating and visualising confidence intervals, e.g. for A/B test analysis.
@@ -21,8 +21,8 @@ packages = find:
 python_requires = >=3.8
 install_requires =
     numpy>=1.20.0,<2.0.0
-    scipy>=1.6.0,<1.8.0
-    pandas>=1.2.0,<2.0.0
+    scipy>=1.6.0
+    pandas>=1.2.0
     statsmodels>=0.13.0,<1.0.0
     chartify>=4.0.3
     ipywidgets>=8.0.0

spotify_confidence/analysis/frequentist/confidence_computers/z_test_computer.py

@@ -4,7 +4,11 @@
 from pandas import DataFrame, Series
 from scipy import optimize
 from scipy import stats as st
-from scipy.stats.stats import _unequal_var_ttest_denom
+
+try:
+    from scipy.stats._stats_py import _unequal_var_ttest_denom
+except ImportError:  # Fallback for scipy<1.8.0
+    from scipy.stats.stats import _unequal_var_ttest_denom
 
 from statsmodels.stats.weightstats import _zconfint_generic, _zstat_generic
 

tests/frequentist/test_freqsamplesizecalculator.py

@@ -819,6 +819,6 @@ def test_sample_size_with_nan(self):
         )
 
         assert len(ss) == len(df)
-        assert ss[REQUIRED_SAMPLE_SIZE_METRIC].values[0] is None
+        assert ss[REQUIRED_SAMPLE_SIZE_METRIC].isna()[0]
         assert 0.999 < ss[REQUIRED_SAMPLE_SIZE_METRIC].values[1] / 95459 < 1.001
         assert ss[CI_WIDTH].isna().all()

tests/frequentist/test_ztest.py

@@ -203,8 +203,8 @@ def test_powered_effect(self):
         assert np.isclose(powered_effect[POWERED_EFFECT][1], 0.5291, atol=0.001)
         assert np.isclose(powered_effect[POWERED_EFFECT][2], 0.4596, atol=0.001)
         assert np.isclose(powered_effect[POWERED_EFFECT][3], 0.4869, atol=0.001)
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][0] is None
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][1] is None
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[0]
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[1]
         assert np.isclose(powered_effect[REQUIRED_SAMPLE_SIZE][2], 16487886, atol=100)
         assert np.isclose(powered_effect[REQUIRED_SAMPLE_SIZE][3], 3083846, atol=100)
 
@@ -329,10 +329,10 @@ def test_powered_effect(self):
         assert np.isclose(powered_effect[POWERED_EFFECT][6], 0.4995, atol=0.001)
         assert np.isclose(powered_effect[POWERED_EFFECT][7], 0.5291, atol=0.001)
 
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][0] is None
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][1] is None
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][2] is None
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][3] is None
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[0]
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[1]
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[2]
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[3]
         assert np.isclose(powered_effect[REQUIRED_SAMPLE_SIZE][4], 19475238, atol=100)
         assert np.isclose(powered_effect[REQUIRED_SAMPLE_SIZE][5], 3642591, atol=100)
         assert np.isclose(powered_effect[REQUIRED_SAMPLE_SIZE][6], 19475238, atol=100)
@@ -733,12 +733,12 @@ def test_powered_effect(self):
         #  assert np.isclose(powered_effect[POWERED_EFFECT][10], 0.2663, atol=0.001)
         #  assert np.isclose(powered_effect[POWERED_EFFECT][11], 0.2479, atol=0.001)
 
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][0] is None
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][1] is None
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][2] is None
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][3] is None
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][4] is None
-        assert powered_effect[REQUIRED_SAMPLE_SIZE][5] is None
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[0]
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[1]
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[2]
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[3]
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[4]
+        assert powered_effect[REQUIRED_SAMPLE_SIZE].isna()[5]
         assert np.isclose(powered_effect[REQUIRED_SAMPLE_SIZE][6], 260541, atol=100)
         assert np.isclose(powered_effect[REQUIRED_SAMPLE_SIZE][7], 361863, atol=100)
         assert np.isclose(powered_effect[REQUIRED_SAMPLE_SIZE][8], 326159, atol=100)