ENH ability to prefix and suffix columns

README.md

@@ -18,6 +18,7 @@ All the indicators return a named Series or a DataFrame in uppercase underscore
 * Example Jupyter Notebook under the examples directory.
 * Abbreviated Indicator names as listed below.
 * *Extended Pandas DataFrame* as 'ta'.  See examples below.
+* Easily add prefixes or suffixes or both to columns names.
 * Categories similar to [TA-lib](https://github.com/mrjbq7/ta-lib/tree/master/docs/func_groups).
 
 
@@ -41,7 +42,7 @@ All the indicators return a named Series or a DataFrame in uppercase underscore
     - __Aberration__ (aberration)
     - __BRAR__ (brar)
 
-### What is a Pandas DataFrame Extension?
+## What is a Pandas DataFrame Extension?
 
 A [Pandas DataFrame Extension](https://pandas.pydata.org/pandas-docs/stable/extending.html), extends a DataFrame allowing one to add more functionality and features to Pandas to suit your needs.  As such, it is now easier to run Technical Analysis on existing Financial Time Series without leaving the current DataFrame.  This extension by default returns the Indicator result or it can append the result to the existing DataFrame by including the parameter 'append=True' in the method call. Examples below.
 
@@ -101,6 +102,18 @@ help(ta.log_return)
 help(pd.DataFrame().ta.log_return)
 ```
 
+## New DataFrame kwargs: *prefix* and *suffix*
+
+```python
+prehl2 = df.ta.hl2(prefix="pre")
+print(prehl2.columns)  # "pre_HL2"
+
+endhl2 = df.ta.hl2(suffix="end")
+print(endhl2.columns)  # "HL2_end"
+
+bothhl2 = df.ta.hl2(suffix="end")
+print(bothhl2.columns)  # "pre_HL2_end"
+```
 
 ## New DataFrame Properties: *reverse* & *datetime_ordered*
 

pandas_ta/core.py

@@ -169,12 +169,12 @@ class AnalysisIndicators(BasePandasObject):
         """Add prefix and/or suffix to the result columns"""
         if result is None: return
         else:
-            prefix = ''
+            prefix = suffix = ''
+
             if 'prefix' in kwargs:
-                prefix = kwargs['prefix'] + '_'
-            suffix = ''
+                prefix = f"{kwargs['prefix']}_"
             if 'suffix' in kwargs:
-                suffix = '_' + kwargs['suffix']
+                suffix = f"_{kwargs['suffix']}"
 
             if isinstance(result, pd.Series):
                 result.name = prefix + result.name + suffix

setup.py

@@ -6,7 +6,7 @@ long_description = "An easy to use Python 3 Pandas Extension with 95+ Technical
 setup(
     name ="pandas_ta",
     packages =['pandas_ta', 'pandas_ta.momentum', 'pandas_ta.overlap', 'pandas_ta.performance', 'pandas_ta.statistics', 'pandas_ta.trend', 'pandas_ta.volatility', 'pandas_ta.volume'],
-    version ="0.1.47b",
+    version ="0.1.48b",
     description =long_description,
     long_description =long_description,
     author ="Kevin Johnson",

tests/test_utils.py

@@ -1,3 +1,4 @@
+from .config import sample_data
 from .context import pandas_ta
 
 from unittest import TestCase
@@ -16,6 +17,14 @@ data = {
 }
 
 class TestUtilities(TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.data = sample_data
+
+    @classmethod
+    def tearDownClass(cls):
+        del cls.data
+
     def setUp(self):
         self.crosseddf = DataFrame(data)
         self.utils = pandas_ta.utils
@@ -24,6 +33,23 @@ class TestUtilities(TestCase):
         del self.crosseddf
         del self.utils
 
+    def test__add_prefix_suffix(self):
+        result = self.data.ta.hl2(append=False, prefix="pre")
+        self.assertEqual(result.name, 'pre_HL2')
+
+        result = self.data.ta.hl2(append=False, suffix="suf")
+        self.assertEqual(result.name, 'HL2_suf')
+
+        result = self.data.ta.hl2(append=False, prefix="pre", suffix="suf")
+        self.assertEqual(result.name, 'pre_HL2_suf')
+
+        result = self.data.ta.hl2(append=False, prefix=1, suffix=2)
+        self.assertEqual(result.name, '1_HL2_2')
+
+        result = self.data.ta.macd(append=False, prefix="pre", suffix="suf")
+        for col in result.columns:
+            self.assertTrue(col.startswith('pre_') and col.endswith('_suf'))
+
     def test__above_below(self):
         result = self.utils._above_below(self.crosseddf['a'], self.crosseddf['zero'], above=True)
         self.assertIsInstance(result, Series)