From 25b30d2c2be7ad6e2034b72028ef5da799c160ca Mon Sep 17 00:00:00 2001
From: Steve Nyemba <steve@the-phi.com>
Date: Tue, 5 Mar 2019 15:47:34 -0600
Subject: [PATCH] bug fix with documentation

---
 README.md        | 6 +++---
 risk/__init__.py | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 96103de..36cdb6d 100644
--- a/README.md
+++ b/README.md
@@ -39,10 +39,10 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample
 
     import numpy as np
     import pandas as pd
-    from pandas_risk import *
+    import risk
 
     mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50),"z":np.random.choice( np.random.randint(1,10),50),"r":np.random.choice( np.random.randint(1,10),50)  })
-    print mydf.risk.evaluate()
+    print (mydf.risk.evaluate())
 
 
 
@@ -52,7 +52,7 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample
     #   - Insure the fields are identical in both sample and population
     #
     pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"z":np.random.choice( np.random.randint(1,10),150),"r":np.random.choice( np.random.randint(1,10),150)})
-    mydf.risk.evaluate(pop=pop)
+    print (mydf.risk.evaluate(pop=pop))
 
 
 @TODO:
diff --git a/risk/__init__.py b/risk/__init__.py
index fff471f..57e7ee1 100644
--- a/risk/__init__.py
+++ b/risk/__init__.py
@@ -43,10 +43,10 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample
 
     import numpy as np
     import pandas as pd
-    from pandas_risk import *
+    import risk
 
     mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50),"z":np.random.choice( np.random.randint(1,10),50),"r":np.random.choice( np.random.randint(1,10),50)  })
-    print mydf.risk.evaluate()
+    print (mydf.risk.evaluate())
 
 
 
@@ -56,7 +56,7 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample
     #   - Insure the fields are identical in both sample and population
     #
     pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"z":np.random.choice( np.random.randint(1,10),150),"r":np.random.choice( np.random.randint(1,10),150)})
-    mydf.risk.evaluate(pop=pop)
+    print (mydf.risk.evaluate(pop=pop))
 
 
 @TODO: