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privacykit
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bug fix with documentation

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pull/2/head
Steve L. Nyemba 6 years ago
parent 30fe9cf9fc
commit 25b30d2c2b
2 changed files with 6 additions and 6 deletions
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6
README.md
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@ -39,10 +39,10 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample
import numpy as np import numpy as np
import pandas as pd 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) }) 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 # - 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)}) 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: @TODO:

6
risk/__init__.py
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@ -43,10 +43,10 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample
import numpy as np import numpy as np
import pandas as pd 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) }) 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 # - 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)}) 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: @TODO:

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