Applying a function to columns within a dataframe based on a condition

Question

The goal of the below code is to loop through 5 columns of data and apply a function to that row within the column if a condition is met within the ‘condition’ column.

I have a data frame where I have set up a column as a result of a condition:

def WanCon(y):
  if (30 <= y <= 48):
    return 1
  else:
    return 2
time = four70['WY WEEK']
Wandata = pd.DataFrame(time)
Wandata['Condition'] = list(map(WanCon,Wandata['WY WEEK']))

I have then copied the values for 5 columns into the new data frame Wandata where objects a-e are the x values within my below functions.

name = 'WANAP'

a = four40[name]
b = eight40[name]
c = four70[name]
d = eight70[name]
e = Historical[name]

Wandata[['440','840','470','870','Historical']] = [a,b,c,d,e]
Wandata.replace("",float('NaN'),inplace=True)

I then have two formula’s I would like to apply to the newly added columns based on the above condition where any row in condition where the value is one Waneq1() is applied otherwise apply Waneq2():

def Waneq1(x):
  return((model1.c[0]*x**3)+(model1.c[1]*x**2)+(model1.c[2]*x)+(model1.c[3]))

def Waneq2(x):
  return((model2.c[0]*x**3)+(model2.c[1]*x**2)+(model2.c[2]*x)+(model2.c[3]))


for column in Wandata[['440','840','470','870','Historical']]:
  if Wandata['Condition']==1:
    Waneq1()
  else:
    Waneq2()

Im fairly new to python and this is the farthest I have gotten. I am wondering if any one knows of a better way to achieve this as step three is fairly challenging to me and i’ve hit a roadblock.

Asked By: rweber

||

Source

Answer 1

It is unclear what specifically you are trying to do (we also don’t know what four40 etc. objects are), but the right way to apply conditions on a pandas dataframe is generally:

wandata['output column'] = (model2.c[0]*x**3)+(model2.c[1]*x**2)+(model2.c[2]*x)+(model2.c[3]) # The default value of the column
wandata.loc[wandata['condition'] == 1, 'output columns'] = (model1.c[0]*x**3)+(model1.c[1]*x**2)+(model1.c[2]*x)+(model1.c[3]) # The value if the condition is true

Answered By: Aryerez

Answer 2

I was able to find a solution to my problem by employing an .apply() and lambda statement within the df.

The generalized solution is as below:

df['f(x)'] = df.apply(lambda r: function1(r['x']) if r['Condition'] == 1 else function2(r['x']), axis=1)

Answered By: rweber

Applying a function to columns within a dataframe based on a condition

Question:

Answers: