assign value to new column [Python pandas]

Question

I have a scenario where I am running two functions in a script:

test.py :

def func1():
    df1=pd.read_csv('test1.csv')
    val1=df['col1'].mean().round(2)
    return va11

def func2():
    df2=pd.read_csv('test2.csv')
    val2=df['col1'].mean().round(2)
    return val2

def func3():
    dataf = pd.read_csv('test3.csv')
    col1=dataf['area']
    col2 = dataf['overall']
    dataf['overall']=val1 # value from val1 ->leads to error
    dataf['overall']=val2 #value from val2 ->leads to error

Here I am reading test1.csv & test2.csv file and I am storing the mean value in variable "val1" & "val2" respectively and returning the same. These variable values I want to store in a new test3.csv file which is having two cols and values should be stored one after one(appending). By the above it is not working out & couldn't find anything on internet as such. Any help would be great.

Answer 1

You need pass variables as parameters in function func3, and if only difference in func1 and func2 is file name, create only one function with parameetr .

Thanks for idea cᴏʟᴅsᴘᴇᴇᴅ ;)

def func1(file):
    df=pd.read_csv(file)
    val=df['col1'].mean().round(2)
    return val

a = func1('test1.csv')
b = func1('test2.csv')

def func3(val1=a, val2=b):
    dataf = pd.read_csv('test3.csv')
    col1=dataf['area']
    col2 = dataf['overall']
    dataf.iloc[::2, dataf.columns.get_loc('overall')] = val1 
    dataf.iloc[1::2, dataf.columns.get_loc('overall')] = val2
    return dataf

Sample:

dataf = pd.DataFrame({'overall':[1,7,8,9,4],
                      'col':list('abcde')})

print (dataf)
  col  overall
0   a        1
1   b        7
2   c        8
3   d        9
4   e        4

val1 = 20
val2 = 50

dataf.iloc[::2, dataf.columns.get_loc('overall')] = val1 
dataf.iloc[1::2, dataf.columns.get_loc('overall')] = val2
print (dataf)
  col  overall
0   a       20
1   b       50
2   c       20
3   d       50
4   e       20

General solution for append N values from list - create array by numpy.tile and then assign to new column:

val =[1,8,4]
a = np.tile(val, int(len(dataf) / len(val))+2)[:len(dataf)]
dataf['overall'] = a
print (dataf)
  col  overall
0   a        1
1   b        8
2   c        4
3   d        1
4   e        8