pythonpandasdataframefunctiontypeerror

TypeError: 'DataFrame' object is not callable

I've programmed these for calculating Variance

credit_card = pd.read_csv("default_of_credit_card_clients_Data.csv", skiprows=1)
    
for col in credit_card:
    var[col]=np.var(credit_card(col))

I'm getting this error

Traceback (most recent call last):
   File "C:/Python34/project.py", line 11, in <module>
     var[col]=np.var(credit_card(col)) 
TypeError: 'DataFrame' object is not callable
Solution

  • It seems you need DataFrame.var:

    Normalized by N-1 by default. This can be changed using the ddof argument

    var1 = credit_card.var()

    Sample:

    #random dataframe
np.random.seed(100)
credit_card = pd.DataFrame(np.random.randint(10, size=(5,5)), columns=list('ABCDE'))
print (credit_card)
   A  B  C  D  E
0  8  8  3  7  7
1  0  4  2  5  2
2  2  2  1  0  8
3  4  0  9  6  2
4  4  1  5  3  4

var1 = credit_card.var()
print (var1)
A     8.8
B    10.0
C    10.0
D     7.7
E     7.8
dtype: float64

var2 = credit_card.var(axis=1)
print (var2)
0     4.3
1     3.8
2     9.8
3    12.2
4     2.3
dtype: float64

    If need numpy solutions with numpy.var:

    print (np.var(credit_card.values, axis=0))
[ 7.04  8.    8.    6.16  6.24]

print (np.var(credit_card.values, axis=1))
[ 3.44  3.04  7.84  9.76  1.84]

    Differences are because by default ddof=1 in pandas, but you can change it to 0:

    var1 = credit_card.var(ddof=0)
print (var1)
A    7.04
B    8.00
C    8.00
D    6.16
E    6.24
dtype: float64

var2 = credit_card.var(ddof=0, axis=1)
print (var2)
0    3.44
1    3.04
2    7.84
3    9.76
4    1.84
dtype: float64