I've programmed these for calculating Variance
import pandas as pd import xlrd import numpy as np import matplotlib.pyplot as plt credit_card=pd.read_csv("default_of_credit_card_clients_Data.csv",skiprows=1) print(credit_card.head()) for col in credit_card: var[col]=np.var(credit_card(col)) print(var) 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 1 Answer
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 
