'Percentage difference between any two columns of pandas dataframe
I would like to have a function defined for percentage diff calculation between any two pandas columns. Lets say that my dataframe is defined by:
R1  R2    R3    R4   R5    R6
 A   B     1     2    3     4
I would like my calculation defined as
df['R7'] = df[['R3','R4']].apply( method call to calculate perc diff)
and
df['R8'] = df[['R5','R6']].apply(same method call to calculate perc diff)
How can i do that?
I have tried below
df['perc_cnco_error'] = df[['CumNetChargeOffs_x','CumNetChargeOffs_y']].apply(lambda x,y: percCalc(x,y))
def percCalc(x,y):
    if x<1e-9:
        return 0
    else:
        return (y - x)*100/x
and it gives me the error message
TypeError: ('() takes exactly 2 arguments (1 given)', u'occurred at index CumNetChargeOffs_x')
Solution 1:[1]
At it's simplest terms:
def percentage_change(col1,col2):
    return ((col2 - col1) / col1) * 100
You can apply it to any 2 columns of your dataframe:
df['a'] = percentage_change(df['R3'],df['R4'])    
df['b'] =  percentage_change(df['R6'],df['R5'])
>>> print(df)
 
  R1 R2  R3  R4  R5  R6      a     b
0  A  B   1   2   3   4  100.0 -25.0
Equivalently using pandas arithmetic operation functions
def percentage_change(col1,col2):
    return ((col2.sub(col1)).div(col1)).mul(100)
You can also utilise pandas built-in pct_change  which computes the percentage change across all the columns passed, and select the column you want to return:
df['R7'] = df[['R3','R4']].pct_change(axis=1)['R4']
df['R8'] = df[['R6','R5']].pct_change(axis=1)['R5']
>>> print(df)
  R1 R2  R3  R4  R5  R6      a     b   R7    R8
0  A  B   1   2   3   4  100.0 -25.0  1.0 -0.25
Setup:
df = pd.DataFrame({'R1':'A','R2':'B',
                   'R3':1,'R4':2,'R5':3,'R6':4},
                  index=[0])
    					Solution 2:[2]
To calculate percent diff between R3 and R4 you can use:
df['R7'] = (df.R3 - df.R4) / df.R3 * 100
    					Solution 3:[3]
This would give you the deviation in percentage:
df.apply(lambda row: (row.iloc[0]-row.iloc[1])/row.iloc[0]*100, axis=1)
If you have more than two columns try,
df[['R3', 'R5']].apply(lambda row: (row.iloc[0]-row.iloc[1])/row.iloc[0]*100, axis=1)
    					Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source | 
|---|---|
| Solution 1 | |
| Solution 2 | Daniil Mashkin | 
| Solution 3 | pdubucq | 
