Pandas: df.groupby(x, y).apply() across multiple columns parameter error

Question

Basic Problem:

I have several 'past' and 'present' variables that I'd like to perform a simple percent change 'row-wise' on. For example: ((exports_now - exports_past)/exports_past)).

These two questions accomplish this but when I try a similar method I get an error that my function deltas gets an unknown parameter axis.

• How to apply a function to two columns of Pandas dataframe
• Pandas: How to use apply function to multiple columns

Data Example :

exports_ past    exports_ now    imports_ past    imports_ now    ect.(6 other pairs)
   .23               .45             .43             .22              1.23
   .13               .21             .47             .32               .23
    0                 0              .41             .42               .93
   .23               .66             .43             .22               .21
    0                .12             .47             .21              1.23

Following the answer in the first question,

My solution is to use a function like this:

def deltas(row):
    '''
    simple pct change
    '''
    if int(row[0]) == 0 and int(row[1]) == 0:
        return 0
    elif int(row[0]) == 0:
        return np.nan
    else:
        return ((row[1] - row[0])/row[0])

And apply the function like this:

df['exports_delta'] = df.groupby(['exports_past', 'exports_now']).apply(deltas, axis=1)

This generates this error : TypeError: deltas() got an unexpected keyword argument 'axis' Any Ideas on how to get around the axis parameter error? Or a more elegant way to calculate the pct change? The kicker with my problem is that I needs be able to apply this function across several different column pairs, so hard coding the column names like the answer in 2nd question is undesirable. Thanks!

Answer 1

Consider using the pct_change Series/DataFrame method to do this.

df.pct_change()

The confusion stems from two different (but equally named) apply functions, one on Series/DataFrame and one on groupby.

In [11]: df
Out[11]:
   0  1  2
0  1  1  1
1  2  2  2

The DataFrame apply method takes an axis argument:

In [12]: df.apply(lambda x: x[0] + x[1], axis=0)
Out[12]:
0    3
1    3
2    3
dtype: int64

In [13]: df.apply(lambda x: x[0] + x[1], axis=1)
Out[13]:
0    2
1    4
dtype: int64

The groupby apply doesn't, and the kwarg is passed to the function:

In [14]: g.apply(lambda x: x[0] + x[1])
Out[14]:
0    2
1    4
dtype: int64

In [15]: g.apply(lambda x: x[0] + x[1], axis=1)
TypeError: <lambda>() got an unexpected keyword argument 'axis'

Note: that groupby does have an axis argument, so you can use it there, if you really want to:

In [16]: g1 = df.groupby(0, axis=1)

In [17]: g1.apply(lambda x: x.iloc[0, 0] + x.iloc[1, 0])
Out[17]:
0
1    3
2    3
dtype: int64