How to calculate pct_change in pandas with reference to just the first column

Question

I have a dataframe as:

df = pd.DataFrame({
    "A": [1, 5, 2, 5, 6],
    "B": [-12, 23, 5, 22, 35],
    "C": [-32, 12, -10, 3, 2],
    "D": [2, 13, 6, 2, 8]
})

Now, I want to calculate the percentage change on axis=1 but with reference to just "A" for all the columns like the percentage change for "B" wrt "A" , "C" wrt "A" and so on.

The pct_change function does a similar job but it calculates the percent change for successive rows or columns which I don't want.

Right now I'm thinking of acheiving this by probably a for loop and adding on the percentages OR splitting the dataframe like ["A", "B"] , ["A", "C"] , so forth and then applying pct_change to all separately.

The latter approach is I think better, but the question is,

Is there an even better approach which will do the same job?

Answer 1

You can use divide function in pandas, diving all columns with column A

pct = df.divide(df["A"], axis="index") - 1
pct.head()

Results:

	A	B	C	D
0	0.0	-13.000000	-33.000000	1.000000
1	0.0	3.600000	1.400000	1.600000
2	0.0	1.500000	-6.000000	2.000000
3	0.0	3.400000	-0.400000	-0.600000
4	0.0	4.833333	-0.666667	0.333333

Answer 2

You can emulate pct_change wrt A easily with DataFrame.sub and DataFrame.div for your case.

df.loc[:, 'B':].sub(df['A'], axis=0).div(df['A'], axis=0)

           B          C         D
0 -13.000000 -33.000000  1.000000
1   3.600000   1.400000  1.600000
2   1.500000  -6.000000  2.000000
3   3.400000  -0.400000 -0.600000
4   4.833333  -0.666667  0.333333

Answer 3

You can use ** kwargs to assign()

    "A": [1, 5, 2, 5, 6],
    "B": [-12, 23, 5, 22, 35],
    "C": [-32, 12, -10, 3, 2],
    "D": [2, 13, 6, 2, 8]
})

basecol = "A"
df.assign(**{f"{c}_pct":df[basecol]/df[c] for c in df.columns if c!=basecol})

	A	B	C	D	B_pct	C_pct	D_pct
0	1	-12	-32	2	-0.0833333	-0.03125	0.5
1	5	23	12	13	0.217391	0.416667	0.384615
2	2	5	-10	6	0.4	-0.2	0.333333
3	5	22	3	2	0.227273	1.66667	2.5
4	6	35	2	8	0.171429	3	0.75