Divide a dataframe by another dataframe according to index

Question

I am trying to divide rows of a dataframe by the same index row in another dataframe. There are the same amount of columns in each dataframe.

The goal is to divide a list of columns by another list of columns. Is there a way to do this in Pandas?

Here is a sample data:

import pandas as pd
import numpy as np
data1 = {"a":[10.,20.,30.,40.,50.],
         "b":[900.,800.,700.,600.,500.],
         "c":[2.,4.,6.,8.,10.]}
data2 = {"f":[1.,2.,3.,4.],
         "g":[900.,800.,700.,600.],
         "h":[10.,20.,30.,40.]}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2) 

Expected output:

    a/f  b/g  c/h
0  10.0  1.0  0.2
1  10.0  1.0  0.2
2  10.0  1.0  0.2
3  10.0  1.0  0.2
4   NaN  NaN  NaN

As of now, I am using this little function I wrote:

def divDF(df1, df2):
    nRow, nCol = df1.shape
    result = pd.DataFrame(np.empty((nRow, nCol)), index=df1.index)
    for col in range(nCol):
        result.iloc[:,col] = df1.iloc[:,col] / df2.iloc[:,col]
    return result

Is this the only way or is there a faster way of doing this?

Answer 1

divide by values to get around index alignment

dfd = df1.div(df2.values)
dfd.columns = df1.columns + '/' + df2.columns

dfd

    a/f  b/g  c/h
0  10.0  1.0  0.2
1  10.0  1.0  0.2
2  10.0  1.0  0.2
3  10.0  1.0  0.2

Or

c = df1.columns + '/' + df2.columns
pd.DataFrame(df1.values / df2.values, df1.index, c)

    a/f  b/g  c/h
0  10.0  1.0  0.2
1  10.0  1.0  0.2
2  10.0  1.0  0.2
3  10.0  1.0  0.2

---

rebirth of @ScottBoston's answer

c = df1.columns + '/' + df2.columns
d1 = dict(zip(df1.columns, c))
d2 = dict(zip(df2.columns, c))
df1.rename(columns=d1) / df2.rename(columns=d2)

    a/f  b/g  c/h
0  10.0  1.0  0.2
1  10.0  1.0  0.2
2  10.0  1.0  0.2
3  10.0  1.0  0.2
4   NaN  NaN  NaN

Answer 2

Using align to force index alignment:

df3 = np.divide(*df1.align(df2, axis=0))
df3.columns = df1.columns + '/' + df2.columns

The resulting output:

    a/f  b/g  c/h
0  10.0  1.0  0.2
1  10.0  1.0  0.2
2  10.0  1.0  0.2
3  10.0  1.0  0.2
4   NaN  NaN  NaN