How to divide two columns element-wise in a pandas dataframe

I have two columns in my pandas dataframe. I'd like to divide column A by column B, value by value, and show it as follows:

import pandas as pd

csv1 = pd.read_csv('auto$0$0.csv')
csv2 = pd.read_csv('auto$0$8.csv')

df1 = pd.DataFrame(csv1, columns=['Column A', 'Column B'])
df2 = pd.DataFrame(csv2, columns=['Column A', 'Column B'])

dfnew = pd.concat([df1, df2])

The columns:

Column A  Column B
12        2
14        7
16        8
20        5

And the expected result:

Result
6
2
2
4

How do I do this?

Just divide the columns:

In [158]:
df['Result'] = df['Column A']/df['Column B']
df

Out[158]:
   Column A  Column B  Result
0        12         2     6.0
1        14         7     2.0
2        16         8     2.0
3        20         5     4.0

Series.div()

Equivalent to the / operator but with support to substitute a fill_value for missing data in either one of the inputs.

So normally div() is the same as /:

df['C'] = df.A.div(df.B)
#         df.A / df.B

But div()'s fill_value is more concise than 2x fillna():

df['C'] = df.A.div(df.B, fill_value=-1)
#         df.A.fillna(-1) / df.B.fillna(-1)

And div()'s method chaining is more idiomatic:

df['C'] = df.A.div(df.B).cumsum().add(1).gt(10)
#         ((df.A / df.B).cumsum() + 1) > 10

Note that when dividing a DataFrame with another DataFrame or Series, DataFrame.div() also supports broadcasting across an axis or MultiIndex level.