pandas string replace TypeError - replace words using pandas df column

Question

I am using Python 3.7 and have a large data frame that includes a column of addresses, like so:

c = ['420 rodeo drive', '22 alaska avenue', '919 franklin boulevard', '39 emblem alley', '67 fair way']
df1 = pd.DataFrame(c, columns = ["address"])

There is also a dataframe of address abbrevations and their usps version. An example:

x = ['avenue', 'drive', 'boulevard', 'alley', 'way']
y = ['aly', 'dr', 'blvd', 'ave', 'way']
df2 = pd.DataFrame(list(zip(x,y)), columns = ['common_abbrev', 'usps_abbrev'], dtype = "str")

What I would like to do is as follows:

1. Search df1['address'] for occurrences of words in df2['common_abbrev'] and replace with df2['usps_abbrev'] .
2. Return the transformed df1 .

To this end I have tried in a few ways what appears to be the canonical strategy of df.str.replace() like so:

df1["address"] = df1["address"].str.replace(df2["common_abbrev"], df2["usps_abbrev"])

However, I get the following error:

`TypeError: repl must be a string or callable`.

My question is:

1. Since I've already given the dtype for df2 , why am I receiving the error?

2. How can I produce my desired result of:

    address 0 420 rodeo dr 1 22 alaska ave 2 919 franklin blvd 3 39 emblem aly 4 67 fair way

Thanks for your help.

Answer 1

First create a dict using zip . Then use Series.replace :

In [703]: x = ['avenue', 'drive', 'boulevard', 'alley', 'way']
     ...: y = ['aly', 'dr', 'blvd', 'ave', 'way']

In [686]: d = dict(zip(x, y))

In [691]: df1.address = df1.address.replace(d, regex=True)

In [692]: df1
Out[692]: 
             address
0       420 rodeo dr
1      22 alaska aly
2  919 franklin blvd
3      39 emblem ave
4        67 fair way