Complete Addresses
Start Timer
0:00:00
You’re given two dataframes. One contains information about addresses and the other contains relationships between various cities and states:
Example:
df_addresses
| address |
|---|
| 4860 Sunset Boulevard, San Francisco, 94105 |
| 3055 Paradise Lane, Salt Lake City, 84103 |
| 682 Main Street, Detroit, 48204 |
| 9001 Cascade Road, Kansas City, 64102 |
| 5853 Leon Street, Tampa, 33605 |
df_cities
| city | state |
|---|---|
| Salt Lake City | Utah |
| Kansas City | Missouri |
| Detroit | Michigan |
| Tampa | Florida |
| San Francisco | California |
Write a function complete_address to create a single dataframe with complete addresses in the format of street, city, state, zip code.
Input:
import pandas as pd
addresses = {"address": ["4860 Sunset Boulevard, San Francisco, 94105", "3055 Paradise Lane, Salt Lake City, 84103", "682 Main Street, Detroit, 48204", "9001 Cascade Road, Kansas City, 64102", "5853 Leon Street, Tampa, 33605"]}
cities = {"city": ["Salt Lake City", "Kansas City", "Detroit", "Tampa", "San Francisco"], "state": ["Utah", "Missouri", "Michigan", "Florida", "California"]}
df_addresses = pd.DataFrame(addresses)
df_cities = pd.DataFrame(cities)
Output:
def complete_address(df_addresses,df_cities) ->
| address |
|---|
| 4860 Sunset Boulevard, San Francisco, California, 94105 |
| 3055 Paradise Lane, Salt Lake City, Utah, 84103 |
| 682 Main Street, Detroit, Michigan, 48204 |
| 9001 Cascade Road, Kansas City, Missouri, 64102 |
| 5853 Leon Street, Tampa, Florida, 33605 |
.
.
.
.
.
.
.
.
.
Comments