How can two dataframes containing location data with identical resolution be merged?

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Two dataframes containing location data with identical resolution can be merged using the pandas merge function. The merge function combines rows from two dataframes based on a common column. To merge two dataframes based on location data, the common column should be the location information, such as latitude and longitude.

Here's an example:

import pandas as pd # create first dataframe with location data df1 = pd.DataFrame({ 'latitude': [37.7749, 37.7749, 37.7504], 'longitude': [-122.4194, -122.4194, -122.4477], 'name': ['San Francisco', 'San Francisco', 'Daly City'] }) # create second dataframe with location data df2 = pd.DataFrame({ 'latitude': [37.7816, 37.7677, 37.7449], 'longitude': [-122.3933, -122.4331, -122.5083], 'population': [881549, 419267, 104007] }) # merge the two dataframes based on latitude and longitude merged_df = pd.merge(df1, df2, on=['latitude', 'longitude']) print(merged_df) 

This code will merge the two dataframes based on their latitude and longitude columns and will create a new dataframe that contains all of the columns from both input dataframes. In this example, the resulting dataframe will have three rows for the three shared locations: San Francisco, San Francisco, and Daly City.