In pandas we can merge two or more Dataframes based on a common column by using the pandas.merge() function and setting on parameter as the common column name.
Let's take a look at an example:
Example: Pandas Merge on Common Column
import pandas as pd
data_city_NYC = {
'Dates': ['2023-07-01', '2023-07-02', '2023-07-03'],
'Temp_NYC': [25, 30, 27],
'Humidity_NYC': [50, 45, 55]
}
data_city_Chicago = {
'Dates': ['2023-07-01', '2023-07-02', '2023-07-03'],
'Temp_Chicago': [28, 32, 29],
'Humidity_Chicago': [60, 58, 62]
}
df_city_NYC = pd.DataFrame(data_city_NYC).set_index('Dates')
df_city_Chicago = pd.DataFrame(data_city_Chicago).set_index('Dates')
print("DataFrame: NYC City")
print(df_city_NYC)
print("\nDataFrame: Chicago City")
print(df_city_Chicago)
# Merge the DataFrames on the common column 'Dates'
merged_cities_df = pd.merge(df_city_NYC, df_city_Chicago, on='Dates')
print("\nMerged DataFrames on Common Column Dates:")
print(merged_cities_df)
Output:
DataFrame: NYC City
Temp_NYC Humidity_NYC
Dates
2023-07-01 25 50
2023-07-02 30 45
2023-07-03 27 55
DataFrame: Chicago City
Temp_Chicago Humidity_Chicago
Dates
2023-07-01 28 60
2023-07-02 32 58
2023-07-03 29 62
Merged DataFrames on Common Column Dates:
Temp_NYC Humidity_NYC Temp_Chicago Humidity_Chicago
Dates
2023-07-01 25 50 28 60
2023-07-02 30 45 32 58
2023-07-03 27 55 29 62
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