Python: Pandas Merge DataFrames on Index Example

In order to merge DataFrames with pandas in Python we can make use of the pandas.merge() function.

Example:

import pandas as pd

data_city_NYC = {
    'Temp_NYC': [25, 30, 27, 25, 24, 24],
    'Humidity_NYC': [50, 45, 55, 30, 55, 45]
}

data_city_Chicago = {
    'Temp_Chicago': [28, 32, 29, 28, 29, 30],
    'Humidity_Chicago': [60, 58, 62, 61, 65, 67]
}

dates = pd.date_range(start='2023-07-01', periods=6, freq='D')
df_city_NYC = pd.DataFrame(data_city_NYC, index=dates)
df_city_Chicago = pd.DataFrame(data_city_Chicago, index=dates)

print("DataFrame: NYC City")
print(df_city_NYC)
print("\nDataFrame: Chicago City")
print(df_city_Chicago)

merged_cities_df = pd.merge(df_city_NYC, df_city_Chicago, left_index=True, right_index=True)

print("\nMerged DataFrame Data:")
print(merged_cities_df)

Output:

DataFrame: NYC City
            Temp_NYC  Humidity_NYC
2023-07-01        25            50
2023-07-02        30            45
2023-07-03        27            55
2023-07-04        25            30
2023-07-05        24            55
2023-07-06        24            45

DataFrame: Chicago City
            Temp_Chicago  Humidity_Chicago
2023-07-01            28                60
2023-07-02            32                58
2023-07-03            29                62
2023-07-04            28                61
2023-07-05            29                65
2023-07-06            30                67

Merged DataFrame Data:
            Temp_NYC  Humidity_NYC  Temp_Chicago  Humidity_Chicago
2023-07-01        25            50            28                60
2023-07-02        30            45            32                58
2023-07-03        27            55            29                62
2023-07-04        25            30            28                61
2023-07-05        24            55            29                65
2023-07-06        24            45            30                67

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