Compare specific Timestamps for a Pandas DataFrame – Python

To compare specific timestamps in a Pandas DataFrame, you can access individual rows using index numbers and calculate the difference between timestamp columns. This is useful for analyzing time intervals between related events.

Creating a DataFrame with Timestamps

First, let's create a DataFrame containing timestamp data ?

import pandas as pd

# Create a DataFrame with timestamp columns
dataFrame = pd.DataFrame({
    "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW"],
    "Date_of_Purchase": [
        pd.Timestamp("2021-06-10"),
        pd.Timestamp("2021-07-11"),
        pd.Timestamp("2021-06-25"),
        pd.Timestamp("2021-06-29"),
        pd.Timestamp("2021-03-20"),
    ],
    "Date_of_Service": [
        pd.Timestamp("2021-11-05"),
        pd.Timestamp("2021-12-03"),
        pd.Timestamp("2021-10-30"),
        pd.Timestamp("2021-11-29"),
        pd.Timestamp("2021-08-20"),
    ]
})

print("DataFrame...")
print(dataFrame)

DataFrame...
        Car Date_of_Purchase Date_of_Service
0      Audi        2021-06-10      2021-11-05
1     Lexus        2021-07-11      2021-12-03
2     Tesla        2021-06-25      2021-10-30
3  Mercedes        2021-06-29      2021-11-29
4       BMW        2021-03-20      2021-08-20

Comparing Specific Timestamps

Use index numbers in square brackets to access specific rows and calculate timestamp differences ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW"],
    "Date_of_Purchase": [
        pd.Timestamp("2021-06-10"),
        pd.Timestamp("2021-07-11"),
        pd.Timestamp("2021-06-25"),
        pd.Timestamp("2021-06-29"),
        pd.Timestamp("2021-03-20"),
    ],
    "Date_of_Service": [
        pd.Timestamp("2021-11-05"),
        pd.Timestamp("2021-12-03"),
        pd.Timestamp("2021-10-30"),
        pd.Timestamp("2021-11-29"),
        pd.Timestamp("2021-08-20"),
    ]
})

# Compare specific timestamps using index positions
timestamp1_diff = abs(dataFrame['Date_of_Purchase'][0] - dataFrame['Date_of_Service'][0])
timestamp2_diff = abs(dataFrame['Date_of_Purchase'][1] - dataFrame['Date_of_Service'][1])
timestamp3_diff = abs(dataFrame['Date_of_Purchase'][2] - dataFrame['Date_of_Service'][2])

print("Difference between Car 1 Date of Purchase and Service:")
print(timestamp1_diff)
print("\nDifference between Car 2 Date of Purchase and Service:")
print(timestamp2_diff)
print("\nDifference between Car 3 Date of Purchase and Service:")
print(timestamp3_diff)

Difference between Car 1 Date of Purchase and Service:
148 days 00:00:00

Difference between Car 2 Date of Purchase and Service:
145 days 00:00:00

Difference between Car 3 Date of Purchase and Service:
127 days 00:00:00

Using iloc for More Flexible Access

You can also use iloc for position-based indexing ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW"],
    "Date_of_Purchase": [
        pd.Timestamp("2021-06-10"),
        pd.Timestamp("2021-07-11"),
        pd.Timestamp("2021-06-25"),
        pd.Timestamp("2021-06-29"),
        pd.Timestamp("2021-03-20"),
    ],
    "Date_of_Service": [
        pd.Timestamp("2021-11-05"),
        pd.Timestamp("2021-12-03"),
        pd.Timestamp("2021-10-30"),
        pd.Timestamp("2021-11-29"),
        pd.Timestamp("2021-08-20"),
    ]
})

# Using iloc for position-based access
for i in range(3):
    purchase_date = dataFrame['Date_of_Purchase'].iloc[i]
    service_date = dataFrame['Date_of_Service'].iloc[i]
    car_name = dataFrame['Car'].iloc[i]
    diff = abs(service_date - purchase_date)
    print(f"{car_name}: {diff}")

Audi: 148 days 00:00:00
Lexus: 145 days 00:00:00
Tesla: 127 days 00:00:00

Conclusion

Use square bracket indexing or iloc to access specific timestamp rows. The abs() function ensures positive differences regardless of which date is earlier. This approach is perfect for comparing individual timestamp pairs in your DataFrame.