How to Combine Date and Time Columns in Pandas?

Combine Date and Time Columns Using Pandas

When working with temporal data, it's often necessary to combine date and time columns to obtain a single timestamp value. Pandas provides various options for achieving this, including the pd.to_datetime() function.

Concatenating Strings and Using pd.to_datetime()

In some scenarios, your date and time columns are stored as strings. To combine them, you can simply concatenate them with a space as follows:

df['Date'] + ' ' + df['Time']

Once the strings are concatenated, you can use pd.to_datetime() to convert them into a DatetimeIndex object:

pd.to_datetime(df['Date'] + ' ' + df['Time'])

This approach allows you to utilize the inferred format of the concatenated string, which is typically a combination of the date and time formats of the individual columns.

Using the format= Parameter

However, if your date and time strings are not in a standardized format, or if you want to explicitly specify the format, you can use the format= parameter as follows:

pd.to_datetime(df['Date'] + df['Time'], format='%m-%d-%Y%H:%M:%S')

Here, you specify the exact format of the concatenated string, ensuring accurate conversion.

Parsing Dates Directly

As an alternative to concatenating strings, you can also parse the date and time information directly using pd.read_csv() with the parse_dates parameter. This parameter allows you to specify a list of columns to be parsed as datetime objects.

For example, if your data is stored in a CSV file named "data.csv":

import pandas as pd

df = pd.read_csv("data.csv", parse_dates=[['Date', 'Time']])

In this case, Pandas will automatically parse the specified columns into a DatetimeIndex.

Performance Considerations

When working with large datasets, performance becomes crucial. Concatenating strings and then converting them to datetime takes significantly longer than directly parsing the date and time information. As shown by the following timing results using the %timeit magic command:

# Sample dataframe with 10 million rows
df = pd.concat([df for _ in range(1000000)]).reset_index(drop=True)

# Time to combine strings and convert to datetime
%timeit pd.to_datetime(df['Date'] + ' ' + df['Time'])

# Time to parse dates directly
%timeit pd.to_datetime(df['Date'] + df['Time'], format='%m-%d-%Y%H:%M:%S')

The results indicate that direct parsing is significantly faster, especially for large datasets.

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