Convert Float to Datetime in Pandas DataFrame

Pandas is a powerful data manipulation library widely used in Python for data analysis and preprocessing tasks. When working with data, it is common to encounter situations where dates and times are represented as floating?point numbers instead of the expected datetime format. In such cases, it becomes essential to convert the float values to datetime objects to perform accurate time?based analysis.

This article aims to provide a comprehensive guide on how to convert float values to datetime objects in a Pandas DataFrame.

Understanding the importance of converting float to datetime

Datetime objects offer several advantages over float representations of dates and times. By converting float values to datetime objects, we can leverage the rich set of datetime functionalities provided by Pandas and Python, including date arithmetic, time zone handling, resampling, and plotting.

Additionally, converting float to datetime allows for precise time?based comparisons and calculations, enabling accurate analysis and visualisation of time series data.

Below are two different approaches to convert float values to datetime in a Pandas DataFrame.

Using Pandas' built?in functions

Consider the code shown below.

Example

import pandas as pd

# Sample DataFrame with float column representing Unix timestamps
data = {'timestamp': [1620619200.0, 1620705600.0, 1620792000.0]}
df = pd.DataFrame(data)

# Convert float to datetime using Pandas' to_datetime() function
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')

# Print the updated DataFrame
print(df)

Explanation

• We start by importing the required libraries, including Pandas.

• We create a sample DataFrame df with a column named 'timestamp' that contains float values representing Unix timestamps.

• Using the pd.to_datetime() function, we convert the 'timestamp' column to datetime format. The unit='s' argument specifies that the float values represent timestamps in seconds. If the float values are in milliseconds, you can use unit='ms'.

• The original 'timestamp' column is replaced with the converted datetime values.

• Finally, we print the updated DataFrame, showing the conversion results.

Output

  timestamp
0 2021-05-10 04:00:00
1 2021-05-11 04:00:00
2 2021-05-12 04:00:00

Using custom function

Consider the code shown below.

Example

import pandas as pd

# Sample DataFrame with float column representing fractional years
data = {'year': [2021.5, 2022.25, 2023.75]}
df = pd.DataFrame(data)

# Custom function to convert fractional years to datetime
def convert_to_datetime(year):
    year = int(year)
    days = int((year - int(year)) * 365.25)
    base_date = pd.to_datetime(f'{year}-01-01')
    return base_date + pd.DateOffset(days=days)

# Apply the custom function to the 'year' column
df['year'] = df['year'].apply(convert_to_datetime)

# Print the updated DataFrame
print(df)

Explanation

• After importing the required libraries, we create a sample DataFrame df with a column named 'year' containing float values representing fractional years.

• We define a custom function convert_to_datetime(year) to convert fractional years to datetime objects.

• Within the custom function, we convert the float value to an integer representing the year and calculate the number of days corresponding to the fractional part of the year.

• We create a base date using pd.to_datetime() and specifying the year as January 1st.

• By adding the calculated number of days using pd.DateOffset(), we obtain the final datetime value.

• We apply the custom function to the 'year' column using the apply() function.

• The 'year' column is updated with the converted datetime values.

• Finally, we print the updated DataFrame to display the conversion results.

Output

    year
0 2021-01-01
1 2022-01-01
2 2023-01-01

Conclusion

In conclusion, converting float values to datetime in a Pandas DataFrame is a common task when dealing with time?related data. In this article, we explored two different approaches to accomplish this task.

The first approach leverages Pandas' built?in functionality, specifically the pd.to_datetime() function.

The second approach demonstrated a custom function to handle more specific scenarios. In this case, we tackled the conversion of fractional years to datetime objects.

Both approaches showcased the power and flexibility of Pandas for data manipulation and conversion. Depending on the specific requirements of your dataset, you can choose the most appropriate method.