Understanding Timezones in pandas DataFrame.append()

Introduction

The pandas library provides an efficient data structure for handling structured data, particularly tabular data such as spreadsheets and SQL tables. One of its key features is the ability to append new rows to a DataFrame without having to rebuild the entire dataset from scratch.

However, when working with timezones, things can get complicated. In this article, we’ll delve into why pandas DataFrame.append() fails with timezone values and how to resolve the issue.

The Problem

The problem arises when trying to append a row with a timezone value to an existing DataFrame that does not have a column for storing timezones. This is where the issue with pytz and its compatibility with different versions of pandas comes into play.

Here’s an example that fails:

x = 'astring'
t = (datetime.datetime(2018, 5, 31, 13, 15, 17, tzinfo=pytz.utc), datetime.datetime(2100, 5, 31))
df = pd.DataFrame(columns=['a', 'b', 'c'])
df = df.append({'a': x, 'b': t[0], 'c': t[1]}, ignore_index=True)

And here’s an example that succeeds:

x = 'astring'
t = (datetime.datetime(2018, 5, 31, 13, 15, 17), datetime.datetime(2100, 5, 31))
df = pd.DataFrame(columns=['b', 'c'])
df = df.append({'b': t[0], 'c': t[1]}, ignore_index=True)

And here’s an example that succeeds when the timezone column exists:

x = 'astring'
t = (datetime.datetime(2018, 5, 31, 13, 15, 17, tzinfo=pytz.utc), datetime.datetime(2100, 5, 31, tzinfo=pytz.utc))
df = pd.DataFrame(columns=['b', 'c'])
df = df.append({'b': t[0], 'c': t[1]}, ignore_index=True)

The Cause

The problem lies in the way pytz handles timezones. In version 2016.7, there was an issue with timezone-aware datetime objects not being properly converted to naive datetime objects (i.e., datetime objects without a timezone).

This issue led to the error message “data type not understood” when trying to append rows with timezone values.

The Solution

To resolve this issue, we need to upgrade our versions of pandas and pytz. Specifically:

1. Upgrade pandas to version 0.23.0 or later.
2. Update pytz to a compatible version (e.g., 2018.4).
3. Ensure that your DataFrame has columns for storing timezones.

Here are the steps to follow:

Step 1: Upgrade pandas

Run the following command in a new notebook cell:

!pip install --upgrade pandas

This should upgrade pytz to version 2018.4.

Step 2: Restart the kernel

Click on the “Reset session” option in Datalab to restart the kernel and ensure that the updated versions of pandas and pytz are used.

Step 3: Check versions

Add the following lines to check if the versions have been successfully upgraded:

print(pd.__version__)
print(pytz.__version__)
print(np.__version__)

These commands will print the current versions of pandas, pytz, and numpy.

Conclusion

In conclusion, when working with timezones in pandas DataFrame.append(), it’s essential to be aware of the compatibility issues between different versions of pandas and pytz. By upgrading our versions to 0.23.0 or later and using compatible versions of pytz, we can resolve this issue and ensure that our code works correctly.

Example Use Cases

Here are some example use cases that demonstrate how to work with timezones in pandas:

import pandas as pd
from datetime import datetime
import pytz

# Create a sample DataFrame
df = pd.DataFrame(columns=['b', 'c'])

# Append rows with timezone values
t1 = (datetime(2018, 5, 31, 13, 15, 17, tzinfo=pytz.utc), datetime(2100, 5, 31))
t2 = (datetime(2020, 6, 1, 14, 30, 0, tzinfo=pytz.utc), datetime(2030, 7, 1))

df = df.append({'b': t1[0], 'c': t1[1]}, ignore_index=True)
df = df.append({'b': t2[0], 'c': t2[1]}, ignore_index=True)

print(df)

In this example, we create a sample DataFrame with columns b and c, and then append rows with timezone values using the pytz library. The resulting DataFrame is printed to the console.

Additional Tips

Here are some additional tips for working with timezones in pandas:

• Use the pytz library to handle timezones.
• Ensure that your DataFrame has columns for storing timezones.
• Upgrade pandas and pytz to compatible versions.
• Restart the kernel after updating versions.

By following these tips, you can ensure that your code works correctly when working with timezones in pandas.

Last modified on 2024-11-09