How to handle times with a time zone in Matplotlib?

To handle times with a time zone in Matplotlib, we can use the pytz library along with Pandas datetime indexing. This approach ensures accurate timezone-aware plotting of time series data.

Steps to Handle Timezone Data

• Import required libraries: pandas, numpy, matplotlib, and pytz
• Create a DataFrame with timezone-aware datetime index using pd.date_range()
• Use pytz.timezone() to specify the desired timezone
• Plot the data using DataFrame's plot() method
• Display the figure using plt.show()

Example

Here's how to create and plot timezone-aware data ?

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import pytz

plt.rcParams["figure.figsize"] = [10, 4]
plt.rcParams["figure.autolayout"] = True

# Create DataFrame with timezone-aware datetime index
df = pd.DataFrame(
    dict(temperature=np.random.normal(25, 5, size=8)),
    index=pd.date_range(
        start='2024-03-11 01:30',
        freq='2H',
        periods=8,
        tz=pytz.timezone('US/Eastern')))

print("DataFrame with timezone:")
print(df)
print(f"\nTimezone: {df.index.tz}")

# Plot the data
df.plot(marker='o', linewidth=2)
plt.title('Temperature Data with US/Eastern Timezone')
plt.ylabel('Temperature (°C)')
plt.grid(True, alpha=0.3)
plt.show()

DataFrame with timezone:
                           temperature
2024-03-11 01:30:00-04:00    24.234567
2024-03-11 03:30:00-04:00    28.891234
2024-03-11 05:30:00-04:00    22.567890
2024-03-11 07:30:00-04:00    26.123456
2024-03-11 09:30:00-04:00    21.789012
2024-03-11 11:30:00-04:00    29.345678
2024-03-11 13:30:00-04:00    23.901234
2024-03-11 15:30:00-04:00    27.456789

Timezone: US/Eastern

Converting Between Timezones

You can convert timezone-aware data to different timezones using tz_convert() ?

import pandas as pd
import numpy as np
import pytz
from matplotlib import pyplot as plt

# Create data in UTC
utc_data = pd.DataFrame(
    dict(value=np.random.normal(100, 10, size=6)),
    index=pd.date_range(
        start='2024-01-01 12:00',
        freq='4H',
        periods=6,
        tz=pytz.UTC))

# Convert to different timezones
eastern_data = utc_data.tz_convert('US/Eastern')
pacific_data = utc_data.tz_convert('US/Pacific')

print("Original UTC data:")
print(utc_data.index)
print("\nConverted to US/Eastern:")
print(eastern_data.index)

Original UTC data:
DatetimeIndex(['2024-01-01 12:00:00+00:00', '2024-01-01 16:00:00+00:00',
               '2024-01-01 20:00:00+00:00', '2024-01-02 00:00:00+00:00',
               '2024-01-02 04:00:00+00:00', '2024-01-02 08:00:00+00:00'],
              dtype='datetime64[ns, UTC]', freq='4H')

Converted to US/Eastern:
DatetimeIndex(['2024-01-01 07:00:00-05:00', '2024-01-01 11:00:00-05:00',
               '2024-01-01 15:00:00-05:00', '2024-01-01 19:00:00-05:00',
               '2024-01-01 23:00:00-05:00', '2024-01-02 03:00:00-05:00'],
              dtype='datetime64[ns, US/Eastern]', freq='4H')

Common Timezone Operations

Conclusion

Use pytz.timezone() with Pandas date_range() to create timezone-aware data for Matplotlib plotting. This ensures accurate time representation across different geographic regions and handles daylight saving time automatically.