Pandas timeseries plot setting X-axis major and minor ticks and labels

When working with Pandas time series data, you often need to customize the X-axis ticks and labels for better visualization. This involves setting both major and minor ticks to display dates at appropriate intervals.

Steps

• Create a random number generator with a fixed seed for reproducible results.

• Generate a fixed frequency DatetimeIndex using pd.date_range() from '2020-01-01' to '2021-01-01'.

• Create sample data using a mathematical function or random distribution.

• Build a DataFrame with the time series data.

• Create a plot with custom figure size and configure major/minor ticks.

• Display the plot using plt.show().

Basic Time Series Plot

Let's start by creating a basic time series plot with Pandas ?

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

# Create random number generator
rng = np.random.default_rng(seed=1)

# Generate date range
date_day = pd.date_range(start='2020-01-01', end='2021-01-01', freq='D')

# Create sample data (exponential decay)
df_day = pd.DataFrame(dict(speed=[pow(2, -i/50) for i in range(len(date_day))]),
                      index=date_day)

# Create the plot
df_day.plot(figsize=(10, 5))
plt.title('Time Series Plot with Default Ticks')
plt.show()

Customizing Major and Minor Ticks

To have better control over the X-axis, we can set custom major and minor ticks using matplotlib's date formatting ?

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib.dates as mdates

# Create data
rng = np.random.default_rng(seed=1)
date_day = pd.date_range(start='2020-01-01', end='2021-01-01', freq='D')
df_day = pd.DataFrame(dict(speed=[pow(2, -i/50) for i in range(len(date_day))]),
                      index=date_day)

# Create plot
fig, ax = plt.subplots(figsize=(10, 5))
df_day.plot(ax=ax)

# Set major ticks to show every 2 months
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=2))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))

# Set minor ticks to show every month
ax.xaxis.set_minor_locator(mdates.MonthLocator())

# Rotate labels for better readability
plt.xticks(rotation=45)
plt.title('Time Series with Custom Major and Minor Ticks')
plt.tight_layout()
plt.show()

Advanced Tick Configuration

For more detailed control, you can customize both the frequency and format of ticks ?

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib.dates as mdates

# Create sample data
date_day = pd.date_range(start='2020-01-01', end='2021-01-01', freq='D')
df_day = pd.DataFrame({
    'speed': [pow(2, -i/50) for i in range(len(date_day))],
    'acceleration': [np.sin(i/30) * 0.5 + 1 for i in range(len(date_day))]
}, index=date_day)

# Create subplot
fig, ax = plt.subplots(figsize=(12, 6))
df_day.plot(ax=ax)

# Configure major ticks (every 3 months)
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))

# Configure minor ticks (every month)
ax.xaxis.set_minor_locator(mdates.MonthLocator())

# Customize grid
ax.grid(True, which='major', alpha=0.7)
ax.grid(True, which='minor', alpha=0.3)

# Format plot
plt.title('Multi-Series Time Plot with Custom Ticks and Grid')
plt.xlabel('Date')
plt.ylabel('Values')
plt.xticks(rotation=30)
plt.tight_layout()
plt.show()

Common Date Formatters

Conclusion

Customizing X-axis ticks in Pandas time series plots involves using matplotlib's mdates module. Set major ticks with MonthLocator() or WeekdayLocator(), and format labels with DateFormatter() for optimal readability.