How to plot int to datetime on X-axis using Seaborn?

When working with Seaborn plots, you may need to display integer timestamps as readable datetime labels on the X-axis. This is commonly required when dealing with Unix timestamps or other integer-based date representations.

Understanding the Problem

Integer timestamps (like Unix timestamps) are not human-readable. Converting them to datetime format on the X-axis makes your plots more interpretable and professional-looking.

Complete Example

Here's how to convert integer timestamps to datetime labels on the X-axis ?

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Set the figure size
plt.rcParams["figure.figsize"] = [10, 6]
plt.rcParams["figure.autolayout"] = True

# Create DataFrame with integer timestamps
df = pd.DataFrame([
    [23, 'James', 1259969793926],
    [39, 'Jimmy', 1259969793927],
    [56, 'Jack', 1259969793929],
    [60, 'Tom', 1259969793928],
    [80, 'Tim', 1259969793939]
], columns=['marks', 'names', 'dob'])

print("Original DataFrame:")
print(df)
print("\nData types:")
print(df.dtypes)

# Create countplot with integer timestamps
ax = sns.countplot(x="dob", data=df)

# Convert integer timestamps to datetime labels
ax.set_xticklabels([
    pd.to_datetime(tm, unit='ms').strftime('%Y-%m-%d %H:%M:%S')
    for tm in ax.get_xticks()
], rotation=45)

# Add labels and title
ax.set_xlabel('Date of Birth')
ax.set_ylabel('Count')
ax.set_title('Count Plot with DateTime X-axis Labels')

plt.tight_layout()
plt.show()

Original DataFrame:
   marks  names           dob
0     23  James  1259969793926
1     39  Jimmy  1259969793927
2     56   Jack  1259969793929
3     60    Tom  1259969793928
4     80    Tim  1259969793939

Data types:
marks      int64
names     object
dob        int64
dtype: object

How It Works

The key steps in this process are ?

• ax.get_xticks() − Retrieves the current X-axis tick positions (integer values)

• pd.to_datetime(tm, unit='ms') − Converts each integer timestamp to datetime object

• strftime('%Y-%m-%d %H:%M:%S') − Formats datetime as readable string

• ax.set_xticklabels() − Replaces integer labels with formatted datetime strings

Alternative Approach with Line Plot

For time series data, you might prefer a line plot ?

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Create sample time series data
data = pd.DataFrame({
    'timestamp': [1609459200, 1609545600, 1609632000, 1609718400, 1609804800],
    'value': [10, 25, 30, 15, 35]
})

# Convert timestamp to datetime for plotting
data['datetime'] = pd.to_datetime(data['timestamp'], unit='s')

plt.figure(figsize=(10, 6))
sns.lineplot(x='datetime', y='value', data=data, marker='o')
plt.xticks(rotation=45)
plt.title('Time Series Plot with DateTime X-axis')
plt.tight_layout()
plt.show()

print("Converted DataFrame:")
print(data)

Converted DataFrame:
   timestamp  value    datetime
0  1609459200     10  2021-01-01
1  1609545600     25  2021-01-02
2  1609632000     30  2021-01-03
3  1609718400     15  2021-01-04
4  1609804800     35  2021-01-05

Key Parameters

Conclusion

Converting integer timestamps to datetime labels improves plot readability. Use pd.to_datetime() with appropriate units and set_xticklabels() to format the X-axis properly. Consider using rotation for better label visibility.