如何对含UUT的Pandas时间序列重采样至1秒并补0以分组绘图？

Got it, let's walk through how to handle your time series resampling and plotting task step by step using pandas and matplotlib. Here's a complete solution:

Step 1: Import Required Libraries #

First, we'll need pandas for data manipulation and matplotlib for plotting:

import pandas as pd
import matplotlib.pyplot as plt

Step 2: Load and Prepare the Raw Data #

We'll start by loading your provided data into a pandas DataFrame and converting the DateTime column to a proper datetime type:

# Raw data as a string (you can also load this from a CSV file directly)
raw_data = """DateTime       UUT    Sent  Received  Latency(ms)  Sum
2018-01-25 15:03:05  uut-1    1        1          427      2
2018-01-25 15:03:05  uut-2    1        1          664      2
2018-01-25 15:03:17  uut-1    1        1          637      2
2018-01-25 15:03:17  uut-2    1        1         1229      2
2018-01-25 15:03:29  uut-1    1        1         1154      2
2018-01-25 15:03:29  uut-2    1        1         1148      2
2018-01-25 15:04:00  uut-1    1        1          279      2"""

# Load into DataFrame
df = pd.read_csv(pd.compat.StringIO(raw_data), sep='\s+')

# Convert DateTime column to datetime type
df['DateTime'] = pd.to_datetime(df['DateTime'])

Step 3: Resample to 1-Second Intervals #

We'll create a full 1-second interval time range covering your data's start and end times, then fill missing values with 0 for each UUT:

# Generate a complete 1-second time index from the earliest to latest timestamp
min_timestamp = df['DateTime'].min()
max_timestamp = df['DateTime'].max()
full_time_index = pd.date_range(start=min_timestamp, end=max_timestamp, freq='1S')

# Process each UUT group separately
resampled_data = []
for uut_id, group in df.groupby('UUT'):
    # Set DateTime as the index for resampling
    group_indexed = group.set_index('DateTime')
    # Reindex to the full 1-second timeline, filling missing values with 0
    resampled_group = group_indexed.reindex(full_time_index, fill_value=0)
    # Add back the UUT column (since it gets dropped during reindex)
    resampled_group['UUT'] = uut_id
    # Reset index to get DateTime back as a column
    resampled_data.append(resampled_group.reset_index().rename(columns={'index': 'DateTime'}))

# Combine all resampled UUT data into one DataFrame
final_resampled_df = pd.concat(resampled_data, ignore_index=True)

Sample Output of Resampled Data #

Here's how the first 5 rows for uut-1 look after resampling:

Step 4: Plot Trends by UUT #

We'll create subplots for each metric (Sent, Received, Latency(ms)) to compare trends across UUTs:

# Set a clean plotting style
plt.style.use('seaborn-v0_8')

# Create a figure with 3 subplots (one for each metric)
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(12, 15))
axes = axes.flatten()

# Define which columns to plot
metrics = ['Sent', 'Received', 'Latency(ms)']

# Plot each metric
for ax_idx, metric in enumerate(metrics):
    for uut_id, group in final_resampled_df.groupby('UUT'):
        axes[ax_idx].plot(group['DateTime'], group[metric], label=uut_id, linewidth=2)
    # Customize the plot
    axes[ax_idx].set_title(f'Time vs {metric}', fontsize=14)
    axes[ax_idx].set_xlabel('DateTime', fontsize=12)
    axes[ax_idx].set_ylabel(metric, fontsize=12)
    axes[ax_idx].legend(fontsize=10)
    axes[ax_idx].tick_params(axis='x', rotation=45)

# Adjust layout to prevent overlapping labels
plt.tight_layout()
plt.show()

Key Notes #

• The resampling ensures every 1-second interval is present, with non-UUT columns filled to 0 where there's no original data.
• Grouping by UUT before plotting lets you easily compare each device's performance over time.
• The subplot layout keeps each metric's trend clear and easy to interpret.

内容的提问来源于stack exchange，提问作者user97662