如何基于未知格式EventTime列与Price列绘制CSV数据图表？

Got it, let's tackle this step by step. First, let's decode that tricky EventTime format, then we'll convert it into a usable datetime object, and finally plot your Price data over time.

Step 1: Understand the EventTime Format #

Looking at your example 20180402-070000.164-0400, here's what each part breaks down to:

• 20180402: YYYYMMDD (Year: 2018, Month: 04, Day: 02)
• 070000.164: HHMMSS.sss (Hour: 07, Minute: 00, Second: 00, Millisecond: 164)
• -0400: UTC timezone offset (UTC minus 4 hours)

The full format string we'll use to parse this is %Y%m%d-%H%M%S.%f-%z — it perfectly matches each segment of the timestamp.

Step 2: Parse the CSV and Convert EventTime #

We'll use Python's pandas (the standard tool for this kind of data work) to read the CSV and convert the EventTime column into proper datetime values. Here's the code:

import pandas as pd
import matplotlib.pyplot as plt

# Read the CSV (skip the comment line starting with #)
df = pd.read_csv('your_data.csv', comment='#')

# Convert EventTime to datetime type using our format string
df['EventTime'] = pd.to_datetime(df['EventTime'], format='%Y%m%d-%H%M%S.%f-%z')

# Quick check to confirm the conversion worked
print(df[['EventTime', 'Price']].head())

Step 3: Plot the Price vs. Time #

Now that we have properly formatted datetime values, plotting is straightforward with matplotlib:

# Set up the plot size
plt.figure(figsize=(12, 6))
# Plot Price against EventTime with small markers for clarity
plt.plot(df['EventTime'], df['Price'], marker='o', linestyle='-', markersize=3, color='steelblue')

# Add labels and a title to make the plot readable
plt.xlabel('Event Time')
plt.ylabel('Price')
plt.title('AKER Price Over Time')

# Rotate x-axis labels to prevent overlap
plt.xticks(rotation=45)

# Adjust layout so labels don't get cut off
plt.tight_layout()

# Display the plot
plt.show()

Quick Edge Case Tips #

• If timezone handling causes issues, you can convert all timestamps to UTC (or your local timezone) with df['EventTime'] = df['EventTime'].tz_convert('UTC')
• If your CSV has missing values, clean the data first with df.dropna(subset=['EventTime', 'Price']) to avoid plotting errors

内容的提问来源于stack exchange，提问作者Jeremie