Python 如何对列进行裁剪和匹配，得到常用的时间值和对应的值

在下面的答案中，我已经使用numpy将Scope 2数据内插到Scope 1时间轴上.我还包括了将结果可视化的绘图.

进口和数据

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt

#The data
scope1Data = pd.DataFrame({
'TIME': [-1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2,
         -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
'V': [-0.841470985, -0.78332691, -0.717356091, -0.644217687, -0.564642473,
      -0.479425539, -0.389418342, -0.295520207, -0.198669331, -0.099833417,
      0, 0.099833417, 0.198669331, 0.295520207, 0.389418342, 0.479425539,
      0.564642473, 0.644217687, 0.717356091, 0.78332691, 0.841470985],
})

scope2Data = pd.DataFrame({
'TIME': [-1.05, -0.9, -0.75, -0.6, -0.45, -0.3, -0.15, 0, 0.15,
         0.3, 0.45, 0.6, 0.75, 0.9, 1.05],
'I': [-0.887362369, -0.946300088, -0.983985947, -0.999573603, -0.992712991,
      -0.963558185, -0.91276394, -0.841470985, -0.751280405, -0.644217687,
      -0.522687229, -0.389418342, -0.247403959, -0.099833417, 0.049979169]
})

内插，并将结果放入新的数据帧中.

#interpolate scope2 data onto the time axis of scope1
scope2_interpolated = np.interp(x=scope1Data['TIME'],
                                xp=scope2Data['TIME'],
                                fp=scope2Data['I'])
#Put it in a dataframe
scope2_interpolated = pd.DataFrame({
    'TIME': scope1Data['TIME'],
    'I_scope2_interp': scope2_interpolated,
     })

#new dataframe that has both scope1 and the interpolated scope2
combined_data = pd.merge(scope1Data, scope2_interpolated, on='TIME')

#Optionally rename columns
combined_data = combined_data.rename(columns={'TIME': 'TIME_scope1', 'V': 'V_scope1'})

将结果可视化

#As a sanity check, visualise the results.
#Interpolated should overlay with the original scope2 data
f, ax1 = plt.subplots(figsize=(7, 3))
ax1.plot(combined_data.TIME_scope1, combined_data.V_scope1,
         linewidth=2, label='V [scope1]')
ax1.set_xlabel('time (ms)')
ax1.set_ylabel('V (mV)')
ax1.legend()

ax1.grid(axis='x', which='both')
ax1.set_title('Original and interpolated scope data')

ax2 = ax1.twinx()
ax2.plot(scope2Data.TIME, scope2Data.I, label='original I [scope2]',
         color='tab:red', marker='d', linestyle='')
ax2.plot(combined_data.TIME_scope1, combined_data.I_scope2_interp,
         color='tab:red', linestyle=':', label='interpolated I [scope2]')
ax2.set_ylabel('I (mA)')
ax2.legend(loc='lower right')

#Rather than trimming the data, you can trim the
# plot limits if you only want to see a smaller range
x_limits = [-1.2, 1.2]
ax1.set_xlim(x_limits);