数据以纪元纳秒为单位，如下例所示：
time duration
1.530706769E+018 40000000000
1.53070683E+018 41000000000
1.530706891E+018 41000000000
1.530706951E+018 41000000000
1.530707011E+018 41000000000
1.530707068E+018 37000000000
1.530707128E+018 41000000000
1.530707188E+018 41000000000
1.530707248E+018 40000000000
1.530707313E+018 45000000000
1.530707368E+018 40000000000
1.530707432E+018 44000000000
1.530707492E+018 44000000000
1.530707552E+018 44000000000
1.530707612E+018 39000000000
1.530707673E+018 41000000000
1.530707729E+018 37000000000
1.530712952E+018 45000000000
1.530713007E+018 40000000000
1.530713067E+018 35000000000
1.530713132E+018 40000000000
1.530713187E+018 36000000000
1.530713248E+018 36000000000
1.530713308E+018 40000000000
1.530713369E+018 41000000000
1.530713429E+018 40000000000
1.53071349E+018 41000000000
1.530713551E+018 42000000000
1.530713611E+018 40000000000
1.530713671E+018 40000000000
1.530713732E+018 40000000000
1.530713792E+018 39000000000
1.530713852E+018 39000000000
1.530713913E+018 40000000000
1.530713973E+018 40000000000
1.530714029E+018 36000000000
1.53071409E+018 36000000000
1.530714151E+018 41000000000
1.530714212E+018 41000000000
1.530714272E+018 41000000000
1.530714332E+018 40000000000
1.530714393E+018 41000000000
1.530714454E+018 41000000000
1.530714515E+018 42000000000
1.530714574E+018 39000000000
1.53071463E+018 39000000000
1.530714694E+018 44000000000
1.530714753E+018 40000000000
1.530714814E+018 41000000000
1.530714874E+018 42000000000
1.530714934E+018 41000000000
1.530714995E+018 41000000000
1.530715056E+018 41000000000
1.530715112E+018 37000000000
1.530715245E+018 16000000000
1.530715275E+018 15000000000
1.530715305E+018 15000000000
1.530715335E+018 15000000000
1.530715366E+018 16000000000
1.530715396E+018 15000000000
1.530715427E+018 16000000000
1.530715456E+018 15000000000
1.530715487E+018 16000000000
1.530715517E+018 15000000000
1.530715548E+018 16000000000
1.530715578E+018 15000000000
要进行转换，我在熊猫中使用以下代码：
import pandas as pd
import matplotlib.pyplot as plt
import datetime from datetime
df = pd.read_csv('d1.csv')
df['time']=pd.to_datetime(df['time'], unit='ns')
df['Time'] = df['time'].dt.time
以上代码将数据转换为以下形式：
df.head(20)
Out[100]:
time d Time
0 2018-07-04 12:19:29 4.000000e+10 12:19:29
1 2018-07-04 12:20:30 4.100000e+10 12:20:30
2 2018-07-04 12:21:31 4.100000e+10 12:21:31
3 2018-07-04 12:22:31 4.100000e+10 12:22:31
4 2018-07-04 12:23:31 4.100000e+10 12:23:31
5 2018-07-04 12:24:28 3.700000e+10 12:24:28
6 2018-07-04 12:25:28 4.100000e+10 12:25:28
7 2018-07-04 12:26:28 4.100000e+10 12:26:28
8 2018-07-04 12:27:28 4.000000e+10 12:27:28
9 2018-07-04 12:28:33 4.500000e+10 12:28:33
10 2018-07-04 12:29:28 4.000000e+10 12:29:28
11 2018-07-04 12:30:32 4.400000e+10 12:30:32
12 2018-07-04 12:31:32 4.400000e+10 12:31:32
13 2018-07-04 12:32:32 4.400000e+10 12:32:32
14 2018-07-04 12:33:32 3.900000e+10 12:33:32
15 2018-07-04 12:34:33 4.100000e+10 12:34:33
16 2018-07-04 12:35:29 3.700000e+10 12:35:29
17 2018-07-04 14:02:32 4.500000e+10 14:02:32
18 2018-07-04 14:03:27 4.000000e+10 14:03:27
19 2018-07-04 14:04:27 3.500000e+10 14:04:27
现在我需要一个Time和d之间的散点图
所以我编码为：
x = df['Time']
y = df['d']
df.plot(x,y, 'scatter')
但是出现错误：
KeyError: '[datetime.time(12, 19, 29) datetime.time(12, 20, 30)\n datetime.time(12, 21, 31) ... datetime.time(23, 54, 39)\n datetime.time(23, 55, 16) datetime.time(23, 55, 52)] not in index'
此错误是什么意思...如何解决此问题并在此处获取散点图？
答案 0 :(得分：1)
将enter code here设置为索引并绘制
time答案 1 :(得分：1)
您可以使用此：
GetDomainByName答案 2 :(得分：0)
pandas.DataFrame.plot.scatter — pandas 0.23.4 documentation：
x：int或str
用作每个点的水平坐标的列名或列位置。
y：int或str
用作每个点的垂直坐标的列名或列位置。
所以应该是
df.plot('Time','d','scatter')
答案 3 :(得分：0)
将matplotlib.pyplot导入为plt
plt.plot（df ['Time']，df ['d']）