我想将DataFrame重新采样到每五秒钟,其中原始数据的时间戳是不规则的。抱歉,如果这看起来像一个重复的问题,但我有插值排列到数据时间戳的问题,这就是我在这个问题中包含我的DataFrame的原因。 The graph in this answer显示了我想要的结果,但我不能使用那里建议的 Altitude Time
1 0.00 0.00000
2 1000.00 16.45350
3 2000.00 33.19584
4 3000.00 50.25330
5 4000.00 67.64580
6 5000.00 85.38720
7 6000.00 103.56720
8 7000.00 122.29260
9 8000.00 141.61440
10 9000.00 161.59140
11 9999.67 182.27940
12 10000.30 182.33940
13 10000.30 199.76880
14 10000.30 199.82880
15 11000.00 221.67660
16 12000.00 244.36260
17 13000.00 267.93900
18 14000.00 292.46940
19 15000.00 318.01080
20 16000.00 344.36820
21 17000.00 371.32200
22 18000.00 398.91420
23 19000.00 427.19100
24 20000.00 456.24900
25 21000.00 486.38940
26 22000.00 517.91640
27 23000.00 550.96140
28 24000.00 585.65460
29 25000.00 622.12800
30 26000.00 660.35400
31 27000.00 700.37400
32 28000.00 742.39200
33 29000.00 786.57600
34 30000.00 833.13000
35 31000.00 882.09000
36 32000.00 933.46200
37 33000.00 987.40800
38 34000.00 1044.06000
39 35000.00 1103.85000
40 36000.00 1167.52200
41 36088.90 1173.39000
42 36089.60 1173.45000
43 36671.70 1216.60200
44 36672.40 1216.66200
45 38000.00 1295.80200
46 39000.00 1368.45000
47 40000.00 1458.00000
48 41000.00 1574.08200
49 42000.00 1730.97000
50 42231.00 1775.19600
包。我使用df = df.set_index(pd.to_datetime(df['Time'], unit='s'), drop=False)
resample_index = pd.date_range(start=df.index[0], end=df.index[-1], freq='5s')
dummy_frame = pd.DataFrame(np.NaN, index=resample_index, columns=df.columns)
df.combine_first(dummy_frame).interpolate().iloc[:6]
Time Altitude
1970-01-01 00:00:00.000000 0.000000 0.0
1970-01-01 00:00:05.000000 4.113375 250.0
1970-01-01 00:00:10.000000 8.226750 500.0
1970-01-01 00:00:15.000000 12.340125 750.0
1970-01-01 00:00:16.453500 16.453500 1000.0
1970-01-01 00:00:20.000000 20.639085 1250.0
。
考虑以下飞机的爬升路径(as dict on pastebin):
df.resample('1s').interpolate(method='linear').resample('5s').asfreq()
Time Altitude
1970-01-01 00:00:00 0.0 0.000000
1970-01-01 00:00:05 5.0 137.174211
1970-01-01 00:00:10 10.0 274.348422
1970-01-01 00:00:15 15.0 411.522634
1970-01-01 00:00:20 20.0 548.696845
1970-01-01 00:00:25 25.0 685.871056
1970-01-01 00:00:30 30.0 823.045267
1970-01-01 00:00:35 35.0 960.219479
1970-01-01 00:00:40 40.0 1097.393690
1970-01-01 00:00:45 45.0 1234.567901
1970-01-01 00:00:50 50.0 1371.742112
1970-01-01 00:00:55 55.0 1508.916324
1970-01-01 00:01:00 60.0 1646.090535
1970-01-01 00:01:05 65.0 1783.264746
1970-01-01 00:01:10 70.0 1920.438957
1970-01-01 00:01:15 75.0 2057.613169
1970-01-01 00:01:20 80.0 2194.787380
1970-01-01 00:01:25 85.0 2331.961591
1970-01-01 00:01:30 90.0 2469.135802
1970-01-01 00:01:35 95.0 2606.310014
1970-01-01 00:01:40 100.0 2743.484225
1970-01-01 00:01:45 105.0 2880.658436
1970-01-01 00:01:50 110.0 3017.832647
1970-01-01 00:01:55 115.0 3155.006859
1970-01-01 00:02:00 120.0 3292.181070
1970-01-01 00:02:05 125.0 3429.355281
1970-01-01 00:02:10 130.0 3566.529492
1970-01-01 00:02:15 135.0 3703.703704
1970-01-01 00:02:20 140.0 3840.877915
1970-01-01 00:02:25 145.0 3978.052126
... ... ...
1970-01-01 00:27:10 1458.0 40000.000000
1970-01-01 00:27:15 1458.0 40000.000000
1970-01-01 00:27:20 1458.0 40000.000000
1970-01-01 00:27:25 1458.0 40000.000000
1970-01-01 00:27:30 1458.0 40000.000000
1970-01-01 00:27:35 1458.0 40000.000000
1970-01-01 00:27:40 1458.0 40000.000000
1970-01-01 00:27:45 1458.0 40000.000000
1970-01-01 00:27:50 1458.0 40000.000000
1970-01-01 00:27:55 1458.0 40000.000000
1970-01-01 00:28:00 1458.0 40000.000000
1970-01-01 00:28:05 1458.0 40000.000000
1970-01-01 00:28:10 1458.0 40000.000000
1970-01-01 00:28:15 1458.0 40000.000000
1970-01-01 00:28:20 1458.0 40000.000000
1970-01-01 00:28:25 1458.0 40000.000000
1970-01-01 00:28:30 1458.0 40000.000000
1970-01-01 00:28:35 1458.0 40000.000000
1970-01-01 00:28:40 1458.0 40000.000000
1970-01-01 00:28:45 1458.0 40000.000000
1970-01-01 00:28:50 1458.0 40000.000000
1970-01-01 00:28:55 1458.0 40000.000000
1970-01-01 00:29:00 1458.0 40000.000000
1970-01-01 00:29:05 1458.0 40000.000000
1970-01-01 00:29:10 1458.0 40000.000000
1970-01-01 00:29:15 1458.0 40000.000000
1970-01-01 00:29:20 1458.0 40000.000000
1970-01-01 00:29:25 1458.0 40000.000000
1970-01-01 00:29:30 1458.0 40000.000000
1970-01-01 00:29:35 1458.0 40000.000000
首先,我已经尝试重新取样,同时保持原始索引不变,如this question所示,所以我可以线性插值,但我发现没有插值方法可以产生正确的结果(注意仅在16.45s匹配的原始时间列):
$(document).ready(function() {
var voices = window.speechSynthesis.getVoices();
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第二,我尝试重新取样而不保留原始索引,首先降至1秒,然后升至5秒,如this answer所示,但插值值不在数据结束,高度值也不是(1000英尺应该在15到20秒之间)。刚刚重新采样到1s已经产生了错误的结果。
<Directory /var/www/html>
Options Indexes FollowSymLinks MultiViews
AllowOverride All
Order allow,deny
allow from all
</Directory>
如何在执行正确插值的同时将原始数据重采样为5s?我只是使用错误的插值方法吗?
答案 0 :(得分:2)
在@Martin Schmelzer的帮助下(谢谢!)我在将time
用作pandas插值方法的method
参数时,找到了问题中第一个建议的方法:
resample_index = pd.date_range(start=df.index[0], end=df.index[-1], freq='5s')
dummy_frame = pd.DataFrame(np.NaN, index=resample_index, columns=df.columns)
df.combine_first(dummy_frame).interpolate('time').iloc[:6]
Altitude Time
1970-01-01 00:00:00.000000 0.000000 0.0000
1970-01-01 00:00:05.000000 303.886711 5.0000
1970-01-01 00:00:10.000000 607.773422 10.0000
1970-01-01 00:00:15.000000 911.660133 15.0000
1970-01-01 00:00:16.453500 1000.000000 16.4535
1970-01-01 00:00:20.000000 1211.828215 20.0000
然后,我可以将其重新取样为5秒或其他,结果是准确的。
df.combine_first(dummy_frame).interpolate('time').resample('5s').asfreq().head()
Altitude Time
1970-01-01 00:00:00 0.000000 0.0
1970-01-01 00:00:05 303.886711 5.0
1970-01-01 00:00:10 607.773422 10.0
1970-01-01 00:00:15 911.660133 15.0
1970-01-01 00:00:20 1211.828215 20.0
所以最后我发现我只是使用了错误的插值方法。
答案 1 :(得分:0)
我发现这个问题出奇的困难。特别是如果date_range()不容易定义内插值集。有很多陷阱:
此代码对我有用:
const { circularProperty, ...rest } = item;
$.extend(true, {}, rest);
import pandas as pd
import numpy as np
def interpolate_into(df, interpolate_keys, index_name, columns):
# Downselect to only those columns necessary
# Also, remove duplicated values in the data frame. Eye roll.
df = df[[index_name] + columns]
df = df.drop_duplicates(subset=[index_name], keep="first")
df = df.set_index(index_name)
# Only interpolate into values that don't already exist. This is not handled manually.
needed_interpolate_keys = [i for i in interpolate_keys if i not in df.index]
# Create a dummy DF that has the x or time values we want to interpolate into.
dummy_frame = pd.DataFrame(np.NaN, index=needed_interpolate_keys, columns=df.columns)
dummy_frame[index_name] = pd.to_datetime(needed_interpolate_keys)
dummy_frame = dummy_frame.set_index(index_name)
# Combine the dataframes, sort, interpolate, downselect.
df = dummy_frame.combine_first(df)
df = df.sort_values(by=index_name, ascending=True)
df = df.interpolate()
df = df[df.index.isin(interpolate_keys)]
return df
是原始数据帧。
df
是要为其内插新值的“ x”值的列表。
interpolated_keys
是这些键的列的名称
index_name
是要为其插值的其他列。