In [ ]:
In [ ]:
In [ ]:
%reset
In [1]:
%matplotlib inline
In [23]:
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import csv
import math
import matplotlib.pyplot as plt
from scipy.signal import hilbert, chirp
import scipy
import networkx as nx
from pandas.tools.plotting import parallel_coordinates
In [3]:
data = pd.read_table('D:\\zzzLola\\PhD\\DataSet\\US101\\test\\small.txt', sep='\t', header=None)
In [4]:
data[:10]
Out[4]:
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1073
3211
587
1118847300000
20.097
1999.367
6452595.286
1872026.290
12.5
5
2
59.92
-1.71
2
0
1083
0
0
1
1073
3212
587
1118847300100
20.025
2005.368
6452600.004
1872022.536
12.5
5
2
59.60
-4.46
2
0
1083
0
0
2
1073
3213
587
1118847300200
19.973
2011.314
6452604.649
1872018.817
12.5
5
2
59.14
-5.47
2
0
1083
0
0
3
1073
3214
587
1118847300300
19.886
2017.183
6452609.253
1872015.175
12.5
5
2
58.78
-2.54
2
0
1083
0
0
4
1073
3215
587
1118847300400
19.817
2023.016
6452613.792
1872011.563
12.5
5
2
58.78
2.52
2
0
1083
0
0
5
1073
3216
587
1118847300500
19.731
2028.885
6452618.396
1872007.921
12.5
5
2
59.14
5.47
2
0
1083
0
0
6
1073
3217
587
1118847300600
19.678
2034.831
6452623.041
1872004.202
12.5
5
2
59.60
4.46
2
0
1083
0
0
7
1073
3218
587
1118847300700
19.606
2040.831
6452627.759
1872000.448
12.5
5
2
59.91
1.71
2
0
1083
0
0
8
1073
3219
587
1118847300800
19.535
2046.858
6452632.453
1871996.712
12.5
5
2
59.90
-2.03
2
0
1083
0
0
9
1073
3220
587
1118847300900
19.464
2052.849
6452637.148
1871992.976
12.5
5
2
59.60
-3.95
2
0
1083
0
0
In [5]:
data.groupby([3])
Out[5]:
<pandas.core.groupby.DataFrameGroupBy object at 0x0000000006D032E8>
In [31]:
for i, group in data.groupby([3]):
print group
0 1 2 3 4 5 6 \
0 1073 3211 587 1118847300000 20.097 1999.367 6452595.286
22 1077 3211 637 1118847300000 7.393 2086.090 6452670.807
7 8 9 10 11 12 13 14 15 16 17
0 1872026.290 12.5 5.0 2 59.92 -1.71 2 0 1083 0 0
22 1871981.512 13.0 5.9 2 64.98 -0.04 1 0 1082 0 0
0 1 2 3 4 5 6 \
1 1073 3212 587 1118847300100 20.025 2005.368 6452600.004
23 1077 3212 637 1118847300100 7.393 2092.585 6452675.893
7 8 9 10 11 12 13 14 15 16 17
1 1872022.536 12.5 5.0 2 59.6 -4.46 2 0 1083 0 0
23 1871977.465 13.0 5.9 2 65.0 0.46 1 0 1082 0 0
0 1 2 3 4 5 6 \
2 1073 3213 587 1118847300200 19.973 2011.314 6452604.649
24 1077 3213 637 1118847300200 7.392 2099.058 6452680.957
7 8 9 10 11 12 13 14 15 16 17
2 1872018.817 12.5 5.0 2 59.14 -5.47 2 0 1083 0 0
24 1871973.436 13.0 5.9 2 65.30 4.71 1 0 1082 0 0
0 1 2 3 4 5 6 \
3 1073 3214 587 1118847300300 19.886 2017.183 6452609.253
25 1077 3214 637 1118847300300 7.375 2105.593 6452686.092
7 8 9 10 11 12 13 14 15 16 17
3 1872015.175 12.5 5.0 2 58.78 -2.54 2 0 1083 0 0
25 1871969.370 13.0 5.9 2 65.93 8.08 1 0 1082 0 0
0 1 2 3 4 5 6 \
4 1073 3215 587 1118847300400 19.817 2023.016 6452613.792
26 1077 3215 637 1118847300400 7.387 2112.099 6452691.152
7 8 9 10 11 12 13 14 15 16 17
4 1872011.563 12.5 5.0 2 58.78 2.52 2 0 1083 0 0
26 1871965.322 13.0 5.9 2 65.93 0.00 1 0 1082 0 0
0 1 2 3 4 5 6 \
5 1073 3216 587 1118847300500 19.731 2028.885 6452618.396
27 1077 3216 637 1118847300500 7.381 2119.098 6452696.629
7 8 9 10 11 12 13 14 15 16 17
5 1872007.921 12.5 5.0 2 59.14 5.47 2 0 1083 0 0
27 1871960.964 13.0 5.9 2 65.93 0.00 1 0 1082 0 0
0 1 2 3 4 5 6 \
6 1073 3217 587 1118847300600 19.678 2034.831 6452623.041
28 1077 3217 637 1118847300600 7.377 2125.598 6452701.715
7 8 9 10 11 12 13 14 15 16 17
6 1872004.202 12.5 5.0 2 59.60 4.46 2 0 1083 0 0
28 1871956.916 13.0 5.9 2 65.93 0.00 1 0 1082 0 0
0 1 2 3 4 5 6 7 \
7 1073 3218 587 1118847300700 19.606 2040.831 6452627.759 1872000.448
8 9 10 11 12 13 14 15 16 17
7 12.5 5 2 59.91 1.71 2 0 1083 0 0
0 1 2 3 4 5 6 7 \
8 1073 3219 587 1118847300800 19.535 2046.858 6452632.453 1871996.712
8 9 10 11 12 13 14 15 16 17
8 12.5 5 2 59.9 -2.03 2 0 1083 0 0
0 1 2 3 4 5 6 7 \
9 1073 3220 587 1118847300900 19.464 2052.849 6452637.148 1871992.976
8 9 10 11 12 13 14 15 16 17
9 12.5 5 2 59.6 -3.95 2 0 1083 0 0
0 1 2 3 4 5 6 7 \
10 1073 3221 587 1118847301000 19.393 2058.777 6452641.843 1871989.24
8 9 10 11 12 13 14 15 16 17
10 12.5 5 2 59.39 -1.76 2 0 1083 0 0
0 1 2 3 4 5 6 \
11 1073 3222 587 1118847301100 19.383 2064.687 6452646.538
7 8 9 10 11 12 13 14 15 16 17
11 1871985.504 12.5 5 2 59.39 1.79 2 0 1083 0 0
0 1 2 3 4 5 6 \
12 1073 3223 587 1118847301200 19.382 2070.617 6452651.233
7 8 9 10 11 12 13 14 15 16 17
12 1871981.768 12.5 5 2 59.59 3.72 2 0 1083 0 0
0 1 2 3 4 5 6 \
13 1073 3224 587 1118847301300 19.383 2076.605 6452655.927
7 8 9 10 11 12 13 14 15 16 17
13 1871978.031 12.5 5 2 59.88 1.98 2 0 1083 0 0
0 1 2 3 4 5 6 \
14 1073 3225 587 1118847301400 19.383 2082.618 6452660.622
7 8 9 10 11 12 13 14 15 16 17
14 1871974.295 12.5 5 2 60 0 2 0 1083 0 0
0 1 2 3 4 5 6 \
15 1073 3226 587 1118847301500 19.382 2088.617 6452665.317
7 8 9 10 11 12 13 14 15 16 17
15 1871970.559 12.5 5 2 60.01 0.12 2 0 1083 0 0
0 1 2 3 4 5 6 \
16 1073 3227 587 1118847301600 19.382 2094.618 6452670.012
7 8 9 10 11 12 13 14 15 16 17
16 1871966.823 12.5 5 2 60.02 0.22 2 0 1083 0 0
0 1 2 3 4 5 6 \
17 1073 3228 587 1118847301700 19.381 2100.622 6452674.707
7 8 9 10 11 12 13 14 15 16 17
17 1871963.087 12.5 5 2 60.04 0.1 2 0 1083 0 0
0 1 2 3 4 5 6 7 \
18 1073 3229 587 1118847301800 19.38 2106.627 6452679.402 1871959.351
8 9 10 11 12 13 14 15 16 17
18 12.5 5 2 60.04 -0.1 2 0 1083 0 0
0 1 2 3 4 5 6 \
19 1073 3230 587 1118847301900 19.376 2112.631 6452684.096
7 8 9 10 11 12 13 14 15 16 17
19 1871955.615 12.5 5 2 60.04 0 2 0 1083 0 0
0 1 2 3 4 5 6 \
20 1073 3231 587 1118847302000 19.372 2118.632 6452688.791
7 8 9 10 11 12 13 14 15 16 17
20 1871951.878 12.5 5 2 60.04 0 2 0 1083 0 0
0 1 2 3 4 5 6 \
21 1073 3232 587 1118847302100 19.367 2124.632 6452693.486
7 8 9 10 11 12 13 14 15 16 17
21 1871948.142 12.5 5 2 60.04 0 2 0 1083 0 0
In [22]:
for i, group in data.groupby([3]):
print i
print group[0]+group[1]
1118847300000
0 4284
22 4288
dtype: int64
1118847300100
1 4285
23 4289
dtype: int64
1118847300200
2 4286
24 4290
dtype: int64
1118847300300
3 4287
25 4291
dtype: int64
1118847300400
4 4288
26 4292
dtype: int64
1118847300500
5 4289
27 4293
dtype: int64
1118847300600
6 4290
28 4294
dtype: int64
1118847300700
7 4291
dtype: int64
1118847300800
8 4292
dtype: int64
1118847300900
9 4293
dtype: int64
1118847301000
10 4294
dtype: int64
1118847301100
11 4295
dtype: int64
1118847301200
12 4296
dtype: int64
1118847301300
13 4297
dtype: int64
1118847301400
14 4298
dtype: int64
1118847301500
15 4299
dtype: int64
1118847301600
16 4300
dtype: int64
1118847301700
17 4301
dtype: int64
1118847301800
18 4302
dtype: int64
1118847301900
19 4303
dtype: int64
1118847302000
20 4304
dtype: int64
1118847302100
21 4305
dtype: int64
In [29]:
parallel_coordinates(data,0)
Out[29]:
<matplotlib.axes._subplots.AxesSubplot at 0xb33d5f8>
In [ ]:
Content source: lalonica/PhD
Similar notebooks: