plotting both x and y axis in log scale in pandas

Everything is fine when I plot only y axis in log scale.

import pandas as pd
import numpy
import matplotlib.pyplot as plt

iplevel = pd.read_csv('iplevel.csv')
fig = plt.figure()
#plt.xscale('log')
plt.yscale('log')
plt.title(' Size Vs Duration (at IP level) for ')

plt.xlabel('Duration (in seconds)')
plt.ylabel('Size (in bytes)')
plt.scatter(iplevel['Time'], iplevel['Length'])
fig.tight_layout()
fig.savefig('iplevel_timevdur.png', dpi=fig.dpi)

Something strange happens when I plot both x and y axis in log scale.

iplevel = pd.read_csv('iplevel.csv')
fig = plt.figure()
plt.xscale('log')
plt.yscale('log')
plt.title(' Size Vs Duration (at IP level) for ')

plt.xlabel('Duration (in seconds)')
plt.ylabel('Size (in bytes)')
plt.scatter(iplevel['Time'], iplevel['Length'])
fig.tight_layout()
fig.savefig('iplevel_timevdur.png', dpi=fig.dpi)

I am not sure where I am going wrong. Any ideas/suggestions welcome

My trace file can be downloaded from here. Here are the top 1800 of 2715 rows sorted by 'Time'.

Length,Time
64,0.0
64,0.0
64,0.0
863,0.0
259,0.0
64,0.0
64,0.0
78,0.0
64,0.0
64,0.0
64,0.0
86,0.0
64,0.0
102,0.0
64,0.0
140,0.0
90,0.0
140,0.0
152,0.0
260,0.0
64,0.0
1419,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
140,0.0
64,0.0
140,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
634,0.0
64,0.0
64,0.0
64,0.0
64,0.0
78,0.0
64,0.0
1241,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
66,0.0
64,0.0
64,0.0
64,0.0
66,0.0
66,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
78,0.0
66,0.0
66,0.0
66,0.0
64,0.0
66,0.0
66,0.0
64,0.0
64,0.0
64,0.0
140,0.0
140,0.0
140,0.0
66,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
70,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
64,0.0
70,0.0
64,0.0
64,0.0
64,0.0
70,0.0
64,0.0
82,0.0
70,0.0
122,0.0
70,0.0
174,0.0
64,0.0
911,0.0
64,0.0
64,0.0
64,0.0
128,0.0
129,0.0
64,0.0
64,0.0
140,0.0
140,0.0
64,0.0
94,0.0
1518,0.0
64,0.0
64,0.0
68,0.0
127,0.0
70,0.0
86,0.0
66,0.0
66,0.0
64,0.0
66,0.0
66,0.0
66,0.0
64,0.0
70,0.0
86,0.0
64,0.0
66,0.0
64,0.0
64,0.0
64,0.0
64,0.0
140,0.0
1045,0.0
64,0.0
70,0.0
64,0.0
64,0.0
70,0.0
64,0.0
140,0.0
66,0.0
70,0.0
140,0.0
64,0.0
64,0.0
171,0.0
128,4.00000000411e-06
128,4.99999999803e-06
128,8.00000000112e-06
128,2.29999999988e-05
448,3.00000000002e-05
140,4.40000000026e-05
256,7.19999999959e-05
384,0.000106999999986
320,0.000121000000007
384,0.000253999999984
128,0.000342
204,0.000381000000004
140,0.000453
204,0.000878999999998
358,0.001082
448,0.001192
315,0.00134000000003
448,0.00136600000002
280,0.001679
128,0.001698
635,0.001723
324,0.00185
174,0.00224799999998
128,0.002389
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128,0.00279799999999
635,0.00283899999997
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2917,0.00290999999999
128,0.002927
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198,0.00299800000001
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128,0.00318299999998
128,0.003261
391,0.00328500000001
128,0.003311
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128,0.003875
634,0.00393600000001
128,0.00412900000001
128,0.00423000000001
642,0.00425000000001
128,0.00425200000001
128,0.00436499999995
128,0.00437899999999
128,0.00458800000001
128,0.00463000000002
128,0.00491100000001
128,0.00500199999999
724,0.005042
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128,0.00520300000005
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146,0.00525199999999
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128,0.0054
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1128,0.00558500000001
128,0.00563299999999
128,0.0057
128,0.00574699999999
1454,0.00586200000001
2202,0.00588400000001
128,0.00593999999995
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128,0.00613000000001
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128,0.00663800000001
420,0.006648
128,0.00665499999999
2917,0.00671500000001
889,0.00673999999998
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146,0.00725600000004
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128,0.00841500000001
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128,0.00867199999999
128,0.00873899999999
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128,0.016338
128,0.018201
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340,0.019342
174,0.019462
128,0.019505
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248,0.020937
128,0.021647
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128,0.022295
637,0.022531
128,0.024646
555,0.025917
194,0.029173
637,0.030238
1096,0.033326
1186,0.033387
1416,0.039525
1282,0.04019
37292,0.042872
1920,0.050076
198,0.05401
2613,0.0541250000001
5408,0.058094
194,0.062085
1163,0.063978
4145,0.064394
401,0.065523
256,0.069589
194,0.071795
174,0.073762
1835,0.07433
1763,0.074372
5808,0.07657
1198,0.078694
192,0.079372
740,0.079504
1017,0.083836
357,0.084731
1311,0.084966
646,0.085513
382,0.085933
355,0.089751
356,0.092304
354,0.095439
375,0.095455
64438,0.097073
356,0.098272
1085,0.104543
1707,0.106295
128,0.107193
128,0.116315
128,0.11694
256,0.117687
194,0.120159
128,0.121077
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421,0.124742
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