%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import math
import pytz
tz = pytz.timezone('America/New_York')
def geodistance(lng1, lat1, lng2, lat2):
lng1, lat1, lng2, lat2 = map(math.radians, [float(lng1), float(lat1), float(lng2), float(lat2)])
dlon = lng2 - lng1
dlat = lat2 - lat1
a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2
distance = 2 * math.asin(math.sqrt(a)) * 6371 * 1000
distance = round(distance / 1000, 3)
return distance
def total_distance(d):
lngs = d['longitude'].values.tolist()
lats = d['latitude'].values.tolist()
total = 0
for i in range(len(lngs) - 1):
lng1 = lngs[i]
lat1 = lats[i]
lng2 = lngs[i + 1]
lat2 = lats[i + 1]
total += geodistance(lng1, lat1, lng2, lat2)
return total
def time_4am(ts):
dt = pytz.datetime.datetime.fromtimestamp(ts, tz)
n = dt.hour * 3600 + dt.minute * 60 + dt.second
return abs(n - 4 * 3600)
def most_location(d):
values = []
lngs = d['longitude'].values.tolist()
lats = d['latitude'].values.tolist()
counts = [1] * len(lngs)
for i in range(len(lngs)):
for j in range(len(lngs)):
if i == j:
continue
if geodistance(lngs[i], lats[i], lngs[j], lats[j]) <= 0.1:
counts[i] += 1
i = counts.index(max(counts))
return lngs[i], lats[i]
def location_near4am(d):
values = []
lngs = d['longitude'].values.tolist()
lats = d['latitude'].values.tolist()
times = d['timestamp'].values.tolist()
for i in range(len(lngs)):
values.append((lngs[i], lats[i], times[i]))
values.sort(key=lambda x:x[2])
return values[0][0], values[0][1]
pd.set_option('display.max_colwidth', 500)
data = pd.read_csv('d:/2016100200_part.csv')
data = data[[ 'device_id', 'latitude', 'longitude', 'timestamp']]
counts = data.groupby('device_id', as_index=False).count()
print('Number of cell phones:')
print(len(counts))
print('Top 200 active users with their location counts:')
result = counts.sort_values(['latitude'], ascending=False)
# print(result.info())
print(result[['device_id', 'longitude']].head(200).to_string(index=False))
data20 = result.loc[(result.longitude > 50) & (result.longitude < 200)].head(20);
for id in data20['device_id']:
print(id)
d = data.loc[data.device_id == id]
d.sort_values('timestamp')
print('Total distance:')
print(total_distance(d))
print('Home locaiton:')
print(location_near4am(d))
print('Most location:')
print(most_location(d))
print()
plt.title(id)
d.plot(x="longitude", y="latitude", kind="scatter")
d.plot(x="longitude", y="latitude", kind="line" , xlim=(-74.025, -73.9), ylim=(40.7, 40.9))
Number of cell phones:
151032
Top 200 active users with their location counts:
device_id longitude
ebfe4ed5130dd5bdfdf7f5d1fb6b977875512e7da3fa9efb190fedd767b8a559 10466
9a47b0c904dcf078fe18fbb977fe86a092c601eb5752115639f4ebeb8c66e859 9811
289d023006092608245ad6bccd7512fb99778fd3316e219091a1b1cb7a09d119 5060
4e005a81c7394afbae96894f1af997d6353e35af5b31795d05e24cc6896870c9 4097
1e70c3e3dc519b99ea23b34fb499bf850e560853c2032a257e8af1ecb58fa677 3309
61033d5156177fd4177bb84c988a7f4cc59a3ef78bc512e80c1908e60c9d953b 3160
765131a0e1bc3028ee6e9e8a664b1ffab475f48a761cbe72076410454ddf8037 3012
cf61b5c95baf3044b8219d3ebeebc4af94ba7db86a359ecd1278a69601866bb4 2828
ad56f264eed42bef641db2fd8680424e9fe55614e9f8433274d885279ff53462 2276
147a9dd32da4c3e09d2782579ad9a5973e9a0d7da93d162bb35de47f5b75ef79 2235
b812fd0667403efcdda53af273c45a88686f67e442c934a8a27c07678b51005d 2184
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58552b1defe8a59bfd5a6cdc886f77da78116d584477c424215856334fab4f6d 1972
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7c33d7bfaaf0e894b62c9180fc78b7fa02a7aa4553e74593d3b661196b8a2819 715
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7b67f17a237e409ce76b31403238673573034cd87304be044cde6f8664f48de6 713
6c63cae9b3937f70ab980b87848d96f2a84fe2c7eb4cdc3b95aa4096678bec8b 713
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09c7f656bdf3668035dea006a48bdf455c2c989e09d5fd2861ed6777167d7807 712
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ae11a9620b5ac2e23c5ae76aa6a3c441dfb19f52c724127570937ae765716eda 701
01e15a45d5f57d18a3171546a3c198eb7beca000f7956526f327a1b772c7ba08 701
8a0592c96b3f77f12919130564c15a07460b5166a4af2d212af915b4ab023ad5 700
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0471b1e0203c2609e25fda179fa0e5fa4261ac317346e412e9893d79f08a5a29 698
35c6570be53d819b2ffce57dcb0cc7500cf6af6b48b3894612e324df29603617 695
73c6a747d756d3914942b5ae6d6507c134ffe66c87582942ce3a20f00cdcbf5a 695
7db7f05013e9e067ee2bf9d288b93e657d1da965e6b6724910bc57d75d1c76ce 695
486b675c3e96ffe9e0737cda7288aa4511676e7db97718bfa4ff14c5f57fc5e6 690
e594d1ad6343c14e16cf7afc6abf3357cddd9ef0985ec788d873a210b1b3a175 690
5e3962be50de420124e0970aaad946b318898125c09ef814b508533cab03de3f 689
b261457ac583d2849153e1cf5b8c70fca45bc1bd92b4dc4945c6ed4f773c85b3 688
bc25731a9e73beab42d44fab80e9b28bff6eafb29277e753825655f1a6b3f925 687
f9db8772519d873be0928423806419b2763e3aa94743ad9467fae55eca9fdfc6 685
6aba83caa52ab02761b1e1a507331e32e39f2d41202b5579d90413978f3fb536 684
5d955f2624763b8e789d0eb1a0af7a8118b424afe5fcb528e91a6974c5418bed 684
d73f609df2becc79cdfb5f18718d015e64a74c681f490fd5dea1036d9f3e009a 683
0595fb8f7a055d0f0b6d3c4ba27c994d89ced784c7d8a7cc9fbcf23746bc5925 683
7233982d86ccce3def19bd6d150975e71a4b0c85e187d61afb7a897e309a1171 683
6eed9bacb3fce73528b8f983f433b05f12830b7556e9ef2be1c8815b7fc37247 682
86a7c0bd114b65961e2829ea5b4e1acc033efeab3bd10380ddc17632d66c2282 681
f9090af049dfdbbedcbabb449337fd9e5790e4e792e052aa9a362feb535c0e9e
Total distance:
21.519000000000005
Home locaiton:
(-73.944016, 40.798355)
Most location:
(-73.94428, 40.79831)
f35e7343009f7c1c34b19abe25f3969f5a17782068fb0e4ba13928f3a2e45469
Total distance:
21.951000000000004
Home locaiton:
(-73.981415, 40.780582)
Most location:
(-73.98142, 40.780605)
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Total distance:
35.60699999999996
Home locaiton:
(-73.95577, 40.77905)
Most location:
(-73.95591, 40.779568)
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Total distance:
20.862000000000002
Home locaiton:
(-73.936844, 40.804165000000005)
Most location:
(-73.95486, 40.80514)
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Total distance:
24.275000000000006
Home locaiton:
(-73.96891, 40.7904)
Most location:
(-73.96863, 40.790596)
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Total distance:
10.179000000000002
Home locaiton:
(-73.98846400000001, 40.75869)
Most location:
(-73.98865, 40.758484)
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Total distance:
4.694999999999994
Home locaiton:
(-73.98814, 40.721413)
Most location:
(-73.98731, 40.72185)
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Total distance:
76.55500000000005
Home locaiton:
(-74.00014, 40.732665999999995)
Most location:
(-74.00816, 40.714962)
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Total distance:
210.19699999999975
Home locaiton:
(-73.981445, 40.766068)
Most location:
(-73.981445, 40.766068)
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Total distance:
246.84100000000004
Home locaiton:
(-73.99802, 40.75588)
Most location:
(-73.97561, 40.750923)
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Total distance:
63.553999999999995
Home locaiton:
(-73.94573000000001, 40.82177)
Most location:
(-73.9446, 40.819373999999996)
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Total distance:
7.441000000000001
Home locaiton:
(-73.97606999999999, 40.72018)
Most location:
(-73.97606999999999, 40.72018)
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Total distance:
22.081000000000003
Home locaiton:
(-74.00168599999999, 40.717403000000004)
Most location:
(-74.001274, 40.71777)
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Total distance:
23.066999999999986
Home locaiton:
(-73.960365, 40.800373)
Most location:
(-73.941, 40.792103000000004)
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Total distance:
147.91200000000015
Home locaiton:
(-73.9544, 40.779083)
Most location:
(-73.94575999999999, 40.828133)
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Total distance:
54.27599999999998
Home locaiton:
(-73.97414, 40.746128000000006)
Most location:
(-73.97403, 40.78116)
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Total distance:
72.93499999999993
Home locaiton:
(-73.988556, 40.758396000000005)
Most location:
(-73.99296600000001, 40.743120000000005)
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Total distance:
115.94800000000002
Home locaiton:
(-73.99517, 40.753777)
Most location:
(-73.988556, 40.721333)
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Total distance:
0.6020000000000004
Home locaiton:
(-73.99557, 40.72497)
Most location:
(-73.99561, 40.724968)
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Total distance:
218.0840000000002
Home locaiton:
(-73.942825, 40.81908)
Most location:
(-73.94001999999999, 40.810513)
In [ ]: