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pandas 数据分析展示

2020-02-06 10:35:54  阅读:229  来源: 互联网

标签:数据分析 distance 展示 locaiton Most location Home Total pandas


%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
 d1a42dd42384d79786969a964ed4d79ddbd73c7996b38e1aaba530482677656f       2021
 58552b1defe8a59bfd5a6cdc886f77da78116d584477c424215856334fab4f6d       1972
 351171f8856be01e1a08a154e4e797070c21632a53a1692749df39ae2e3e12e1       1885
 d196c74da3838c80f6dbb5aac93581eae24404980e66488780dbbd47c2aa1fc6       1786
 de8fdc96c10b4c4ad108af613556baf2e4f735d08a87eb91a0c01d1328ef353b       1708
 c6e084452f38daeeeadaf3461accfe7aed374aef20685e4cc14deeac596946ab       1581
 fa07ac4156b257b5137e1030ec08d2e1b89bb9e59e1224680e60d3d5d76231f9       1461
 678d6cbe48a7cee15adb9655435a76a33a01a0fa45a55543f15d28466c7edef2       1406
 0ef45caf5971d1891bead27a9e72d0541dcdb5bfecb3867401e1927b3850d019       1382
 1dfa2685048e6f0fe2626618325f984aeef8fac9a88a0fa4aa8afd2ac1def49a       1377
 1795f9e9d5249f6772f315b005a8f0ea49c306f6b0643c05937a21bab8fcca77       1364
 6ae19d86e9b5b130da7899d68fcb74577ea1fb705c3c93ded9c0528144f49099       1315
 f7497dbc6997480e5f561705dda27d8dbbdb5e330a8aa3f59ffd0dd5ad730eed       1299
 7f628d508d6243f8724d52f4810c0dcd0532a7a50d5a1fed58664168a351605f       1294
 7c305b70f7a9216e3cc795a87b009bddf2d5c981e5f68c2a407dda9f889f6244       1293
 3c37306f6136e75da5a533e68f7479fa940084a437fdd30efcfae1b67f7c5048       1288
 50ddd120b19f287940f0c608969890bad43c6b7f82a626af1662b9d7a15a5027       1286
 32cae7e50bc12d89b0bc5f54a4b1cf37957c72bc28f14e9e00f783a4ed16cd00       1274
 d1f43c194a73bf5c789cb9ea9206785a98118a8cabf4df58fc36888d3fd11913       1248
 6d83e7a97a8a512fccddb44e8309cfbab34d6b29ed1b5aa909c8461af73c1216       1245
 74614751f0de34a1c9147819d425161f717caaaf2d187eb2ca047612350a178c       1228
 c9eefb6a0e0f0dfcdfca36fe554dc34e1aadcbd5248c6d03547f37e9654356a3       1226
 21b885daef682df7c98a3345596b8197a8b3471ef46dc51703ca567bab3203b1       1210
 f7232b213ee8730f87d4b932fac49fe3b7a34410aa696a8a0add61aaf9bf2e90       1209
 9dbc41ac7a8f920944b495143a54178ddf72a0fb253522d427ab66b7a61aadaa       1188
 816a3cbf1285d09c3937d543060c414bc6b77f8a928c9b769b1daf4c9568c5de       1161
 dbfc3799ea6f961789e346addb2c7064b235d6b0182f0b0a5de4f1b333fa4f33       1159
 33168e300c663384187e314626217c486c5466dfd223dd35f07513cca179baba       1144
 4183455b5b86a7b0a1d1b116d57074dc52fd31603fec548236cc4a6c363d8253       1138
 e2f79f94efd44c8f95bf5509a2f42b79f40160d304a04ecd6d71fe095c0f9673       1134
 90c41b3c32e3fd623d691f0dd8dc5240192eb3d0cbd8d139b6175baa20aa3a57       1133
 675656613005c9bbf48013bf97e81ff06f5243d73114888d21d062cd1d8747ac       1128
 eaa44710ebf7e89c24a015a326ac3e846e8adaade1f36701998d54625d2e723f       1121
 483d4b23198f2317b711ba3033e4215fe2afe57268f624d8a74b21daae729864       1105
 a617843488aa3c70962dc6829fd839ac62f697dbe0f6a828b2f3267359396926       1100
 9b875085d4f7898edfb4d01d120071833ce8000b70babb454b06bad8aa9ca45f       1088
 abe05f891cd72f1ef7b8dd1924e7d39a0c181dfd8e7b93e5e6223bcf119d85cc       1079
 73c34901c26f79683f8a082116b9cbfddffd46c9f61a466f066381b032d40b49       1076
 05f59bf3fadbb8495ca603c98c6758cfe9da54131f652622194f35f9d0249b0a       1070
 22cc84534658636c0da1297d9c1b785f29fe92ee5d092a4abf0362efc2e4b9b3       1063
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 dec4e1edb7d790c32e32e33adb086151815b009a761ff6558528ebc1d2230eb5       1049
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 6c3e25df99b496b1b9b4b83271bee441a1a4fdbc1254eef6e76dd06f8d69721f        729
 287aa8c03821f73c98ff92f0fb9af2f50a3d16221bbc2e90f7d11d0b2f61ec27        729
 538afc402b8c4cd627eab5f749820d39dd4b88ffeeb7323ef347e8b4d2a8651a        729
 96382ea191347a4b37f942bbc38569e821638425f01cba286510f39fa3ac030a        722
 c35ae735a3d9fd51f1c1faccb3a9937392e1ded7a06570d5867c195016487b3c        720
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 ad337499c9cf7a49740a6988db6068d24d57902ee0e7496730a0bace4c296039        719
 7c33d7bfaaf0e894b62c9180fc78b7fa02a7aa4553e74593d3b661196b8a2819        715
 34b282c45addfc7b350ae63d45a2a4cfed4796f2d34261daa6421a86b1814c35        714
 7b67f17a237e409ce76b31403238673573034cd87304be044cde6f8664f48de6        713
 6c63cae9b3937f70ab980b87848d96f2a84fe2c7eb4cdc3b95aa4096678bec8b        713
 70d2dd0d3b39ce41e01cf49f5c39c44afe46505bcdd898df30177819e27cfa39        712
 09c7f656bdf3668035dea006a48bdf455c2c989e09d5fd2861ed6777167d7807        712
 cadd2b6bd9af059b6e0b149ec0f7226df90ecbbf943a9d2087d48dda270f5c58        711
 8a9cd3c2fb45855c3b0449e0ce2181612527419fd0282fa0abe7c7245eb09783        709
 eccd9897a5b90547ccf2a47cec4bc556978c6f9bb1d9809b8b78eac587c81d61        708
 7889d0611bd5ace640e85be85fcbe037aa98497b4e568981b96071ea48146bfd        706
 7d2026c4662303c0d1e5baadb208fb82c311ff92f6fe36fbd86354da780725f3        704
 b4876a977b068b0bc85610f7963676c10d4fb29adec91b2d69d10332e737fd2f        703
 ae11a9620b5ac2e23c5ae76aa6a3c441dfb19f52c724127570937ae765716eda        701
 01e15a45d5f57d18a3171546a3c198eb7beca000f7956526f327a1b772c7ba08        701
 8a0592c96b3f77f12919130564c15a07460b5166a4af2d212af915b4ab023ad5        700
 2fe9bb0975fbb0c8e0bb6da527a1af92f11446062365605736a54e84955a4a37        699
 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)

087c00273c18759c9a83691bba0972149b7235d6d0c68d2d67fc306a7ac3e4bc
Total distance:
35.60699999999996
Home locaiton:
(-73.95577, 40.77905)
Most location:
(-73.95591, 40.779568)

5cd6d7ced3a0160d08a112e894d130a38a62cfd7eed7df87f7a69bbb636c6da3
Total distance:
20.862000000000002
Home locaiton:
(-73.936844, 40.804165000000005)
Most location:
(-73.95486, 40.80514)

b19238195a92e8930a1a7b729d4f580e9953b18c0de2682af74a54c6c4dd515d
Total distance:
24.275000000000006
Home locaiton:
(-73.96891, 40.7904)
Most location:
(-73.96863, 40.790596)

80ff716b34f380c3b5457db1409ae44c827375c685cfdfe9749c36a1036908e7
Total distance:
10.179000000000002
Home locaiton:
(-73.98846400000001, 40.75869)
Most location:
(-73.98865, 40.758484)

8c2621a338dd7a8ae204b0596a4a64557a3ea14f70cc915c1be97f2cb8bf3b89
Total distance:
4.694999999999994
Home locaiton:
(-73.98814, 40.721413)
Most location:
(-73.98731, 40.72185)

601907867cba3e384449d2d9dd21f09fb23b07a46187f3111c297c7c4dc34398
Total distance:
76.55500000000005
Home locaiton:
(-74.00014, 40.732665999999995)
Most location:
(-74.00816, 40.714962)

c0a3d6d6c8d2b703b923dcc5c3d1b912c855624d9a8c1072fd3c73e2aff051ed
Total distance:
210.19699999999975
Home locaiton:
(-73.981445, 40.766068)
Most location:
(-73.981445, 40.766068)

63e2706b83d0d8e38ab29bcc0946754e38d10fbc1d5559b06038fd61f2dcb5a2
Total distance:
246.84100000000004
Home locaiton:
(-73.99802, 40.75588)
Most location:
(-73.97561, 40.750923)

f4089b34366a31c6745a529c6cea184488aa7e0e097f3752ea5fb3bdf881db71
Total distance:
63.553999999999995
Home locaiton:
(-73.94573000000001, 40.82177)
Most location:
(-73.9446, 40.819373999999996)

4b82487916526c00dff245d2d157182eaa9cee6189daaef409efd43019dc4d2d
Total distance:
7.441000000000001
Home locaiton:
(-73.97606999999999, 40.72018)
Most location:
(-73.97606999999999, 40.72018)

1c613e326dd4c4e306987a8b5d07001535a4da35ad06837496e75b4fce04acce
Total distance:
22.081000000000003
Home locaiton:
(-74.00168599999999, 40.717403000000004)
Most location:
(-74.001274, 40.71777)

2c7689ac32132798486db4d93c90848d258c53cd35af161e048e2e933336d360
Total distance:
23.066999999999986
Home locaiton:
(-73.960365, 40.800373)
Most location:
(-73.941, 40.792103000000004)

ea3b6aac587ccf8c4bb015999fc6604f9c0325806639910c876796cecb9ac9ed
Total distance:
147.91200000000015
Home locaiton:
(-73.9544, 40.779083)
Most location:
(-73.94575999999999, 40.828133)

80723ce63a5ff66854334042f8f90a7de1ac89ebceb94e13563125e182e6ba24
Total distance:
54.27599999999998
Home locaiton:
(-73.97414, 40.746128000000006)
Most location:
(-73.97403, 40.78116)

03004ff5e72fae238ca63b180676e97a68517f9eb4bb56aeabb465f4ecbd7313
Total distance:
72.93499999999993
Home locaiton:
(-73.988556, 40.758396000000005)
Most location:
(-73.99296600000001, 40.743120000000005)

f454b9ca03ee5ff16e5a821ebb61a5c3adbdbf0b9eb37e3325967e3ddd6f87f3
Total distance:
115.94800000000002
Home locaiton:
(-73.99517, 40.753777)
Most location:
(-73.988556, 40.721333)

8e16a6f53efaeef6f37b909e8de672474f709a83d7df0c64c8c2b9912fbb9fc6
Total distance:
0.6020000000000004
Home locaiton:
(-73.99557, 40.72497)
Most location:
(-73.99561, 40.724968)

e926da376ad454fe20461c023d28e3dfdf2b888f7eab7dfde20cd89e41cd77c9
Total distance:
218.0840000000002
Home locaiton:
(-73.942825, 40.81908)
Most location:
(-73.94001999999999, 40.810513)

In [ ]:


 
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标签:数据分析,distance,展示,locaiton,Most,location,Home,Total,pandas
来源: https://blog.csdn.net/yunfeiw0/article/details/104192586

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