箭头#
Vaex 支持 Arrow。我们将通过快速查看一个不适合内存的大型数据集来展示 vaex+arrow 的功能。2015 年的纽约出租车数据集包含大约 1.5 亿行关于纽约出租车行程的信息，大小约为 23GB。你可以在这里下载它：
如果你想将其转换为箭头格式，请使用以下代码：
ds_hdf5 = vaex.open('/Users/maartenbreddels/datasets/nytaxi/nyc_taxi2015.hdf5')
# this may take a while to export
ds_hdf5.export('./nyc_taxi2015.arrow')
[1]:
!ls -alh /Users/maartenbreddels/datasets/nytaxi/nyc_taxi2015.arrow
-rw-r--r-- 1 maartenbreddels staff 23G Oct 31 18:56 /Users/maartenbreddels/datasets/nytaxi/nyc_taxi2015.arrow
[3]:
import vaex
立即打开#
打开文件是瞬间完成的，因为没有数据被复制到内存中。数据只是被内存映射，这是一种只在需要时读取数据的技术。
[4]:
%time
df = vaex.open('/Users/maartenbreddels/datasets/nytaxi/nyc_taxi2015.arrow')
CPU times: user 3 µs, sys: 1 µs, total: 4 µs
Wall time: 6.91 µs
[5]:
df
[5]:
| # | 供应商ID | 下车星期几 | 下车小时 | 下车纬度 | 下车经度 | 额外费用 | 车费金额 | 改进附加费 | MTA税 | 乘客数量 | 支付类型 | 上车星期几 | 上车小时 | 上车纬度 | 上车经度 | 小费金额 | 通行费金额 | 总金额 | 下车时间 | 上车时间 | 行程距离 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 3.0 | 19.0 | 40.75061798095703 | -73.97478485107422 | 1.0 | 12.0 | 0.3 | 0.5 | 1 | 1 | 3.0 | 19.0 | 40.7501106262207 | -73.993896484375 | 3.25 | 0.0 | 17.05 | numpy.datetime64('2015-01-15T19:23:42.000000000') | numpy.datetime64('2015-01-15T19:05:39.000000000') | 1.59 |
| 1 | 1 | 5.0 | 20.0 | 40.75910949707031 | -73.99441528320312 | 0.5 | 14.5 | 0.3 | 0.5 | 1 | 1 | 5.0 | 20.0 | 40.7242431640625 | -74.00164794921875 | 2.0 | 0.0 | 17.8 | numpy.datetime64('2015-01-10T20:53:28.000000000') | numpy.datetime64('2015-01-10T20:33:38.000000000') | 3.3 |
| 2 | 1 | 5.0 | 20.0 | 40.82441329956055 | -73.95182037353516 | 0.5 | 9.5 | 0.3 | 0.5 | 1 | 2 | 5.0 | 20.0 | 40.80278778076172 | -73.96334075927734 | 0.0 | 0.0 | 10.8 | numpy.datetime64('2015-01-10T20:43:41.000000000') | numpy.datetime64('2015-01-10T20:33:38.000000000') | 1.8 |
| 3 | 1 | 5.0 | 20.0 | 40.71998596191406 | -74.00432586669923 | 0.5 | 3.5 | 0.3 | 0.5 | 1 | 2 | 5.0 | 20.0 | 40.71381759643555 | -74.00908660888672 | 0.0 | 0.0 | 4.8 | numpy.datetime64('2015-01-10T20:35:31.000000000') | numpy.datetime64('2015-01-10T20:33:39.000000000') | 0.5 |
| 4 | 1 | 5.0 | 20.0 | 40.742652893066406 | -74.00418090820312 | 0.5 | 15.0 | 0.3 | 0.5 | 1 | 2 | 5.0 | 20.0 | 40.762428283691406 | -73.97117614746094 | 0.0 | 0.0 | 16.3 | numpy.datetime64('2015-01-10T20:52:58.000000000') | numpy.datetime64('2015-01-10T20:33:39.000000000') | 3.0 | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 146,112,984 | 2 | 4.0 | 0.0 | 40.722469329833984 | -73.98621368408203 | 0.5 | 7.5 | 0.3 | 0.5 | 5 | 1 | 3.0 | 23.0 | 40.72087097167969 | -73.99381256103516 | 1.76 | 0.0 | 10.56 | numpy.datetime64('2016-01-01T00:08:18.000000000') | numpy.datetime64('2015-12-31T23:59:56.000000000') | 1.2 |
| 146,112,985 | 1 | 4.0 | 0.0 | 40.75238800048828 | -73.93951416015625 | 0.5 | 7.5 | 0.3 | 0.5 | 2 | 2 | 3.0 | 23.0 | 40.76028060913085 | -73.96527099609375 | 0.0 | 0.0 | 8.8 | numpy.datetime64('2016-01-01T00:05:19.000000000') | numpy.datetime64('2015-12-31T23:59:58.000000000') | 2.0 |
| 146,112,986 | 1 | 4.0 | 0.0 | 40.69329833984375 | -73.9886703491211 | 0.5 | 13.5 | 0.3 | 0.5 | 2 | 2 | 3.0 | 23.0 | 40.73907852172852 | -73.98729705810547 | 0.0 | 0.0 | 14.8 | numpy.datetime64('2016-01-01T00:12:55.000000000') | numpy.datetime64('2015-12-31T23:59:59.000000000') | 3.8 |
| 146,112,987 | 2 | 4.0 | 0.0 | 40.705322265625 | -74.01712036132812 | 0.5 | 8.5 | 0.3 | 0.5 | 1 | 2 | 3.0 | 23.0 | 40.72569274902344 | -73.99755859375 | 0.0 | 0.0 | 9.8 | numpy.datetime64('2016-01-01T00:10:26.000000000') | numpy.datetime64('2015-12-31T23:59:59.000000000') | 1.96 |
| 146,112,988 | 2 | 4.0 | 0.0 | 40.76057052612305 | -73.99098205566406 | 0.5 | 13.5 | 0.3 | 0.5 | 1 | 1 | 3.0 | 23.0 | 40.76725769042969 | -73.98439788818358 | 2.96 | 0.0 | 17.76 | numpy.datetime64('2016-01-01T00:21:30.000000000') | numpy.datetime64('2015-12-31T23:59:59.000000000') | 1.06 |
快速可视化1.46亿行数据#
可以看出，这个数据集包含1.46亿行。使用plot，我们可以快速生成数据内容的概览。上车地点很好地勾勒出了曼哈顿的轮廓。
[6]:
df.viz.heatmap(df.pickup_longitude, df.pickup_latitude, f='log')
[7]:
df.total_amount.minmax()
[7]:
array([-4.9630000e+02, 3.9506116e+06])
数据清洗：异常值#
从total_amount列（人们支付了多少）可以看出，这个数据集中包含异常值。通过快速的1d图，我们可以看到合理的数据过滤方法。
[8]:
df.plot1d(df.total_amount, shape=100, limits=[0, 100])
[8]:
[<matplotlib.lines.Line2D at 0x121d26320>]
[9]:
# filter the dataset
dff = df[(df.total_amount >= 0) & (df.total_amount < 100)]
浅拷贝#
这个过滤后的数据集没有复制任何数据（否则会消耗我们大约23GB的内存）。相反，创建了数据的浅拷贝，并使用布尔掩码来跟踪应该使用哪些行。
[10]:
dff['ratio'] = dff.tip_amount/dff.total_amount
虚拟列#
新列 ratio 目前还没有进行任何计算，它只存储了表达式并且不占用任何内存。然而，这个新的（虚拟）列可以像普通列一样用于计算。
[11]:
dff.ratio.mean()
<string>:1: RuntimeWarning: invalid value encountered in true_divide
[11]:
0.09601926650107262
结果#
我们的最终结果，小费的百分比，可以很容易地为这个大数据集计算出来，它不需要任何过多的内存。
互操作性#
由于数据以Arrow数组形式存在，我们可以将它们传递给其他库，如pandas，甚至传递给其他进程。
[12]:
arrow_table = df.to_arrow_table()
arrow_table
[12]:
pyarrow.Table
VendorID: int64
dropoff_dayofweek: double
dropoff_hour: double
dropoff_latitude: double
dropoff_longitude: double
extra: double
fare_amount: double
improvement_surcharge: double
mta_tax: double
passenger_count: int64
payment_type: int64
pickup_dayofweek: double
pickup_hour: double
pickup_latitude: double
pickup_longitude: double
tip_amount: double
tolls_amount: double
total_amount: double
tpep_dropoff_datetime: timestamp[ns]
tpep_pickup_datetime: timestamp[ns]
trip_distance: double
[13]:
# Although you can 'convert' (pass the data) in to pandas,
# some memory will be wasted (at least an index will be created by pandas)
# here we just pass a subset of the data
df_pandas = df[:10000].to_pandas_df()
df_pandas
[13]:
| 供应商ID | 下车星期几 | 下车小时 | 下车纬度 | 下车经度 | 额外费用 | 车费金额 | 改进附加费 | MTA税 | 乘客数量 | ... | 上车星期几 | 上车小时 | 上车纬度 | 上车经度 | 小费金额 | 通行费金额 | 总金额 | 下车时间 | 上车时间 | 行程距离 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 3.0 | 19.0 | 40.750618 | -73.974785 | 1.0 | 12.0 | 0.3 | 0.5 | 1 | ... | 3.0 | 19.0 | 40.750111 | -73.993896 | 3.25 | 0.00 | 17.05 | 2015-01-15 19:23:42 | 2015-01-15 19:05:39 | 1.59 |
| 1 | 1 | 5.0 | 20.0 | 40.759109 | -73.994415 | 0.5 | 14.5 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.724243 | -74.001648 | 2.00 | 0.00 | 17.80 | 2015-01-10 20:53:28 | 2015-01-10 20:33:38 | 3.30 |
| 2 | 1 | 5.0 | 20.0 | 40.824413 | -73.951820 | 0.5 | 9.5 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.802788 | -73.963341 | 0.00 | 0.00 | 10.80 | 2015-01-10 20:43:41 | 2015-01-10 20:33:38 | 1.80 |
| 3 | 1 | 5.0 | 20.0 | 40.719986 | -74.004326 | 0.5 | 3.5 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.713818 | -74.009087 | 0.00 | 0.00 | 4.80 | 2015-01-10 20:35:31 | 2015-01-10 20:33:39 | 0.50 |
| 4 | 1 | 5.0 | 20.0 | 40.742653 | -74.004181 | 0.5 | 15.0 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.762428 | -73.971176 | 0.00 | 0.00 | 16.30 | 2015-01-10 20:52:58 | 2015-01-10 20:33:39 | 3.00 |
| 5 | 1 | 5.0 | 20.0 | 40.758194 | -73.986977 | 0.5 | 27.0 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.774048 | -73.874374 | 6.70 | 5.33 | 40.33 | 2015-01-10 20:53:52 | 2015-01-10 20:33:39 | 9.00 |
| 6 | 1 | 5.0 | 20.0 | 40.749634 | -73.992470 | 0.5 | 14.0 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.726009 | -73.983276 | 0.00 | 0.00 | 15.30 | 2015-01-10 20:58:31 | 2015-01-10 20:33:39 | 2.20 |
| 7 | 1 | 5.0 | 20.0 | 40.726326 | -73.995010 | 0.5 | 7.0 | 0.3 | 0.5 | 3 | ... | 5.0 | 20.0 | 40.734142 | -74.002663 | 1.66 | 0.00 | 9.96 | 2015-01-10 20:42:20 | 2015-01-10 20:33:39 | 0.80 |
| 8 | 1 | 5.0 | 21.0 | 40.759357 | -73.987595 | 0.0 | 52.0 | 0.3 | 0.5 | 3 | ... | 5.0 | 20.0 | 40.644356 | -73.783043 | 0.00 | 5.33 | 58.13 | 2015-01-10 21:11:35 | 2015-01-10 20:33:39 | 18.20 |
| 9 | 1 | 5.0 | 20.0 | 40.759365 | -73.985916 | 0.5 | 6.5 | 0.3 | 0.5 | 2 | ... | 5.0 | 20.0 | 40.767948 | -73.985588 | 1.55 | 0.00 | 9.35 | 2015-01-10 20:40:44 | 2015-01-10 20:33:40 | 0.90 |
| 10 | 1 | 5.0 | 20.0 | 40.728584 | -74.004395 | 0.5 | 7.0 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.723103 | -73.988617 | 1.66 | 0.00 | 9.96 | 2015-01-10 20:41:39 | 2015-01-10 20:33:40 | 0.90 |
| 11 | 1 | 5.0 | 20.0 | 40.757217 | -73.967407 | 0.5 | 7.5 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.751419 | -73.993782 | 1.00 | 0.00 | 9.80 | 2015-01-10 20:43:26 | 2015-01-10 20:33:41 | 1.10 |
| 12 | 1 | 5.0 | 20.0 | 40.707726 | -74.009773 | 0.5 | 3.0 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.704376 | -74.008362 | 0.00 | 0.00 | 4.30 | 2015-01-10 20:35:23 | 2015-01-10 20:33:41 | 0.30 |
| 13 | 1 | 5.0 | 21.0 | 40.735210 | -73.997345 | 0.5 | 19.0 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.760448 | -73.973946 | 3.00 | 0.00 | 23.30 | 2015-01-10 21:03:04 | 2015-01-10 20:33:41 | 3.10 |
| 14 | 1 | 5.0 | 20.0 | 40.739895 | -73.995216 | 0.5 | 6.0 | 0.3 | 0.5 | 1 | ... | 5.0 | 20.0 | 40.731777 | -74.006721 | 0.00 | 0.00 | 7.30 | 2015-01-10 20:39:23 | 2015-01-10 20:33:41 | 1.10 |
| 15 | 2 | 3.0 | 19.0 | 40.757889 | -73.983978 | 1.0 | 16.5 | 0.3 | 0.5 | 1 | ... | 3.0 | 19.0 | 40.739811 | -73.976425 | 4.38 | 0.00 | 22.68 | 2015-01-15 19:32:00 | 2015-01-15 19:05:39 | 2.38 |
| 16 | 2 | 3.0 | 19.0 | 40.786858 | -73.955124 | 1.0 | 12.5 | 0.3 | 0.5 | 5 | ... | 3.0 | 19.0 | 40.754246 | -73.968704 | 0.00 | 0.00 | 14.30 | 2015-01-15 19:21:00 | 2015-01-15 19:05:40 | 2.83 |
| 17 | 2 | 3.0 | 19.0 | 40.785782 | -73.952713 | 1.0 | 26.0 | 0.3 | 0.5 | 5 | ... | 3.0 | 19.0 | 40.769581 | -73.863060 | 8.08 | 5.33 | 41.21 | 2015-01-15 19:28:18 | 2015-01-15 19:05:40 | 8.33 |
| 18 | 2 | 3.0 | 19.0 | 40.786083 | -73.980850 | 1.0 | 11.5 | 0.3 | 0.5 | 1 | ... | 3.0 | 19.0 | 40.779423 | -73.945541 | 0.00 | 0.00 | 13.30 | 2015-01-15 19:20:36 | 2015-01-15 19:05:41 | 2.37 |
| 19 | 2 | 3.0 | 19.0 | 40.718590 | -73.952377 | 1.0 | 21.5 | 0.3 | 0.5 | 2 | ... | 3.0 | 19.0 | 40.774010 | -73.874458 | 4.50 | 0.00 | 27.80 | 2015-01-15 19:20:22 | 2015-01-15 19:05:41 | 7.13 |
| 20 | 2 | 3.0 | 19.0 | 40.714596 | -73.998924 | 1.0 | 17.5 | 0.3 | 0.5 | 1 | ... | 3.0 | 19.0 | 40.751896 | -73.976601 | 0.00 | 0.00 | 19.30 | 2015-01-15 19:31:00 | 2015-01-15 19:05:41 | 3.60 |
| 21 | 2 | 3.0 | 19.0 | 40.734650 | -73.999939 | 1.0 | 5.5 | 0.3 | 0.5 | 1 | ... | 3.0 | 19.0 | 40.745079 | -73.994957 | 1.62 | 0.00 | 8.92 | 2015-01-15 19:10:22 | 2015-01-15 19:05:41 | 0.89 |
| 22 | 2 | 3.0 | 19.0 | 40.735512 | -74.003563 | 1.0 | 5.5 | 0.3 | 0.5 | 1 | ... | 3.0 | 19.0 | 40.747063 | -74.000938 | 1.30 | 0.00 | 8.60 | 2015-01-15 19:10:55 | 2015-01-15 19:05:41 | 0.96 |
| 23 | 2 | 3.0 | 19.0 | 40.704220 | -74.007919 | 1.0 | 6.5 | 0.3 | 0.5 | 2 | ... | 3.0 | 19.0 | 40.717892 | -74.002777 | 1.50 | 0.00 | 9.80 | 2015-01-15 19:12:36 | 2015-01-15 19:05:41 | 1.25 |
| 24 | 2 | 3.0 | 19.0 | 40.761856 | -73.978172 | 1.0 | 11.5 | 0.3 | 0.5 | 5 | ... | 3.0 | 19.0 | 40.736362 | -73.997459 | 2.50 | 0.00 | 15.80 | 2015-01-15 19:22:11 | 2015-01-15 19:05:41 | 2.11 |
| 25 | 2 | 3.0 | 19.0 | 40.811089 | -73.953339 | 1.0 | 7.5 | 0.3 | 0.5 | 5 | ... | 3.0 | 19.0 | 40.823994 | -73.952278 | 1.70 | 0.00 | 11.00 | 2015-01-15 19:14:05 | 2015-01-15 19:05:41 | 1.15 |
| 26 | 2 | 3.0 | 19.0 | 40.734890 | -73.988609 | 1.0 | 9.0 | 0.3 | 0.5 | 1 | ... | 3.0 | 19.0 | 40.750080 | -73.991127 | 0.00 | 0.00 | 10.80 | 2015-01-15 19:16:18 | 2015-01-15 19:05:42 | 1.53 |
| 27 | 2 | 3.0 | 19.0 | 40.743530 | -73.985603 | 0.0 | 52.0 | 0.3 | 0.5 | 1 | ... | 3.0 | 19.0 | 40.644127 | -73.786575 | 6.00 | 5.33 | 64.13 | 2015-01-15 19:49:07 | 2015-01-15 19:05:42 | 18.06 |
| 28 | 2 | 3.0 | 19.0 | 40.757721 | -73.994514 | 1.0 | 10.0 | 0.3 | 0.5 | 1 | ... | 3.0 | 19.0 | 40.741447 | -73.993668 | 2.36 | 0.00 | 14.16 | 2015-01-15 19:18:33 | 2015-01-15 19:05:42 | 1.76 |
| 29 | 2 | 3.0 | 19.0 | 40.704689 | -74.009079 | 1.0 | 17.5 | 0.3 | 0.5 | 6 | ... | 3.0 | 19.0 | 40.744083 | -73.985291 | 3.70 | 0.00 | 23.00 | 2015-01-15 19:21:40 | 2015-01-15 19:05:42 | 5.19 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9970 | 1 | 4.0 | 11.0 | 40.719917 | -73.955521 | 0.0 | 20.0 | 0.3 | 0.5 | 1 | ... | 4.0 | 10.0 | 40.725979 | -74.009071 | 4.00 | 0.00 | 24.80 | 2015-01-30 11:20:08 | 2015-01-30 10:51:40 | 3.70 |
| 9971 | 1 | 4.0 | 10.0 | 40.720398 | -73.984940 | 1.0 | 6.5 | 0.3 | 0.5 | 1 | ... | 4.0 | 10.0 | 40.732452 | -73.985001 | 1.65 | 0.00 | 9.95 | 2015-01-30 10:58:58 | 2015-01-30 10:51:40 | 1.10 |
| 9972 | 1 | 4.0 | 11.0 | 40.755405 | -74.002457 | 0.0 | 8.5 | 0.3 | 0.5 | 2 | ... | 4.0 | 10.0 | 40.751358 | -73.990479 | 1.00 | 0.00 | 10.30 | 2015-01-30 11:03:41 | 2015-01-30 10:51:41 | 0.70 |
| 9973 | 2 | 1.0 | 19.0 | 40.763626 | -73.969666 | 1.0 | 24.5 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.708790 | -74.017281 | 5.10 | 0.00 | 31.40 | 2015-01-13 19:22:18 | 2015-01-13 18:55:41 | 7.08 |
| 9974 | 2 | 1.0 | 19.0 | 40.772366 | -73.960800 | 1.0 | 5.5 | 0.3 | 0.5 | 5 | ... | 1.0 | 18.0 | 40.780003 | -73.954681 | 1.00 | 0.00 | 8.30 | 2015-01-13 19:02:03 | 2015-01-13 18:55:41 | 0.64 |
| 9975 | 2 | 1.0 | 19.0 | 40.733429 | -73.984154 | 1.0 | 9.0 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.749680 | -73.991531 | 0.00 | 0.00 | 10.80 | 2015-01-13 19:06:56 | 2015-01-13 18:55:41 | 1.67 |
| 9976 | 2 | 1.0 | 19.0 | 40.774780 | -73.957779 | 1.0 | 20.0 | 0.3 | 0.5 | 3 | ... | 1.0 | 18.0 | 40.751801 | -74.002327 | 2.00 | 0.00 | 23.80 | 2015-01-13 19:18:39 | 2015-01-13 18:55:42 | 5.28 |
| 9977 | 2 | 1.0 | 19.0 | 40.751698 | -73.989746 | 1.0 | 8.5 | 0.3 | 0.5 | 2 | ... | 1.0 | 18.0 | 40.768433 | -73.986137 | 0.00 | 0.00 | 10.30 | 2015-01-13 19:06:38 | 2015-01-13 18:55:42 | 1.38 |
| 9978 | 2 | 1.0 | 19.0 | 40.752941 | -73.977470 | 1.0 | 7.5 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.745071 | -73.987068 | 1.00 | 0.00 | 10.30 | 2015-01-13 19:05:34 | 2015-01-13 18:55:42 | 0.88 |
| 9979 | 2 | 1.0 | 19.0 | 40.735130 | -73.976120 | 1.0 | 8.5 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.751259 | -73.977814 | 0.00 | 0.00 | 10.30 | 2015-01-13 19:05:41 | 2015-01-13 18:55:42 | 1.58 |
| 9980 | 2 | 1.0 | 19.0 | 40.745541 | -73.984383 | 1.0 | 8.5 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.731110 | -74.001350 | 0.00 | 0.00 | 10.30 | 2015-01-13 19:05:32 | 2015-01-13 18:55:42 | 1.58 |
| 9981 | 2 | 1.0 | 19.0 | 40.793671 | -73.974327 | 1.0 | 5.0 | 0.3 | 0.5 | 2 | ... | 1.0 | 18.0 | 40.791222 | -73.965118 | 0.00 | 0.00 | 6.80 | 2015-01-13 19:00:05 | 2015-01-13 18:55:42 | 0.63 |
| 9982 | 2 | 1.0 | 19.0 | 40.754639 | -73.986343 | 1.0 | 11.0 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.764175 | -73.968994 | 1.00 | 0.00 | 13.80 | 2015-01-13 19:11:57 | 2015-01-13 18:55:43 | 1.63 |
| 9983 | 2 | 1.0 | 18.0 | 40.723721 | -73.989494 | 1.0 | 4.5 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.714985 | -73.992409 | 2.00 | 0.00 | 8.30 | 2015-01-13 18:59:19 | 2015-01-13 18:55:43 | 0.70 |
| 9984 | 2 | 1.0 | 19.0 | 40.774590 | -73.963249 | 1.0 | 5.5 | 0.3 | 0.5 | 5 | ... | 1.0 | 18.0 | 40.764881 | -73.968529 | 1.30 | 0.00 | 8.60 | 2015-01-13 19:01:19 | 2015-01-13 18:55:44 | 0.94 |
| 9985 | 2 | 1.0 | 19.0 | 40.774872 | -73.982613 | 1.0 | 7.0 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.762344 | -73.985695 | 1.60 | 0.00 | 10.40 | 2015-01-13 19:03:54 | 2015-01-13 18:55:44 | 1.04 |
| 9986 | 2 | 1.0 | 19.0 | 40.787998 | -73.953888 | 1.0 | 5.0 | 0.3 | 0.5 | 2 | ... | 1.0 | 18.0 | 40.779526 | -73.957619 | 1.20 | 0.00 | 8.00 | 2015-01-13 19:00:06 | 2015-01-13 18:55:44 | 0.74 |
| 9987 | 2 | 1.0 | 19.0 | 40.790218 | -73.975128 | 1.0 | 11.5 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.762226 | -73.985916 | 2.50 | 0.00 | 15.80 | 2015-01-13 19:10:46 | 2015-01-13 18:55:44 | 2.19 |
| 9988 | 2 | 1.0 | 19.0 | 40.739487 | -73.989059 | 1.0 | 9.5 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.725056 | -73.984329 | 2.10 | 0.00 | 13.40 | 2015-01-13 19:08:40 | 2015-01-13 18:55:44 | 1.48 |
| 9989 | 2 | 1.0 | 19.0 | 40.780548 | -73.959030 | 1.0 | 8.5 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.778542 | -73.981949 | 1.00 | 0.00 | 11.30 | 2015-01-13 19:04:44 | 2015-01-13 18:55:45 | 1.83 |
| 9990 | 2 | 1.0 | 19.0 | 40.761524 | -73.960602 | 1.0 | 15.0 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.746319 | -74.001114 | 0.00 | 0.00 | 16.80 | 2015-01-13 19:14:59 | 2015-01-13 18:55:45 | 3.27 |
| 9991 | 2 | 1.0 | 19.0 | 40.720646 | -73.989716 | 1.0 | 8.0 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.738167 | -73.987434 | 1.00 | 0.00 | 10.80 | 2015-01-13 19:04:58 | 2015-01-13 18:55:45 | 1.56 |
| 9992 | 2 | 1.0 | 19.0 | 40.795898 | -73.972610 | 1.0 | 20.5 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.740582 | -73.989738 | 4.30 | 0.00 | 26.60 | 2015-01-13 19:18:18 | 2015-01-13 18:55:45 | 5.40 |
| 9993 | 2 | 1.0 | 18.0 | 40.769939 | -73.981316 | 1.0 | 4.5 | 0.3 | 0.5 | 1 | ... | 1.0 | 18.0 | 40.772015 | -73.979416 | 1.10 | 0.00 | 7.40 | 2015-01-13 18:59:40 | 2015-01-13 18:55:45 | 0.34 |
| 9994 | 2 | 4.0 | 18.0 | 40.773521 | -73.955353 | 1.0 | 31.0 | 0.3 | 0.5 | 1 | ... | 4.0 | 18.0 | 40.713215 | -74.013542 | 5.00 | 0.00 | 37.80 | 2015-01-23 18:59:52 | 2015-01-23 18:22:55 | 9.05 |
| 9995 | 2 | 4.0 | 18.0 | 40.774670 | -73.947845 | 1.0 | 11.5 | 0.3 | 0.5 | 1 | ... | 4.0 | 18.0 | 40.773186 | -73.978043 | 0.00 | 0.00 | 13.30 | 2015-01-23 18:37:44 | 2015-01-23 18:22:55 | 2.32 |
| 9996 | 2 | 4.0 | 18.0 | 40.758148 | -73.985626 | 1.0 | 8.5 | 0.3 | 0.5 | 2 | ... | 4.0 | 18.0 | 40.752003 | -73.973198 | 0.00 | 0.00 | 10.30 | 2015-01-23 18:34:48 | 2015-01-23 18:22:56 | 0.92 |
| 9997 | 2 | 4.0 | 18.0 | 40.768131 | -73.964516 | 1.0 | 10.5 | 0.3 | 0.5 | 1 | ... | 4.0 | 18.0 | 40.740456 | -73.986252 | 2.46 | 0.00 | 14.76 | 2015-01-23 18:33:58 | 2015-01-23 18:22:56 | 2.36 |
| 9998 | 2 | 4.0 | 18.0 | 40.759171 | -73.975189 | 1.0 | 6.5 | 0.3 | 0.5 | 3 | ... | 4.0 | 18.0 | 40.770500 | -73.981323 | 2.08 | 0.00 | 10.38 | 2015-01-23 18:29:22 | 2015-01-23 18:22:56 | 1.05 |
| 9999 | 2 | 4.0 | 18.0 | 40.752113 | -73.975189 | 1.0 | 5.0 | 0.3 | 0.5 | 1 | ... | 4.0 | 18.0 | 40.761505 | -73.968452 | 0.00 | 0.00 | 6.80 | 2015-01-23 18:27:58 | 2015-01-23 18:22:57 | 0.75 |
10000 行 × 21 列
教程#
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