5

streamz和包协同工作，hvplot为使用 pandas 数据帧绘制流数据提供支持。

例如，该streamz包有一个用于创建随机流数据帧的便利实用程序：

import hvplot.streamz
from streamz.dataframe import Random

sdf = Random(interval='200ms', freq='50ms')
sdf
# Stop the streaming with: sdf.stop()

这可以使用以下方法在流图中简单地绘制hvplot：

sdf.hvplot()

是否有一种简单的方法可以从预先存在的pandas数据帧中流式传输数据？

例如，我希望能够这样说：

import pandas as pd

df=pd.DataFrame({'a':range(0,100),'b':range(5,105)})

sdf = StreamingDataFrame(df, interval='200ms', freq='50ms')

然后，我可以简单地使用来自预先存在的数据帧的示例数据，而不是使用随机示例数据pandas。

4

1 回答 1

2

据我所知，这是...

from streamz import Stream
from streamz.dataframe import DataFrame

from time import sleep

from datetime import datetime

#Set up a source stream
source = Stream()

#Create a sample pandas dataframe
samples = pd.DataFrame({'x':[0],'y':[0]})

#The streaming dataframe takes the source stream and sample pandas dataframe
#The sample defines the dataframe schema, maybe?
sdf = DataFrame(source, example=samples)

def stest(r):
    print(datetime.now())
    print(r)

#I don't recall what this does
#I think what I was looking to do was display the last 3 items...?
# ...which this doesn't appear to do!
df = sdf.window(3).full()

#This seems to set a callback on stest when a stream element appears
df.stream.sink(stest)


for i in range(5):

    #pull the next item in the streaming dataframe into the stream
    #We could iloc on an existing dataframe?
    source.emit(pd.DataFrame({'x': [i,i,i], 'y' :[i,i,i]}))

    #Pause for a short while...
    sleep(2)


--------
2020-01-27 19:13:06.816315
   x  y
0  0  0
1  0  0
2  0  0
2020-01-27 19:13:08.824016
   x  y
0  1  1
1  1  1
2  1  1
2020-01-27 19:13:10.829178
   x  y
0  2  2
1  2  2
2  2  2
2020-01-27 19:13:12.835948
   x  y
0  3  3
1  3  3
2  3  3
2020-01-27 19:13:14.843432
   x  y
0  4  4
1  4  4
2  4  4

……

啊..找到了一个似乎效果更好的例子。

设置我们要流式传输的数据框：

import pandas as pd
from time import sleep
from datetime import datetime

from streamz import Stream
from streamz.dataframe import DataFrame


#Set up a dummy dataframe
ddf=pd.DataFrame({'a':range(1,5),'b':range(11,15)})
print(ddf)

---
   a   b
0  1  11
1  2  12
2  3  13
3  4  14

并流式传输...

#Create a stream source
source = Stream()

#Create a dataframe model for the stream
samples = pd.DataFrame({'a':[0],'b':[0]})

#Creating the streaming dataframe
sdf = DataFrame(source, example=samples)

#That window thing again...
df = sdf.window(4).full()

#FUnction to run when an item appears on the stream
def stest(r):
    print(datetime.now())
    print(r)

#Add the callback function to streamer
df.stream.sink(stest)

#This does stream from the dataframe
for i in range(len(ddf)):
    source.emit(ddf.iloc[i])
    sleep(2)

df
---
2020-01-27 19:28:05.503123
a     1
b    11
Name: 0, dtype: int64
2020-01-27 19:28:07.505536
a     1
b    11
a     2
b    12
dtype: int64
2020-01-27 19:28:09.508925
a     2
b    12
a     3
b    13
dtype: int64
2020-01-27 19:28:11.514117
a     3
b    13
a     4
b    14
dtype: int64

我还找到了另一种让数据流入全息图的方法：

from tornado import gen
from tornado.ioloop import PeriodicCallback

from holoviews.streams import Buffer

import holoviews as hv
hv.extension('bokeh')


import numpy as np
import pandas as pd

df = pd.DataFrame({'x':range(1000), 'y':np.sin(range(1000))})

rowcount = 0
maxrows = 1000

dfbuffer = Buffer(np.zeros((0, 2)), length=20)

@gen.coroutine
def g():
    global rowcount
    item = df[['x','y']].iloc[rowcount].values.tolist()
    dfbuffer.send(np.array([item]))
    rowcount += 1

    if rowcount>=maxrows:
        cbdf.stop()



#How can we get the thing to stop?

cbdf = PeriodicCallback(g, 500)
cbdf.start()
hv.DynamicMap(hv.Curve, streams=[dfbuffer]).opts(padding=0.1, width=600, color = 'green',)

然后停止它：（cbdf.stop()当我刚尝试它时，这似乎对我不起作用......）

我似乎没有将streamz组件连接到图表的示例（除非streamz在下面使用全息视图？）

于 2020-01-27T19:14:09.687 回答