psy*_*dia 5 pandas holoviews hvplot holoviz
和包一起工作streamz，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()
可以使用以下命令在流图表中简单地绘制此图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，我可以简单地使用预先存在的pandas数据帧中的示例数据，而不是使用随机示例数据。
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。
设置我们想要流式传输的数据帧：
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
我还找到了另一种将数据传输到全息图中的方法：
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
然后停止它：（cbdf.stop()只是当我刚刚尝试时这似乎对我不起作用......）
我似乎还没有为自己提供将streamz组件连接到图表的示例（除非 Holoviews 在streamz下面使用？）