Data Cleaning(Flagging) Dead Sensor
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I have a large timeseries(pandas dataframe) of windspeed (10min average) which contains error data (dead sensor). How can it be flagged automatically. I was trying with moving average.
Some other approach other then moving average is much appreciated. I have attached the sample data image below.
python pandas
asked Nov 15 '18 at 11:22
Bhuvan KumarBhuvan Kumar
219212
add a comment |
I have a large timeseries(pandas dataframe) of windspeed (10min average) which contains error data (dead sensor). How can it be flagged automatically. I was trying with moving average.
Some other approach other then moving average is much appreciated. I have attached the sample data image below.
python pandas
asked Nov 15 '18 at 11:22
Bhuvan KumarBhuvan Kumar
219212
1
You did not attach the data. Also share the work you have done (aka code)
– Jorge
Nov 15 '18 at 11:35
add a comment |
I have a large timeseries(pandas dataframe) of windspeed (10min average) which contains error data (dead sensor). How can it be flagged automatically. I was trying with moving average.
Some other approach other then moving average is much appreciated. I have attached the sample data image below.
python pandas
asked Nov 15 '18 at 11:22
Bhuvan KumarBhuvan Kumar
219212
I have a large timeseries(pandas dataframe) of windspeed (10min average) which contains error data (dead sensor). How can it be flagged automatically. I was trying with moving average.
Some other approach other then moving average is much appreciated. I have attached the sample data image below.
python pandas
python pandas
asked Nov 15 '18 at 11:22
Bhuvan KumarBhuvan Kumar
219212
asked Nov 15 '18 at 11:22
Bhuvan KumarBhuvan Kumar
219212
asked Nov 15 '18 at 11:22
Bhuvan KumarBhuvan Kumar
219212
asked Nov 15 '18 at 11:22
Bhuvan KumarBhuvan Kumar
219212
asked Nov 15 '18 at 11:22
Bhuvan KumarBhuvan Kumar
219212
219212
1
You did not attach the data. Also share the work you have done (aka code)
– Jorge
Nov 15 '18 at 11:35
add a comment |
1
You did not attach the data. Also share the work you have done (aka code)
– Jorge
Nov 15 '18 at 11:35
1
1
You did not attach the data. Also share the work you have done (aka code)
– Jorge
Nov 15 '18 at 11:35
You did not attach the data. Also share the work you have done (aka code)
– Jorge
Nov 15 '18 at 11:35
add a comment |
1 Answer
1
active
oldest
votes
There are several ways to deal with this problem. I will first pass to differences:
%matplotlib inline
import pandas as pd
import numpy as np
np.random.seed(0)
n = 200
y = np.cumsum(np.random.randn(n))
y[100:120] = 2
y[150:160] = 0
ts = pd.Series(y)
ts.diff().plot();
The next step is to find how long are the strikes of consecutive zeros.
def getZeroStrikeLen(x):
""" Accept a boolean array only
"""
res = np.diff(np.where(np.concatenate(([x[0]],
x[:-1] != x[1:],
[True])))[0])[::2]
return res
vec = ts.diff().values == 0
out = getZeroStrikeLen(vec)
Now if len(out)>0 you can conclude that there is a problem. If you want to go one step further you can have a look to this. It is in R but it's not that hard to replicate in Python.
answered Nov 15 '18 at 11:45
user32185user32185
2,13311028
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
There are several ways to deal with this problem. I will first pass to differences:
%matplotlib inline
import pandas as pd
import numpy as np
np.random.seed(0)
n = 200
y = np.cumsum(np.random.randn(n))
y[100:120] = 2
y[150:160] = 0
ts = pd.Series(y)
ts.diff().plot();
The next step is to find how long are the strikes of consecutive zeros.
def getZeroStrikeLen(x):
""" Accept a boolean array only
"""
res = np.diff(np.where(np.concatenate(([x[0]],
x[:-1] != x[1:],
[True])))[0])[::2]
return res
vec = ts.diff().values == 0
out = getZeroStrikeLen(vec)
Now if len(out)>0 you can conclude that there is a problem. If you want to go one step further you can have a look to this. It is in R but it's not that hard to replicate in Python.
answered Nov 15 '18 at 11:45
user32185user32185
2,13311028
add a comment |
There are several ways to deal with this problem. I will first pass to differences:
%matplotlib inline
import pandas as pd
import numpy as np
np.random.seed(0)
n = 200
y = np.cumsum(np.random.randn(n))
y[100:120] = 2
y[150:160] = 0
ts = pd.Series(y)
ts.diff().plot();
The next step is to find how long are the strikes of consecutive zeros.
def getZeroStrikeLen(x):
""" Accept a boolean array only
"""
res = np.diff(np.where(np.concatenate(([x[0]],
x[:-1] != x[1:],
[True])))[0])[::2]
return res
vec = ts.diff().values == 0
out = getZeroStrikeLen(vec)
Now if len(out)>0 you can conclude that there is a problem. If you want to go one step further you can have a look to this. It is in R but it's not that hard to replicate in Python.
answered Nov 15 '18 at 11:45
user32185user32185
2,13311028
add a comment |
There are several ways to deal with this problem. I will first pass to differences:
%matplotlib inline
import pandas as pd
import numpy as np
np.random.seed(0)
n = 200
y = np.cumsum(np.random.randn(n))
y[100:120] = 2
y[150:160] = 0
ts = pd.Series(y)
ts.diff().plot();
The next step is to find how long are the strikes of consecutive zeros.
def getZeroStrikeLen(x):
""" Accept a boolean array only
"""
res = np.diff(np.where(np.concatenate(([x[0]],
x[:-1] != x[1:],
[True])))[0])[::2]
return res
vec = ts.diff().values == 0
out = getZeroStrikeLen(vec)
Now if len(out)>0 you can conclude that there is a problem. If you want to go one step further you can have a look to this. It is in R but it's not that hard to replicate in Python.
answered Nov 15 '18 at 11:45
user32185user32185
2,13311028
There are several ways to deal with this problem. I will first pass to differences:
%matplotlib inline
import pandas as pd
import numpy as np
np.random.seed(0)
n = 200
y = np.cumsum(np.random.randn(n))
y[100:120] = 2
y[150:160] = 0
ts = pd.Series(y)
ts.diff().plot();
The next step is to find how long are the strikes of consecutive zeros.
def getZeroStrikeLen(x):
""" Accept a boolean array only
"""
res = np.diff(np.where(np.concatenate(([x[0]],
x[:-1] != x[1:],
[True])))[0])[::2]
return res
vec = ts.diff().values == 0
out = getZeroStrikeLen(vec)
Now if len(out)>0 you can conclude that there is a problem. If you want to go one step further you can have a look to this. It is in R but it's not that hard to replicate in Python.
answered Nov 15 '18 at 11:45
user32185user32185
2,13311028
answered Nov 15 '18 at 11:45
user32185user32185
2,13311028
answered Nov 15 '18 at 11:45
user32185user32185
2,13311028
answered Nov 15 '18 at 11:45
user32185user32185
2,13311028
2,13311028
add a comment |
add a comment |
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1
You did not attach the data. Also share the work you have done (aka code)
– Jorge
Nov 15 '18 at 11:35