We Are Going To Discuss About Pandas How to filter a Series. So lets Start this Python Article.
Pandas How to filter a Series
In [5]: import pandas as pd test = { 383: 3.000000, 663: 1.000000, 726: 1.000000, 737: 9.000000, 833: 8.166667 } s = pd.Series(test) s = s[s != 1] s Out[0]: 383 3.000000 737 9.000000 833 8.166667 dtype: float64
In [5]: import pandas as pd test = { 383: 3.000000, 663: 1.000000, 726: 1.000000, 737: 9.000000, 833: 8.166667 } s = pd.Series(test) s = s[s != 1] s Out[0]: 383 3.000000 737 9.000000 833 8.166667 dtype: float64
Solution 1
In [5]:
import pandas as pd
test = {
383: 3.000000,
663: 1.000000,
726: 1.000000,
737: 9.000000,
833: 8.166667
}
s = pd.Series(test)
s = s[s != 1]
s
Out[0]:
383 3.000000
737 9.000000
833 8.166667
dtype: float64
Original Author Andrew Of This Content
Solution 2
From pandas version 0.18+ filtering a series can also be done as below
test = {
383: 3.000000,
663: 1.000000,
726: 1.000000,
737: 9.000000,
833: 8.166667
}
pd.Series(test).where(lambda x : x!=1).dropna()
Checkout:
http://pandas.pydata.org/pandas-docs/version/0.18.1/whatsnew.html#method-chaininng-improvements
Original Author DACW Of This Content
Solution 3
As DACW pointed out, there are method-chaining improvements in pandas 0.18.1 that do what you are looking for very nicely.
Rather than using .where, you can pass your function to either the .loc indexer or the Series indexer [] and avoid the call to .dropna:
test = pd.Series({
383: 3.000000,
663: 1.000000,
726: 1.000000,
737: 9.000000,
833: 8.166667
})
test.loc[lambda x : x!=1]
test[lambda x: x!=1]
Similar behavior is supported on the DataFrame and NDFrame classes.
Original Author Gordon Bean Of This Content
Solution 4
A fast way of doing this is to reconstruct using numpy to slice the underlying arrays. See timings below.
mask = s.values != 1
pd.Series(s.values[mask], s.index[mask])
0
383 3.000000
737 9.000000
833 8.166667
dtype: float64
naive timing
Original Author piRSquared Of This Content
Conclusion
So This is all About This Tutorial. Hope This Tutorial Helped You. Thank You.
