Python pandas slice dataframe by multiple index ranges
22,416
Solution 1
You can use numpy's r_
"slicing trick":
df = pd.DataFrame({'a':range(10,100)})
df.iloc[pd.np.r_[10:12, 25:28]]
NOTE: this now gives a warning The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead
. To do that, you can import numpy as np
and then slice the following way:
df.iloc[np.r_[10:12, 25:28]]
This gives:
a
10 20
11 21
25 35
26 36
27 37
Solution 2
You can take advantage of pandas isin function.
df = pd.DataFrame({'a':range(10,100)})
ls = [i for i in range(10,12)] + [i for i in range(25,28)]
df[df.index.isin(ls)]
a
10 20
11 21
25 35
26 36
27 37
Comments
-
ragesz over 2 years
What is the pythonic way to slice a dataframe by more index ranges (eg. by
10:12
and25:28
)?I want this in a more elegant way:
df = pd.DataFrame({'a':range(10,100)}) df.iloc[[i for i in range(10,12)] + [i for i in range(25,28)]]
Result:
a 10 20 11 21 25 35 26 36 27 37
Something like this would be more elegant:
df.iloc[(10:12, 25:28)]