Populating pandas dataframe based on indexes between two values

Question

I'm trying to generate a mask for broadcasting into dataframes: a boolean series that indicates whether a given row is between two values. This is easy to do for single logical statement, say the last five elements in a dataframe:

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(10,1))
mask = (df.index.values>4)
df.loc[mask,'column'] = range(0,5)

But how does one do the same thing with more intersectional statements? For example, instead of the last five components in the array, can I address rows 2 through 6? Trying to use an AND statement for the mask fails, and I can't use Between on dataframe index values.

jezrael · Accepted Answer · 2017-06-26 07:48:18Z

6

I think you can use mask mainly if duplicated index values.

So if want use between working only with Series, is possible use to_series or Series constructor.

mask = df.index.to_series().between(2,6)
#mask = pd.Series(df.index, index=df.index).between(2,6)
print (mask)
0    False
1    False
2     True
3     True
4     True
5     True
6     True
7    False
8    False
9    False
dtype: bool

mask = df.index.to_series().between(2,6).values
print (mask)
[False False  True  True  True  True  True False False False]

Or chain conditions with &:

mask = (df.index >= 2) & (df.index <= 6)
print (mask)
[False False  True  True  True  True  True False False False]

But maybe better is use loc if unique monotonic index:

df.loc[2:6, 0] = range(5)
print (df)
          0
0  0.642933
1  0.912846
2  0.000000
3  1.000000
4  2.000000
5  3.000000
6  4.000000
7  0.504830
8  0.000422
9  0.029358

edited Jun 26, 2017 at 7:48

answered Jun 26, 2017 at 7:22

jezrael

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