df.iloc[i] returns the ith row of df. i does not refer to the index label, i is a 0-based index.
In contrast, the attribute index returns actual index labels, not numeric row-indices:
df.index[df[‘BoolCol’] == True].tolist()
or equivalently,
df.index[df[‘BoolCol’]].tolist()
You can see the difference quite clearly by playing with a DataFrame with
a non-default index that does not equal to the row’s numerical position:
df = pd.DataFrame({‘BoolCol’: [True, False, False, True, True]},
index=[10,20,30,40,50])
In [53]: df
Out[53]:
BoolCol
10 True
20 False
30 False
40 True
50 True
[5 rows x 1 columns]
In [54]: df.index[df[‘BoolCol’]].tolist()
Out[54]: [10, 40, 50]
If you want to use the index,
In [56]: idx = df.index[df[‘BoolCol’]]
In [57]: idx
Out[57]: Int64Index([10, 40, 50], dtype=’int64′)
then you can select the rows using loc instead of iloc:
In [58]: df.loc[idx]
Out[58]:
BoolCol
10 True
40 True
50 True
[3 rows x 1 columns]
Note that loc can also accept boolean arrays:
In [55]: df.loc[df[‘BoolCol’]]
Out[55]:
BoolCol
10 True
40 True
50 True
[3 rows x 1 columns]
If you have a boolean array, mask, and need ordinal index values, you can compute them using np.flatnonzero:
In [110]: np.flatnonzero(df[‘BoolCol’])
Out[112]: array([0, 3, 4])
Use df.iloc to select rows by ordinal index:
In [113]: df.iloc[np.flatnonzero(df[‘BoolCol’])]
Out[113]:
BoolCol
10 True
40 True
50 True
Can be done using numpy where() function:
import pandas as pd
import numpy as np
In [716]: df = pd.DataFrame({“gene_name”: [‘SLC45A1’, ‘NECAP2’, ‘CLIC4’, ‘ADC’, ‘AGBL4′] , “BoolCol”: [False, True, False, True, True] },
index=list(“abcde”))
In [717]: df
Out[717]:
BoolCol gene_name
a False SLC45A1
b True NECAP2
c False CLIC4
d True ADC
e True AGBL4
In [718]: np.where(df[“BoolCol”] == True)
Out[718]: (array([1, 3, 4]),)
In [719]: select_indices = list(np.where(df[“BoolCol”] == True)[0])
In [720]: df.iloc[select_indices]
Out[720]:
BoolCol gene_name
b True NECAP2
d True ADC
e True AGBL4
Though you don’t always need index for a match, but incase if you need:
In [796]: df.iloc[select_indices].index
Out[796]: Index([u’b’, u’d’, u’e’], dtype=’object’)
In [797]: df.iloc[select_indices].index.tolist()
Out[797]: [‘b’, ‘d’, ‘e’]