Python Pandas: Get index of rows which column matches certain value

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']

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