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

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

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 rows 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 dont always need index for a match, but incase if you need:

In [796]: df.iloc[select_indices].index
Out[796]: Index([ub, ud, ue], dtype=object)

In [797]: df.iloc[select_indices].index.tolist()
Out[797]: [b, d, e]

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

If you want to use your dataframe object only once, use:

df[BoolCol].loc[lambda x: x==True].index