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


Given a DataFrame with a column "BoolCol", we want to find the indexes of the DataFrame in which the values for "BoolCol" == True

I currently have the iterating way to do it, which works perfectly:

for i in range(100,3000):
    if df.iloc[i]['BoolCol']== True:
         print i,df.iloc[i]['BoolCol']

But this is not the correct pandas way to do it. After some research, I am currently using this code:

df[df['BoolCol'] == True].index.tolist()

This one gives me a list of indexes, but they don’t match, when I check them by doing:


The result is actually False!!

Which would be the correct pandas way to do this?


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,


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]},

In [53]: df
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]
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']]
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'])]
10    True
40    True
50    True


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