python提取某列特定值,Python Pandas：获取列匹配特定值的行的索引
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 panda's 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 dont match, when I check them by doing:
df.iloc[i]['BoolCol']
The result is actually False!!
Which would be the correct Pandas way to do this?
解决方案
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
an "unusual" index:
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