Selecting with complex criteria from pandas.DataFrame

Sure! Setup:

>>> import pandas as pd
>>> from random import randint
>>> df = pd.DataFrame({'A': [randint(1, 9) for x in range(10)],
                   'B': [randint(1, 9)*10 for x in range(10)],
                   'C': [randint(1, 9)*100 for x in range(10)]})
>>> df
   A   B    C
0  9  40  300
1  9  70  700
2  5  70  900
3  8  80  900
4  7  50  200
5  9  30  900
6  2  80  700
7  2  80  400
8  5  80  300
9  7  70  800

We can apply column operations and get boolean Series objects:

>>> df["B"] > 50
0    False
1     True
2     True
3     True
4    False
5    False
6     True
7     True
8     True
9     True
Name: B
>>> (df["B"] > 50) & (df["C"] != 900)

or

>>> (df["B"] > 50) & ~(df["C"] == 900)
0    False
1    False
2     True
3     True
4    False
5    False
6    False
7    False
8    False
9    False

[Update, to switch to new-style .loc]:

And then we can use these to index into the object. For read access, you can chain indices:

>>> df["A"][(df["B"] > 50) & (df["C"] != 900)]
2    5
3    8
Name: A, dtype: int64

but you can get yourself into trouble because of the difference between a view and a copy doing this for write access. You can use .loc instead:

>>> df.loc[(df["B"] > 50) & (df["C"] != 900), "A"]
2    5
3    8
Name: A, dtype: int64
>>> df.loc[(df["B"] > 50) & (df["C"] != 900), "A"].values
array([5, 8], dtype=int64)
>>> df.loc[(df["B"] > 50) & (df["C"] != 900), "A"] *= 1000
>>> df
      A   B    C
0     9  40  300
1     9  70  700
2  5000  70  900
3  8000  80  900
4     7  50  200
5     9  30  900
6     2  80  700
7     2  80  400
8     5  80  300
9     7  70  800

Another solution is to use the query method:

import pandas as pd

from random import randint
df = pd.DataFrame({'A': [randint(1, 9) for x in xrange(10)],
                   'B': [randint(1, 9) * 10 for x in xrange(10)],
                   'C': [randint(1, 9) * 100 for x in xrange(10)]})
print df

   A   B    C
0  7  20  300
1  7  80  700
2  4  90  100
3  4  30  900
4  7  80  200
5  7  60  800
6  3  80  900
7  9  40  100
8  6  40  100
9  3  10  600

print df.query('B > 50 and C != 900')

   A   B    C
1  7  80  700
2  4  90  100
4  7  80  200
5  7  60  800

Now if you want to change the returned values in column A you can save their index:

my_query_index = df.query('B > 50 & C != 900').index

....and use .iloc to change them i.e:

df.iloc[my_query_index, 0] = 5000

print df

      A   B    C
0     7  20  300
1  5000  80  700
2  5000  90  100
3     4  30  900
4  5000  80  200
5  5000  60  800
6     3  80  900
7     9  40  100
8     6  40  100
9     3  10  600

And remember to use parenthesis!

Keep in mind that & operator takes a precedence over operators such as > or < etc. That is why

4 < 5 & 6 > 4

evaluates to False. Therefore if you're using pd.loc, you need to put brackets around your logical statements, otherwise you get an error. That's why do:

df.loc[(df['A'] > 10) & (df['B'] < 15)]

instead of

df.loc[df['A'] > 10 & df['B'] < 15]

which would result in

TypeError: cannot compare a dtyped [float64] array with a scalar of type [bool]

You can use pandas it has some built in functions for comparison. So if you want to select values of "A" that are met by the conditions of "B" and "C" (assuming you want back a DataFrame pandas object)

df[['A']][df.B.gt(50) & df.C.ne(900)]

df[['A']] will give you back column A in DataFrame format.

pandas gt function will return the positions of column B that are greater than 50 and ne will return the positions not equal to 900.