Selecting with complex criteria from pandas.DataFrame

For example I have simple DF:

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)]})

Can I select values from 'A' for which corresponding values for 'B' will be greater than 50, and for 'C' - not equal 900, using methods and idioms of Pandas?

Solution 1:

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)
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

Note that I accidentally typed == 900 and not != 900, or ~(df["C"] == 900), but I'm too lazy to fix it. Exercise for the reader. :^)

Solution 2:

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

Solution 3:

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]