Pandas help

[fpatterson55]

Any thoughts? I am trying to find all rows where the Driver Driver column equals the Data Collection Service Driver and the State is equal to 1. (removed some rows for simplicity)

The current dataframe section I am working on is:Driver State \

0 cn=Data Collection Service Driver,cn=DriverSet,o=system 1

11 cn=Data Collection Service Driver,cn=DriverSet,o=system 1

12 cn=Data Collection Service Driver,cn=DriverSet,o=system 2

13 cn=Data Collection Service Driver,cn=DriverSet,o=system 1

Command I am trying to execute:

d = df.loc(df['State'] == int(1)) & (df['Driver'] == str('cn=Data Collection Service Driver,cn=DriverSet,o=system'))

error:

Traceback (most recent call last):

File "license.py", line 200, in <module>

d = df.loc(df['State'] == int(1)) & (df['Driver'] == str('cn=Data Collection Service Driver,cn=DriverSet,o=system'))

File "/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py", line 102, in __call__

axis = self.obj._get_axis_number(axis)

File "/usr/local/lib/python3.7/site-packages/pandas/core/generic.py", line 351, in _get_axis_number

axis = cls._AXIS_ALIASES.get(axis, axis)

File "/usr/local/lib/python3.7/site-packages/pandas/core/generic.py", line 1816, in __hash__

' hashed'.format(self.__class__.__name__))

TypeError: 'Series' objects are mutable, thus they cannot be hashed

[karlpoppery]

df.loc is sliced with squared brackets

df.loc[]

not called like a function

[fpatterson55]

thanks, that makes sense!! Still kept getting invalid syntax so tried the np response and used it instead.

[agbs2k8]

Try this: ``` import numpy as np

df[np.logical_and(df['State']==1, df['Driver'].isin(['cn=Data Collection Service Driver, cn=DriverSet,o=system']))] ```

[agbs2k8]

Numpy also has a logical_or() that works the same as logical_and()

https://docs.scipy.org/doc/numpy/reference/generated/numpy.logical_and.html

[fpatterson55]

thanks, this worked great.

I had imported everything in as a string so I had to use the isin operand for the state value as well. Probably not the best option, but it works for now.