checking for computers in another dataframe-CodePudding

I have one data frame that outputs hostname and agent_version this is named df_filter output:

              hostname agent_version
10      LN07WXDHQ92196  6.16.13008.0
56      LN20WXDHQ58722  6.16.13008.0
61      PC08W7D0811273  6.13.12708.0
100    PC09WXD09003874  6.13.12708.0
128    PC09WXD09003764  6.20.13408.0
155    PC07WXD07024782  6.16.13008.0
162     TB56WXDHQ54910  6.20.13408.0
164    PC12WXD12024954  6.13.12708.0
207    TB07WXD07018788  6.14.12806.0
354    PC10WXD10006757  6.14.12806.0
602     PC59WXDHQ80140  6.14.12806.0

i have another sccm data frame called df_sccm Name

0        Provisioning Device (Provisioning Device)
1      x64 Unknown Computer (x64 Unknown Computer)
2      x86 Unknown Computer (x86 Unknown Computer)
3                                   PC20WXDHQ60970
4                                     SV03TMCCMPRI
...                                            ...
27231                               LN51WXDHQ57087
27232                              LN07WXDHQ098858
27233                              LN08WXDHQ100237
27234                              LN43WXDHQ100397
27235                              PC01WXD03437647

check cs dataframe is in sccm df

#df_sccm_check = df_filter.isin(df_sccm)
#print(df_sccm_check)

i am trying to see if my output of the first dataframe is in my second dataframe (df_sccm), when i run it, it just says false, even though there are some machines thare in there

CodePudding user response：

I am not sure if you can use .isin() like that. I would probably use a left join like that:

import pandas as pd

df_filter = pd.DataFrame(
    data={
        'hostname': ['PC20WXDHQ60970', 'LN07WXDHQ92196','PC09WXD09003874'], 
        'agent_version': ['6.16.13008.0', '6.16.12038.0', '6.16.14008.0']
        }
)
df_sccm = pd.DataFrame(
    data={'agent_version': ['LN07WXDHQ92196', 'PC01WXD03437647', 'x86 Unknown Computer (x86 Unknown Computer)']}
    )
df_filter['Check'] = True
pd.merge(df_sccm, df_filter[['hostname', 'Check']].rename(columns={'hostname': 'agent_version'}), on=['agent_version'], how='left').fillna(False)

which yields the following data frame:

    agent_version                               Check
0   LN07WXDHQ92196                              True
1   PC01WXD03437647                             False
2   x86 Unknown Computer (x86 Unknown Computer) False

EDIT

You could try this then:

import pandas as pd
from itertools import chain

df_filter = pd.DataFrame(
    data={
        'hostname': ['PC20WXDHQ60970', 'LN07WXDHQ92196','PC09WXD09003874'], 
        'agent_version': ['6.16.13008.0', '6.16.12038.0', '6.16.14008.0']
        }
)
df_sccm = pd.DataFrame(
    data={'agent_version': ['LN07WXDHQ92196', 'PC01WXD03437647', 'x86 Unknown Computer (x86 Unknown Computer)']}
    )

df_filter.isin(list(chain.from_iterable(df_sccm.values.tolist())))

It relies on the isna() method and returns:

    hostname  agent_version
0   False     False
1   True      False
2   False     False

Now, you can get the True rows only by the following line:

df_filter[df_filter.hostname.isin(list(chain.from_iterable(df_sccm.values.tolist())))]

-----------------------------------------------
    hostname        agent_version
1   LN07WXDHQ92196  6.16.12038.0
-----------------------------------------------