Drop row in pandas dataframe if value is an array instead of a single value

Question

I have a dataframe where one of the columns have some rows with an array value instead of a single int64 value. I want to drop all such rows.

I am using the following code to do this but this is not working (for obvious reasons as it is being compared to a string).

handover_data.drop(handover_data[handover_data['S-PCI'] == '[105 106]'].index, inplace=True)

In the dataframe it should either have 105 or 106 not both but some of the palces have [105 106]

What are the ways to compare this to check if there is an array instead of the expected value:

Data set looks like the following:

S-Cell ID  N-Cell ID S-PLMN  S-PCI  N-PCI        S-BW        N-BW  \
73        257          0    105    105    106  2147483647  2147483647   

    S-EARFCN  N-EARFCN  
73      3025      3025  30102 

     Elapsed RT  Time (ms)  RSRP-105  RSRP-106  RSRQ-105  RSRQ-106  
73  41846000000  2947094.0       -84       -90        -4       -14

EDIT:

s_Cell = 105
for i, j in hd_data.iterrows():
        if(hd_data.at[i,'S-PCI'].all() != s_Cell):
        hd_data.at[i,'H_Event'] = 1

This is failing with following error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-399-d4c47a34a73e> in <module>
     19         print(i)
     20         print(handover_data.at[i,'S-PCI'])
---> 21     if(handover_data.at[i,'S-PCI'].all() != starting_Cell):
     22         handover_data.at[i,'Handover_Event'] = 1
     23         #handover_data.at[i,'Time_to_Handover'] = handover_data.at[i,'TimeInterval']-last_HO_time

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Answer 1

you can filter by the values you want like

wanted_values = [105, 106]
handover_data = handover_data[handover_data['S-PCI'].isin(wanted_values)]

if you want to remove the items that are specifically a list then it'd be a bit more resource intensive

import numpy as np
handover_data = handover_data.apply(lambda x: np.nan if isinstance(x['S-PCI'], list) else x).dropna(subset=['S-PCI'])

Answer 2

try this

handover_data.drop(handover_data[handover_data['S-PCI'].apply(lambda x: not str(x).isdigit())].index, inplace=True)