TypeError: Cannot perform 'rand_' with a dtyped [float64] array and scalar of type [bool]

Question

I ran a command in python pandas as follows:

q1_fisher_r[(q1_fisher_r['TP53']==1) & q1_fisher_r[(q1_fisher_r['TumorST'].str.contains(':1:'))]] 

I got following error:

TypeError: Cannot perform 'rand_' with a dtyped [float64] array and scalar of type [bool] 

the solution i tried using this: error link.

changed the code accordingly as:

q1_fisher_r[(q1_fisher_r['TumorST'].str.contains(':1:')) & (q1_fisher_r[(q1_fisher_r['TP53']==1)])] 

But still I got the same error as TypeError: Cannot perform 'rand_' with a dtyped [float64] array and scalar of type [bool]

Answer 1

For filtering by multiple conditions chain them by & and filter by boolean indexing:

q1_fisher_r[(q1_fisher_r['TP53']==1) & q1_fisher_r['TumorST'].str.contains(':1:')]                ^^^^                        ^^^^            first condition            second condition 

Problem is this code returned filtered data, so cannot chain by condition:

q1_fisher_r[(q1_fisher_r['TumorST'].str.contains(':1:'))] 

Similar problem:

q1_fisher_r[(q1_fisher_r['TP53']==1)] 

Sample:

q1_fisher_r = pd.DataFrame({'TP53':[1,1,2,1], 'TumorST':['5:1:','9:1:','5:1:','6:1']}) print (q1_fisher_r)    TP53 TumorST 0     1    5:1: 1     1    9:1: 2     2    5:1: 3     1     6:1  df = q1_fisher_r[(q1_fisher_r['TP53']==1) & q1_fisher_r['TumorST'].str.contains(':1:')] print (df)    TP53 TumorST 0     1    5:1: 1     1    9:1: 

Answer 2

Had a similar problem with a setup as below, which yielded the same error message. The very simple solution for me was to have each individual condition between brackets. Should have known, but want to highlight in case someone else has the same problem.

Incorrect code:

conditions = [     (df['A'] == '15min' & df['B'].dt.minute == 15),  # Note brackets only surrounding both conditions together, not each individual condition     df['A'] == '30min' & df['B'].dt.minute == 30,  # Note no brackets at all  ]  output = [     df['Time'] + dt.timedelta(minutes = 45),     df['Time'] + dt.timedelta(minutes = 30), ]  df['TimeAdjusted'] = np.select(conditions, output, default = np.datetime64('NaT')) 

Correct code:

conditions = [         (df['A'] == '15min') & (df['B'].dt.minute == 15),  # Note brackets surrounding each condition         (df['A'] == '30min') & (df['B'].dt.minute == 30),  # Note brackets surrounding each condition ]      output = [         df['Time'] + dt.timedelta(minutes = 45),         df['Time'] + dt.timedelta(minutes = 30), ]  df['TimeAdjusted'] = np.select(conditions, output, default = np.datetime64('NaT'))