Data Wrangling in Python to Create a List-CodePudding

I have a dataframe in Python below:

import pandas as pd
df = pd.DataFrame({
    'CRDACCT_DLQ_CYC_1_MNTH_AGO' : [3, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'], 
    'CRDACCT_DLQ_CYC_2_MNTH_AGO': [4, 3, 3, 3, 3, 3, 2, 0, 5, 4, 3, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2], 
    'CRDACCT_DLQ_CYC_3_MNTH_AGO': [8, 7, 6, 5, 4, 3, 2, 'F', 'F', 0, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'F', 'C', 'C', 'F', 'F'], 
    'CRDACCT_DLQ_CYC_4_MNTH_AGO' : [0, 2, 'F', 'F', 'C', 'C', 'C', 'C', 0, 2, 0, 2, 0, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'F', 'C', 'F'], 
    'CRDACCT_DLQ_CYC_5_MNTH_AGO' : [2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'], 
    'CRDACCT_DLQ_CYC_6_MNTH_AGO' : [2, 2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 0, 2, 0, 2, 0], 
    'CRDACCT_DLQ_CYC_7_MNTH_AGO' : [3, 3, 2, 'C', 'C', 'C', 'F', 0, 6, 5, 4, 3, 2, 2, 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'], 
    'CRDACCT_DLQ_CYC_8_MNTH_AGO' : [5, 4, 4, 3, 3, 2, 3, 2, 2, 2, 1, 2, 0, 2, 'C', 'C', 0, 2, 2, 2, 'C', 'C', 0, 'Z'], 
    'CRDACCT_DLQ_CYC_9_MNTH_AGO' : [2, 2, 'C', 0, 2, 0, 2, 'C', 'C', 'C', 'C', 'C', 0, 3, 2, 'C', 'F', 'C', 'F', 'F', 'F', 'F', 'F', 'F'], 
    'CRDACCT_DLQ_CYC_10_MNTH_AGO' : [5, 4, 3, 2, 3, 2, 0, 2, 0, 2, 'C', 'C', 'F', 2, 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'C'], 
    'CRDACCT_DLQ_CYC_11_MNTH_AGO' : [4, 3, 2, 'F', 2, 0, 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z', 'Z'], 
    'CRDACCT_DLQ_CYC_12_MNTH_AGO' : ['F', 8, 7, 6, 5, 4, 3, 2, 'C', 'C', 'C', 0, 2, 'C', 'C', 0, 2, 0, 3, 2, 'C', 'C', 'F', 2]
})

df.head()

I want to transform those values (string value: C, F, and Z) into some categories with this condition: if values in column CRDACCT_DLQ_CYC_1_MNTH_AGO, CRDACCT_DLQ_CYC_2_MNTH_AGO, ......., CRDACCT_DLQ_CYC_12_MNTH_AGO consist:

C = -1
F = -2
Z = -3
else value  = value

Then I transpose the table to identify Month Since Dlq (MSD).

dfT =pd.DataFrame(df.T).reset_index(inplace=False)
dfT

I want to create a list with the name of MSD. MSD is identified for value if it is greater than 1 ( value > 1). For example, in index 2 CRDACCT_DLQ_CYC_1_MNTH_AGO = C or after it has changed = -1 which is not greater than 1. Then, check CRDACCT_DLQ_CYC_2_MNTH_AGO is greater than 1? CRDACCT_DLQ_CYC_2_MNTH_AGO = 3 is greater than 1. Hence, the MSD is 2 because it's in CRDACCT_DLQ_CYC_2_MNTH_AGO. Detail flow chart & overview table for identification .

The MSD value is between 1 and 12 depends on i in CRDACCT_DLQ_CYC_i_MNTH_AGO, for i = 1,2,3,...,12. So the final result is a MSD list with 24 value, identified for each index 0 -23.

CodePudding user response：

Does it what you are looking for:

# From your dataframe
MSD = df.T.apply(pd.to_numeric, errors='coerce').ge(1).idxmax(axis=0) \
        .str.extract(r'CYC_(\d )_MNTH', expand=False).astype(int).tolist()

print(MSD)

# Output:
[1, 1, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 7, 2, 2, 2, 2, 2, 2, 8, 2, 2, 6, 2]