比较数据框的两个字符串列，其值为“PO”、“GO”等，并创建值为“High”、“Low”和“No Change”的第三列

Question

I have two columns in a dataframe. Column one is named as previous_code and column two is named as New_code.These columns have values as "PO","GO","RO" etc. These codes have priority for example "PO" has higher Priority compared to "GO".I want to compare values of these two columns and Put the output in new column as "High","Low" and "No Change" incase both the columns have same code. Below is the example of how dataframe looks like

CustID|previous_code|New_code
345.    | PO.                   | GO
367.    | RO.                   | PO
385.    |PO.                    | RO
455.    |GO.                    |GO

Expected output Dataframe

CustID|previous_code|New_code|Change

345.    | PO.                   | GO.            | Low
367.    | RO.                   | PO.            |High
385.    |PO.                    | RO.            |Low
455.    |GO.                    |GO.             |No Change

If someone could write a demo code for this in pyspark or Pandasthat will be helpful.

Thanks in advance.

Answer 1

If I understood the ordering correctly, this should work fine:

import pandas as pd
import numpy as np
data = {'CustID':[345,367,385,455],'previous_code':['PO','RO','PO','GO'],'New_code':['GO','PO','RO','GO']}
df = pd.DataFrame(data)
mapping = {'PO':1,'GO':2,'RO':3}
df['previous_aux'] = df['previous_code'].map(mapping)
df['new_aux'] = df['New_code'].map(mapping)
df['output'] = np.where(df['previous_aux'] == df['new_aux'],'No change',np.where(df['previous_aux'] > df['new_aux'],'High','Low'))
df = df[['CustID','previous_code','New_code','output']]
print(df)

Output:

   CustID previous_code New_code     output
0     345            PO       GO        Low
1     367            RO       PO       High
2     385            PO       RO        Low
3     455            GO       GO  No change