<prompt> Etsa lenaneo le ka lemohang liketso tsa bolotsana tse ka bang teng.nan  <response>: Instruction in English: Create a program that can detect potential fraudulent activity. . Response in English: pythonimport pandas as pddef detect_frauddata:pd.DataFrame, rules:dict -list: Function to detect potential fraudulent activity based on a given set of rules :param data: pandas dataframe containing the transaction data :param rules: dictionary with keys as column names and values as threshold values :return: list of indices for rows with potential fraudulent activity fraud_index for key, value in rules.items: fraud_index.extendlistdatadatakey value.index removing duplicate indices fraud_index listsetfraud_index return fraud_indexExamplestart by reading the datatransaction_data pd.read_csvfile.csvdefine rules for fraud detectionrules amount: 10000, location_change: Truecall the detect_fraud functionfraud_index detect_fraudtransaction_data, rulesdisplay rows with potential fraudulent activityprinttransaction_data.ilocfraud_index . Response in Sesotho: python import pandas as pd def detect_frauddata:pd.DataFrame, rules:dict -lethathamo: Mosebetsi oa ho bona tsebetso e ka bang teng ea bomenemene ho ipapisitsoe le sehlopha se itseng sa melao :data ea param: pandas dataframe e na le boitsebiso ba transaction :melao ea param: bukantswe e nang le dinotlelo e le dikholomo le boleng e le ditekanyetso tsa threshold :return: list of indices bakeng sa mela e nang le mesebetsi e ka bang teng ya bomenemene fraud_index bakeng sa senotlolo, boleng ho melao.items: fraud_index .eketsalethathamolethathamodatasenotlolo boleng.index ho tlosa likopi tse peli fraud_index listsetfraud_index khutlisetsa fraud_index Mohlala qala ka ho bala data transaction_data pd.read_csvfile .csv hlalosa melao bakeng sa melao ya ho lemoha bomenemene palo: 10000, location_change: True call the detect_fraud function fraud_index detect_fraudtransaction_data, rules bontša mela e nang le tsebetso e ka bang teng ea bolotsanatransaction_data.iloc index_fraud 