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使用函數(shù)參數(shù)過(guò)濾 CSV 文件

使用函數(shù)參數(shù)過(guò)濾 CSV 文件

慕運(yùn)維8079593 2023-09-26 14:10:14
所以我正在編寫(xiě)一個(gè)函數(shù)來(lái)根據(jù)函數(shù)參數(shù)過(guò)濾 csv 文件，然后在過(guò)濾后找到一列的平均值。我只允許使用 import csv （沒(méi)有 pandas）并且不能使用 lambda 或任何其他 python“高級(jí)”快捷方式。我覺(jué)得我可以輕松獲得平均部分，但我在根據(jù)我提到的參數(shù)和約束對(duì)其進(jìn)行過(guò)濾時(shí)遇到了麻煩。我通常會(huì)使用 pandas 來(lái)解決這個(gè)問(wèn)題，這使得這個(gè)過(guò)程更容易，但我不能。這是我的代碼：def calc_avg(self, specific, filter, logic, threshold):                with open(self.load_data, 'r') as avg_file:            for row in csv.DictReader(avg_file, delimiter= ','):                specific = row[specific]                filter = int(row[filter])                logic = logic                threshold = 0                                if logic == 'lt':                    filter < threshold                                    elif logic == 'gt':                    filter > threshold                                    elif logic == 'lte':                    filter <= threshold                                    elif logic == 'gte':                    filter >= threshold                    它應(yīng)該與這個(gè)命令一起使用print(csv_data.calc_avg("Length_of_stay", filter="SOFA", logic="lt", threshold="15"))這是代碼和列標(biāo)題的格式。樣本數(shù)據(jù)：RecordID SAPS-I SOFA    Length_of_stay  132539    6      1         5    132540    16     8         8    132541    21     11       19    132545    17     2         4    132547    14     11        6    132548    14     4         9    132551    19     8         6    132554    11     0        17    
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此選項(xiàng)計(jì)算一次并返回一個(gè)可在迭代行時(shí)使用的logic函數(shù)。compare當(dāng)數(shù)據(jù)有很多行時(shí)，速度會(huì)更快。


# written as a function because you don't share the definition of load_data

# but the main idea can be translated to a class

def calc_avg(self, specific, filter, logic, threshold):

    if isinstance(threshold, str):

        threshold = float(threshold)

    

    def lt(a, b): return a < b

    def gt(a, b): return a > b

    def lte(a, b): return a <= b

    def gte(a, b): return a >= b

    

    if logic == 'lt': compare = lt

    elif logic == 'gt': compare = gt

    elif logic == 'lte': compare = lte

    elif logic == 'gte': compare = gte

    

    with io.StringIO(self) as avg_file: # change to open an actual file

        running_sum = running_count = 0

        for row in csv.DictReader(avg_file, delimiter=','):

            if compare(int(row[filter]), threshold):

                running_sum += int(row[specific])

                # or float(row[specific])

                running_count += 1

        

    if running_count == 0:

        # no even one row passed the filter

        return 0

    else:

        return running_sum / running_count


print(calc_avg(data, 'Length_of_stay', 'SOFA', 'lt', '15'))

print(calc_avg(data, 'Length_of_stay', 'SOFA', 'lt', '2'))

print(calc_avg(data, 'Length_of_stay', 'SOFA', 'lt', '0'))

輸出


9.25

11.0

0

初步答復(fù)


為了過(guò)濾行，一旦確定應(yīng)該使用哪種類(lèi)型的不等式，就必須進(jìn)行比較。這里的代碼將其存儲(chǔ)在 boolean 中include。


然后你可以有兩個(gè)變量：running_sum和running_count稍后應(yīng)該除以返回平均值。


import io

import csv


# written as a function because you don't share the definition of load_data

# but the main idea can be translated to a class

def calc_avg(self, specific, filter, logic, threshold):

    if isinstance(threshold, str):

        threshold = float(threshold)


    with io.StringIO(self) as avg_file: # change to open an actual file

        running_sum = running_count = 0

        for row in csv.DictReader(avg_file, delimiter=','):

            # your code has: filter = int(row[filter])

            value = int(row[filter]) # avoid overwriting parameters

            

            if logic == 'lt' and value < threshold:

                include = True

            elif logic == 'gt' and value > threshold:

                include = True

            elif logic == 'lte' and value <= threshold: # should it be 'le'

                include = True

            elif logic == 'gte' and value >= threshold: # should it be 'ge'

                include = True

            # or import ast and consider all cases in one line

            # if ast.literal_eval(f'{value}{logic}{treshold}'):

                # include = True

            else:

                include = False

            

            if include:

                running_sum += int(row[specific])

                # or float(row[specific])

                running_count += 1

        

        return running_sum / running_count

    


data = """RecordID,SAPS-I,SOFA,Length_of_stay

132539,6,1,5

132540,16,8,8

132541,21,11,19

132545,17,2,4

132547,14,11,6

132548,14,4,9

132551,19,8,6

132554,11,0,17"""



print(calc_avg(data, 'Length_of_stay', 'SOFA', 'lt', '15'))

print(calc_avg(data, 'Length_of_stay', 'SOFA', 'lt', '2'))

輸出


9.25

11.0


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反對(duì) 回復(fù) 2023-09-26
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TA貢獻(xiàn)1757條經(jīng)驗(yàn) 獲得超8個(gè)贊

您沒(méi)有對(duì)比較結(jié)果做任何事情。您需要在if報(bào)表中使用它們以將特定值包含在平均值計(jì)算中。


def calc_avg(self, specific, filter, logic, threshold):

    with open(self.load_data, 'r') as avg_file:

        values = []

        for row in csv.DictReader(avg_file, delimiter= ','):

            specific = row[specific]

            filter = int(row[filter])

            threshold = 0


            if logic == 'lt' and filter < threshold:

                values.append(specific)

            elif logic == 'gt' and filter > threshold:

                values.append(specific)

            elif logic == 'lte' and filter <= threshold:

                values.append(specific)

            elif logic == 'gte' and filter >= threshold:

                values.append(specific)

        if len(values) > 0:

            return sum(values) / len(values)

        else:

            return 0


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