从csv文件中以嵌套dict格式分别计算雄性和雌性

Question

这段代码运行良好，它以这种格式打印结果

我需要像这样的嵌套dict格式的结果

data = {

           'year': {
                    'male': {'Q1': 1, 'Q2': 1, 'Q3': 1, 'Q4': 1, },
                    'female': { 'Q1': 1, 'Q2': 1, 'Q3': 1, 'Q4': 1, }
                   }
       }

守则：

import csv

results = {'males': {}, 'females': {}}

with open('1000 Records.csv') as csv_file:

    csv_reader = csv.reader(csv_file)

    for row in csv_reader:
        year_of_joining = int(row[17])
        quarter_of_joining = row[15]
        gender = 'males' if row[5] == 'M' else 'females'

        if year_of_joining not in results[gender]:
            results[gender][year_of_joining] = {f'Q{i + 1}': 0 for i in range(4)}
        results[gender][year_of_joining][quarter_of_joining] += 1

years = list(results['males'].keys()) + list(results['females'].keys())
years = sorted(list(set(years)))

for year in years:
    count = [results['males'].get(year, 0), results['females'].get(year, 0)]
    print("Male's and Female's: %s: %s" % (year, count))

Answer 1

你很接近。在你的for year in years之外保留一本字典，其中存储年度盘点的运行结果：

data = {}
for year in years:
    data[year] = {'male':results['males'].get(year, 0), 
                 'female':results['females'].get(year, 0)}

Answer 2

这是一个有效的解决方案：

import csv
import collections

data= {}

with open('1000 Records.csv') as csv_file:

    csv_reader = csv.reader(csv_file)

    for row in csv_reader:
        year_of_joining = int(row[17])
        quarter_of_joining = row[15]
        gender = 'male' if row[5] == 'M' else 'female'

        if year_of_joining not in data:
            data[year_of_joining]={'male': {f'Q{i + 1}': 0 for i in range(4)}, 'female': {f'Q{i + 1}': 0 for i in range(4)}}
        data[year_of_joining][gender][quarter_of_joining] += 1

data = collections.OrderedDict(sorted(data.items())) # sorting

for year in data:
    print("Male's and Female's: %s: %s" % (year, data[year]))

上面代码的唯一区别是，它以稍微不同的格式提供输出，但我怀疑这可能是您首先想要的：

Male's and Female's: 1993: {'male': {'Q1': 0, 'Q2': 0, 'Q3': 0, 'Q4': 1}, 'female': {'Q1': 0, 'Q2': 0, 'Q3': 0, 
'Q4': 0}}
Male's and Female's: 1998: {'male': {'Q1': 0, 'Q2': 0, 'Q3': 0, 'Q4': 1}, 'female': {'Q1': 0, 'Q2': 0, 'Q3': 0, 
'Q4': 0}}
Male's and Female's: 1999: {'male': {'Q1': 0, 'Q2': 1, 'Q3': 1, 'Q4': 0}, 'female': {'Q1': 0, 'Q2': 0, 'Q3': 0, 
'Q4': 1}}
Male's and Female's: 2001: {'male': {'Q1': 0, 'Q2': 0, 'Q3': 0, 'Q4': 0}, 'female': {'Q1': 1, 'Q2': 0, 'Q3': 0, 
'Q4': 0}}
Male's and Female's: 2003: {'male': {'Q1': 0, 'Q2': 0, 'Q3': 0, 'Q4': 0}, 'female': {'Q1': 0, 'Q2': 0, 'Q3': 0, 
'Q4': 1}}

如果没有，请告诉我，我会修改它

Answer 3

我在代码中遇到了一些被认为“工作正常”的错误，所以我也修复了它们，并在这个过程中进行了一些优化。以下是我为测试目的创建的简单示例CSV文件的结果：

import csv
from pprint import pprint

#YOJ, QOJ, GEN = 17, 15, 3
YOJ, QOJ, GEN = 0, 1, 2  # For testing since no sample CSV provided.


results = {'males': {}, 'females': {}}

with open('1000 Records.csv') as csv_file:
    for row in csv.reader(csv_file):
        year_of_joining = int(row[YOJ])
        quarter_of_joining = int(row[QOJ])
        gender = 'males' if row[GEN] == 'M' else 'females'

        if year_of_joining not in results[gender]:
            results[gender][year_of_joining] = {f'Q{i + 1}': 0 for i in range(4)}

        QOJ_key = f'Q{quarter_of_joining+1}'  # Convert to dict key format.
        results[gender][year_of_joining][QOJ_key] += 1

years = sorted(results['males'].keys() | results['females'].keys())

data = {year: {'males': results['males'][year],
               'females': results['females'][year]}
        for year in years}

pprint(data, sort_dicts=False)

样本输出：

{1980: {'males': {'Q1': 0, 'Q2': 1, 'Q3': 1, 'Q4': 0},
        'females': {'Q1': 0, 'Q2': 0, 'Q3': 1, 'Q4': 0}},
 1981: {'males': {'Q1': 0, 'Q2': 0, 'Q3': 1, 'Q4': 0},
        'females': {'Q1': 0, 'Q2': 0, 'Q3': 0, 'Q4': 2}}}