如果另一行具有特定值，如何计算csv文件行中字符串的出现次数？

Question

我有一个csv文件，其中包含一排国家/地区值和一排奥运奖牌。 我想计算哪个国家获得了多少枚奖牌。

我用熊猫尝试了一下，但是它给我一个错误消息，即虽然该行确实存在并且被命名为country，但找不到行“ country”。CSV文件：

from collections import Counter
import pandas as pd
import csv

with open('olympics.csv', 'r', encoding='utf-8', newline='') as csv_file:

    df = pd.read_csv("olympics.csv",
                     index_col=["NOC","Medal"])
                     #usecols=["NOC","Medal"])
    #df.sort_index()

    #grouped = df.groupby(['NOC', 'Medal'])

    grouped = df.groupby(['NOC','Medal'])['NOC'].count()

print(grouped)

输出应类似于：

USA 2366 
UK  2189 
Denmark 1455 
....

Answer 1

可以使用“ csv”库中的DictReader来解决。

import csv
import collections

csv_file = open('olympics.csv', 'r', encoding='utf-8')
csv_input = csv.DictReader(csv_file)
country_medals_map = collections.defaultdict(int)
# initializes the country_medals_map with default value as int which will be 0.
# e.g test_dict = defaultdict(int), print(test_dict['not_set_key']) => 0

for input in csv_input:
    country_medals_map[input['NOC']] += 1 if input['Medal'] in ['Gold', 'Silver', 'Bronze'] else 0
    # set dict key as the rows country('NOC') value and increasing the dict value if medal present in GOLD SILVER or BRONZE.

 # finally printing our dict formed.
 for key, value in country_medals_map.items():
     print(key, '-', value)

 # output
 # GRE - 10
 # AUS - 5
 # Uk - 20

如果您希望对输出进行排序，而不是在创建时对其进行排序（更新），请在之后对其进行排序。

sorted_by_medals = sorted(country_medals_map.items(), key=lambda kv: kv[1], reverse=True)

# printing our sorted output
for item in sorted_by_medals:
     print(item)

# output
# ('Uk', 20)
# ('GRE', 10)
# ('AUS', 5)

Answer 2

最终，我自己没有使用熊猫找到了答案。

import csv

d = {}

with open('olympics.csv', 'r', encoding='utf-8', newline='') as f:

    dialect = csv.Sniffer().sniff(f.read(1024))
    #print(dialect)

    # Move to beginning of file
    f.seek(0)

    # DictReader uses the first row in the file as headers.
    r = csv.DictReader(f, dialect=dialect)

    for row in r:

        if row['Medal'] == 'Gold'or 'Silver'or 'Bronze':
            d[row['NOC']] = d.get(row['NOC'], 0) + 1
            #d.sort(key=itemgetter(1)
for k in d:
    #k.sort(key=lambda element: element[1]

    print('{}  {}'.format(k, d[k]))```````