Question

我是Python新手，在目录上我有两个csv文件

file1.csv

Id place,Date and hour, Value
1,2018.09.17.12.54,200000
2,2018.09.18.14.16,150000
1,2018.09.19.15.06,78000
3,2018.09.17.16.26,110000
2,2018.09.20.13.54,200000
3,2018.09.17.14.16,150000
1,2018.09.21.12.54,200000

file2.csv

Id place,Date and hour, Value
1,2018.09.24.12.54,200000
3,2018.09.24.14.16,150000
1,2018.09.24.15.06,78000
2,2018.09.26.16.26,110000
1,2018.09.27.12.54,200000
3,2018.09.25.14.16,150000
1,2018.09.28.12.54,200000
3,2018.09.28.14.16,150000

我已阅读direcory中的所有csv文件，并将有关的信息保存在新的csv文件中

求和列文件中的值

输出

Id place, Value
1, 1 156 000
2, 460 000
3, 710 000

平均周数

输出

Week, average Value
1 ,  155428,57   (1088000 / 7)
2 ,  154750   (1238000 / 8)

平均数（以周为单位）

输出

Id place,Week, average Value
1, 1 , 159 333  (478000 / 3)
2, 1 , 175 000  (350000 / 2)
3, 1 , 130 000  (260 000/ 2)
1, 2 , 169 500  (678000 / 4) 
2, 2 , 110 000  (110000 / 1)
3, 2 , 150 000  (450000 / 3)

我不知道该怎么做

Answer 1

我建议使用pandas：

import glob
import pandas as pd

#get all files
files = glob.glob('files/*.csv')
#create list of DataFrames, if necessary remove traling whitespaces in csv headers
dfs = [pd.read_csv(fp).rename(columns=lambda x: x.strip()) for fp in files]
#join together all files
df = pd.concat(dfs, ignore_index=True)

#convert column to datetimes
df['Date and hour'] = pd.to_datetime(df['Date and hour'], format='%Y.%m.%d.%H.%M')
#convert to weeks and for starting with 1 add factorize
df['week'] = pd.factorize(df['Date and hour'].dt.weekofyear)[0] + 1
print (df)
    Id place       Date and hour   Value  week
0          1 2018-09-17 12:54:00  200000     1
1          2 2018-09-18 14:16:00  150000     1
2          1 2018-09-19 15:06:00   78000     1
3          3 2018-09-17 16:26:00  110000     1
4          2 2018-09-20 13:54:00  200000     1
5          3 2018-09-17 14:16:00  150000     1
6          1 2018-09-21 12:54:00  200000     1
7          1 2018-09-24 12:54:00  200000     2
8          3 2018-09-24 14:16:00  150000     2
9          1 2018-09-24 15:06:00   78000     2
10         2 2018-09-26 16:26:00  110000     2
11         1 2018-09-27 12:54:00  200000     2
12         3 2018-09-25 14:16:00  150000     2
13         1 2018-09-28 12:54:00  200000     2
14         3 2018-09-28 14:16:00  150000     2

#aggregate sum
df1 = df.groupby('Id place', as_index=False)['Value'].sum()
print (df1)
   Id place    Value
0         1  1156000
1         2   460000
2         3   710000

#aggregate mean
df2 = df.groupby('week', as_index=False)['Value'].mean()
print (df2)
   week          Value
0     1  155428.571429
1     2  154750.000000

#aggregate mean per 2 columns
df3 = df.groupby(['Id place','week'], as_index=False)['Value'].mean()
print (df3)

   Id place  week          Value
0         1     1  159333.333333
1         1     2  169500.000000
2         2     1  175000.000000
3         2     2  110000.000000
4         3     1  130000.000000
5         3     2  150000.000000

#write output DataFrames to files
df1.to_csv('out1.csv', index=False)
df2.to_csv('out2.csv', index=False)
df3.to_csv('out3.csv', index=False)

Answer 2

绝对不推荐使用，pandas到目前为止是更好的方法，但是手动执行此操作的方法是使用defaultdict对项目进行分组并最后进行计算。

演示：

from csv import reader
from os import listdir
from collections import defaultdict
from datetime import datetime
from operator import itemgetter
from pprint import pprint

# Collect sums first in a defaultdict
sums = defaultdict(list)

# Collect dates seperately since they are more complicated
dates = []

# Get all csv files and open them
for file in listdir("."):
    if file.endswith(".csv"):
        with open(file) as f:
            csv_reader = reader(f)

            # Skip headers
            next(csv_reader)

            # Separately get sums and dates stuff
            for place, date, value in csv_reader:
                sums[int(place)].append(int(value))
                dates.append(
                    (place, datetime.strptime(date, "%Y.%m.%d.%H.%M"), int(value))
                )

# Print out sum of columns
sum_column_values = {k: sum(v) for k, v in sums.items()}
pprint(sum_column_values)

# Get Minimum date to get weeknumber
min_date = min(map(itemgetter(1), dates)).date().isocalendar()[1]

# Collect weeks stuff in separate dicts
weeks = defaultdict(list)
place_weeks = defaultdict(list)

for place, date, value in dates:

    # Weeknumber calculation
    week_number = date.date().isocalendar()[1] - min_date + 1

    # Collect week stuff
    weeks[week_number].append(value)
    place_weeks[int(place), week_number].append(value)

# Print out week averages
week_averages = {k: sum(v) / len(v) for k, v in weeks.items()}
pprint(week_averages)

# Print out place/week averages
place_week_averages = {k: sum(v) / len(v) for k, v in place_weeks.items()}
pprint(place_week_averages)

给出以下结果存储在单独的词典中：

# place averages
{1: 1156000, 2: 460000, 3: 710000}

# week averages
{1: 155428.57142857142, 2: 154750.0}

# place/week averages
{(1, 1): 159333.33333333334,
 (1, 2): 169500.0,
 (2, 1): 175000.0,
 (2, 2): 110000.0,
 (3, 1): 130000.0,
 (3, 2): 150000.0}

Python多个文件的CSV总和列，一周的平均值和分支的平均值

2 个答案: