Question

我有一个csv文件，如下所示：

Date     Name    Wage
5/1/19   Joe     $100
5/1/19   Sam     $120
5/1/19   Kate    $30
5/2/19   Joe     $120
5/2/19   Sam     $134
5/2/19   Kate    $56
5/3/19   Joe     $89
5/3/19   Sam     $90
5/3/19   Kate    $231

我想将其重组为如下形式：

Date      Joe    Sam    Kate
5/1/19    $100   $120   $30
5/2/19    $120   $134   $56
5/3/19    $89    $90    $231

我不确定该如何处理。这是我开始写的东西：

import csv

with open ('myfile.csv', 'rb') as filein, open ('restructured.csv', 'wb') as fileout:
  rows = list(csv.DictReader(filein, skipinitialspace=True))
  names = NOT SURE HOW TO GET THIS
  fieldnames = ['Date'] + ['{}'.format(i) for i in names]
  csvout = csv.DictWriter(fileout, fieldnames=fieldnames, extrasaction='ignore', restval='NA')
  csvout.writeheader()
  for row in rows:
    row['{}'.format(row['Name'].strip())] = row['Wage']
    csvout.writerow(row)

Answer 1

仅使用pandas库：

import pandas as pd

df = pd.read_csv("test.csv", sep="\s+")
p_table = pd.pivot_table(df, values='Wage', columns=['Name'], index='Date', 
                         aggfunc=lambda x:x)
p_table = p_table.reset_index()
p_table.columns.name = None

print(p_table)

输出：

     Date   Joe  Kate   Sam
0  5/1/19  $100   $30  $120
1  5/2/19  $120   $56  $134
2  5/3/19   $89  $231   $90

参考链接：

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html

http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html

Answer 2

可以使用csv模块来完成。这是Python 3的方法：

import csv
import collections

with open ('myfile.csv', 'r') as filein, open ('restructured.csv', 'w', newline='') as fileout:
    data = collections.defaultdict(dict)
    names = set()
    for row in csv.DictReader(filein, skipinitialspace=True):
        data[row['Date']][row['Name']] = row['Wage']
        names.add(row['Name'])
    csvout = csv.DictWriter(fileout, fieldnames = ['Date'] + list(names))
    csvout.writeheader()
    for dat in sorted(data.keys()):
        row = data[dat]
        row['Date'] = dat
        csvout.writerow(row)

生成的csv应该如下所示：

Date,Kate,Joe,Sam
5/1/19,$30,$100,$120
5/2/19,$56,$120,$134
5/3/19,$231,$89,$90

与Python 2相同，除了第一行应为：

with open ('myfile.csv', 'rb') as filein, open ('restructured.csv', 'wb') as fileout:

Answer 3

您要执行的操作也称为从长格式转换为宽格式。使用pandas，您可以通过

轻松完成此操作

import pandas as pd

df = pd.read_csv("myfile.csv", sep = ',')

# Restructure the dataframe
tdf = df.pivot(index = 'Date', columns = 'Name', values = 'Wage')

tdf.to_csv("restructured.csv", sep = ',')

print(tdf)
Name     Joe  Kate   Sam
Date                    
5/1/19  $100   $30  $120
5/2/19  $120   $56  $134
5/3/19   $89  $231   $90

Answer 4

这应该使您走上正确的轨道

data.csv

5/1/19,Joe,$100
5/1/19,Sam,$120
5/1/19,Kate,$30
5/2/19,Joe,$120
5/2/19,Sam,$134
5/2/19,Kate,$56
5/3/19,Joe,$89
5/3/19,Sam,$90
5/3/19,Kate,$231

data = {}
people = set()
with open('data.csv', 'r') as f:
    for line in f.read().splitlines():
        values = line.split(',')

        if values[0] not in data:
            data[values[0]] = {}

        data[values[0]][values[1]] = values[2]
        people.add(values[1])

print('Date,' + ','.join([per for per in people]))
for date in data:
    print(f"{date},{','.join([data[date][per] for per in people])}"

输出：

Date,Sam,Kate,Joe
5/1/19,$120,$30,$100
5/2/19,$134,$56,$120
5/3/19,$90,$231,$89

使用Python重组CSV文件

4 个答案: