如何根据多列拆分csv

Question

我正在尝试根据两列值将 csv 拆分为多个文件。 例如，

源文件：

Header1  Header2             Header3
Alpha    energy              0.1
Alpha    energy              0.34
Beta     energy_imbalance    0.66
Beta     energy              0.7
Beta     energy              0.1
Gamma    energy_imbalance    0.3

预期输出：

输出文件 1：

Header1  Header2             Header3
Alpha    energy              0.1
Alpha    energy              0.34

输出文件2：

Header1  Header2             Header3
Beta     energy_imbalance    0.66

输出文件3：

Header1  Header2             Header3
Beta     energy              0.7
Beta     energy              0.1

输出文件4：

Header1  Header2             Header3
Gamma    energy_imbalance    0.3

以下是我开始的内容：

filein = open('test.csv')
csvin = csv.DictReader(filein)

outputs = {}
for row in csvin:
    primaryValue = row['Header1']
    secondaryValue = row['Header2']
    if primaryValue not in outputs:
        fileout = open('{}_{}.csv'.format(primaryValue,secondaryValue),'w')
        dw = csv.DictWriter(fileout, fieldnames=csvin.fieldnames)
        dw.writeheader()
        outputs[primaryValue] = fileout, dw
    outputs[primaryValue][1].writerow(row)

for fileout, _ in outputs.values():
    fileout.close()

我能够根据 column = Header1 拆分文件，但是我不确定如何进一步进行。

Answer 1

在这里尝试：

csvin = csv.DictReader(filein)
csv_files = {}
files = []

for row in csvin:
    key = (row['Header1'], row['Header2'])
    if key not in csv_files:
        # create the csv file
        fileout = open('{}_{}.csv'.format(*key), 'w')
        dw = csv.DictWriter(fileout, fieldnames=csvin.fieldnames)
        dw.writeheader()
        csv_files[key] = dw
        files.append(fileout)  # to close them later

    # write the line into to corresponding csv writer
    csv_files[key].writerow(row)

# close all files
for f in files: f.close()

Answer 2

这是一种按照@Barmar的建议行事的方法（只是它不使用f字符串来定义csv_files字典键值）：

import csv


infile_name = 'test.csv'

with open(infile_name, newline='') as infile:
    reader = csv.DictReader(infile)
    csv_files = {}
    files = []

    for row in reader:
        key = '{}_{}'.format(row['Header1'], row['Header2'])
        if key not in csv_files:
            # Create the csv file
            outfile_name = '{}.csv'.format(key)
            fileout = open(outfile_name, 'w', newline='')
            writer = csv.DictWriter(fileout, fieldnames=reader.fieldnames)
            writer.writeheader()
            csv_files[key] = writer
            files.append(fileout)  # To close them later.

        # Write the line to corresponding csv writer.
        csv_files[key].writerow(row)

    # Close all csv output files.
    for f in files:
        f.close()

应用于示例输入文件，这将产生以下csv输出文件：

Alpha_energy.csv
Beta_energy.csv
Beta_energy_imbalance.csv
Gamma_energy_imbalance.csv

包含您期望的数据。

Answer 3

使用 pandas df.groupby()是另一种基于多列值拆分 csv 的选项。

工作示例：

import pandas as pd
df = pd.read_csv('test.csv')
def df_to_grouped_csv(df):
    df_group = df.groupby(['Header1', 'Header2'])
    for name, group in df_group:
        outfile = '_'.join(name) + '.csv'
        group.to_csv(outfile, index=False)

输出：

Alpha_energy.csv
  Header1 Header2  Header3
0   Alpha  energy     0.10
1   Alpha  energy     0.34
Beta_energy.csv
  Header1 Header2  Header3
3    Beta  energy      0.7
4    Beta  energy      0.1
Beta_energy_imbalance.csv
  Header1           Header2  Header3
2    Beta  energy_imbalance     0.66
Gamma_energy_imbalance.csv
  Header1           Header2  Header3
5   Gamma  energy_imbalance      0.3

在性能方面，与csv.DictWriter 方法相比，这应该显示出改进（特别是对于大文件）。 但它确实需要导入熊猫。

表现：

Larger file (500,000 rows)
In [1]: %timeit df_to_grouped_csv()
865 ms ± 36.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [2]: %timeit csv_DictWriter_approach()
2.71 s ± 40.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)