將多個 CSV 文件中的列數據合並到一個 CSV 文件中

Question

我是 Python 新手，尤其是數據處理。 這就是我正在努力實現的目標-

我在多台服務器上運行 CIS 測試並為每個服務器生成一個 CSV 文件（文件名與服務器名相同）。 將所有服務器的輸出文件復制到中央服務器產生的輸出如下所示（截斷的輸出）-

File1: dc1pp1v01.co.uk.csv
Description,Outcome,Result
1.1 Database Placement,/var/lib/mysql,PASSED
1.2 Use dedicated least privilaged account,mysql,PASSED
1.3 Diable MySQL history,Not Found,PASSED

File2: dc1pp2v01.co.uk.csv
Description,Outcome,Result
1.1 Database Placement,/var/lib/mysql,PASSED
1.2 Use dedicated least privilaged account,mysql,PASSED
1.3 Diable MySQL history,Not Found,PASSED

File..n: dc1pp1v02.co.uk.csv
Description,Outcome,Result
1.1 Database Placement,/var/lib/mysql,PASSED
1.2 Use dedicated least privilaged account,mysql,PASSED
1.3 Diable MySQL history,Found,FAILED

我想要的是輸出應該看起來像-

Description  dc1pp1v01 dc1pp2v01 dc1pp1v02 
0  1.1 Database Placement PASSED   PASSED   PASSED
1  1.2 Use dedicated least privilaged account PASSED   PASSED   PASSED
2  1.3 Diable MySQL history PASSED   PASSED   FAILED

為了合並這些文件，我創建了另一個文件，其中只有描述字段和兩列標題，如下所示 -

file: cis_report.csv
Description,Result
1.1 Database Placement,
1.2 Use dedicated least privilaged account,
1.3 Diable MySQL history,

我寫了下面的代碼來進行基於列的合並-

import glob
import os
import pandas as pd 

col_list = ["Description","Result"]
path = "/Users/Python/Data"
all_files = glob.glob(os.path.join(path, "dc*.csv"))

cis_df = pd.read_csv("/Users/Python/Data/cis_report.csv")

for fl in all_files:
   d = pd.read_csv(fl, usecols=col_list)
   f = cis_df.merge(d, on='Description')
   cis_df = f.copy()
   
print(cis_df.head())

我得到的輸出是-

Description Result_x Result_y Result_x Result_y
0                      1.1 Database Placement      NaN   PASSED   PASSED   PASSED
1  1.2 Use dedicated least privilaged account      NaN   PASSED   PASSED   PASSED
2                    1.3 Diable MySQL history      NaN   PASSED   PASSED   FAILED

在我的代碼中，我不確定如何將文件名作為結果的標題並擺脫 NaN。

另外，是否有更好的方法來實現我正在尋找的輸出而不使用虛擬文件（cis_report.csv）？ 非常感謝您的幫助。

Answer 1

您需要DataFrme.pivot()函數。 下面的代碼有很好的注釋和一個完整的工作示例。 根據需要進行更改

import os
import pandas as pd

# Get all file names in a directory
# Use . to use current working directory or replace it with
# e.g. r'C:\Users\Dames\Desktop\csv_files'
file_names = os.listdir('.')

# Filter out all non .csv files
# You can skip this if you know that only .csv files will be in that folder
csv_file_names = [fn for fn in file_names if fn[-4:] == '.csv']

# This Loads a csv file into a dataframe and sets the Server column
def load_csv(file_name):
    df = pd.read_csv(file_name)
    df['Server'] = file_name.split('.')[0]
    return df

# Append all the csvfiles after being processed by load_csv
df = pd.DataFrame().append([load_csv(fn) for fn in csv_file_names])

# Turn DataFrame into Pivot Table
df = df.pivot('Description', 'Server', 'Result')

# Save DataFrame into CSV File
# If this script runs multiple times make sure that the final.csv is saved elsewhere!
# Or it will be read by the code above as an input file
df.to_csv('final.csv')

最終的 DataFrame 看起來像這樣

Server                                     dc1pp1v01 dc1pp1v02 dc1pp2v01
Description
1.1 Database Placement                        PASSED    PASSED    PASSED
1.2 Use dedicated least privilaged account    PASSED    PASSED    PASSED
1.3 Diable MySQL history                      PASSED    FAILED    PASSED

和這樣的 CSV 文件

Description,dc1pp1v01,dc1pp1v02,dc1pp2v01
1.1 Database Placement,PASSED,PASSED,PASSED
1.2 Use dedicated least privilaged account,PASSED,PASSED,PASSED
1.3 Diable MySQL history,PASSED,FAILED,PASSED

Answer 2

用 -

import glob
import os
import pandas as pd 

col_list = ["Description","Result"]
path = "/Users/Python/Data"
all_files = glob.glob(os.path.join(path, "dc*.csv"))

cis_df = pd.read_csv("/Users/Python/Data/cis_report.csv")
from functools import reduce
df_final = reduce(lambda left,right: pd.merge(left,right,on='Description'), [cis_df]+[pd.read_csv(i, usecols=col_list) for i in all_files])
df_final.drop([i for i in df_final.columns if 'Outcome' in i], axis=1).rename(columns={i:j for i,j in zip([i for i in df_final.columns if 'Result' in i], [i.replace('.co.uk.csv','') for i in all_files])})

輸出

    Description dc1pp1v01   dc1pp2v01   dc1pp1v02
0   1.1 Database Placement  PASSED  PASSED  PASSED
1   1.2 Use dedicated least privilaged account  PASSED  PASSED  PASSED
2   1.3 Diable MySQL history    PASSED  PASSED  FAILED

Answer 3

最后，我設法自己做到了。 下面的解決方案對我有用，但我相信有更簡潔的方法 -

import glob
import os
import pandas as pd 
from functools import reduce

col_list = ["Description","Result"]
path = "/Users/Python/Data"
all_files = glob.glob(os.path.join(path, "dc*.csv"))

final_cols = ['Description']
for j in all_files:
    final_cols.append(os.path.basename(j).split('.',1)[0]) 

cis_df = pd.read_csv("/Users/Python/Data/cis_report.csv")

df_final = reduce(lambda left,right: pd.merge(left,right,on='Description'), [cis_df]+[pd.read_csv(i, usecols=col_list) for i in all_files])
df_final.rename(columns=dict(zip(df_final.columns,final_cols)),inplace=True)

print(df_final.head())

我對描述保存文件做了一個小改動。 刪除了每行末尾的結果字段和“,”

文件：cis_report.csv

Description
1.1 Database Placement
1.2 Use dedicated least privilaged account
1.3 Diable MySQL history

我得到的輸出是-

Description dc1pp1v01 dc1pp2v01 dc2pp1v01
0                      1.1 Database Placement        PASSED        PASSED        PASSED
1  1.2 Use dedicated least privilaged account        PASSED        PASSED        PASSED
2                    1.3 Diable MySQL history        PASSED        PASSED        FAILED

Answer 4

你已經有一個贏家，但是：

import csv
from pathlib import Path

path = Path('/Users/Python/Data')

# Read the reports and store the results in a 2-dim list
results = []
for file in path.glob('dc*.co.uk.csv'):
    with open(file, 'r') as fin:
        results += [[file.name.split('.')[0]]
                    + [row[2] for row in csv.reader(fin)][1:]]

# Read the row labels
with open(path / 'cis_report.csv', 'r') as fin:
    labels = [row[0] for row in csv.reader(fin)]

# Prepare the output
output = [[label] + [result[i] for result in results]
          for i, label in enumerate(labels)]

# Write the output
with open(path / 'cis_reports_merged.csv', 'w') as fout:
    csv.writer(fout, delimiter='\t').writerows(output)