I have a large set of CSV files. Approx. 15 000 files. And would like to figure out how to join them together as one file for data processing.
Each file is in a simple pattern with timestamp that corresponds to a period of time that represent the data in the each CSV file.
Ex.
file1.csv
2021-07-23 08:00:00
Unit.Device.No03.ErrorCode;11122233
Unit.Device.No04.ErrorCode;0
Unit.Device.No05.ErrorCode;0
Unit.Device.No11.ErrorCode;0
file2.csv
2021-07-23 08:15:00
Unit.Device.No03.ErrorCode;0
Unit.Device.No04.ErrorCode;44556666
Unit.Device.No05.ErrorCode;0
Unit.Device.No11.ErrorCode;0
Each file starts with the timestamp. I would like to join all the files in a directory, and transpose the "Unit.Device" to columns. And then use the original header as a timestamp column. For each file add a new row with the corresponding "ErrorCode" to each column.
Like this:
Timestamp;Unit.Device.No03.ErrorCode;Unit.Device.No04.ErrorCode;Unit.Device.No05.ErrorCode..
2021-07-23 08:00:00;11122233;0;0;0;0....
2021-07-23 08:15:00;0;44556666;0;0;0....
Any simple tools for this, or Python routines?
CodePudding user response:
Try the following approach:
from datetime import datetime
import csv
import glob
data = []
fieldnames = set()
for fn in glob.glob('file*.csv'):
with open(fn) as f_input:
csv_input = csv.reader(f_input, delimiter=';')
timestamp = next(csv_input)[0]
row = {'Timestamp' : timestamp}
for device, error_code in csv_input:
row[device] = error_code
fieldnames.add(device)
data.append(row)
with open('output.csv', 'w', newline='') as f_output:
csv_output = csv.DictWriter(f_output, fieldnames=['Timestamp', *sorted(fieldnames)], delimiter=';')
csv_output.writeheader()
csv_output.writerows(sorted(data, key=lambda x: datetime.strptime(x['Timestamp'], '%Y-%m-%d %H:%M:%S')))
This gives output.csv
as:
Timestamp;Unit.Device.No03.ErrorCode;Unit.Device.No04.ErrorCode;Unit.Device.No05.ErrorCode;Unit.Device.No11.ErrorCode
2021-07-23 08:00:00;11122233;0;0;0
2021-07-23 08:15:00;0;44556666;0;0
CodePudding user response:
Thanks for the reply on my first question here! I will also contribute with a solution for this problem.
I did some read up on Pandas after I found something similar to what I wanted to do. I found that the transform method was very easy to use, and put together this snippet of Python code instead.
import pandas as pd
import os
folder = 'in'
df_out = pd.DataFrame()
for filename in os.scandir(folder):
if filename.is_file():
print('Working on file' filename.path)
df = pd.read_csv(filename.path, encoding='utf-16', sep=';',header =[0])
# Transpose data with timestamp header to columns
df_tranposed = df.T
df_out = df_out.append(df_tranposed)
df_out.to_csv('output.csv')