Python Export Dictionary to CSV-CodePudding

I'm new in Python and I've been trying to create csv file and save each result in a new row. The results consist of several rows and each line should be captured in csv. However, my csv file separate each letter into new row. I also need to add new key values for the filename, but I dont know how to get the image filename (input is images). I used the search bar searching for similar case/recommended solution but still stumped. Thanks in advance.

with open('glaresss_result.csv','wt') as f:
f.write(",".join(res1.keys())   "\n")
for imgpath in glob.glob(os.path.join(TARGET_DIR, "*.png")):
    res1,res = send_request_qcglare(imgpath)
    for row in zip(*res1.values()):
        f.write(",".join(str(n) for n in row)   "\n")
f.close()

dictionary res1 printed during the iteration returns:

{'glare': 'Passed', 'brightness': 'Passed'}

The results should be like this (got 3 rows):

glare brightness
Passed Passed
Passed Passed
Passed. Passed

But the current output looks like this:

CodePudding user response：

Few things I changed.

w is enough, since t for text mode is default
no need to close the csv when using a context manager
no need for zip and str(n) for n in row. Just join the 2 values of the dictionary

UPDATED

with open('glaresss_result.csv','w') as f: 
    f.write(",".join([*res1]   ['filename'])   "\n") # replace filename with whatever columnname you want
    for imgpath in glob.glob(os.path.join(TARGET_DIR, "*.png")):
        res1,res = send_request_qcglare(imgpath)
        f.write(",".join([*res1.values()]   [imgpath])   "\n") # imgpath (which needs to be a string) will be the value of each row, replace with whatever you suits

CodePudding user response：

If you plan to do more with data, you might want to check out the pandas library. In your use case p.ex DataFrame.from_records

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

It provides a lot of out of the box functionalities to read, transform and write data.

import pandas as pd
results = []
for imgpath in glob.glob(os.path.join(TARGET_DIR, "*.png")):
    res1,res = send_request_qcglare(imgpath)
    result.append(res1)

df = pd.DataFrame.from_records(results)
df.to_csv("glaresss_result.csv", index=False)