In CSV, how to write the header after writing the body?

[Larz60+]

Raj, just click on new thread

Tim,

OK, I got it to work. This will make life a lot easier for you.
first the code, and a sample run. Explanation after that:

Code:

import json


class ReadBib2:
    def __init__(self):
        self.csv_file = 'ConferencePublications.csv'
        self.bib_file = 'ConferencePublications.bib'
        self.temp = 'temp.txt'
        self.ieee = {}

    def make_dict(self):
        with open(self.bib_file, 'r', encoding="utf8") as bf:
            line1 = ''
            longline = False
            for line in bf:
                line = line.strip()
                if '@' not in line and not line.endswith('},') and not line.endswith('},}'):
                    line1 += line
                    longline = True
                    continue
                if longline:
                    longline = False
                    line = line1 + line
                    line1 = ''
                if line.startswith('@'):
                    id = line[line.index('{')+1:]
                    id = id.replace(',', '')
                    self.ieee[id] = {}
                else:
                    name = line[:line.index('=')]
                    value = line[line.index('=')+2:line.index('}')]
                    self.ieee[id][name] = value
        # Save it to json file for future use
        with open('ConferensePublications.json', 'w') as jo:
            json.dump(self.ieee, jo)


    def query_dict(self):
        # first test json load:
        with open('ConferensePublications.json', 'r') as jin:
            local_dict = json.load(jin)

        key = input('Enter ieee id: ')
        print(f'data for {key}:')

        # To get a list of keys:
        all_keys = list(local_dict.keys())
        all_keys.sort()
        print(f'allkeys: {all_keys}')

        # you can get all fields with this
        for key1, value1 in local_dict[key].items():
            print(f'    {key1}, value: {value1}')
        # Or select individual fields like this:
        print(f"\nid 6292950 abstract = {local_dict['6292950']['abstract']}")

        # Or to get entire dictionary:
        for key, value in local_dict.items():
            print(f'Data for ieee id: {key}')
            for key1, value1 in value.items():
                print(f'    {key1}: {value1}')

def main():
    rb = ReadBib2()
    rb.make_dict()
    rb.query_dict()

if __name__ == '__main__':
    main()

Test:

Output:
allkeys: ['1040292', '1188147', '1188376', '1204080', '1378454', '1506224', '4036232', '4401396', '4480099', '4518238', '4557318', '4595196', '4728502', '5069522', '5201243', '5205261', '5206022', '5407520', '5425904', '5450184', '5478770', '5502530', '5671265', '5711782', '5744885', '5745174', '5938020', '5947173', '6034093', '6058866', '6120179', '6221220', '6292950', '6292976', '6310445', '6487117', '6487241', '6487274', '6579580', '6602377', '6602383', '6612051', '6638540', '6645331', '6655322', '6655557', '6673389', '6692503', '6811744', '6811776', '681783', '6855176', '6941904', '6952399', '6952628', '6952925', '6959000', '6966079', '7037034', '7037286', '7037599', '7060279', '7075229', '7080617', '7086838', '7145750', '7151123', '7156411', '7176779', '7176783', '7178352', '7178393', '7178506', '7178517', '7178607', '7247067', '7247160', '7248768', '7249036', '7326474', '7362511', '7369548', '7383821', '7390898', '7391001', '7391114', '7394416', '7414149', '7417276', '7417578', '7417787', '7418208', '7472232', '7472609', '750925', '7510709', '7510959', '7511143', '7521914', '7536773', '7539180', '7541684', '7551750', '7760279', '778822', '7794612', '7794681', '7841677', '7841695', '7841744', '7848801', '7881128', '7905806', '7952375', '7952960', '7959945', '8006695', '8006951', '8081160', '8081408', '8081637', '8081645', '862014', '940688', '940689', '987630']
Enter ieee id: 4036232
data for 4036232:
    author, value: T. Y. Al-naffouri and M. Sharif and B. Hassibi
    booktitle, value: 2006 IEEE International Symposium on Information Theory
    title, value: How Much Does Transmit Correlation Affect the Sum-Rate of MIMO Downlink Channels?
    year, value: 2006
    volume, value: 
    number, value: 
    pages, value: 1574-1578
    abstract, value: This paper considers the effect of spatial correlation between transmit antennas on the sum-rate capacity of the MIMO broadcast channel (i.e., downlink of a cellular system).Specifically, for a system with a large number of users n, we analyze the scaling laws of the sum-rate for the dirty paper coding and for different types of beamforming transmissionschemes. When the channel is i.i.d., it has been shown that for large n, the sum rate is equal to M log log n + M log P/M + o(1) where M is the number of transmit antennas, P is theaverage signal to noise ratio, and o(1) refers to terms that go to zero as n rarr infin. When the channel exhibits some spatial correlation with a covariance matrix R (non-singularwith tr(R) = M), we prove that the sum rate of dirty paper coding is M log log n + M log P/M + log det(R) + o(1). We further show that the sum-rate of various beamforming schemesachieves M log log n + M log P/M + M log c + o(1) where c les 1 depends on the type of beamforming. We can in fact compute c for random beamforming proposed in M. Sharif et al. (2005)and more generally, for random beamforming with preceding in which beams are pre-multiplied by a fixed matrix. Simulation results are presented at the end of the paper
    keywords, value: MIMO systems;antenna arrays;broadcast channels;channel capacity;computational complexity;covariance matrices;radio links;transmitting antennas;wireless channels;MIMObroadcast channel;MIMO downlink channel sum-rate capacity;average signal to noise ratio;beamforming transmission schemes;covariance matrix;dirty paper coding;multiple input multipleoutput systems;random beamforming;spatial correlation;transmit antennas;transmit correlation;Array signal processing;Base stations;Broadband antennas;Broadcasting;Covariancematrix;Downlink;MIMO;Signal to noise ratio;Transmitters;Transmitting antennas
    doi, value: 10.1109/ISIT.2006.261541
    ISSN, value: 2157-8095
    month, value: July

id 6292950 abstract = This paper presents a maximum likelihood (ML) approach to mitigate the effect of narrow band interference (NBI) in a zero padded orthogonal frequency division multiplexing(ZP-OFDM) system. The NBI is assumed to be time variant and asynchronous with the frequency grid of the ZP-OFDM system. The proposed structure based technique uses the fact that theNBI signal is sparse as compared to the ZP-OFDM signal in the frequency domain. The structure is also useful in reducing the computational complexity of the proposed method. The paperalso presents a data aided approach for improved NBI estimation. The suitability of the proposed method is demonstrated through simulations.

Look at the example. Now you can pick and choose what you would like to save to your csv file.
If you want to save the entire dictionary, you can do it either by:

• Get a list of keys
  
• Access each record by key
  
• Or get the whole shebang