<jupyter_start><jupyter_text>Registation date for the companyThe raw data can be found [here](https://data.world/telangwana/company-master-data-of-telangana-upto-31st-march-2015/workspace/file?filename=company_master_data_upto_Mar_2015_Telangana.csv/)- `CORPORATE_IDENTIFICATION_NUMBER` - Corporate ID- `DATE_OF_REGISTRATION` - Registation Date- `COMPANY_NAME` - Company Name- `AUTHORIZED_CAPITAL` - Authorized Capital- `PAIDUP_CAPITAL` - Paidup Captital<jupyter_code>import csv

inputList = []
with open('company_master_data_upto_Mar_2015_Telangana.csv','r',encoding='utf-8') as f:
    reader = csv.reader(f,dialect='excel')
    for row in reader:
        inputList.append(row)<jupyter_output><empty_output><jupyter_text>Data DenoiseThe raw data behind the story  **Some people are too superstitious to have a baby on Friday the 13th**, which you can read [here](https://fivethirtyeight.com/features/some-people-are-too-superstitious-to-have-a-baby-on-friday-the-13th/)- `Header` - Remove CSV Header- `NA` - Remove record if field is NA- `Output` - DenoiseList1.csv<jupyter_code>import pandas as pd
#Remove the Header
inputList = inputList[1:len(inputList)]

#Remove the NA Element
DenoiseList1 = []
for i in inputList:
    if i[0] != "":
        if i[1] != "NA" and i[4] != "NA":
            DenoiseList1.append([i[0],i[1],i[2],i[3],i[4]])
name=['CORPORATE_IDENTIFICATION_NUMBER','DATE_OF_REGISTRATION','COMPANY_NAME','AUTHORIZED_CAPITAL','PAIDUP_CAPITAL']
csv=pd.DataFrame(columns=name,data=DenoiseList1)
csv.to_csv('DenoiseList1.csv',encoding='utf-8')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13753943217665615, 'fraction_numerical': 0.02145110410094637, 'mean_word_length': 5.986784140969163, 'pattern_counts': {'":': 2, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '528371', 'license': 'MIT', 'n_tokens_mistral': 612, 'n_tokens_neox': 579, 'n_words': 105, 'path': 'Step1-RawDataDenoise.ipynb', 'repo_name': 'randyguogithub/BeautyData', 'size': '1585'}