Dateutil: The parser.parse() function is applied to each date string to convert it into a datetime object, handling various formats.

done once again

aaj te baba g ne pent coat paya e

I learned about Data Inconsistencies: 1: Inconsistent format (i.e different date formats in the date column) 2: Inconsistent Naming Conventions (i.e Usa, U.S.A, usa, United States, United States of America) 3: Typographical Errors (Mistakes in data entry i.e Pakistan, Paaakistannn) 4: Duplicates (Multiple Occurrence of same row) 5: Contradictory (Logical inconsistency/contradiction i.e father_age<son_age [logically not possible])

done

Done

df = pd.DataFrame(data)# Define a function to standardize the date format def standardize_date(date_str): try: # Try parsing the date with different formats date_obj = pd.to_datetime(date_str, errors='coerce') return date_obj.strftime("%m/%d/%Y") except ValueError: # If parsing fails, return NaN return pd.NaT# Apply the function to the 'date' column df['date'] = df['date'].apply(standardize_date)print(df)

import pandas as pd# Sample DataFrame data = { 'date': ['2021-12-01', '01-12-2022', 'dec-22-2022', '2021/12/12'], 'country': ['USA', 'UK', 'United States of America', 'UK'], 'name': ['nazra', 'nazra', 'Nazar', 'naz'], 'age': [21, 22, 23, 24], 'city': ['lahore', 'karach', 'lahore', 'lahore'], 'sale': [100, None, 300, 400] }df = pd.DataFrame(data)# Define a function to standardize the date format def standardize_date(date_str): try: # Try parsing the date with different formats date_obj = pd.to_datetime(date_str, errors='coerce') return date_obj.strftime("%m/%d/%Y") except ValueError: # If parsing fails, return NaN return pd.NaT# Apply the function to the 'date' column df['date'] = df['date'].apply(standardize_date)print(df)

import pandas as pd# Sample DataFrame data = { 'date': ['2021-12-01', '01-12-2022', 'dec-22-2022', '2021/12/12'], 'country': ['USA', 'UK', 'United States of America', 'UK'], 'name': ['nazra', 'nazra', 'Nazar', 'naz'], 'age': [21, 22, 23, 24], 'city': ['lahore', 'karach', 'lahore', 'lahore'], 'sale': [100, None, 300, 400] }df = pd.DataFrame(data)# Define a function to standardize the date format def standardize_date(date_str): try: # Try parsing the date with different formats date_obj = pd.to_datetime(date_str, errors='coerce') return date_obj.strftime("%m/%d/%Y") except ValueError: # If parsing fails, return NaN return pd.NaT# Apply the function to the 'date' column df['date'] = df['date'].apply(standardize_date)print(df)date country name age city sale 0 12/01/2021 USA nazra 21 lahore 100.0 1 01/12/2022 UK nazra 22 karach NaN 2 12/22/2022 United States of America Nazar 23 lahore 300.0 3 12/12/2021 UK naz 24 lahore 400.0

How to remove inconsistencies in big data