Handling mixed-format data within a single column can be challenging when working with CSV files using Python Pandas. This article aims to provide a comprehensive guide on overcoming this issue and parsing diverse data formats within a column using various techniques and Pandas functionalities.
1. Understanding the Problem.
- Dealing with mixed formats in a CSV column often leads to parsing issues, especially when certain values are not properly quoted.
- Unquoted values and those enclosed in double quotes pose challenges during the reading process.
2. How to Fix the Issue.
2.1 Importing Necessary Libraries.
- First, you should import the Python pandas library using the below code.
import pandas as pd
2.2 Reading CSV File Basics.
- Below is the content of the example csv file ./resource-files/mixed_format_data.csv.
id,name,mixed_column,value 1,John,0,100 2,Jane,1,200 3,Bob,"(10,12)",300 4,Alice,"(20,11)",400 5,Charlie,9,500
- Below is the source code that read the first n rows of the above example csv file.
import pandas as pd csv_file_path = "./resource-files/mixed_format_data.csv" def read_csv_file_first_n_rows(n): # Attempting to read the CSV file df = pd.read_csv(csv_file_path) print(df.head(n)) if __name__ == "__main__": read_csv_file_first_n_rows(3) - Output.
id name mixed_column value 0 1 John 0 100 1 2 Jane 1 200 2 3 Bob (10,12) 300
2.3 Using Additional `read_csv` Options to Parse csv File.
- Now we change the csv file mixed_format_data.csv content to below.
id,name,mixed_column,value 1,John,0,'100' 2,"Jane",1,"200" 3,Bob,'(10,12)',300 4,Alice,'(20,11)',400 5,Charlie,9,500
- We change the double quotes to single quote and add some single or double quote to the column item.
- When you run the code in the section 2.2, you will get the below error message.
File "parsers.pyx", line 890, in pandas._libs.parsers.TextReader._check_tokenize_status File "parsers.pyx", line 2058, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: Expected 4 fields in line 4, saw 5
- This is because of the single quote we used in the csv file, so we need to change the source code to below to parse the new data in the csv file.
import pandas as pd import csv csv_file_path = "./resource-files/mixed_format_data.csv" def read_csv_parse_single_quoting(): # Attempting to use additional options df = pd.read_csv(csv_file_path, quotechar='\'', quoting=csv.QUOTE_ALL, doublequote=True) print(df.head()) if __name__ == "__main__": read_csv_parse_single_quoting() - Output.
id name mixed_column value 0 1 John 0 100 1 2 "Jane" 1 "200" 2 3 Bob (10,12) 300 3 4 Alice (20,11) 400 4 5 Charlie 9 500
- `quotechar`: This defines the single character used to enclose field values when needed. The default is `“`.
- `quoting`: This controls how the `csv` module handles quotes during reading and writing. It has four main options:
- `csv.QUOTE_MINIMAL`: (default) Only quotes fields containing the `quotechar` or the delimiter.
- `csv.QUOTE_ALL`: Quotes all fields regardless of content.
- `csv.QUOTE_NONNUMERIC`: Quotes all fields except integers and floats.
- `csv.QUOTE_NONE`: Never adds quotes during writing, but still interprets existing quotes while reading.
2.4 Custom Parsing with Pandas.
- Change the content of the file mixed_format_data.csv to below.
id,name,mixed_column,value 1,John,0,100 2,"Jane",1,"200" 3,Bob,"(10,12)",300 4,Alice,"(20,11)",400 5,Charlie,9,500
- Now we can use the apply function to trip the “(” and “)” from the mixed format column.
import pandas as pd import csv csv_file_path = "./resource-files/mixed_format_data.csv" def use_apply_function(): df = pd.read_csv(csv_file_path) # Custom parsing using apply function df['mixed_column'] = df['mixed_column'].apply(lambda x: x.strip('()"') if isinstance(x, str) else x) print(df.head()) if __name__ == "__main__": use_apply_function() - Output.
id name mixed_column value 0 1 John 0 100 1 2 Jane 1 200 2 3 Bob 10,12 300 3 4 Alice 20,11 400 4 5 Charlie 9 500
2.5 Replacing and Cleaning Data.
- We can also use the replace() function to replace the character in the mixed column to clean the data.
import pandas as pd import csv csv_file_path = "./resource-files/mixed_format_data.csv" def replace_clean_data(): df = pd.read_csv(csv_file_path) # Replacing unwanted characters df['mixed_column'] = df['mixed_column'].str.replace('(', '').str.replace(')', '').str.replace(',', ' ') print(df.head()) if __name__ == "__main__": replace_clean_data() - Output.
id name mixed_column value 0 1 John 0 100 1 2 Jane 1 200 2 3 Bob 10 12 300 3 4 Alice 20 11 400 4 5 Charlie 9 500
2.6 Using Regular Expressions for Advanced Parsing.
- Source code.
import pandas as pd import re import csv csv_file_path = "./resource-files/mixed_format_data.csv" def parse_with_regular_expression(): df = pd.read_csv(csv_file_path) df['mixed_column'] = df['mixed_column'].apply(lambda x: re.sub(r'\((\d+),(\d+)\)', r'\1-\2', str(x))) print(df.head()) if __name__ == "__main__": parse_with_regular_expression() - Output.
id name mixed_column value 0 1 John 0 100 1 2 Jane 1 200 2 3 Bob 10-12 300 3 4 Alice 20-11 400 4 5 Charlie 9 500
2.7 Handling Numeric and String Formats.
- The to_numeric() function can convert the column to numeric value.
- The astype() function can convert the column to the specified data type.
- The below source code will add 2 new columns by converting the mixed_column to a numeric column and a string column.
import pandas as pd import re import csv csv_file_path = "./resource-files/mixed_format_data.csv" def handling_numeric_and_string_formats(): df = pd.read_csv(csv_file_path) # Differentiating between numeric and string formats df['numeric_column'] = pd.to_numeric(df['mixed_column'], errors='coerce') df['string_column'] = df['mixed_column'].astype(str) print(df.head()) if __name__ == "__main__": handling_numeric_and_string_formats() - Output.
id name mixed_column value numeric_column string_column 0 1 John 0 100 0.0 0 1 2 Jane 1 200 1.0 1 2 3 Bob (10,12) 300 NaN (10,12) 3 4 Alice (20,11) 400 NaN (20,11) 4 5 Charlie 9 500 9.0 9
