Solve pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 48, saw 2

Table of Contents

wrong reason

Solution

Resolve delimiter errors

Solve the problem of non-standard data format

Summarize

Solve pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 48, saw 2

When using Pandas for data processing, you sometimes encounter the error “pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 48, saw 2”. This error is usually caused by delimiter errors in the data file or non-standard data format. This blog will introduce how to solve this problem.

Error reason

There are two possibilities for causing the error:

Delimiter error exists in the data file. When reading data, Pandas uses commas as the delimiter by default. If the delimiter in the data file does not match the default delimiter, an error will result.
The data format of a certain row in the data file is not standardized. For example, some rows have an incorrect number of data fields, or data fields contain special characters, etc., which may cause parsing errors.

Solution

For these two possibilities, we can take the following solutions respectively.

Resolve delimiter errors

If the delimiter in the data file is inconsistent with the default delimiter, we can use the ??delimiter?? parameter of the ??read_csv?? function to specify the correct delimiter.

pythonCopy codeimport pandas as pd
data = pd.read_csv('data.csv', delimiter=';')

In the above code, we set the delimiter to a semicolon (;) to suit the format of the data file.

Solve the problem of non-standard data format

If the data format of some lines in the data file is not standardized, we can use the ??error_bad_lines?? parameter to skip the error lines and continue reading valid data.

pythonCopy codeimport pandas as pd
data = pd.read_csv('data.csv', error_bad_lines=False)

In the above code, we set the ??error_bad_lines?? parameter to False, so that when an error line is encountered, Pandas will skip these lines and continue reading the next line. If we want to skip the error line and know the specific location of the error line, we can set the ??on_bad_lines?? parameter to ??warn??, so that Pandas will print A warning message is issued to inform us that an error line has been encountered.

pythonCopy codeimport pandas as pd
data = pd.read_csv('data.csv', error_bad_lines=False, on_bad_lines='warn')

Summary

Through the above two solutions, we can solve the “pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 48, saw 2” error. Choose an appropriate solution according to the actual situation, and make corresponding settings according to the format of the specific data file. I hope this blog will help solve this error. If you have other questions about Pandas, please leave a message for discussion.

Suppose there is a data file named “data.csv” that contains some student performance information. Each row of data includes the student’s name and grade, using commas as delimiters. However, on line 48, a piece of data contains extra fields, resulting in non-standard format, resulting in the error “pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 48, saw 2”. Here is sample code on how to solve this problem:

pythonCopy codeimport pandas as pd
try:
    data = pd.read_csv('data.csv')
except pd.errors.ParserError as e:
    if 'Expected 1 fields in line 48, saw 2' in str(e):
        # If an unexpected field number error occurs, take appropriate actions.
        print("Data field error in line 48")
        # You can choose to skip the error line and continue reading the next line
        data = pd.read_csv('data.csv', error_bad_lines=False, warn_bad_lines=True)
    else:
        # Other errors, such as incorrect file path, etc.
        print("Error reading file:", str(e))
else:
    # The data is read successfully and subsequent operations can be performed.
    print(data.head())

In the above code, we used the ??try-except?? statement to catch the ??pd.errors.ParserError?? exception. In the ??ParserError?? exception, we check whether the exception information contains the specific error message “Expected 1 fields in line 48, saw 2”. If so, it is determined that the number of fields is wrong. You can Select to skip the error line and continue reading the next line. If it is not this specific error message, it may be other errors, such as file path errors, etc., which can be handled accordingly. Through the above example code, we solved the “pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 48, saw 2” error in actual application scenarios, and performed corresponding error handling operations to ensure Correct reading and processing of data.

In Pandas, the format of data usually needs to meet certain specifications so that the reading and processing of data can proceed smoothly. The following is a detailed introduction to the specifications of Pandas data format:

Delimiter: Data fields in data files are usually separated by specific delimiters. In Pandas, the default delimiter is comma (,). When using the ??read_csv()?? function to read data, if the data file uses other delimiters, you can set it by setting ? The ?delimiter?? or ??sep?? parameter specifies the correct delimiter.

pythonCopy codeimport pandas as pd
data = pd.read_csv('data.csv', delimiter=';')

Missing values: There may be some missing data in the data file, usually represented by specific symbols or strings. In Pandas, missing data is represented as NaN (Not a Number) by default. You can use the ??na_values?? parameter to specify other symbols or strings.

pythonCopy codeimport pandas as pd
data = pd.read_csv('data.csv', na_values=['-', 'NA'])

Blank lines and comment lines: Data files may contain blank lines or comment lines, which can be set by setting ??skip_blank_lines?? and ??comment?? parameter is skipped.

pythonCopy codeimport pandas as pd
data = pd.read_csv('data.csv', skip_blank_lines=True, comment='#')

Data type: Different fields in the data file may have different data types, such as integers, floating point numbers, strings, etc. Pandas will infer the data type of the field based on the data in the data file. You can also specify the data type of the field by setting the ??dtype?? parameter.

pythonCopy codeimport pandas as pd
data = pd.read_csv('data.csv', dtype={'Age': int, 'Salary': float})

Quotation marks: If the data fields in the data file are enclosed in quotation marks, you can use the ??quotechar?? parameter to specify the correct quotation mark character.

pythonCopy codeimport pandas as pd
data = pd.read_csv('data.csv', quotechar='"')

Header and index: The first line in the data file may be the field name or the data. You can specify the number of header lines by setting the ??header?? parameter. If there is no header in the data file, you can use ??header=None??. By default, Pandas automatically infers index columns.

pythonCopy codeimport pandas as pd
data = pd.read_csv('data.csv', header=0) # Take the first row as the header
data = pd.read_csv('data.csv', header=None) # No header

The above are some of the main points of the Pandas data format specification. In actual use, according to the specific conditions of the data file, the corresponding parameters are set according to these specifications to ensure that the data can be read and processed correctly.

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