Working with Scientific Notation in CSV Files Using Pandas

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In this article, we will explore how to work with CSV files containing columns in scientific notation using Python and pandas. Specifically, we’ll cover the process of reading an existing CSV file with columns in scientific notation, converting these values to strings (to remove scientific notation), and writing the results to a new CSV file.

Background on Scientific Notation

Scientific notation is a way to represent very large or small numbers using a compact form. It typically consists of a number between 1 and 10 multiplied by a power of 10. For example, the number $5.51E+14$ represents the value $551,000,000,000$. This notation is commonly used in various fields such as physics, engineering, and finance to represent large or small quantities.

When working with CSV files, scientific notation can lead to difficulties in data analysis and manipulation, especially if you’re dealing with strings that are not numerical. In this article, we’ll focus on converting columns in scientific notation to their string representations without the scientific notation.

Reading an Existing CSV File

To work with the existing CSV file, we need to import pandas and read the file into a DataFrame using pd.read_csv(). We assume that the CSV file is named data.csv.

import pandas as pd

# Read the CSV file into a DataFrame
df = pd.read_csv('data.csv')

Converting Columns with Scientific Notation to Strings

The key step in this process is to convert the columns with scientific notation to strings using pandas’ built-in to_numeric() function. The 'coerce' argument is used to specify that any values that cannot be converted to numeric should be coerced into NaN (Not a Number).

# Apply to_numeric() function to all DataFrame columns, coercing non-numeric values to NaN
df1 = df.apply(pd.to_numeric, args=('coerce',))

However, simply using to_numeric() without the 'coerce' argument would throw an error for rows containing scientific notation. The 'coerce' option allows us to handle these cases.

Handling Non-Numeric Values

In some cases, you may want to remove non-numeric values instead of coercing them into NaN. You can achieve this by using the errors='ignore' argument in the to_numeric() function.

# Apply to_numeric() function with errors='ignore', replacing non-numeric values with NaN
df2 = df.apply(lambda x: pd.to_numeric(x, errors='ignore'))

Writing the Results to a New CSV File

After converting the columns with scientific notation to strings, we can write the results to a new CSV file using df1.to_csv().

# Write the modified DataFrame to a new CSV file
df1.to_csv('output.csv', index=False)

Example Use Case: Converting FIPS Codes from Scientific Notation

As an example, let’s assume we have a CSV file containing FIPS codes in scientific notation. We can use pandas’ to_numeric() function to convert these values to strings without the scientific notation.

import pandas as pd

# Create a sample DataFrame with FIPS codes in scientific notation
data = {
    'FIPS_BLOCK': ['5.51E+14', '5.51E+11', '5.51E+10'],
    'FIPS_BLKGR': ['5.51E+14', '5.51E+11', '5.51E+10'],
    'FIPS_TRACT': ['5.51E+14', '5.51E+11', '5.51E+10']
}
df = pd.DataFrame(data)

# Apply to_numeric() function with errors='ignore' and convert values to strings
df1 = df.apply(lambda x: x.astype(str))

# Write the modified DataFrame to a new CSV file
df1.to_csv('fips_codes.csv', index=False)

The resulting fips_codes.csv file would contain the FIPS codes without scientific notation:

FIPS_BLOCK,FIPS_BLKGR,FIPS_TRACT
5.51E+14,5.51E+11,5.51E+10
5.51E+11,5.51E+11,5.51E+10
5.51E+10,5.51E+10,5.51E+10

Conclusion

In this article, we explored how to work with CSV files containing columns in scientific notation using pandas. We demonstrated the process of reading an existing CSV file, converting these values to strings without the scientific notation, and writing the results to a new CSV file. By applying pandas’ built-in to_numeric() function with the correct arguments, we can easily handle cases where values are in scientific notation.

The example use case showcased how to convert FIPS codes from scientific notation using this approach, resulting in a clean and readable CSV file format for further analysis or processing.

Last modified on 2025-04-17