When working with large datasets in Python using the pandas library, it is common to encounter issues with data types and formatting. One such issue is the conversion of large numbers to exponential notation in Excel sheets. This can lead to confusion and inaccuracies in data analysis. In this article, we will delve into the reasons behind this conversion and provide solutions to prevent it.

Understanding the Problem

Pandas, a powerful data manipulation library in Python, is widely used for data analysis and visualization. When exporting data from pandas to an Excel sheet, large numbers are often converted to exponential notation. This is because Excel has a default limit for displaying large numbers, and when this limit is exceeded, it automatically converts the number to scientific notation.

For example, if you have a column with large numbers like 1234567890, when you export this data to an Excel sheet using pandas, it might appear as 1.23457E+09. This conversion can be problematic, especially when working with financial or scientific data where precision is crucial.

This conversion can lead to difficulties in data interpretation and analysis, especially when precise values are required. The challenge is to prevent this automatic formatting and ensure that large numbers are displayed in their full numeric form in the exported Excel sheet.

Reasons Behind the Conversion

There are several reasons why pandas converts large numbers to exponential notation in Excel sheets:

1. Excel’s Default Limit: As mentioned earlier, Excel has a default limit for displaying large numbers. When this limit is exceeded, it automatically converts the number to scientific notation.
2. Data Type: The data type of the column in the pandas DataFrame also plays a role in this conversion. If the data type is set to float, pandas will convert large numbers to exponential notation.
3. Excel’s Formatting: Excel’s default formatting for large numbers is set to scientific notation. This formatting can be changed, but it is the default behavior.

Solutions to Prevent the Conversion

To prevent pandas from converting large numbers to exponential notation in Excel sheets, you can use the following solutions:

1. Set the precision of Pandas to a large value of Decimal Places

One of the simple techniques is to change the value of Pandas precision value by using “pd.set_option” to prevent exponential formatting.

Example: Setting the precision value to 30.

Implementation:

import pandas as pd
import numpy as np

# Install xlsxwriter
!pip install xlsxwriter

# Set Pandas display precision to 30 decimal places
pd.set_option('display.precision', 30)

# Example data with high precision
data = {
    'ID': [1, 2, 3],
    'HighPrecisionNumber': [np.pi, np.e, np.sqrt(2)]
}

# Create DataFrame
df = pd.DataFrame(data)

# Display DataFrame to check precision
print(df)

# Export to Excel with high precision
import xlsxwriter # Import the installed module
with pd.ExcelWriter('high_precision_numbers.xlsx', engine='xlsxwriter') as writer:
    df.to_excel(writer, sheet_name='Sheet1', index=False)
    workbook  = writer.book
    worksheet = writer.sheets['Sheet1']

    # Define a format with 30 decimal places
    number_format = workbook.add_format({'num_format': '0.' + '0'*30})

    # Apply the format to the relevant column (B in this case)
    worksheet.set_column('B:B', 30, number_format)

Output:

      ID               HighPrecisionNumber
0    1               3.141592653589793115997963468544
1     2               2.718281828459045090795598298428
2    3              1.414213562373095145474621858739

2. Convert Numbers to Strings

Convert the large numbers to strings before exporting. This ensures that the numbers are written as plain text and are not formatted in scientific notation.

Implementation:

import pandas as pd

# Example data
data = {
    'ID': [1, 2, 3],
    'LargeNumber': [12345678901234567890, 98765432109876543210, 19283746556473829101]
}

# Create DataFrame
df = pd.DataFrame(data)

# Convert large numbers to strings
df['LargeNumber'] = df['LargeNumber'].astype(str)

# Export to Excel
df.to_excel('large_numbers_as_text.xlsx', index=False)

# Display DataFrame to show the output
print(df)

Output:

   ID           LargeNumber
0   1    12345678901234567890
1   2    98765432109876543210
2   3    19283746556473829101

3. Excel Writer with Formats

Use ExcelWriter from Pandas with specific number formats to control the display of large numbers in Excel. The xlsxwriter library provides more control over the formatting of Excel files.

Implementation:

import pandas as pd

# Example data
data = {
    'ID': [1, 2, 3],
    'LargeNumber': [12345678901234567890, 98765432109876543210, 19283746556473829101]
}

# Create DataFrame
df = pd.DataFrame(data)

# Convert large numbers to strings
df['LargeNumber'] = df['LargeNumber'].astype(str)

# Export to Excel
df.to_excel('large_numbers_as_text.xlsx', index=False)

print(df)

Output:

   ID           LargeNumber
0   1    12345678901234567890
1   2    98765432109876543210
2   3   19283746556473829101

4. Disable Scientific Notation in Pandas

In Pandas, scientific notation can be disablled globally.

Implentation:

import pandas as pd

# Disable scientific notation for large numbers
pd.options.display.float_format = '{:.0f}'.format

# Example DataFrame with large numbers
data = {
    'large_number': [123456789012345, 987654321098765]
}
df = pd.DataFrame(data)

# Write to Excel
df.to_excel('output_file.xlsx', index=False)
print(df)

Output:

     large_number
0  123456789012345
1  987654321098765

Conclusion

Handling large numbers in Pandas and Excel can be challenging due to the automatic conversion to scientific notation. However, by using the methods outlined in this article, you can ensure that your data is accurately represented and easily readable. Whether you choose to adjust Pandas’ display options, use specific formatting functions, or leverage Excel’s formatting capabilities, these solutions will help you maintain data integrity and readability in your projects.