# How to Suppress Scientific Notation in Python

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Summary: Use the string literal syntax f"{number:.nf}" to suppress the scientific notation of a number to its floating-point representation.

Problem Formulation: How will you suppress a number represented in scientific notation by default to a floating-point value?

Note: Generally, Python represents huge floating-point numbers or very small floating-point numbers in their scientific form. Scientific notation of a number represents the number in terms of powers of 10. For example, the scientific notation for the number 0.0000025 is 2.5e-6.

Example: Consider the following code snippet.

number = 2.5 / 1000000
print(number)

The output is 2.5e-06. How will you suppress the scientific notation and display the output as 0.000025?

## Solution 1: Using String Literal (f-string)

Approach: If you are using Python 3.6 or above, then the simplest solution to our problem is to use the string literal syntax f"{number:.nf}" to represent the given floating-point number in its decimal format with n places of decimal points.

Code:

number = 2.5 / 1000000
print(f"Scientific Notation: {number}")
print(f"Decimal Representation: {number:.7f}")

Output:

Scientific Notation: 2.5e-06
Decimal Representation: 0.0000025

TRIVIA: The string literal technique relies upon str.format() implicitly.

## Solution 2: Using str.format()

format() is a built-in function in Python that returns a formatted string. It formats the specific values and inserts them in the placeholder of the string.

Approach: To represent the scientific notation of the output in decimal format with n places following the decimal point, use str.format(number) where str is “{:.nf}” .

Code:

number = 2.5 / 1000000
print("Scientific Notation: ", number)
print("Decimal Representation: {:.7f}".format(number))

Output:

Scientific Notation:  2.5e-06
Decimal Representation: 0.0000025

Note: Ensure that you manage the value of n, that is, the number after the decimal points you want to display properly, depending on how small the floating-point number is. For example, have a look at the following example:

a = -7.1855143557448603e-17
print('{:f}'.format(a))

# -0.000000

Since the default number of digits after the decimal point is 6, the output of the above snippet will be -0.000000. This is not helpful for the given example. Hence, we could use something which looks like this:

a = -7.1855143557448603e-17
print('{:.30f}'.format(a))

# OUTPUT: -0.000000000000000071855143557449

Recommended Tutorials to understand how string formatting works in Python:

## Solution 3: Using % Formatting

The % operator allows you to format a string object in Python. Conversion specifiers, like %f and %s, are used in the format string as placeholders. These specifiers dictate how % will format the values.

Approach: If you are using an older version of Python, use %.nf to suppress the scientific notation to a floating-point value upto n decimal places. Remember to manage the precision manually.

Code:

number = 2.5 / 1000000
print("Scientific Notation: ", number)
print("Decimal Representation: %.7f" % number)

Output:

Scientific Notation:  2.5e-06
Decimal Representation: 0.0000025

## Solution 4: Using Pandas

Approach: Another option, if you are using pandas and would like to suppress scientific notation for all floats, is to adjust Pandas options as shown below.

Code:

import pandas as pd

pd.options.display.float_format = '{:.10f}'.format
df = pd.DataFrame([1.192433e-06, 1.293066e-06, 1.077142e-06], index=['foo', 'bar', 'baz'], columns=['time'])
print(df)

Output:

time
foo 0.0000011924
bar 0.0000012931
baz 0.0000010771

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## Solution 5: Using Numpy

In the above approaches, we had to specify the precision manually. But, what if you do not have an idea about the precision? This is where the Numpy library will help you.

Approach: format_float_positional() is a function of the Numpy library in Python that formats a floating-point scalar. It then returns the decimal string representation of the floating-point value passed. The optional trim argument, when set to '-' trims trailing zeros and any trailing decimal point if not needed.

Code:

import numpy as np
number = 2.5 / 1000000
print("Scientific Notation: ", number)
print("Decimal Representation: ", np.format_float_positional(number, trim='-'))

Output:

Scientific Notation:  2.5e-06
Decimal Representation:  0.0000025

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## Solution 6: Using decimal Module

Another workaround to the given problem is to import the decimal module and use its Decimal instance, as shown below.

from decimal import Decimal
number = str(Decimal(2.5) / Decimal(1000000))
print(number)

Output:

0.0000025

⚠️Caution: This approach won’t work for values smaller than 1e-6. It turns values lesser than 1e-6 to their scientific notation.

### ✨How to Print a Number in Scientific Notation in Python?

You can convert a number to its scientific notation using "{:e}".format(num). To specify the precision, i.e., the number of digits after the decimal point, use "{:.Ne}", where N represents the number of digits.

Example:

num = 1250000000
# format number into scientific notation
print("{:e}".format(num))
# format scientific notation with 2 digits
print("{:.2e}".format(num))

Output:

1.250000e+09
1.25e+09

### ✨How to Limit Floats to Two Decimal Points in Python?

Use the built-in function round() to round off the given decimal number to the given precision.

Example:

x = 3.14667
y = round(x)
z = round(x, 3)  # Limit the float value to three decimal points
print("x=", x)
print("y=", y)
print("z=", z)

Output:

x= 3.14667
y= 3
z= 3.147

Read Here: How to Limit Floats to Two Decimal Points in Python?

### ✨How to Match Scientific Notation in a String?

Leverage the power of regex to detect the presence of numbers represented in their scientific notation in a given string.

Example:

import re

val = "the scientific notation for the number 0.0000025 is 2.5e-6"
num = re.compile('[-+]?[\d]+\.?[\d]*[Ee](?:[-+]?[\d]+)?')
print(re.findall(num, val))

Output:

['2.5e-6']

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## Conclusion

To wrap things up, if you are on a newer version of Python, then it is advisable to use the string literal approach to suppress the scientific notation to a floating-point value. In case you are dealing with a Pandas dataframe or a Numpy array, then proposed solutions 4 and 5 are worth the try.

With that, we come to the end of this tutorial. Please subscribe and stay tuned for more interesting tutorials in the future. Happy learning!