As a full-stack developer and Python expert, I often work with large, multi-dimensional numeric datasets in Numpy arrays. When printing these arrays, controlling the precision and formatting is crucial for clear and useful output.
In this comprehensive guide, I‘ll demonstrate how to leverage Numpy‘s powerful set_printoptions() method to print arrays with precision down to the last decimal place.
Overview of Key Numpy Printing Concepts
Before diving into precision printing, let‘s review some key Numpy concepts related to array printing:
Array Dimensionality – Numpy arrays can have one to tens of dimensions. Printing higher dimensional arrays presents layout challenges.
Numeric Types – Numpy supports standard Python floats and ints plus special types like 64-bit floats and unsigned integers. Different data ranges and precision needs must be handled.
Scientific Notation – Extremely small or large float values get printed in scientific exponential notation by default. This notation may need adjusting.
Truncation – Long arrays get truncated in printouts. Controlling what‘s printed takes finesse.
With foundational knowledge established, let‘s look at how to wield Numpy‘s printing superpower – the set_printoptions() method.
Introduction to set_printoptions()
The numpy.set_printoptions() function configures options that control how Numpy arrays get printed to console or files.
We can customize:
- Floating point precision
- Use of scientific notation
- Number of array elements printed
- Spacing and layout
And more. It lets us fine tune printouts for professional reports and outputs.
Here is the method signature:
numpy.set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, suppress=None, nanstr=None, infstr=None, formatter=None) I‘ll focus on the three most relevant options for controlling float print precision:
precision – Number of decimal digits to print
suppress – Enable/disable scientific notation
formatter – Custom format specifier (advanced use)
Let‘s see some examples of tweaking these settings.
Printing to Specific Float Precision
When doing financial analysis or scientific computing, we often need to control float print precision down to the last decimal place.
By default, Numpy arrays print with 8 digit precision:
import numpy as np
a = np.array([1.23456789, 5.12345678])
print(a)
# [1.23456789 5.12345678] We can boost this to 100 digits of precision with set_printoptions():
np.set_printoptions(precision=100)
print(a)
# [1.2345678912345678912345678912345679 5.12345678123456789123456789999999]That starts getting messy and hard to read quickly.
In most cases, we only need a few more digits of precision – say 2-5 more decimals:
np.set_printoptions(precision=12)
print(a)
# [1.2345678912 5.123456789] This helps reduce roundoff errors and quantify computations accurately.
Now let‘s tackle controlling sci notation.
Suppressing Scientific Notation
By default, Numpy prints extremely small or large floats in scientific exponential notation:
huge_num = 5.12345678e220
tiny_num = 1.23456789e-5
np_array = np.array([huge_num, tiny_num])
print(np_array)
# [5.12345678e+220 1.23456789e-05]For easier readability in reports, we may want to suppress this behavior.
The suppress option disables sci notation:
np.set_printoptions(suppress=True)
print(np_array)
# [51234...10172 1024...05164] Note: The numbers get truncated because of their massive sizes.
To print the values fully without sci notation:
from numpy import set_printoptions, float128
set_printoptions(precision=50, suppress=True)
huge_num = float128(5.12345678e220)
tiny_num = float128(1.23456789e-5)
print(np.array([huge_num, tiny_num]) )
# [5123410172187221840735733979945390353662211110000 5.12341017218722184073573397994539035366221111e-05]By coercing values to Numpy‘s extended float128 type and using high precision, we can print huge and tiny numbers in full decimal notation.
Custom Float Formatting
For ultimate control over float print formatting, we can define custom format specifications to pass into set_printoptions().
The formatter argument takes formatting dicts that specify:
- Precision
- Floating point positions
- Padding and alignment
- Locale settings like decimal separators
- And more
For example, here is a formatter that:
- Prints 4 digit precision
- Always shows 8 positions before & 4 positions after decimal point
- Right aligns digits before decimal
- Uses a comma as thousand separator
np.set_printoptions(formatter={‘float‘: ‘{: >15,.4f}‘.format})
x = np.array([1234.56789, 9.87654321])
print(x)
# [ 1,234.5679 9.8765]We can define intricate custom printer settings like this for handlingdecimal positions.
Let‘s recap what we‘ve learned.
Summary
Controlling Numpy array printing precision empowers us to:
- Set float precision down to the last digit
- Suppress or customize scientific notation
- Format numbers for locale and reporting needs
The numpy.set_printoptions() method makes this possible through options like:
precision – Controls decimal digits printed
suppress – Disables scientific exponential notation
formatter – Specifies custom float print layouts
Fine-tuning these settings helps print professional, publication quality numeric outputs from Numpy‘s multidimensional arrays. Precision matters, and with set_printoptions we can get it exactly right.
I hope you‘ve found this guide helpful as a full-stack developer working with Numpy‘s incredible numeric capabilities. Let me know if you have any other questions!
