DEV Community: Vee Satayamas

Validating Native Python: A Practical Approach with baredtype

Vee Satayamas — Mon, 19 Jan 2026 00:35:05 +0000

In Python development, we often default to using basic data structures like dict and list to move data around. They are flexible, JSON-compatible, and require no special setup. With the recent advancements in TypedDict and Annotated, the Python type system has become powerful enough to describe these structures with high precision.

baredtype is a library built to bridge the gap between these native structures and formal validation. It allows you to enforce strict rules on your data without ever forcing that data to change its shape or type.

The Strength of Basic Data Structures

The main draw of baredtype is that it works directly with the data structures you already use. There is no custom class instantiation or "wrapping" of data into library-specific objects.

Serialization and Deserialization

Because your data remains a standard list or dict, you don't need special methods to get your data in or out of a system. You use the standard tools you already know:

To JSON: json.dumps(my_data)
From JSON: json.loads(input_string)
Validation: validate(MySchema, my_data)

Handling Keys and `None` Values

Working with native structures means using standard Python idioms. baredtype respects TypedDict definitions for required fields and Annotated for value constraints, allowing you to handle None values and missing keys naturally.

from typing_extensions import TypedDict, NotRequired, Annotated
from baredtype import validate

class UserData(TypedDict):
    username: str
    email: Annotated[str | None, {"pattern": r"^[\w.-]+@[\w.-]+\.\w+$"}]
    age: NotRequired[int]

# Use standard Python to check your data:
data = {"username": "tech_lead", "email": None}

if data.get("email") is None:
    print("No email provided.")

if "age" not in data:
    print("Age is an optional key.")

# Validation checks the types and the constraints
is_valid = validate(UserData, data)

Mapping to JSON Schema and OpenAPI

A core objective of baredtype is making it easy to map Python types to external standards like JSON Schema and OpenAPI. It handles the "quantifiers" that define how different types can be combined.

Quantifier Logic

oneOf: By using Annotated metadata, you can specify that data must match exactly one type in a Union. This is crucial for precise API definitions where ambiguity can lead to bugs.
allOf: This is naturally supported through TypedDict inheritance. When one TypedDict inherits from others, baredtype generates a schema representing the union of those requirements, staying consistent with OpenAPI standards.

Two Ways to Verify the Spec

When we define our data structures, we are writing a specification for our code. There are two primary ways to ensure our code follows this spec, both of which are supported by baredtype.

1. Static Type Checking

Because the library is built on TypedDict, you get the full benefit of tools like Mypy or Pyright. Your IDE can catch missing keys or type mismatches while you are writing the code. This finds errors before the code ever runs.

2. Property-Based Checking

While static types prove what should happen, property-based checking proves what can happen under stress. baredtype integrates with Hypothesis to automatically generate data that fits your TypedDict definitions, including constraints like min_length or regex patterns.

from baredtype import given_from_annotations

@given_from_annotations
def test_process_payload(payload: UserData):
    # Hypothesis generates a wide variety of valid UserData dicts
    # to find edge cases in your logic.
    result = my_function(payload)
    assert result is not None

Validation: The Constant Requirement

Regardless of whether you use static checking, property-based checking, or both, runtime validation is a must. External data is inherently untrusted. Static checking verifies your internal logic, and property-based testing verifies your code's resilience during development. At the moment data enters your system, baredtype provides the final, essential check to ensure the data actually matches your specification.

Explore the Project

baredtype offers a lightweight validation layer that stays out of your data's way, leveraging the emerging strengths of the Python type system.

Check out the project on Codeberg:
👉 https://codeberg.org/veer66/baredtype

Revisiting codebase organization practices from 2004

Vee Satayamas — Sat, 27 Dec 2025 02:06:57 +0000

I reviewed the file and directory structure of several web-based free software projects released in 2003–2004: the Koha 1.2.0 library management system, the MRBS 1.2.1 meeting room booking system, and the DSpace 1.2 content repository system. These were written in Perl, PHP, and Java, respectively.

Endpoint files are named using an action, optionally followed by an entity, following patterns such as search.pl, search.php, SimpleSearchServlet.java, updatebibitem.pl, edit_entry.php, and EditProfileServlet.java. These verbs or verb phrases may or may not align with user workflows. For example, Koha includes opac_reserve.pl, indicating it handles the reservation workflow via the Online Public Access Catalog (OPAC) API. However, there is no dedicated "borrow" endpoint file.

Each system is designed in a modular style. Koha has a Borrower.pm module that provides a reserveslist function; MRBS uses functions.inc with functions such as make_room_select_html; and DSpace's Item.java includes methods like findAll. That said, they do not strictly follow a multi-tier architecture, for instance, database queries are not isolated into a dedicated data access layer. Still, DSpace, by using Servlets and JSPs, does establish a distinct presentation layer.

Because of the verb-based naming convention, files emphasize what they do rather than which architectural layer they belong to. This makes their purpose easy to understand at a glance.

However, this approach can make organization and testing more challenging. Logic, database queries, and presentation code are often mixed; sometimes not even separated into distinct functions.

Global Variables in Python Are Not That Global

Vee Satayamas — Sat, 13 Dec 2025 05:51:25 +0000

In the context of programming in the 1980s, "global variables" likely brings to mind languages like MBASIC. However, using MBASIC as an example today would be challenging, as it is now rarely used or known. Instead, GNU Bash, which is the default shell scripting language for many systems—will be used to illustrate what global variables were traditionally like. Anyway, some might think of Fortran II, but I'm not familiar with it.

Bash Example

I wrote a Bash script consisting of four files:

a.bash

a.bash orchestrates everything. "Orchestration" might be too grand a word for these toy scripts, but I want to convey that it performs a role similar to Apache Airflow.

#!/bin/bash

. ./b.bash
. ./c.bash
. ./d.bash

init
print_count
inc
print_count

b.bash

b.bash contains only one function, inc, which increments the counter, which is the global variable in this example.

inc() {
    counter=$((counter + 1))
}

c.bash

c.bash contains print_count, which simply displays the value of the global variable counter.

print_count() {
    echo $counter
}

d.bash

In Bash, counter can be initialized globally from within a function by default.

init() {
    counter=1
}

Python Example

The following section shows the result of porting the Bash script above to Python, highlighting the key differences.

a.py

This is the orchestration part. Note the use of namespaces or module names.

import c, b, d

d.init()
c.print_count()
b.inc()
c.print_count()

b.py

In the Python version, inc must refer to the module d to access the variable. Alternatively, counter could be explicitly imported.

import d

def inc():
    d.counter += 1

c.py

Similarly, print_count in Python must also refer to module d.

import d

def print_count():
    print(d.counter)

d.py

Unlike in Bash, initializing a global variable from within a function—even in the same module—requires an explicit global declaration.

def init():
    global counter
    counter = 1

Key Differences

As you can see, a global variable in Bash is truly global across files. In Python, however, a global variable is only global within its module, i.e., file. Furthermore, mutating a global variable in Bash requires no special syntax, whereas in Python a function must explicitly declare global to modify a module-level variable.

Consequently, although both are called "global variables," Python's are scoped to the module. This means they won’t interfere with variables in other modules unless we deliberately make them do so. For developers who use one class per module, a Python global variable behaves much like a class variable. Additionally, variable assignment inside a Python function is local by default, preventing accidental modification of global state unless explicitly intended.

In short, many traditional precautions about global variables in languages like Bash or MBASIC no longer apply in Python. Therefore, we might reconsider automatically rejecting global variables based on past advice and instead evaluate their use case thoughtfully.

A lesson about abstraction from Arch Linux

Vee Satayamas — Sun, 23 Nov 2025 09:23:50 +0000

I was impressed by pkgsrc because it is a powerful package management system that defines packages using a common tool like a Makefile. I later discovered that the PKGBUILD file is even more impressive, as its purpose is immediately clear. I initially attributed this to my greater familiarity with Bash scripting compared to Makefiles, but I now believe the true reason is PKGBUILD's level of abstraction.

It retains explicit calls to configure and make, preserving the transparency of a manual installation. This demonstrates that while increased abstraction can make code shorter, it can also hinder understanding.

Another potential advantage of greater abstraction is the ability to change the configure command for every package by modifying just one location. However, since GNU Autotools has continued to use the configure command for decades, it may not be worth sacrificing clarity for this particular benefit.

Reasons to use Emacs in 2025

Vee Satayamas — Tue, 02 Sep 2025 15:52:03 +0000

Expanding Emacs functionality is as simple as defining a new function instead of creating an entire extension package, as is often done in many other extensible editors. This function can then be re-evaluated, tested, and modified entirely within Emacs using just a few clicks or keyboard shortcuts, with no need to restart or reload Emacs.

Reasons to use Common Lisp in 2025

Vee Satayamas — Sun, 17 Aug 2025 16:14:26 +0000

Reasons to use Common Lisp

An actively-maintained-implementation, long-term-stable-specification programming language

There are many programming languages that don't change much, including
Common Lisp, but Common Lisp implementations continue to be developed.
For example, SBCL (Steel Bank Common Lisp) released its latest version
just last month.

Common Lisp can be extended through libraries. For example, cl-interpol
enables Perl-style strings to Common Lisp without requiring a new
version of Common Lisp. cl-arrows allows Common Lisp to create pipelines
using Clojure-style syntax without needing to update the Common Lisp
specification. This exceptional extensibility stems from macro and
particularly reader macro support in Common Lisp.

Feature-packed

Common Lisp includes many features found in modern programming
languages, such as:

Garbage collection
Built-in data structures (e.g., vectors, hash tables)
Type hints
Class definitions
A syntactic structure similar to list comprehensions

Multi-paradigm

While Lisp is commonly associated with functional programming, Common
Lisp doesn't enforce this paradigm. It fully supports imperative
programming (like Pascal), and its object-oriented programming system
even includes advanced features. Best of all, you can freely mix all
these styles. Common Lisp even embraces goto-like code via TAGBODY-GO.

Performance

Common Lisp has many implementations, and some of them, such as SBCL,
are compilers that can generate efficient code.

With some (of course, not all) implementations, many programs written in
dynamic programming languages run slower than those in static ones, such
as C and Modula-2.

First, an example of the generated assembly will be shown, along with
more explanation about why it might be slowed down by some dynamic
implementations

The code listing below is a part of a program written in Modula-2, which
must be easy to read by programmers of languages in the extended ALGOL
family.

    TYPE
      Book = RECORD
        title: ARRAY[1..64] OF CHAR;
        price: REAL;
      END;

    PROCEDURE SumPrice(a, b: Book): REAL;
    BEGIN
      RETURN a.price + b.price;
    END SumPrice;

The code is mainly for summing the price of books, and only the part
'a.price + b.price' will be focused on.

'a.price + b.price' is translated into X86-64 assembly code list below
using the GNU Modula-2 compiler.

    movsd   80(%rbp), %xmm1
    movsd   152(%rbp), %xmm0
    addsd   %xmm1, %xmm0

"movsd 80(%rbp), %xmm1' and 'movsd 152(%rbp), %xmm0' are for loading
'prices' to registers '%xmm1' and '%xmm0', respectively. Finally, 'addsd
%xmm1, %xmm0' is for adding prices together. As can be seen, the prices
are loaded from exact locations relative to the value of the '%rbp'
register, which is one of the most efficient ways to load data from
memory. The instruction 'addsd' is used because prices in this program
are REAL (floating point numbers), and '%xmm0', '%xmm1', and 'movsd' are
used for the same reason. This generated code should be reasonably
efficient. However, the compiler needs to know the type and location of
the prices beforehand to choose the proper instructions and registers to
use.

In dynamic languages, 'SumPrice' can be applied to a price whose type is
an INTEGER instead of a REAL, or it can even be a string/text. A
straightforward implementation would check the type of 'a' and 'b' at
runtime, which makes the program much less efficient. The checking and
especially branching can cost more time than adding the numbers
themselves. Moreover, obtaining the value of the price attribute from
'a' and 'b' might be done by accessing a hash table instead of directly
loading the value from memory. Of course, while a hash-table has many
advantages, it's less efficient because it requires many steps,
including comparing the attribute name and generating a hash value.

However, compilers for dynamic languages can be much more advanced than
what's mentioned above, and SBCL is one such advanced compiler. SBCL can
infer types from the code, especially from literals. Moreover, with
information from type hints and 'struct' usage, SBCL can generate code
that's comparably as efficient as static language compilers.

Given, the Common Lisp code listing below:

    (defstruct book
      title
      (price 0 :type double-float))

    (declaim (ftype (function (book book) double-float) add-price)
         (optimize (speed 3) (debug 0) (safety 0)))
    (defun add-price (a b)
      (+ (book-price a)
         (book-price b)))

SBCL can generate assembly code for '(+ (book-price a) (book-price b))'
as shown below:

    ; 86:       F20F104A0D       MOVSD XMM1, [RDX+13]
    ; 8B:       F20F10570D       MOVSD XMM2, [RDI+13]
    ; 90:       F20F58D1         ADDSD XMM2, XMM1

The assembly code format is slightly different from the one generated by
the GNU Modula-2 compiler, but the main parts, the 'MOVSD' and 'ADDSD'
instructions and the use of XMM registers—are exactly the same. This
shows that we can write efficient code in Common Lisp at least for this
case. This shows that we can write efficient code in Common Lisp, at
least in this case, that is as efficient as, or nearly as efficient as,
a static language.

This implies that Common Lisp is good both for high-level rapid
development and optimized code, which has two advantages: (1) in many
cases, there is no need to switch between two languages, i.e., a
high-level one and a fast one; (2) the code can be started from
high-level and optimized in the same code after a profiler finds
critical parts. This paradigm can prevent premature optimization.

Interactive programming

Interactive programming may not sound familiar. However, it is a common
technique that has been used for decades. For example, a database engine
such as PostgreSQL doesn't need to be stopped and restarted just to run
a new SQL statement. Similarly, it is akin to a spreadsheet like Lotus
1-2-3 or Microsoft Excel, which can run a new formula without needing to
reload existing sheets or restart the program.

Common Lisp is exceptionally well-suited for interactive programming
because of (1) integrated editors with a REPL (Read Eval Print Loop),
(2) the language's syntax, and (3) the active community that has
developed libraries specifically designed to support interactive
programming.

Integrated editors with a REPL

With an integrated with a REPL, any part of the code can be evaluated
immediately without copying and pasting from an editor into a REPL. This
workflow provides feedback even faster than hot reloading because the
code can be evaluated and its results seen instantaneously, even before
it is saved. There are many supported editors, such as Visual Studio
Code, Emacs, Neovim, and others.

the language's syntax

Instead of marking region arbitrarily for evaluating, which is not very
convenient when it is done every few seconds, in Common Lisp, we can
mark a form (which is similar to a block in ALGOL) by moving a cursor to
one of the parentheses in the code, which is very easy with structural
editing, which will be discussed in the next section.

Moreover, even a method definition can be evaluated immediately without
resetting the state of the object in Common Lisp. Since method
definitions are not nested in defclass, this allows mixing interactive
programming and object-oriented programming (OOP) smoothly.

Here's the corrected code listing:

    (defclass toto ()
      ((i :initarg :i :accesor i)))

    (defmethod update-i ((obj toto))
      (setf (i obj) (+ i obj) 1))

According to the code listing above, the method 'update-i' can be
redefined without interfering with the pre-existing value of 'i'.

Structural editing

Instead of editing Lisp code like normal text, tree-based operations can
be used instead, such as paredit-join-sexps and
paredit-forward-slurp-sexp. Moving cursor operations, such as
paredit-forward, which moves the cursor to the end of the form (a
block). These structural moving operations are also useful for selecting
regions to be evaluated in a REPL.

Conclusion

In brief, Common Lisp has unparalleled combined advantages, which are
relevant to software development especially now, not just an archaic
technology that just came earlier. For example, Forth has a
long-term-stable specification, and works well with interactive
programming, but it is not designed for defining classes and adding type
hints. Julia has similar performance optimization and OOP is even
richer, but it doesn't have a long-term-stable specification. Moreover,
Common Lisp's community is still active, as libraries, apps, and even
implementations continue to receive updates.

My programming environment journey

Vee Satayamas — Sat, 19 Jul 2025 04:58:58 +0000

No one actually cares about my programming environment journey, but I’ve often been asked to share it, perhaps for the sake of social media algorithms. I post it here, so later, I can copy and paste this conveniently.

My first computer, in the sense that I, not someone else, made the decision to buy it, ran Debian in 2002. It was a used Compaq desktop with a Pentium II processor, which I bought from Zeer Rangsit, a used computer market that may be the most famous in Thailand these days. When I got it home, I installed Debian right away. Before I bought my computer, I had used MBasic, mainly MS-DOS, Windows 3.1 (though rarely), and Solaris (remotely). For experimentation, I used Xenix, AIX, and one on DEC PDP-11 that I forgot.

Since I started with MBasic, that was my first programming environment. I learned Logo at a summer camp, so that became my second. Later, my father bought me a copy of Turbo Basic, and at school, I switched to Turbo Pascal.

After moving to GNU/Linux, I used more editors instead IDEs. From 1995 to 2010, my editors were pico, nvi, vim, TextMate, and Emacs paired with GCC (mostly C, not C++), PHP, Perl, Ruby, Python, JavaScript, and SQL. I also used VisualAge to learn Java in the 90s. I tried Haskell, OCaml, Objective C, Lua, Julia, and Scala too, but it was strictly for learning only.

After 2010, I used IntelliJ IDEA and Eclipse for Java and Kotlin. For Rust (instead of C), I used Emacs and Visual Studio Code. I explored Racket for learning purposes, then later started coding seriously in Clojure and Common Lisp. I tried using Vim 9.x and Neovim too, they were great, but not quite my cup of tea.

In 2025, a few days ago, I learned Smalltalk with Pharo to deepen my understanding of OOP and exploratory programming.

Update 2025/07/20: I forgot to mention xBase. In the '90s, I used it in a programming competition, but none of my programs in xBase reach production.

I have been told to avoid linked lists.

Vee Satayamas — Sun, 23 Mar 2025 08:18:12 +0000

I've been told to avoid linked lists because their elements are scattered everywhere, which can be true in some cases. However, I wonder what happens in loops, which I use frequently. I tried to inspect memory addresses of list elements of these two programs run on SBCL.

CL-USER> (setq *a-list* (let ((a nil)) (push 10 a) (push 20 a) (push 30 a) a))
(30 20 10)
CL-USER> (sb-kernel:get-lisp-obj-address *a-list*)
69517814583 (37 bits, #x102F95AB37)
CL-USER> (sb-kernel:get-lisp-obj-address (cdr *a-list*))
69517814567 (37 bits, #x102F95AB27)
CL-USER> (sb-kernel:get-lisp-obj-address (cddr *a-list*))
69517814551 (37 bits, #x102F95AB17)
CL-USER> (sb-kernel:get-lisp-obj-address (cddr *a-list*))
69517814551 (37 bits, #x102F95AB17)

CL-USER> (setq *a-list* (let ((a nil))
              (push 10 a)
              (push 20 a)
              (push 30 a) 
              a))
(30 20 10)
CL-USER> (sb-kernel:get-lisp-obj-address *a-list*)
69518319943 (37 bits, #x102F9D6147)
CL-USER> (sb-kernel:get-lisp-obj-address (cdr *a-list*))
69518319927 (37 bits, #x102F9D6137)
CL-USER> (sb-kernel:get-lisp-obj-address (cddr *a-list*))
69518319911 (37 bits, #x102F9D6127)

In both programs, the list elements are not scattered. So, if scattered list elements were an issue for these simple cases, you probably used the wrong compiler or memory allocator.

JSON Manipulation

Vee Satayamas — Sun, 19 Jan 2025 11:55:57 +0000

There are many ways to manipulate JSON. I reviewed a few rapid ways today, which are using a command line tool called jq and libraries that support JSONPath query language.

jq

Awk is a powerful domain-specific language for text processing, but it lacks built-in support for manipulating hierarchical data structures like trees. jq fills this gap by providing a tool for transforming JSON data. However, one potential drawback is that jq requires a separate installation, which may add complexity to my workflow.

So I write a shell script using jq to extract user's names and user's urls from a cURL response, and then output it in TSV format.

curl -s 'https://mstdn.in.th/api/v1/timelines/public?limit=10' | jq '.[].account | [.username, .url] | @tsv' -r

The result looks like this:

kuketzblog      https://social.tchncs.de/@kuketzblog
cats    https://social.goose.rodeo/@cats
AlJazeera       https://flipboard.com/@AlJazeera
TheHindu        https://flipboard.com/@TheHindu
GossiTheDog     https://cyberplace.social/@GossiTheDog
kuketzblog      https://social.tchncs.de/@kuketzblog
weeklyOSM       https://en.osm.town/@weeklyOSM
juanbellas      https://masto.es/@juanbellas
noborusudou     https://misskey.io/@noborusudou
jerryd  https://mastodon.social/@jerryd

With a TSV file, we can use Awk, sed, etc. to manipulate them as usual.

JSONPath

JSONPath, which was explained in RFC 9535, is supported by many libraries and applications, e.g. PostgreSQL. Still, I try to it in Python by the jsonpath_nq library.

from jsonpath_ng import parse
import requests


res = requests.get("https://mstdn.in.th/api/v1/timelines/public?limit=10")

compiled_path = parse("$[*].account")


for matched_node in compiled_path.find(res.json()):
    print(matched_node.value["username"] + "\t" + matched_node.value["url"])

It gave the same result to the shell script above. The code is a bit longer than the shell script. However, it can be integrated with many Python libraries.

Four Reasons that I Sometimes Use Awk Instead of Python

Vee Satayamas — Tue, 31 Dec 2024 17:44:34 +0000

Python is a fantastic language, but in specific situations, Awk can offer significant advantages, particularly in terms of portability, longevity, conciseness, and interoperability.

While Python scripts are generally portable, they may not always run seamlessly on popular Docker base images like Debian and Alpine. In contrast, Awk scripts are often readily available and executable within these environments.

Although Python syntax is relatively stable, its lifespan is shorter compared to Awk. For example, the print 10 syntax from the early 2000s is no longer valid in modern Python. However, Awk scripts from the 1980s can still be executed in current environments.

Python is known for its conciseness, especially when compared to languages like Java. However, when it comes to text processing and working within shell pipelines, Awk often provides more concise solutions. For instance, extracting text blocks between "REPORT" and "END" can be achieved with a single line in Awk: /REPORT/,/END/ { print }. Achieving the same result in Python typically involves more lines of code, including handling file input and pattern matching.

While Python can be embedded within shell scripts like Bash, aligning the indentation of multiline Python code with the surrounding shell script can often break the Python syntax. Awk, on the other hand, is less sensitive to indentation, making it easier to integrate into shell scripts.

Although different Awk implementations (such as Busybox Awk and GNU Awk) may have minor variations, Awk generally offers advantages over Python in the situations mentioned above.

A shell script that I usually run after installing SBCL

Vee Satayamas — Sun, 21 Jul 2024 14:58:53 +0000

I usually run this shell script after installing SBCL because to develop a program in Common Lisp practically, I usually need libraries. Thus, this script install Quicklisp and Ultralisp as a package manager and a package repository, respectively. Moreover, I set working directory for my Common Lisp projects to Develop in my home directory because when I put them in quicklisp/local-projects, I usually forget to backup or even forget where that the projects exist.

#!/bin/bash

# My working directory is $HOME/Develop. You probably want to change it.

rm -rf ~/quicklisp
rm quicklisp.lisp
wget https://beta.quicklisp.org/quicklisp.lisp
sbcl --load quicklisp.lisp \
        --eval '(quicklisp-quickstart:install)' \
        --eval '(ql-util:without-prompting (ql:add-to-init-file))' \
        --quit
rm quicklisp.lisp

sbcl --eval '(ql-dist:install-dist "http://dist.ultralisp.org/" :prompt nil)' --quit
if [ -e ~/.config/common-lisp ]; then
    cp -rp ~/.config/common-lisp ~/.config/common-lisp.bak-$(date -I)-$$
fi
mkdir -p ~/.config/common-lisp

cat <<EOF > ~/.config/common-lisp/source-registry.conf
(:source-registry
     (:tree (:home "Develop"))
     :inherit-configuration)
EOF

Pyenv and Pipenv on Fedora

Vee Satayamas — Sun, 14 Jul 2024 08:32:47 +0000

I wonder if I can use pyenv and pipenv on Fedora Workstation 40 although I don't use these utilities in my personal projects. And the answer is yes.

The steps are as follow:

Install dependencies

sudo dnf builddep python3

Install pyenv

curl https://pyenv.run | bash

I know that you don't like running Bash script immediately from cURL.

Modify .bashrc

Pyenv normally told you to append these lines to .bashrc, and the restart your terminal.

export PYENV_ROOT="$HOME/.pyenv"
[[ -d $PYENV_ROOT/bin ]] && export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"

# Restart your shell for the changes to take effect.

# Load pyenv-virtualenv automatically by adding
# the following to ~/.bashrc:

eval "$(pyenv virtualenv-init -)"

Install Python via Pyenv

pyenv install 3.10 # You can choose other versions

I know that we can install Python using configure and make like many other packages, but you can use pyenv as well.

Set default python

pyenv global 3.10 # or other version

And then restart your terminal.

And finally, install pipenv

pip install pipenv