# Starting guide for Julia, coming from e.g. Python or C++

**URL:** <https://discourse.julialang.org/t/starting-guide-for-julia-coming-from-e-g-python-or-c/139139>\
**Category:** New to Julia\
**Created:** [September 1, 2026, 3:01pm UTC](https://discourse.julialang.org/t/starting-guide-for-julia-coming-from-e-g-python-or-c/139139 "2026-09-01T15:01:03Z")\
**Posts on this page:** 6\
**Page:** 1

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**Author:** ![Palli](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/palli/32/3380_2.png) [@Palli](https://discourse.julialang.org/u/Palli)\
**Post date:** [September 1, 2026, 3:01pm UTC](https://discourse.julialang.org/t/starting-guide-for-julia-coming-from-e-g-python-or-c/139139/1 "2026-09-01T15:01:03Z")

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AI answers are often unbelievably good, and I believe also for this topic: [ChatGPT - Julia Dict Comparisons](https://chatgpt.com/share/6a96e54c-c3d0-83eb-90d0-7784e09b8e2d)

> ### 21. A particularly important Python → Julia mapping

I’m not new to Julia actually, I just though there should be a list of recommend packages coming from e.g. Python, and I recommend `using OrderedCollections, Revise` as a starting point, the latter just during development.

Python’s standard library is incredibly broad, you can e.g. send email with the standard library. Consider `using PythonCall` to get access to all of it, to use it as such as a “tool”. I’m most interested in A. more core capability/data structures, and to some degree about B. tools people know of in e.g. Python, and want a similar native Julia package for. Feel free to list a minimal list of packages for A (and/or B), even for C. for (web) frameworks for Julia parity. For those who know, or for new users who do not know the mapping, so we can help. The manual has a section for difference with many languages vs. Python, C++ etc. It and the help might need amending:

```julia-auto
help?> Dict
..
  Dict{K,V}() constructs a hash table with keys of type K and values of type V. Keys are compared with isequal and hashed with hash.

```

The example is only implicit about unordered, so I suggest changing to:

```julia-auto
  Dict{K,V}() constructs a (unordered) hash table with keys of type K and values of type V. Consider `using OrderedCollections` for access to `OrderedDict`. Keys are compared with isequal and hashed with hash.

```

To partially answer my own question, there is e.g. SMTPClient.jl to send email, no need for PythonCall.jl there (for basic capability, i.e. the actual sending). You can use all of Python’s current email capability, including `email` to _construct_ the email, before sending; note `smtpd` was removed in Python 3.12, with `aiosmtpd` a substitute, and my answer doesn’t cover it or such _servers_.

I’ll just post this too even though AI generated (at least good for discussion if anything wrong there):

> ## 1. The genuinely important Python stdlib gaps
> 
> I’d put these near the top.
> 
> | Python | Julia stdlib/Base | My assessment |
> | --- | --- | --- |
> | `heapq` | `DataStructures.Heap` | **Missing from stdlib** , but trivial package substitute |
> | `collections.deque` | `DataStructures.Deque` | Same |
> | `collections.defaultdict` | `get!` / custom types | `DataStructures.DefaultDict` [Here AI was wrong: “ **No exact equivalent** ”] |
> | `collections.Counter` | `StatsBase.countmap` etc. | Package |
> | `enum` | `@enum` | Julia actually has it |
> | `dataclasses` | structs | Julia doesn’t need the mechanism |
> | `functools` | `do` blocks, closures, higher-order functions | Mostly unnecessary |
> | `itertools` | Base.Iterators | **Julia is arguably better here** |
> | `operator` | operators/functions directly | Mostly unnecessary |
> | `weakref` | `WeakRef` | Julia has it |
> | `copy` | `copy`, `deepcopy` | yes |
> | `pickle` | `Serialization` | yes, though not equivalent in purpose/portability |
> | `json` | `JSON` package | **not stdlib** |
> | `re` | `Regex` | yes |
> | `argparse` | ArgTools.jl / packages | **not a real equivalent** |
> | `subprocess` | `Cmd`, `run`, pipelines | yes |
> | `pathlib` | `Base.Filesystem` | yes, although less elegant |
> | `tempfile` | `mktemp`, `mktempdir` | yes |
> | `shutil` | filesystem functions | mostly yes |
> | `logging` | `Logging` | yes |
> | `warnings` | `Logging` / warnings machinery | not quite equivalent |
> | `traceback` | stacktrace machinery | yes |
> | `inspect` | reflection | **Julia has a different, often more powerful model** |
> | `ast` | `Meta`, JuliaSyntax | yes, but fundamentally different |
> | `dis` | `code_*`, `@code_*` | yes, arguably much richer |
> | `threading` | `Base.Threads` | yes |
> | `multiprocessing` | distributed processes | yes |
> | `asyncio` | `Task`s/`Channel`s | yes, but different model |
> | `socket` | `Base.Sockets` | yes |
> | `ssl` | `MbedTLS` [or rather OpenSSL.jl?] | stdlib package |
> | `http.client` / `urllib` | `Downloads` | yes |
> | `email` | ??? | **real gap** |
> | `smtplib` | [SMTPClient.jl] | **real-ish gap** |
> | `imaplib` | ??? | **gap** |
> | `ftplib` | ??? | **gap** |
> | `zipfile` | `Tar` etc. | **ZIP is surprisingly awkward** |
> | `gzip` | CodecZlib.jl | package |
> | `bz2` | CodecBzip2.jl | package |
> | `lzma` | CodecXz.jl | package |
> | `sqlite3` | SQLite.jl | package |
> | `dbm` | [`SQLite.jl`] | gap |
> | `configparser` | [avoid .ini files, rather use TOML or PythonCall.jl] | gap |
> | `tomllib` | TOML | yes |
> | `csv` | CSV.jl | package |
> | `xml` | XML packages | package |
> | `html.parser` | Gumbo etc. | package |
> | `zipapp` | [AppBundler.jl or JuliaC.jl] | gap |
> | `venv` | Pkg environments | Julia arguably **better** |
> | `venv` + `pip` | `Pkg` | major Julia advantage |

`zipapp` is a rather unusual “library”/concept, and not a substitute to simply call it with PythonCall.jl, I replaced its ??? (and edited more such) with plausible alternatives. `dbm` is likely a real gap, but who cares… I put in indirect substitute…

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**Author:** ![karei](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/karei/32/214809_2.png) [@karei](https://discourse.julialang.org/u/karei)\
**Post date:** [September 2, 2026, 3:52am UTC](https://discourse.julialang.org/t/starting-guide-for-julia-coming-from-e-g-python-or-c/139139/3 "2026-09-02T03:52:39Z")

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I once had a MATLAB-Python-Julia cheatsheet saved in my collection. [https://cheatsheets.quantecon.org/](https://cheatsheets.quantecon.org/)

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**Author:** ![langestefan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/langestefan/32/207923_2.png) [@langestefan](https://discourse.julialang.org/u/langestefan)\
**Post date:** [September 2, 2026, 1:12pm UTC](https://discourse.julialang.org/t/starting-guide-for-julia-coming-from-e-g-python-or-c/139139/4 "2026-09-02T13:12:54Z")

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seems quite old, from 2017?

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**Author:** ![Benny](https://avatars.discourse-cdn.com/v4/letter/b/49beb7/32.png) [@Benny](https://discourse.julialang.org/u/Benny)\
**Post date:** [September 2, 2026, 1:35pm UTC](https://discourse.julialang.org/t/starting-guide-for-julia-coming-from-e-g-python-or-c/139139/5 "2026-09-02T13:35:55Z")

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Limiting this to Python’s standard library isn’t practical, including third-party Julia packages looks unfair by comparison, and it’s misleading to list those under “Julia stdlib/Base”.

> [@Palli](#):
>
> | | | |
> | --- | --- | --- |
> | `venv` | Pkg environments | Julia arguably **better** |
> | `venv` + `pip` | `Pkg` | major Julia advantage |

For example, I would suggest uv, poetry, pixi, or pdm before venv and pip if this mapping went the other way.

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**Author:** ![Palli](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/palli/32/3380_2.png) [@Palli](https://discourse.julialang.org/u/Palli)\
**Post date:** [September 2, 2026, 2:29pm UTC](https://discourse.julialang.org/t/starting-guide-for-julia-coming-from-e-g-python-or-c/139139/6 "2026-09-02T14:29:30Z")

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> [@Benny](#):
>
> Limiting this to Python’s standard library isn’t practical, including third-party Julia packages looks unfair by comparison

It’s true that Python users don’t just use its standard library. I was just curious about the overlap, and well, what each language has additionally. I’m open to a list of any mapping you have or find relevant to have. “`venv` + `pip`” may not be important, I just kept it from the AI generated list. I suppose uv (and Conda etc.) very relevant, all map top Pkg (though there is I think one unofficial alternative by now…).

I asked the AI “What are some majorly used Python packages?” made sure to not ask for mapping right away, then for that. It seemed way too long to post, also might be based on outdated info, anyone can ask. But maybe helpful to post a partial answer:

> ## 1. General-purpose
> 
> | Python | Julia equivalent | Status | Comment |
> | --- | --- | --- | --- |
> | **NumPy** | `Base` arrays + `LinearAlgebra` + `Statistics` | **✓** | Not really a gap; Julia’s array system is built into the language |
> | **SciPy** | `SpecialFunctions`, `Distributions`, `Optim`, `NLsolve`, `DifferentialEquations`, etc. | **✓/≈** | Functionally enormous coverage, but distributed across many packages |
> | **pandas** | `DataFrames.jl`, `CSV.jl`, `Tables.jl` [I would add Tidier.jl ecosystem here] | **✓** | Probably the clearest equivalent; DataFrames is explicitly the Julia analogue to pandas-style tabular work. |
> | **requests** | `HTTP.jl` / `Downloads` | **✓** | HTTP.jl is much more capable; `Downloads` handles simple fetching |
> | **httpx** | `HTTP.jl` | **✓** | Async HTTP etc. |
> | **Pydantic** | `StructTypes.jl`, `JSON3.jl`, `Parameters.jl`, `Accessors.jl` | **≈** | No single equally dominant “typed data model/validation” framework |
> | **PyYAML** | `YAML.jl` | **✓** | |
> | **tomlkit** | `TOML` stdlib | **✓/≈** | TOML itself is excellent in Julia; TOML.jl/std library covers parsing. `tomlkit` has specialized round-tripping/editing semantics |
> | **orjson** | `JSON3.jl`, `StructTypes.jl` | **✓** | Fast JSON; Julia’s JSON ecosystem is quite good |
> | **python-dateutil** | `Dates` stdlib | **✓** | Julia has this natively |
> 
> ..
> 
> # 5. Async/concurrency
> 
> | Python | Julia | Status |
> | --- | --- | --- |
> | **asyncio** | Julia Tasks / Channels / `Distributed` / Threads | **✓** |
> | **anyio** | Julia’s native concurrency model | **✓** |
> | **trio** | Tasks/Channels | **✓/≈** |
> | **greenlet** | Tasks / coroutines | **✓** |
> | **celery** | Dagger / Distributed / message-queue packages | **≈** |
> 
> This is one of the categories where a direct package comparison is misleading. …
> 
> # 6. Scientific / visualization
> 
> | Python | Julia | Status |
> | --- | --- | --- |
> | **Matplotlib** | `Makie.jl`, `Plots.jl`, `Gadfly.jl` | **✓/≈** |
> | **SymPy** | `Symbolics.jl`, `SymPy.jl` | **✓/≈** |
> | **Numba** | Julia compiler itself | **✓** |
> | **h5py** | `HDF5.jl` | **✓** |
> | **networkx** | `Graphs.jl` | **✓** |
> | **Pillow** | `Images.jl`, `ImageIO.jl`, `FileIO.jl` | **✓** |
> | **OpenCV** | `OpenCV.jl` / image ecosystem | **≈** |

> These are things I wouldn’t regard as reasons to use Python:
> 
> ```julia-auto
> NumPy
> SciPy
> pandas
> Matplotlib
> Requests
> PyYAML
> python-dateutil
> orjson
> httpx
> 
> psycopg
> redis
> pyarrow
> duckdb
> openpyxl
> h5py
> NetworkX
> Pillow
> 
> Numba
> pytest
> Black
> Jupyter
> XGBoost
> LightGBM
> 
> ```
> 
> In several cases Julia is arguably **better positioned** , because the equivalent isn’t an external library.

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**Author:** ![Benny](https://avatars.discourse-cdn.com/v4/letter/b/49beb7/32.png) [@Benny](https://discourse.julialang.org/u/Benny)\
**Post date:** [September 3, 2026, 10:42am UTC](https://discourse.julialang.org/t/starting-guide-for-julia-coming-from-e-g-python-or-c/139139/7 "2026-09-03T10:42:15Z")

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That’s definitely a practical improvement, though the conclusion is still very overblown.