DataFrames_benchmarks.jl

### A Pluto.jl notebook ###
# v0.19.26

using Markdown
using InteractiveUtils

# ╔═╡ 91a33052-6ccf-4285-a027-66cbc1259f89
using PyCall , PlutoUI, DataFrames, CSV, BenchmarkTools

# ╔═╡ b12b2b13-caa1-426f-b80b-08886a10fddb
md""" ## DataFrame Library Benchmarks: Pandas, Polars, Julia 
"""

# ╔═╡ 095e81e1-4901-439b-b522-3f02cd59cb86
html""" <style> main {
     margin: 0 auto;
     max-width: 1250px;
     padding-left: max(30px, 13%);
     padding-right: max(120px, 15%);
} </style> """

# ╔═╡ a226c07d-9625-41ee-b8f6-a946bd6c50b5
if false
	run(`$(PyCall.python) -m pip install --upgrade pip`)
	run(`$(PyCall.python) -m pip install polars pandas`)
end

# ╔═╡ 35aea64c-3805-413a-bdf0-f8e66cf9ef11
begin
	pd = pyimport("pandas")
	pl = pyimport("polars")
end

# ╔═╡ 8b91d461-e46c-47a0-9e91-d65edc2d70eb
begin
	println("Pandas version:\t", pd.__version__)
	println("Polars version:\t", pl.__version__)
	println("Julia version:\t", VERSION)
end

# ╔═╡ ba4428c5-5929-49de-b57b-9319d95535c3
begin
	r(x) = round(x; digits=1) 
	r3(x) = round(x; digits=3) 
end

# ╔═╡ f3a042c4-86a9-4aac-9409-e3f59d1bd502
md""" ### Ingest CSV File
"""

# ╔═╡ 8360e30d-9775-43f8-9f3d-ae637f26146d
b_i_1 = @benchmark pd.read_csv("dataset.csv")

# ╔═╡ be981e6f-77bb-4ea5-ad8b-df2679a02b9a
b_i_2 = @benchmark pl.read_csv("dataset.csv")

# ╔═╡ 70f7179e-8264-4b03-b420-6056137b1f35
b_i_3 = @benchmark CSV.read("dataset.csv", DataFrame)

# ╔═╡ b70b41fe-17e2-416a-86a4-ff424e82eb87
begin
	df_pd = pd.read_csv("dataset.csv")
	df_pl = pl.read_csv("dataset.csv")
	df_jl = CSV.read("dataset.csv", DataFrame)
end

# ╔═╡ d622c667-c9c1-4fc0-b6e1-1c447a25157b
md"""  
[Julia](https://julialang.org/) is rapidly on pace to become the open-source de-facto application for scientific programming.  This set of  benchmarks demonstrates the performance advantages, in many but not all cases, of using Julia over other popular DataFrame libraries [Pandas](https://pandas.pydata.org/docs/index.html) and _lightning-fast_ [Polars](https://www.pola.rs/).  


To evaluate each of the DataFrame libraries, a large record consisting of [Employee Salary data](https://drive.google.com/file/d/1XqzFjBSj4HTOZK62lx1i134pZzvhH0nd/view?usp=sharing) (_source_: [A. Chawla](https://github.com/ChawlaAvi/Daily-Dose-of-Data-Science/blob/main/Pandas/Polars-vs-Pandas.ipynb)) with over 4 million rows (and $(size(df_jl)[2]) columns) was utilized. 

The following processes were exercised and benchmarked using [BenchmarkTools.jl](https://github.com/JuliaCI/BenchmarkTools.jl)

- CSV file ingestion
- Writing the CSV file (to disk)
- Memory allocation 
- Selecting DataFrame columns
- Filtering the DataFrame
- Sorting the DataFrame
- Grouping the DataFrame
"""

# ╔═╡ b9ebe150-20d1-403d-866b-7aa9426682dd
md""" ### Write to CSV File
"""

# ╔═╡ 7eef3b74-67cd-4a5f-a3d4-c46f14b9c375
b_w_1 = @benchmark df_pd.to_csv("dataset_dummy_pandas.csv")

# ╔═╡ e1a31d97-4ce6-4210-9eab-266e126b81c5
b_w_2 = @benchmark df_pl.write_csv("dataset_dummy_polars.csv")

# ╔═╡ 4cb70ec5-0f63-435c-950a-2c07268df674
b_w_3 = @benchmark CSV.write("dataset_dummy_julia.csv", df_jl)

# ╔═╡ 8237abd1-2e50-44cb-b6ce-669e82bea126
md""" ### Memory Allocation
"""

# ╔═╡ be9129d1-862a-4ba7-8755-028b297f8a46
md""" Estimation of the total (heap) allocated size of the DataFrame
"""

# ╔═╡ 7354b274-4f98-4be1-98e5-b07c30595329
# df_pd.info(memory_usage="deep")
m_1 = float(df_pd.memory_usage(deep=true).sum() * 1e-9)# GB

# ╔═╡ cd590e5d-48bc-4a16-ba56-93a4081c964f
m_2 = df_pl.estimated_size("gb") # GB

# ╔═╡ 9f144c7c-1afd-44d0-8643-3c7a5034f053
m_3 = Base.summarysize(df_jl) * 1e-9 # GB

# ╔═╡ 4b18a08c-2286-440f-804b-cccc904228e8
md""" ### Selecting Columns
"""

# ╔═╡ bbb0a3b0-1a5c-4c9e-a68c-89b6e7d36604
# df_pd.columns
b_c_1 = @benchmark get(df_pd, py"['Name', 'Employee_Rating']")

# ╔═╡ 16038a29-6a65-40c6-9555-4eb6973348f4
b_c_2 = @benchmark get(df_pl, py"['Name', 'Employee_Rating']")

# ╔═╡ dbd69695-84a6-4cf5-94a9-e06ce546c7e2
b_c_3 = @benchmark df_jl[!,[:Name,:Employee_Rating]]

# ╔═╡ 22152a33-e19a-4718-88fb-d48e006853aa
md""" ### Filtering
"""

# ╔═╡ bdce368d-4277-4aaf-beeb-3f99a22d65fd
b_f_1 = @benchmark get(df_pd, df_pd.Credits>2)

# ╔═╡ 6fd84710-e40e-42c9-9515-72b1c413c0f4
b_f_2 = @benchmark df_pl.filter(pl.col("Credits") > 2)

# ╔═╡ de0f17bb-8510-451b-ba26-e8f3a0cceaf0
b_f_3 = @benchmark filter([:Credits] => (x) -> x .> 2, df_jl)

# ╔═╡ 7c1a8f64-0304-41a4-86d8-cc5b9633d6df
md""" ### Grouping
"""

# ╔═╡ 231d9db0-6b08-412d-94f8-17e75e164393
b_g_1 = @benchmark df_pd.groupby("Company_Name").Employee_Salary.mean().reset_index()

# ╔═╡ e185aa68-66d5-4549-9f94-9622aee39960
b_g_2 = @benchmark df_pl.groupby("Company_Name").agg(pl.mean("Employee_Salary"))

# ╔═╡ 21b03cc4-56be-47c3-971f-a3d599fb50db
b_g_3 = @benchmark combine(groupby(df_jl, :Company_Name), [:Employee_Salary] .=> mean; renamecols=false)

# ╔═╡ 38a41a9c-5740-4724-9199-e34d9ffe51c5
md""" ### Sorting
"""

# ╔═╡ 88820b7b-db6f-4023-93bc-6faf36e61509
b_s_1 = @benchmark df_pd.sort_values("Employee_Salary")

# ╔═╡ 286f2c5f-22d0-4dd1-acc3-0d005c1be634
b_s_2 = @benchmark df_pl.sort("Employee_Salary")

# ╔═╡ ef3942fa-214f-4527-ab65-6bec0df79403
b_s_3 = @benchmark sort!(df_jl, :Employee_Salary, rev=false)

# ╔═╡ 29331c71-bf08-4135-a0e2-74ec2da65ae5
md""" ## Benchmark Results
The following table provides the aggregated _median statistics_ for the data processing tasks considered.
"""

# ╔═╡ 5ef2b97b-4ff3-4d3e-8737-a089c58c304c
md"""
| DataFrame $-$ size: $(size(df_jl)[1]) × $(size(df_jl)[2]) | _Pandas_ v"$(pd.__version__)" | _Polars_ v"$(pl.__version__)" | _Julia_ $(VERSION) | _Polars_ perf. rel. _Pandas_ | _Julia_ perf. rel. _Pandas_ |
| :-------: | :----: | :----: | :---: | :---: | :---: | 
| CSV Ingestion | $(r(median(b_i_1.times)*1e-9)) sec | $(r(median(b_i_2.times)*1e-9)) sec | $(r(median(b_i_3.times)*1e-9)) sec | $(r(median(b_i_1.times)/median(b_i_2.times))) 🔥 | $(r(median(b_i_1.times)/median(b_i_3.times))) |
| Memory Utilization | $(r(m_1)) GB | $(r(m_2)) GB | $(r(m_3)) GB | $(r(m_1/m_2)) | $(r(m_1/m_3)) 🔥 |
| CSV Write | $(r(median(b_w_1.times)*1e-9)) sec | $(r(median(b_w_2.times)*1e-9)) sec | $(r(median(b_w_3.times)*1e-9)) sec | $(r(median(b_w_1.times)/median(b_w_2.times))) 🔥| $(r(median(b_w_1.times)/median(b_w_3.times))) |
| Column Selection | $(r(median(b_c_1.times)*1e-3)) μsec | $(r3(median(b_c_2.times)*1e-3)) μsec | $(r3(median(b_c_3.times)*1e-3)) μsec | $(r(median(b_c_1.times)/median(b_c_2.times))) | $(r(median(b_c_1.times)/median(b_c_3.times))) 🔥 |
| Filtering | $(r(median(b_f_1.times)*1e-6)) msec | $(r(median(b_f_2.times)*1e-6)) msec | $(r(median(b_f_3.times)*1e-6)) msec | $(r(median(b_f_1.times)/median(b_f_2.times))) | $(r(median(b_f_1.times)/median(b_f_3.times))) 🔥 |
| Grouping | $(r(median(b_g_1.times)*1e-6)) msec | $(r(median(b_g_2.times)*1e-6)) msec | $(r(median(b_g_3.times)*1e-6)) msec | $(r(median(b_g_1.times)/median(b_g_2.times))) 🔥 | $(r(median(b_g_1.times)/median(b_g_3.times))) |
| Sorting | $(r(median(b_s_1.times)*1e-6)) msec | $(r(median(b_s_2.times)*1e-6)) msec | $(r(median(b_s_3.times)*1e-6)) msec | $(r(median(b_s_1.times)/median(b_s_2.times))) | $(r(median(b_s_1.times)/median(b_s_3.times))) 🔥 |
"""

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uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
version = "2.7.0"

[[deps.Pkg]]
deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"]
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
version = "1.9.0"

[[deps.PlutoUI]]
deps = ["AbstractPlutoDingetjes", "Base64", "ColorTypes", "Dates", "FixedPointNumbers", "Hyperscript", "HypertextLiteral", "IOCapture", "InteractiveUtils", "JSON", "Logging", "MIMEs", "Markdown", "Random", "Reexport", "URIs", "UUIDs"]
git-tree-sha1 = "b478a748be27bd2f2c73a7690da219d0844db305"
uuid = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
version = "0.7.51"

[[deps.PooledArrays]]
deps = ["DataAPI", "Future"]
git-tree-sha1 = "a6062fe4063cdafe78f4a0a81cfffb89721b30e7"
uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720"
version = "1.4.2"

[[deps.PrecompileTools]]
deps = ["Preferences"]
git-tree-sha1 = "9673d39decc5feece56ef3940e5dafba15ba0f81"
uuid = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
version = "1.1.2"

[[deps.Preferences]]
deps = ["TOML"]
git-tree-sha1 = "7eb1686b4f04b82f96ed7a4ea5890a4f0c7a09f1"
uuid = "21216c6a-2e73-6563-6e65-726566657250"
version = "1.4.0"

[[deps.PrettyTables]]
deps = ["Crayons", "Formatting", "LaTeXStrings", "Markdown", "Reexport", "StringManipulation", "Tables"]
git-tree-sha1 = "213579618ec1f42dea7dd637a42785a608b1ea9c"
uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
version = "2.2.4"

[[deps.Printf]]
deps = ["Unicode"]
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"

[[deps.Profile]]
deps = ["Printf"]
uuid = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"

[[deps.PyCall]]
deps = ["Conda", "Dates", "Libdl", "LinearAlgebra", "MacroTools", "Serialization", "VersionParsing"]
git-tree-sha1 = "08c74e61c63bf63530c03cde3fe59586fcae8941"
uuid = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
version = "1.95.2"

[[deps.REPL]]
deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"

[[deps.Random]]
deps = ["SHA", "Serialization"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

[[deps.Reexport]]
git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
uuid = "189a3867-3050-52da-a836-e630ba90ab69"
version = "1.2.2"

[[deps.SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
version = "0.7.0"

[[deps.SentinelArrays]]
deps = ["Dates", "Random"]
git-tree-sha1 = "04bdff0b09c65ff3e06a05e3eb7b120223da3d39"
uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c"
version = "1.4.0"

[[deps.Serialization]]
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"

[[deps.SnoopPrecompile]]
deps = ["Preferences"]
git-tree-sha1 = "e760a70afdcd461cf01a575947738d359234665c"
uuid = "66db9d55-30c0-4569-8b51-7e840670fc0c"
version = "1.0.3"

[[deps.Sockets]]
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"

[[deps.SortingAlgorithms]]
deps = ["DataStructures"]
git-tree-sha1 = "c60ec5c62180f27efea3ba2908480f8055e17cee"
uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
version = "1.1.1"

[[deps.SparseArrays]]
deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"]
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"

[[deps.Statistics]]
deps = ["LinearAlgebra", "SparseArrays"]
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
version = "1.9.0"

[[deps.StringManipulation]]
git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123"
uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e"
version = "0.3.0"

[[deps.SuiteSparse_jll]]
deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"]
uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c"
version = "5.10.1+6"

[[deps.TOML]]
deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
version = "1.0.3"

[[deps.TableTraits]]
deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
version = "1.0.1"

[[deps.Tables]]
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits", "Test"]
git-tree-sha1 = "1544b926975372da01227b382066ab70e574a3ec"
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.10.1"

[[deps.Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
version = "1.10.0"

[[deps.Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[[deps.TranscodingStreams]]
deps = ["Random", "Test"]
git-tree-sha1 = "9a6ae7ed916312b41236fcef7e0af564ef934769"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.9.13"

[[deps.Tricks]]
git-tree-sha1 = "aadb748be58b492045b4f56166b5188aa63ce549"
uuid = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775"
version = "0.1.7"

[[deps.URIs]]
git-tree-sha1 = "074f993b0ca030848b897beff716d93aca60f06a"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.4.2"

[[deps.UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[deps.VersionParsing]]
git-tree-sha1 = "58d6e80b4ee071f5efd07fda82cb9fbe17200868"
uuid = "81def892-9a0e-5fdd-b105-ffc91e053289"
version = "1.3.0"

[[deps.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
version = "1.4.2"

[[deps.WorkerUtilities]]
git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
version = "1.6.1"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.13+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.8.0+0"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.48.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+0"
"""

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