{ "cells": [ { "cell_type": "markdown", "id": "59cc364b-59b9-42e8-9cb6-431f6cfc9bb8", "metadata": {}, "source": [ "# Sample Julia Notebook - downloading and using data from S3\n", "[![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws/studio-lab-examples/blob/main/use-julia-in-studio-lab/2-sample-julia-nb.ipynb)" ] }, { "cell_type": "markdown", "id": "4e896402-784b-4566-8c14-e7bc8b615007", "metadata": {}, "source": [ "## Installing packages\n", "\n", "As we did in the Installing Julia notebook, we use the Pkg.add command to add a few packages that we will use for this notebook.\n", "\n", " - [Gadfly](http://gadflyjl.org/stable/): grammar of graphics plotting, similar to ggplot in R\n", " - [AWS](https://github.com/JuliaCloud/AWS.jl): third-party SDK for performing tasks in AWS\n", " - [AWSS3](https://github.com/JuliaCloud/AWSS3.jl): high-level third-party interface to Amazon Simple Storage Service\n", " - [DataFrames](https://dataframes.juliadata.org/stable/): data structure for working with tabular data, similar to Pandas in Python\n", " - [Query](http://www.queryverse.org/Query.jl/stable/): convenience functions for working with DataFrames and other queryable data sources, similar to dplyr in R.\n", " - [JSON](https://github.com/JuliaIO/JSON.jl): library for parsing JSON data\n", " - [TranscodingStreams](https://juliaio.github.io/TranscodingStreams.jl/latest/): framework for working with encoded data\n", " - [CodecZlib](https://github.com/JuliaIO/CodecZlib.jl): zlib compression codec for use with TranscodingStreams; allows us to work with gzipped data.\n", " \n", "After installing the packages, we'll load them as we use them in this notebook" ] }, { "cell_type": "code", "execution_count": 1, "id": "b4215fe4-00cb-41d1-b7f7-1f03f247847b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at 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"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m AbstractFFTs ──────────────── v1.0.1\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Retry ─────────────────────── v0.4.1\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Grisu ─────────────────────── v1.0.2\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Libiconv_jll ──────────────── v1.16.1+1\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Formatting ────────────────── v0.4.2\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m IteratorInterfaceExtensions ─ v1.0.0\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m IterableTables ────────────── v1.0.0\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m IniFile ───────────────────── v0.5.0\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Gadfly ────────────────────── v1.3.4\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m URIs ──────────────────────── v1.3.0\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m DataValueInterfaces 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"source": [ "## Example plots using Gadfly\n", "\n", "First up, we'll plot a histogram of some normally-distributed random numbers. 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\n", " 3.4\n", " \n", " \n", " \n", " \n", " 3.6\n", " \n", " \n", " \n", " \n", " 3.8\n", " \n", " \n", " \n", " \n", " 4.0\n", " \n", " \n", " \n", " \n", " 4.2\n", " \n", " \n", " \n", " \n", " 4.4\n", " \n", " \n", " \n", " \n", " 4.6\n", " \n", " \n", " \n", " \n", " 4.8\n", " \n", " \n", " \n", " \n", " 5.0\n", " \n", " \n", " \n", " \n", " 5.2\n", " \n", " \n", " \n", " \n", " 5.4\n", " \n", " \n", " \n", " \n", " 5.6\n", " \n", " \n", " \n", " \n", " 5.8\n", " \n", " \n", " \n", " \n", " 6.0\n", " \n", " \n", " \n", " \n", " 6.2\n", " \n", " \n", " \n", " \n", " 6.4\n", " \n", " \n", " \n", " \n", " 6.6\n", " \n", " \n", " \n", " \n", " 6.8\n", " \n", " \n", " \n", " \n", " 7.0\n", " \n", " \n", " \n", " \n", " 7.2\n", " \n", " \n", " \n", " \n", " 7.4\n", " \n", " \n", " \n", " \n", " 7.6\n", " \n", " \n", " \n", " \n", " 7.8\n", " \n", " \n", " \n", " \n", " 8.0\n", " \n", " \n", " \n", " \n", " 8.2\n", " \n", " \n", " \n", " \n", " 8.4\n", " \n", " \n", " \n", " \n", " 8.6\n", " \n", " \n", " \n", " \n", " 8.8\n", " \n", " \n", " \n", " \n", " 9.0\n", " \n", " \n", " \n", " \n", " \n", " \n", " y\n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(x = randn(1000),\n", " y = randn(1000),\n", " Geom.density2d)" ] }, { "cell_type": "markdown", "id": "b0ebe5dc-5d71-4e09-93ae-a29d718da4f9", "metadata": {}, "source": [ "## Accessing and using data from Amazon S3\n", "\n", "Here we will use the third-party AWS SDK and high-level S3 interface. The data we will use [are listed at the Registry of Open Data on AWS (RODA)](https://registry.opendata.aws/lei/), and are stored in a public S3 bucket s3://gleif/ in the eu-central-1 region. This dataset uniquely identifies legal entities using a code, and also provides information about ownership structures.\n", "\n", "We will access these data anonymously, which can be done by creating and using an `aws_config` structure with `creds=nothing`. Note that we must also supply the bucket's region with our configuration.\n", "\n", "Our first operation will be to list out all the keys in this S3 bucket that begin with \"data/json/lei\". We can see that the data are organized by date and time and have a \".json.gz\" extension, denoting that these data are json formatted and compressed using gzip." ] }, { "cell_type": "code", "execution_count": 4, "id": "75dbc83b-7241-4bc6-a6f0-18cb98d5097f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4231-element Vector{String}:\n", " \"data/json/lei/date=2018-02-09/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-09/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-09/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-10/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-10/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-10/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-11/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-11/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-11/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-12/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-12/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-12/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2018-02-13/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " ⋮\n", " \"data/json/lei/date=2021-12-16/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-17/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-17/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-17/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-18/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-18/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-18/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-19/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-19/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-19/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-20/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"\n", " \"data/json/lei/date=2021-12-20/t\" ⋯ 21 bytes ⋯ \"00-gleif-goldencopy-lei.json.gz\"" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using AWS\n", "using AWSS3 # high-level interface to S3\n", "\n", "# This AWSConfig instance allows us to make anonymous / unsigned requests\n", "aws = global_aws_config(;creds = nothing, region = \"eu-central-1\")\n", "\n", "# s3_list_keys returns a generator that we can collect immediately using collect\n", "obj = collect(s3_list_keys(aws, \"gleif\", \"data/json/lei/\"; delimiter = \"\"))" ] }, { "cell_type": "markdown", "id": "a4a2d5c3-08ba-48fc-b0df-e1dee7aeda16", "metadata": {}, "source": [ "Next, we'll load one of the compressed JSON objects from S3. Since it is gzipped, we'll need to decompress the object. And because the object is also in JSON format, we'll need to parse the JSON to work with it in Julia.\n", "\n", "Objects in S3 can be very large ([up to 5TB in a single object as of this writing](https://docs.aws.amazon.com/AmazonS3/latest/userguide/qfacts.html)). Processing larger objects may require an incremental strategy, however in this case the objects we're interested in can safely fit in memory. We therefore download, decompress, and parse the whole object at once." ] }, { "cell_type": "code", "execution_count": 5, "id": "f9cca693-52c3-4f40-816b-08f6954876e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dict{String, Any} with 1 entry:\n", " \"relations\" => Any[Dict{String, Any}(\"RelationshipRecord\"=>Dict{String, Any}(…" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using JSON, TranscodingStreams, CodecZlib\n", "\n", "rr = JSON.parse(String(transcode(GzipDecompressor, read(S3Path(\"s3://gleif/data/json/rr/date=2021-12-10/time=00:00/20211210-0000-gleif-goldencopy-rr.json.gz\")))))" ] }, { "cell_type": "markdown", "id": "143be21e-32b1-4629-91f9-66992e079d98", "metadata": {}, "source": [ "The resulting dictionary object contains a \"relations\" key that contains a number of records pertaining to the relationships between legal entities. To help us understand the structure of an individual record, we can print the first record as JSON." ] }, { "cell_type": "code", "execution_count": 6, "id": "87b1ba1e-b272-49d1-ace6-d6a977498fc0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"RelationshipRecord\": {\n", " \"Registration\": {\n", " \"InitialRegistrationDate\": {\n", " \"$\": \"2012-10-01T11:09:00.000Z\"\n", " },\n", " \"RegistrationStatus\": {\n", " \"$\": \"PUBLISHED\"\n", " },\n", " \"NextRenewalDate\": {\n", " \"$\": \"2022-06-03T13:04:00.000Z\"\n", " },\n", " \"LastUpdateDate\": {\n", " \"$\": \"2021-06-09T21:32:00.000Z\"\n", " },\n", " \"ValidationSources\": {\n", " \"$\": \"ENTITY_SUPPLIED_ONLY\"\n", " },\n", " \"ValidationDocuments\": {\n", " \"$\": \"SUPPORTING_DOCUMENTS\"\n", " },\n", " \"ManagingLOU\": {\n", " \"$\": \"EVK05KS7XY1DEII3R011\"\n", " }\n", " },\n", " \"Relationship\": {\n", " \"RelationshipStatus\": {\n", " \"$\": \"ACTIVE\"\n", " },\n", " \"EndNode\": {\n", " \"NodeIDType\": {\n", " \"$\": \"LEI\"\n", " },\n", " \"NodeID\": {\n", " \"$\": \"ZZG38T0MDR3QY1ETUA76\"\n", " }\n", " },\n", " \"StartNode\": {\n", " \"NodeIDType\": {\n", " \"$\": \"LEI\"\n", " },\n", " \"NodeID\": {\n", " \"$\": \"010CMKZ3VON21WF2ZD45\"\n", " }\n", " },\n", " \"RelationshipPeriods\": {\n", " \"RelationshipPeriod\": [\n", " {\n", " \"EndDate\": {\n", " \"$\": \"2018-12-31T00:00:00.000Z\"\n", " },\n", " \"StartDate\": {\n", " \"$\": \"2018-01-01T00:00:00.000Z\"\n", " },\n", " \"PeriodType\": {\n", " \"$\": \"ACCOUNTING_PERIOD\"\n", " }\n", " },\n", " {\n", " \"StartDate\": {\n", " \"$\": \"2018-02-06T00:00:00.000Z\"\n", " },\n", " \"PeriodType\": {\n", " \"$\": \"RELATIONSHIP_PERIOD\"\n", " }\n", " }\n", " ]\n", " },\n", " \"RelationshipType\": {\n", " \"$\": \"IS_DIRECTLY_CONSOLIDATED_BY\"\n", " },\n", " \"RelationshipQualifiers\": {\n", " \"RelationshipQualifier\": {\n", " \"QualifierDimension\": {\n", " \"$\": \"ACCOUNTING_STANDARD\"\n", " }\n", " }\n", " }\n", " }\n", " }\n", "}\n" ] } ], "source": [ "print(json(rr[\"relations\"][1], 4)) # the 4 tells, the json library to indent using 4 spaces" ] }, { "cell_type": "markdown", "id": "644deb47-de5d-4d28-bc58-b1b44682bfff", "metadata": {}, "source": [ "To help us do some simple analysis of these relationship records, we'll construct a DataFrame containing just a handful fields that we're interested in. Here we construct the DataFrame from named columns of vectors extracted from the dictionary we generated from the JSON from S3." ] }, { "cell_type": "code", "execution_count": 7, "id": "92d90a1c-c2c1-4770-a5fe-3abe9538dd52", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

173,523 rows × 3 columns

startnodeendnoderelationshiptype
StringStringString
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2010CMKZ3VON21WF2ZD453C7474T6CDKPR9K6YT90IS_ULTIMATELY_CONSOLIDATED_BY
3010PWNH4K3BLIC3I7R03549300COKYB5EGSU1838IS_DIRECTLY_CONSOLIDATED_BY
4010PWNH4K3BLIC3I7R03549300B2Q47IR0CR5B54IS_ULTIMATELY_CONSOLIDATED_BY
501J4SO3XTWZF4PP382095493000HPQ4D2RZ79739IS_DIRECTLY_CONSOLIDATED_BY
601J4SO3XTWZF4PP382095493000HPQ4D2RZ79739IS_ULTIMATELY_CONSOLIDATED_BY
701TRDHWDCL69YP41S025NZKGWYDO5O20EZTLPF09IS_DIRECTLY_CONSOLIDATED_BY
801TRDHWDCL69YP41S025LORM1GNEU1DKEW527V90IS_ULTIMATELY_CONSOLIDATED_BY
9020BQJXAXCZNLKIN7326549300PFEWKNHRG25N08IS_ULTIMATELY_CONSOLIDATED_BY
100292001381F1R1IB5B85029200388A7S7Z5I0H57IS_DIRECTLY_CONSOLIDATED_BY
110292001381F1R1IB5B85029200388A7S7Z5I0H57IS_ULTIMATELY_CONSOLIDATED_BY
120292001568C3M3WGI2920292004558B0R5B7I133IS_DIRECTLY_CONSOLIDATED_BY
130292001568C3M3WGI2920292004558B0R5B7I133IS_ULTIMATELY_CONSOLIDATED_BY
140292002717G4T0CH6E65029200370B2O2ZA1C754IS_DIRECTLY_CONSOLIDATED_BY
150292002717G4T0CH6E65029200370B2O2ZA1C754IS_ULTIMATELY_CONSOLIDATED_BY
16029200302D4K9BC3E535029200343C4N7D2E6H83IS_DIRECTLY_CONSOLIDATED_BY
17029200302D4K9BC3E5356SHGI4ZSSLCXXQSBB395IS_ULTIMATELY_CONSOLIDATED_BY
180292003053E9T6XD4J69029200268F8M5YI5I629IS_DIRECTLY_CONSOLIDATED_BY
190292003053E9T6XD4J69029200268F8M5YI5I629IS_ULTIMATELY_CONSOLIDATED_BY
20029200325G5L3B56F539254900O2M53JMNF7U320IS_DIRECTLY_CONSOLIDATED_BY
21029200325G5L3B56F539029200370B2O2ZA1C754IS_ULTIMATELY_CONSOLIDATED_BY
220292003272D0K9BC3A910292002595F3Q7UF5D85IS_DIRECTLY_CONSOLIDATED_BY
230292003272D0K9BC3A910292002595F3Q7UF5D85IS_ULTIMATELY_CONSOLIDATED_BY
24029200343C4N7D2E6H83CYSD4L2598RQ9BPPCB72IS_DIRECTLY_CONSOLIDATED_BY
25029200343C4N7D2E6H836SHGI4ZSSLCXXQSBB395IS_ULTIMATELY_CONSOLIDATED_BY
260292003540H0S4VA7A500292002717G4T0CH6E65IS_DIRECTLY_CONSOLIDATED_BY
270292003540H0S4VA7A500292002717G4T0CH6E65IS_ULTIMATELY_CONSOLIDATED_BY
28029200355B5N3V4F1F58029200239B8P7V6L1I90IS_DIRECTLY_CONSOLIDATED_BY
29029200355B5N3V4F1F58029200239B8P7V6L1I90IS_ULTIMATELY_CONSOLIDATED_BY
300292003601E9LB4J5157254900O2M53JMNF7U320IS_DIRECTLY_CONSOLIDATED_BY
⋮⋮⋮⋮
" ], "text/latex": [ "\\begin{tabular}{r|ccc}\n", "\t& startnode & endnode & relationshiptype\\\\\n", "\t\\hline\n", "\t& String & String & String\\\\\n", "\t\\hline\n", "\t1 & 010CMKZ3VON21WF2ZD45 & ZZG38T0MDR3QY1ETUA76 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t2 & 010CMKZ3VON21WF2ZD45 & 3C7474T6CDKPR9K6YT90 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t3 & 010PWNH4K3BLIC3I7R03 & 549300COKYB5EGSU1838 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t4 & 010PWNH4K3BLIC3I7R03 & 549300B2Q47IR0CR5B54 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t5 & 01J4SO3XTWZF4PP38209 & 5493000HPQ4D2RZ79739 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t6 & 01J4SO3XTWZF4PP38209 & 5493000HPQ4D2RZ79739 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t7 & 01TRDHWDCL69YP41S025 & NZKGWYDO5O20EZTLPF09 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t8 & 01TRDHWDCL69YP41S025 & LORM1GNEU1DKEW527V90 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t9 & 020BQJXAXCZNLKIN7326 & 549300PFEWKNHRG25N08 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t10 & 0292001381F1R1IB5B85 & 029200388A7S7Z5I0H57 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t11 & 0292001381F1R1IB5B85 & 029200388A7S7Z5I0H57 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t12 & 0292001568C3M3WGI292 & 0292004558B0R5B7I133 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t13 & 0292001568C3M3WGI292 & 0292004558B0R5B7I133 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t14 & 0292002717G4T0CH6E65 & 029200370B2O2ZA1C754 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t15 & 0292002717G4T0CH6E65 & 029200370B2O2ZA1C754 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t16 & 029200302D4K9BC3E535 & 029200343C4N7D2E6H83 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t17 & 029200302D4K9BC3E535 & 6SHGI4ZSSLCXXQSBB395 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t18 & 0292003053E9T6XD4J69 & 029200268F8M5YI5I629 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t19 & 0292003053E9T6XD4J69 & 029200268F8M5YI5I629 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t20 & 029200325G5L3B56F539 & 254900O2M53JMNF7U320 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t21 & 029200325G5L3B56F539 & 029200370B2O2ZA1C754 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t22 & 0292003272D0K9BC3A91 & 0292002595F3Q7UF5D85 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t23 & 0292003272D0K9BC3A91 & 0292002595F3Q7UF5D85 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t24 & 029200343C4N7D2E6H83 & CYSD4L2598RQ9BPPCB72 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t25 & 029200343C4N7D2E6H83 & 6SHGI4ZSSLCXXQSBB395 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t26 & 0292003540H0S4VA7A50 & 0292002717G4T0CH6E65 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t27 & 0292003540H0S4VA7A50 & 0292002717G4T0CH6E65 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t28 & 029200355B5N3V4F1F58 & 029200239B8P7V6L1I90 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t29 & 029200355B5N3V4F1F58 & 029200239B8P7V6L1I90 & IS\\_ULTIMATELY\\_CONSOLIDATED\\_BY \\\\\n", "\t30 & 0292003601E9LB4J5157 & 254900O2M53JMNF7U320 & IS\\_DIRECTLY\\_CONSOLIDATED\\_BY \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m173523×3 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m startnode \u001b[0m\u001b[1m endnode \u001b[0m\u001b[1m relationshiptype \u001b[0m ⋯\n", "\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m ⋯\n", "────────┼───────────────────────────────────────────────────────────────────────\n", " 1 │ 010CMKZ3VON21WF2ZD45 ZZG38T0MDR3QY1ETUA76 IS_DIRECTLY_CONSOLIDATED ⋯\n", " 2 │ 010CMKZ3VON21WF2ZD45 3C7474T6CDKPR9K6YT90 IS_ULTIMATELY_CONSOLIDAT\n", " 3 │ 010PWNH4K3BLIC3I7R03 549300COKYB5EGSU1838 IS_DIRECTLY_CONSOLIDATED\n", " 4 │ 010PWNH4K3BLIC3I7R03 549300B2Q47IR0CR5B54 IS_ULTIMATELY_CONSOLIDAT\n", " 5 │ 01J4SO3XTWZF4PP38209 5493000HPQ4D2RZ79739 IS_DIRECTLY_CONSOLIDATED ⋯\n", " 6 │ 01J4SO3XTWZF4PP38209 5493000HPQ4D2RZ79739 IS_ULTIMATELY_CONSOLIDAT\n", " 7 │ 01TRDHWDCL69YP41S025 NZKGWYDO5O20EZTLPF09 IS_DIRECTLY_CONSOLIDATED\n", " 8 │ 01TRDHWDCL69YP41S025 LORM1GNEU1DKEW527V90 IS_ULTIMATELY_CONSOLIDAT\n", " 9 │ 020BQJXAXCZNLKIN7326 549300PFEWKNHRG25N08 IS_ULTIMATELY_CONSOLIDAT ⋯\n", " 10 │ 0292001381F1R1IB5B85 029200388A7S7Z5I0H57 IS_DIRECTLY_CONSOLIDATED\n", " 11 │ 0292001381F1R1IB5B85 029200388A7S7Z5I0H57 IS_ULTIMATELY_CONSOLIDAT\n", " ⋮ │ ⋮ ⋮ ⋮ ⋱\n", " 173514 │ ZXOVMTW1OS6Z6XPSLX60 5493002N1IVX6KHGYO08 IS_ULTIMATELY_CONSOLIDAT\n", " 173515 │ ZXVOU21SIN3HE96OEI11 549300XL6A5I5G7HDA76 IS_DIRECTLY_CONSOLIDATED ⋯\n", " 173516 │ ZXVOU21SIN3HE96OEI11 549300XL6A5I5G7HDA76 IS_ULTIMATELY_CONSOLIDAT\n", " 173517 │ ZXZDWBXF4U47HEP2EO46 BPE6FTXDZEBNAGIT2T19 IS_DIRECTLY_CONSOLIDATED\n", " 173518 │ ZXZDWBXF4U47HEP2EO46 BPE6FTXDZEBNAGIT2T19 IS_ULTIMATELY_CONSOLIDAT\n", " 173519 │ ZYFWXJ55DN0MMI4LGV67 549300PEPVG75OYCM995 IS_DIRECTLY_CONSOLIDATED ⋯\n", " 173520 │ ZYFWXJ55DN0MMI4LGV67 GLS7OQD0WOEDI8YAP031 IS_ULTIMATELY_CONSOLIDAT\n", " 173521 │ ZYRGHE9I5GFPRVIM1Q91 PKSTQ48QDN0XSVS7YK88 IS_ULTIMATELY_CONSOLIDAT\n", " 173522 │ ZZDAKPSMIRGYQPNY7X10 549300NOJVZRPQGXQV73 IS_DIRECTLY_CONSOLIDATED\n", " 173523 │ ZZDAKPSMIRGYQPNY7X10 WFLLPEPC7FZXENRZV188 IS_ULTIMATELY_CONSOLIDAT ⋯\n", "\u001b[36m 1 column and 173502 rows omitted\u001b[0m" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using DataFrames, Query\n", "\n", "relations = DataFrame(\n", " startnode = map(x -> x[\"RelationshipRecord\"][\"Relationship\"][\"StartNode\"][\"NodeID\"][\"\\$\"], rr[\"relations\"]),\n", " endnode = map(x -> x[\"RelationshipRecord\"][\"Relationship\"][\"EndNode\"][\"NodeID\"][\"\\$\"], rr[\"relations\"]),\n", " relationshiptype = map(x -> x[\"RelationshipRecord\"][\"Relationship\"][\"RelationshipType\"][\"\\$\"], rr[\"relations\"])\n", ")" ] }, { "cell_type": "markdown", "id": "791d1f6f-6ccd-4099-8854-5a6376dc1426", "metadata": {}, "source": [ "Once data are loaded into a DataFrame object, it is straightforward to perform various table operations. Here we use macros from the Query package to refine our DataFrame as a pipeline. Here we limit the results to rows where the relationshiptype is \"IS_DIRECTLY_CONSOLIDATED_BY\", then group by values in the endnode column, then get a count of records per group, and lastly sort the table in descending order." ] }, { "cell_type": "code", "execution_count": 8, "id": "f2dac48e-56d4-4435-9885-c8330767a946", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

34,243 rows × 2 columns

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30335800GRUPRB6ZIXOV3697
⋮⋮⋮
" ], "text/latex": [ "\\begin{tabular}{r|cc}\n", "\t& endnode & count\\\\\n", "\t\\hline\n", "\t& String & Int64\\\\\n", "\t\\hline\n", "\t1 & 784F5XWPLTWKTBV3E584 & 405 \\\\\n", "\t2 & 7LTWFZYICNSX8D621K86 & 365 \\\\\n", "\t3 & K8MS7FD7N5Z2WQ51AZ71 & 362 \\\\\n", "\t4 & W38RGI023J3WT1HWRP32 & 308 \\\\\n", "\t5 & 213800JH9QQWHLO99821 & 274 \\\\\n", "\t6 & TKPE0FGSGCIV1TZ04B42 & 256 \\\\\n", "\t7 & O4QK7KMMK83ITNTHUG69 & 255 \\\\\n", "\t8 & O2RNE8IBXP4R0TD8PU41 & 253 \\\\\n", "\t9 & 5493000GV3I1S3MMF497 & 246 \\\\\n", "\t10 & 549300TRUWO2CD2G5692 & 205 \\\\\n", "\t11 & H7FNTJ4851HG0EXQ1Z70 & 205 \\\\\n", "\t12 & 529900MUF4C20K50JS49 & 202 \\\\\n", "\t13 & 96950066U5XAAIRCPA78 & 187 \\\\\n", "\t14 & 52990063S23N13HU4E98 & 169 \\\\\n", "\t15 & HKUPACFHSSASQK8HLS17 & 164 \\\\\n", "\t16 & 335800AMYJCXE3KCAC26 & 133 \\\\\n", "\t17 & OQ3T05P7YR8P5YJEVI93 & 130 \\\\\n", "\t18 & NNVPP80YIZGEY2314M97 & 123 \\\\\n", "\t19 & E57ODZWZ7FF32TWEFA76 & 122 \\\\\n", "\t20 & R0MUWSFPU8MPRO8K5P83 & 112 \\\\\n", "\t21 & 529900K9B0N5BT694847 & 110 \\\\\n", "\t22 & 335800X9S32QAPOTYJ95 & 107 \\\\\n", "\t23 & I07WOS4YJ0N7YRFE7309 & 106 \\\\\n", "\t24 & F5WCUMTUM4RKZ1MAIE39 & 105 \\\\\n", "\t25 & 549300J4U55H3WP1XT59 & 103 \\\\\n", "\t26 & Q9MAIUP40P25UFBFG033 & 102 \\\\\n", "\t27 & 529900NNUPAGGOMPXZ31 & 101 \\\\\n", "\t28 & 335800BITJZHVZ1CN475 & 98 \\\\\n", "\t29 & 549300PPXHEU2JF0AM85 & 97 \\\\\n", "\t30 & 335800GRUPRB6ZIXOV36 & 97 \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m34243×2 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m endnode \u001b[0m\u001b[1m count \u001b[0m\n", "\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\n", "───────┼─────────────────────────────\n", " 1 │ 784F5XWPLTWKTBV3E584 405\n", " 2 │ 7LTWFZYICNSX8D621K86 365\n", " 3 │ K8MS7FD7N5Z2WQ51AZ71 362\n", " 4 │ W38RGI023J3WT1HWRP32 308\n", " 5 │ 213800JH9QQWHLO99821 274\n", " 6 │ TKPE0FGSGCIV1TZ04B42 256\n", " 7 │ O4QK7KMMK83ITNTHUG69 255\n", " 8 │ O2RNE8IBXP4R0TD8PU41 253\n", " 9 │ 5493000GV3I1S3MMF497 246\n", " 10 │ 549300TRUWO2CD2G5692 205\n", " 11 │ H7FNTJ4851HG0EXQ1Z70 205\n", " ⋮ │ ⋮ ⋮\n", " 34234 │ 529900SOYZIXZBTXB558 1\n", " 34235 │ 54930069AMWJE53ZEK40 1\n", " 34236 │ 969500WUI4C35QFELV89 1\n", " 34237 │ 549300N3CIN473IW5094 1\n", " 34238 │ 529900EVCB17C6S4D127 1\n", " 34239 │ 549300PU661NUSCFHE08 1\n", " 34240 │ 549300JOMD0TGAFKNW16 1\n", " 34241 │ 549300P5P8RXOD23W720 1\n", " 34242 │ BPE6FTXDZEBNAGIT2T19 1\n", " 34243 │ 549300NOJVZRPQGXQV73 1\n", "\u001b[36m 34222 rows omitted\u001b[0m" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "group_sizes = relations |>\n", " @filter(_.relationshiptype == \"IS_DIRECTLY_CONSOLIDATED_BY\") |>\n", " @groupby(_.endnode) |>\n", " @map({endnode=key(_), count = length(_)}) |>\n", " @orderby_descending(_.count) |>\n", " DataFrame" ] }, { "cell_type": "markdown", "id": "f6efab50-36a4-4cc9-b48f-6ff70644f2fc", "metadata": {}, "source": [ "If we wanted to join names for all these groups, we could extract that information from the LEI data similarly housed on S3. Or if we are just casually interested in the top item, we can also [search for a LEI record on GLEIF's website](https://search.gleif.org/#/search/simpleSearch=784F5XWPLTWKTBV3E584&fulltextFilterId=LEIREC_FULLTEXT¤tPage=1&perPage=15&expertMode=false#search-form). In this case, the largest direct consolidator is The Goldman Sachs Group, Inc.\n", "\n", "Lastly, we can plot the distribution of group sizes, once again using the Gadfly plotting library. Here we see that most direct consolidators have far fewer entries—one or two—and that there is a very long tail of larger direct consolidators." ] }, { "cell_type": "code", "execution_count": 9, "id": "cc6469a0-a78e-4ff2-9f05-3abc1c9e9f13", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " count\n", " \n", " \n", " \n", " \n", " \n", " \n", " 0\n", " \n", " \n", " \n", " \n", " 100\n", " \n", " \n", " \n", " \n", " 200\n", " \n", " \n", " \n", " \n", " 300\n", " \n", " \n", " \n", " \n", " 400\n", " \n", " \n", " \n", " \n", " 500\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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