{ "cells": [ { "cell_type": "code", "execution_count": 67, "metadata": { "id": "iNgLgZ27Dlq6" }, "outputs": [], "source": [ "from huggingface_hub import list_models, list_spaces\n", "from pathlib import Path\n", "from toolz import concat\n", "from datasets import Dataset\n", "import polars as pl\n", "from datetime import date\n", "from datetime import date, timedelta\n", "from datasets import load_dataset\n", "import plotly.express as px\n", "import os" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "HF_TOKEN = os.getenv(\"HF_TOKEN\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "mBMDThV4FA3m" }, "outputs": [], "source": [ "def yield_models():\n", " for model in iter(list_models(full=True)):\n", " yield \"model\", model" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "hDqiwFD3uTR8" }, "outputs": [], "source": [ "def yield_spaces():\n", " for space in iter(list_spaces(full=True)):\n", " yield \"space\", space" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "id": "mZEhftoNFHGN" }, "outputs": [], "source": [ "def yield_notebooks():\n", " for repo_type, repo in concat([yield_models(), yield_spaces()]):\n", " files = (f.rfilename for f in repo.siblings)\n", " if jupyter_notebook := [f for f in files if Path(f).suffix == \".ipynb\"]:\n", " yield {\n", " \"date\": date.today(),\n", " \"repo_type\": repo_type,\n", " \"repo_id\": repo.id,\n", " \"repo_notebook_count\": len(jupyter_notebook),\n", " }" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "df = pl.LazyFrame(yield_notebooks())" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "df = (\n", " df.with_columns(pl.col(\"repo_id\").str.split_exact(\"/\", 1))\n", " .unnest(\"repo_id\")\n", " .rename({\"field_0\": \"user\", \"field_1\": \"repo_id\"})\n", ")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "by_user_count = (\n", " df.groupby(\"user\")\n", " .agg(pl.col(\"repo_notebook_count\").sum())\n", " .sort(\"repo_notebook_count\", descending=True)\n", " .collect()\n", ")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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userrepo_notebook_count
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" ], "text/plain": [ "shape: (1540, 2)\n", "┌───────────────────┬─────────────────────┐\n", "│ user ┆ repo_notebook_count │\n", "│ --- ┆ --- │\n", "│ str ┆ i64 │\n", "╞═══════════════════╪═════════════════════╡\n", "│ gradio-pr-deploys ┆ 1798 │\n", "│ gradio ┆ 414 │\n", "│ sgoodfriend ┆ 240 │\n", "│ merve ┆ 63 │\n", "│ … ┆ … │\n", "│ Jack003 ┆ 1 │\n", "│ SiddhantOjha ┆ 1 │\n", "│ lowrollr ┆ 1 │\n", "│ edwardpraveen ┆ 1 │\n", "└───────────────────┴─────────────────────┘" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "by_user_count" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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describeuserrepo_notebook_count
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" ], "text/plain": [ "shape: (7, 3)\n", "┌────────────┬───────────┬─────────────────────┐\n", "│ describe ┆ user ┆ repo_notebook_count │\n", "│ --- ┆ --- ┆ --- │\n", "│ str ┆ str ┆ f64 │\n", "╞════════════╪═══════════╪═════════════════════╡\n", "│ count ┆ 1540 ┆ 1540.0 │\n", "│ null_count ┆ 0 ┆ 0.0 │\n", "│ mean ┆ null ┆ 3.787013 │\n", "│ std ┆ null ┆ 47.455407 │\n", "│ min ┆ 007aneesh ┆ 1.0 │\n", "│ max ┆ zinoubm ┆ 1798.0 │\n", "│ median ┆ null ┆ 1.0 │\n", "└────────────┴───────────┴─────────────────────┘" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "by_user_count.describe()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mean notebooks per user
f64
3.787013
" ], "text/plain": [ "shape: (1, 1)\n", "┌──────────────────────────┐\n", "│ mean notebooks per user │\n", "│ --- │\n", "│ f64 │\n", "╞══════════════════════════╡\n", "│ 3.787013 │\n", "└──────────────────────────┘" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "by_user_count.mean().select(\n", " pl.col(\"repo_notebook_count\").alias(\"mean notebooks per user\")\n", ")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['user', 'repo_notebook_count'],\n", " num_rows: 1540\n", "})" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = Dataset(by_user_count.to_arrow())\n", "ds" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Creating parquet from Arrow format: 100%|██████████| 2/2 [00:00<00:00, 617.08ba/s]\n", "Upload 1 LFS files: 100%|██████████| 1/1 [00:00<00:00, 1.05it/s]\n", "Pushing dataset shards to the dataset hub: 100%|██████████| 1/1 [00:02<00:00, 2.39s/it]\n", "Deleting unused files from dataset repository: 100%|██████████| 1/1 [00:00<00:00, 2.01it/s]\n", "Downloading metadata: 100%|██████████| 406/406 [00:00<00:00, 126kB/s]\n", "Updating downloaded metadata with the new split.\n" ] } ], "source": [ "ds.push_to_hub(\"davanstrien/notebooks_by_user\", token=HF_TOKEN)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "h6AaHRSCV397" }, "outputs": [], "source": [ "grouped = df.groupby(\"repo_type\").agg(pl.col(\"repo_notebook_count\").sum())" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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repo_typerepo_notebook_countdate
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"space"44432023-03-30
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" ], "text/plain": [ "shape: (2, 3)\n", "┌───────────┬─────────────────────┬────────────┐\n", "│ repo_type ┆ repo_notebook_count ┆ date │\n", "│ --- ┆ --- ┆ --- │\n", "│ str ┆ i64 ┆ date │\n", "╞═══════════╪═════════════════════╪════════════╡\n", "│ space ┆ 4443 ┆ 2023-03-30 │\n", "│ model ┆ 1389 ┆ 2023-03-30 │\n", "└───────────┴─────────────────────┴────────────┘" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_df = grouped.with_columns(pl.lit(date.today()).alias(\"date\")).collect()\n", "final_df" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading readme: 100%|██████████| 441/441 [00:00<00:00, 130kB/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading and preparing dataset None/None to /Users/davanstrien/.cache/huggingface/datasets/davanstrien___parquet/davanstrien--notebooks_by_repo_type-1004c11b0535dac5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Downloading data: 100%|██████████| 1.87k/1.87k [00:00<00:00, 705kB/s]\n", "Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.71s/it]\n", "Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 786.78it/s]\n", " " ] }, { "name": "stdout", "output_type": "stream", "text": [ "Dataset parquet downloaded and prepared to /Users/davanstrien/.cache/huggingface/datasets/davanstrien___parquet/davanstrien--notebooks_by_repo_type-1004c11b0535dac5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r" ] }, { "data": { "text/html": [ "
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repo_typerepo_notebook_countdate
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" ], "text/plain": [ "shape: (7, 3)\n", "┌───────────┬─────────────────────┬────────────┐\n", "│ repo_type ┆ repo_notebook_count ┆ date │\n", "│ --- ┆ --- ┆ --- │\n", "│ str ┆ i64 ┆ date │\n", "╞═══════════╪═════════════════════╪════════════╡\n", "│ space ┆ 3956 ┆ 2023-03-27 │\n", "│ model ┆ 1346 ┆ 2023-03-27 │\n", "│ model ┆ 1348 ┆ 2023-03-28 │\n", "│ space ┆ 4386 ┆ 2023-03-28 │\n", "│ space ┆ 4422 ┆ 2023-03-28 │\n", "│ space ┆ 4579 ┆ 2023-03-29 │\n", "│ model ┆ 1384 ┆ 2023-03-29 │\n", "└───────────┴─────────────────────┴────────────┘" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "previous_df = pl.DataFrame(\n", " load_dataset(\"davanstrien/notebooks_by_repo_type\", split=\"train\").data.table\n", ")\n", "previous_df" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "final_df = pl.concat([previous_df, final_df]).unique()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "spaces = final_df.filter(pl.col(\"repo_type\") == \"space\").unique(subset=[\"date\"])\n", "models = final_df.filter(pl.col(\"repo_type\") == \"model\").unique(subset=[\"date\"])" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "final_df = pl.concat([spaces, models]).unique()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "shape: (8, 3)\n", "┌───────────┬─────────────────────┬────────────┐\n", "│ repo_type ┆ repo_notebook_count ┆ date │\n", "│ --- ┆ --- ┆ --- │\n", "│ str ┆ i64 ┆ date │\n", "╞═══════════╪═════════════════════╪════════════╡\n", "│ space ┆ 3956 ┆ 2023-03-27 │\n", "│ model ┆ 1346 ┆ 2023-03-27 │\n", "│ space ┆ 4386 ┆ 2023-03-28 │\n", "│ model ┆ 1348 ┆ 2023-03-28 │\n", "│ space ┆ 4579 ┆ 2023-03-29 │\n", "│ model ┆ 1384 ┆ 2023-03-29 │\n", "│ space ┆ 4443 ┆ 2023-03-30 │\n", "│ model ┆ 1389 ┆ 2023-03-30 │\n", "└───────────┴─────────────────────┴────────────┘" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_df = final_df.sort(\"date\")\n", "final_df" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_df.groupby(\"repo_type\")" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Creating parquet from Arrow format: 100%|██████████| 1/1 [00:00<00:00, 730.46ba/s]\n", "Upload 1 LFS files: 100%|██████████| 1/1 [00:00<00:00, 1.26it/s]\n", "Pushing dataset shards to the dataset hub: 100%|██████████| 1/1 [00:02<00:00, 2.14s/it]\n", "Deleting unused files from dataset repository: 100%|██████████| 1/1 [00:00<00:00, 1.87it/s]\n", "Downloading metadata: 100%|██████████| 441/441 [00:00<00:00, 173kB/s]\n", "Updating downloaded metadata with the new split.\n" ] } ], "source": [ "Dataset(final_df.to_arrow()).push_to_hub(\n", " \"davanstrien/notebooks_by_repo_type\", token=HF_TOKEN\n", ")" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 224 }, "id": "T3vhuLpgdyKh", "outputId": "82a58845-ec9f-40d7-cbad-abddf7ad467a" }, "outputs": [], "source": [ "final_df = final_df.sort(\"date\")\n", "pandas_df = final_df.to_pandas()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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