Datasets:
Add a tested pandas quickstart notebook
Browse files- README.md +6 -0
- notebooks/quickstart.ipynb +96 -0
README.md
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@@ -104,3 +104,9 @@ US Treasury and BLS data are works of the US federal government, in the public d
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- [Insidewell: SEC Form 4 Insider Transactions (latest EDGAR filings)](https://huggingface.co/datasets/CyberMax-tools/sec-form4-insider-transactions)
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- [Eventwren: SEC 8-K Material Events by Item (latest EDGAR filings)](https://huggingface.co/datasets/CyberMax-tools/eventwren-sec-8k-events)
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<!-- cybermax-xlinks:end -->
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- [Insidewell: SEC Form 4 Insider Transactions (latest EDGAR filings)](https://huggingface.co/datasets/CyberMax-tools/sec-form4-insider-transactions)
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- [Eventwren: SEC 8-K Material Events by Item (latest EDGAR filings)](https://huggingface.co/datasets/CyberMax-tools/eventwren-sec-8k-events)
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<!-- cybermax-xlinks:end -->
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<!-- cybermax-notebook:start -->
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## Quickstart notebook
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Load this dataset with pandas and see real outputs in the [quickstart notebook](https://huggingface.co/datasets/CyberMax-tools/curvewire-us-macro-panel/blob/main/notebooks/quickstart.ipynb).
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<!-- cybermax-notebook:end -->
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notebooks/quickstart.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": "# Quickstart: Curvewire: US Treasury yield curve + BLS macro panel (official sources)\n\nThe US numbers macro models, trading bots and LLM agents ask for most, taken straight from the agencies that publish them (US Treasury and the Bureau of Labor Statistics), not scraped from redistributors, and cleaned into three tidy tables: treasury_yields (default), treasury_real_yields…\n\nDataset: [huggingface.co/datasets/CyberMax-tools/curvewire-us-macro-panel](https://huggingface.co/datasets/CyberMax-tools/curvewire-us-macro-panel) · file: `data/treasury_yields.parquet` · by CyberMax.\n\nRuns anywhere with pandas (Colab, Kaggle, Jupyter, VS Code). The outputs below are from a real run of this notebook."
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": "## Load the data"
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "9,189 rows x 17 columns\n"
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},
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{
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"output_type": "execute_result",
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"execution_count": 1,
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"metadata": {},
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"data": {
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"text/plain": " date y_1m y_6w y_2m ... y_20y y_30y spread_10y_2y spread_10y_3m\n0 1990-01-02 NaN NaN NaN ... NaN 8.00 0.07 0.11\n1 1990-01-03 NaN NaN NaN ... NaN 8.04 0.05 0.10\n2 1990-01-04 NaN NaN NaN ... NaN 8.04 0.06 0.14\n3 1990-01-05 NaN NaN NaN ... NaN 8.06 0.09 0.20\n4 1990-01-08 NaN NaN NaN ... NaN 8.09 0.12 0.23\n5 1990-01-09 NaN NaN NaN ... NaN 8.10 0.11 0.22\n6 1990-01-10 NaN NaN NaN ... NaN 8.11 0.12 0.28\n7 1990-01-11 NaN NaN NaN ... NaN 8.11 0.13 0.24\n8 1990-01-12 NaN NaN NaN ... NaN 8.17 0.17 0.36\n9 1990-01-16 NaN NaN NaN ... NaN 8.25 0.10 0.31\n\n[10 rows x 17 columns]",
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"text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>date</th>\n <th>y_1m</th>\n <th>y_6w</th>\n <th>y_2m</th>\n <th>y_3m</th>\n <th>y_4m</th>\n <th>...</th>\n <th>y_7y</th>\n <th>y_10y</th>\n <th>y_20y</th>\n <th>y_30y</th>\n <th>spread_10y_2y</th>\n <th>spread_10y_3m</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1990-01-02</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.83</td>\n <td>NaN</td>\n <td>...</td>\n <td>7.98</td>\n <td>7.94</td>\n <td>NaN</td>\n <td>8.00</td>\n <td>0.07</td>\n <td>0.11</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1990-01-03</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.89</td>\n <td>NaN</td>\n <td>...</td>\n <td>8.04</td>\n <td>7.99</td>\n <td>NaN</td>\n <td>8.04</td>\n <td>0.05</td>\n <td>0.10</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1990-01-04</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.84</td>\n <td>NaN</td>\n <td>...</td>\n <td>8.02</td>\n <td>7.98</td>\n <td>NaN</td>\n <td>8.04</td>\n <td>0.06</td>\n <td>0.14</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1990-01-05</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.79</td>\n <td>NaN</td>\n <td>...</td>\n <td>8.03</td>\n <td>7.99</td>\n <td>NaN</td>\n <td>8.06</td>\n <td>0.09</td>\n <td>0.20</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1990-01-08</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.79</td>\n <td>NaN</td>\n <td>...</td>\n <td>8.05</td>\n <td>8.02</td>\n <td>NaN</td>\n <td>8.09</td>\n <td>0.12</td>\n <td>0.23</td>\n </tr>\n <tr>\n <th>5</th>\n <td>1990-01-09</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.80</td>\n <td>NaN</td>\n <td>...</td>\n <td>8.05</td>\n <td>8.02</td>\n <td>NaN</td>\n <td>8.10</td>\n <td>0.11</td>\n <td>0.22</td>\n </tr>\n <tr>\n <th>6</th>\n <td>1990-01-10</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.75</td>\n <td>NaN</td>\n <td>...</td>\n <td>8.00</td>\n <td>8.03</td>\n <td>NaN</td>\n <td>8.11</td>\n <td>0.12</td>\n <td>0.28</td>\n </tr>\n <tr>\n <th>7</th>\n <td>1990-01-11</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.80</td>\n <td>NaN</td>\n <td>...</td>\n <td>8.01</td>\n <td>8.04</td>\n <td>NaN</td>\n <td>8.11</td>\n <td>0.13</td>\n <td>0.24</td>\n </tr>\n <tr>\n <th>8</th>\n <td>1990-01-12</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.74</td>\n <td>NaN</td>\n <td>...</td>\n <td>8.07</td>\n <td>8.10</td>\n <td>NaN</td>\n <td>8.17</td>\n <td>0.17</td>\n <td>0.36</td>\n </tr>\n <tr>\n <th>9</th>\n <td>1990-01-16</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.89</td>\n <td>NaN</td>\n <td>...</td>\n <td>8.18</td>\n <td>8.20</td>\n <td>NaN</td>\n <td>8.25</td>\n <td>0.10</td>\n <td>0.31</td>\n </tr>\n </tbody>\n</table>"
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}
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}
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],
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"source": "import pandas as pd\n\nURL = \"https://huggingface.co/datasets/CyberMax-tools/curvewire-us-macro-panel/resolve/main/data/treasury_yields.parquet\"\ndf = pd.read_parquet(URL)\nprint(f\"{len(df):,} rows x {len(df.columns)} columns\")\ndf.head(10)"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": "Rows per month by `date`:"
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"execution_count": 2,
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"metadata": {},
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"data": {
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"text/plain": "date\n2025-10 22\n2025-11 18\n2025-12 22\n2026-01 20\n2026-02 19\n2026-03 22\n2026-04 22\n2026-05 20\n2026-06 21\n2026-07 22\n2026-08 21\n2026-09 16\nFreq: M, Name: count, dtype: int64"
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}
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}
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],
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"source": "months = pd.to_datetime(df['date'], errors='coerce', utc=True).dt.tz_localize(None).dt.to_period('M')\nmonths.value_counts().sort_index().tail(12)"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": "Summary statistics for the numeric columns:"
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"execution_count": 3,
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"metadata": {},
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"data": {
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"text/plain": " y_1m y_6w y_2m y_3m y_4m y_6m\ncount 6289.000 401.000 1984.000 9185.000 982.000 9188.000\nmean 1.724 4.020 2.818 2.812 4.658 2.925\nstd 1.840 0.296 2.025 2.263 0.666 2.279\nmin 0.000 3.650 0.000 0.000 3.580 0.020\n25% 0.080 3.720 0.270 0.260 4.025 0.430\n50% 1.070 3.980 3.225 2.910 4.520 3.115\n75% 3.310 4.350 4.460 4.980 5.410 5.080\nmax 6.020 4.530 5.610 8.260 5.640 8.490",
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"text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>y_1m</th>\n <th>y_6w</th>\n <th>y_2m</th>\n <th>y_3m</th>\n <th>y_4m</th>\n <th>y_6m</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>6289.000</td>\n <td>401.000</td>\n <td>1984.000</td>\n <td>9185.000</td>\n <td>982.000</td>\n <td>9188.000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>1.724</td>\n <td>4.020</td>\n <td>2.818</td>\n <td>2.812</td>\n <td>4.658</td>\n <td>2.925</td>\n </tr>\n <tr>\n <th>std</th>\n <td>1.840</td>\n <td>0.296</td>\n <td>2.025</td>\n <td>2.263</td>\n <td>0.666</td>\n <td>2.279</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.000</td>\n <td>3.650</td>\n <td>0.000</td>\n <td>0.000</td>\n <td>3.580</td>\n <td>0.020</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>0.080</td>\n <td>3.720</td>\n <td>0.270</td>\n <td>0.260</td>\n <td>4.025</td>\n <td>0.430</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>1.070</td>\n <td>3.980</td>\n <td>3.225</td>\n <td>2.910</td>\n <td>4.520</td>\n <td>3.115</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>3.310</td>\n <td>4.350</td>\n <td>4.460</td>\n <td>4.980</td>\n <td>5.410</td>\n <td>5.080</td>\n </tr>\n <tr>\n <th>max</th>\n <td>6.020</td>\n <td>4.530</td>\n <td>5.610</td>\n <td>8.260</td>\n <td>5.640</td>\n <td>8.490</td>\n </tr>\n </tbody>\n</table>"
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}
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}
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],
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"source": "df[['y_1m', 'y_6w', 'y_2m', 'y_3m', 'y_4m', 'y_6m']].describe().round(3)"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": "## Go further\n- **All CyberMax tools, datasets and free apps:** [https://cybermax-tools-cybermax.static.hf.space/](https://cybermax-tools-cybermax.static.hf.space/)"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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