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bbchobbcho
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fixed nbs
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‎.github/workflows/ci.yml‎

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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -e ".[dev]"
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pip install -e ".[dev,docs]"
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- name: Run tests
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run: |

‎docs/gallery/facets.ipynb‎

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"id": "cell-12",
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"metadata": {},
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"outputs": [],
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"source": "(ggplot(df, aes(x='x', y='y'))\n + geom_point(alpha=0.5)\n + facet_grid(rows='row_var'))"
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"source": "(ggplot(df, aes(x='x', y='y'))\n + geom_point(alpha=0.5)\n + facet_grid(rows='row_var', cols='.'))"
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},
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{
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"cell_type": "markdown",
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"id": "cell-14",
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"metadata": {},
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"outputs": [],
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"source": "(ggplot(df, aes(x='x', y='y'))\n + geom_point(alpha=0.5)\n + facet_grid(cols='col_var'))"
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"source": "(ggplot(df, aes(x='x', y='y'))\n + geom_point(alpha=0.5)\n + facet_grid(rows='.', cols='col_var'))"
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},
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{
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"cell_type": "markdown",

‎docs/gallery/theming.ipynb‎

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"id": "cell-40",
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"metadata": {},
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"outputs": [],
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"source": "df = pd.DataFrame({\n 'x': np.random.randn(100),\n 'y': np.random.randn(100)\n})\n\n(ggplot(df, aes(x='x', y='y'))\n + geom_point(size=6)\n + geom_smooth(method='lm', color='red')\n + theme_minimal()\n + theme(\n axis_title=element_text(size=12),\n plot_title=element_text(size=14, face='bold')\n )\n + labs(title='Scatter with Linear Fit',\n x='Independent Variable',\n y='Dependent Variable'))"
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"source": "df = pd.DataFrame({\n 'x': np.random.randn(100),\n 'y': np.random.randn(100)\n})\n\n(ggplot(df, aes(x='x', y='y'))\n + geom_point(size=6)\n + geom_smooth(method='lm', color='red')\n + theme_minimal()\n + theme(\n axis_title=element_text(size=12),\n plot_title=element_text(size=14)\n )\n + labs(title='Scatter with Linear Fit',\n x='Independent Variable',\n y='Dependent Variable'))"
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},
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{
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"cell_type": "markdown",

‎docs/getting-started.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 5,
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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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"version": "3.12.0"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "from ggplotly import *\nimport pandas as pd"
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"source": [
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"from ggplotly import *\n",
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"import pandas as pd\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Create some data\ndf = pd.DataFrame({\n 'x': [1, 2, 3, 4, 5],\n 'y': [2, 4, 3, 5, 4]\n})\n\n# Create a scatter plot\n(ggplot(df, aes(x='x', y='y')) + geom_point())"
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"source": [
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"# Create some data\n",
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"df = pd.DataFrame({\n",
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" 'x': [1, 2, 3, 4, 5],\n",
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" 'y': [2, 4, 3, 5, 4]\n",
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"})\n",
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"\n",
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"# Create a scatter plot\n",
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"ggplot(df, aes(x='x', y='y')) + geom_point()"
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]
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},
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{
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"cell_type": "markdown",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "(\n ggplot(df, aes(x='x', y='y', color='category')) # Data + aesthetics\n + geom_point(size=3) # Geom\n + scale_color_brewer(palette='Set1') # Scale\n + theme_minimal() # Theme\n + labs(title='My Plot', x='X Axis', y='Y Axis') # Labels\n)"
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"source": [
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"# More complete sample data\n",
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"np.random.seed(42)\n",
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"df = pd.DataFrame({\n",
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" 'x': np.random.randn(50),\n",
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" 'y': np.random.randn(50),\n",
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" 'category': np.random.choice(['A', 'B', 'C'], 50),\n",
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" 'values': np.random.rand(50) * 10\n",
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"})\n",
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"\n",
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"(\n",
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" ggplot(df, aes(x='x', y='y', color='category')) # Data + aesthetics\n",
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" + geom_point(size=5) # Geom\n",
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" + scale_color_brewer(palette='Set1') # Scale\n",
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" + theme_minimal() # Theme\n",
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" + labs(title='My Plot', x='X Axis', y='Y Axis') # Labels\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "ggplot(df, aes(x='x', y='y', color='category')) + geom_point()"
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"source": [
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"ggplot(df, aes(x='x', y='y', color='category')) + geom_point()"
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]
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},
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{
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"cell_type": "markdown",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "ggplot(df, aes(x='x', y='y')) + geom_point() + geom_line()"
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"source": [
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"ggplot(df, aes(x='x', y='y')) + geom_point() + geom_smooth()"
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]
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},
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{
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"cell_type": "markdown",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "ggplot(df, aes(x='x', y='y')) + geom_point() + facet_wrap('category')"
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"source": [
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"ggplot(df, aes(x='x', y='y')) + geom_point() + facet_wrap('category')"
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]
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},
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{
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"cell_type": "markdown",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Automatically use DataFrame index\nggplot(df, aes(y='values')) + geom_line()\n\n# Or explicitly\nggplot(df, aes(x='index', y='values')) + geom_line()\n\n# Works with Series too\nseries = pd.Series([1, 2, 3], index=['a', 'b', 'c'])\nggplot(series) + geom_point()"
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"source": [
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"# Create time series data with index\n",
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"ts_df = pd.DataFrame({\n",
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" 'values': np.cumsum(np.random.randn(50)) + 50\n",
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"}, index=pd.date_range('2024-01-01', periods=50, freq='D'))\n",
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"\n",
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"# Automatically use DataFrame index\n",
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"ggplot(ts_df, aes(y='values')) + geom_line()"
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]
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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": [
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"## Saving Plots"
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"## Built-in Datasets\n",
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"\n",
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"ggplotly includes classic datasets for learning:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Create plot\np = ggplot(df, aes(x='x', y='y')) + geom_point()\n\n# Save as HTML (interactive)\np.write_html('plot.html')\n\n# Save as image (requires kaleido)\np.write_image('plot.png')\n\n# Or use ggsave\nggsave(p, 'plot.png', width=800, height=600)"
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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": [
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"## Built-in Datasets\n",
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"from ggplotly import data\n",
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"\n",
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"ggplotly includes classic datasets for learning:"
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"# See all available datasets\n",
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"data()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "from ggplotly import data\n\n# Load a dataset\nmpg = data('mpg')\ndiamonds = data('diamonds')\niris = data('iris')\n\n# See all available datasets\ndata()"
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"source": [
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"# Load the iris dataset\n",
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"iris = data('iris')\n",
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"\n",
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"ggplot(iris, aes(x='sepal_length', y='sepal_width', color='species')) + geom_point()"
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]
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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": [
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"## Saving Plots\n",
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"\n",
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"```python\n",
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"# Create plot\n",
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"p = ggplot(df, aes(x='x', y='y')) + geom_point()\n",
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"\n",
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"# Save as HTML (interactive)\n",
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"p.write_html('plot.html')\n",
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"\n",
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"# Save as image (requires kaleido)\n",
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"p.write_image('plot.png')\n",
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"\n",
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"# Or use ggsave\n",
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"ggsave(p, 'plot.png', width=800, height=600)\n",
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"```\n",
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"\n",
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"## Next Steps\n",
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"\n",
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"- [Aesthetics Guide](guide/aesthetics.ipynb) - Learn about mapping data to visual properties\n",
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"- [Geoms Reference](guide/geoms.ipynb) - Explore all available geometric objects\n",
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"- [Themes](guide/themes.ipynb) - Customize the look of your plots"
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]
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}
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]
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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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"version": "3.9.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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