Large timeseries

.pre-commit-config.yaml

@@ -26,9 +26,10 @@ repos:
         args: [hvplot]
         files: hvplot/
 -   repo: https://github.com/hoxbro/clean_notebook
-    rev: v0.1.10
+    rev: v0.1.14
     hooks:
     - id: clean-notebook
+      args: [-i, tags]
 -   repo: https://github.com/codespell-project/codespell
     rev: v2.2.6
     hooks:

doc/user_guide/index.rst

@@ -89,6 +89,9 @@ rather than Matplotlib.
 * `Timeseries Data <Timeseries_Data.html>`_
   Using hvPlot when working with timeseries data.
 
+* `Large Timeseries Data <Large_Timeseries.html>`_
+  Using hvPlot when working with large timeseries data.
+
 * `Statistical Plots <Statistical_Plots.html>`_
   A number of statistical plot types modeled on the pandas.plotting module.
 
@@ -117,5 +120,6 @@ rather than Matplotlib.
     Network Graphs <NetworkX>
     Geographic Data <Geographic_Data>
     Timeseries Data <Timeseries_Data>
+    Large Timeseries <Large_Timeseries>
     Statistical Plots <Statistical_Plots>
     Pandas API <Pandas_API>

examples/getting_started/interactive.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "bcbd5216-59d4-4de9-b576-9727ce9b6435",
    "metadata": {},
    "source": [
     "hvPlot isn't only a plotting library, it is dedicated to make data exploration easier. In this guide you will see how it can help you to get better control over your data pipelines. We define a *data pipeline* as a series of commands that *transform* some data, such as aggregating, filtering, reshaping, renaming, etc. A data pipeline may include a *load* step that will provide the input data to the pipeline, e.g. reading the data from a data base. \n",
@@ -14,6 +15,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "4ff4e131-e046-49a1-85ed-01151299adf8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,6 +25,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "6ece0608-9102-40a6-b649-ee6e46e263b9",
    "metadata": {},
    "source": [
     "We load a dataset and get a handle on its unique *air* variable."
@@ -31,6 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "f34daeaa-bb75-4642-bfbe-c95973e78c5f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,6 +45,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4c105059-2f7d-44ec-ad73-9f65eef4cbc7",
    "metadata": {},
    "source": [
     "We want to better understand the temporal evolution of the air temperature over different latitudes compared to a baseline. The data pipeline we build includes:\n",
@@ -57,6 +62,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "899aa9be-054c-4238-80f3-8706678d7346",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,6 +84,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "66063fd1-32b0-40dd-b2e5-bdb1ed595347",
    "metadata": {},
    "source": [
     "Without `.interactive()` we would manually change the values of `LATITUDE` and `ROLLING_WINDOW` to see how they affect the pipeline output. Instead we create two widgets with the values we expect them to take, we are basically declaring beforehand our parameter space. To create widgets we import [Panel](https://panel.holoviz.org) and pick two appropriate widgets from its [Reference Gallery](https://panel.holoviz.org/reference/index.html#widgets)."
@@ -86,6 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "03ff2aed-65d2-470a-a401-1c6f65fd5d05",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,6 +105,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9a575b14-9973-4308-8b1c-c17b8d8b415e",
    "metadata": {},
    "source": [
     "Now we instantiate an *Interactive* object by calling `.interactive()` on our data. This object mirrors the underlying object API, it accepts all of its natural operations. We replace the data by the interactive object in the pipeline, and replace the constant parameters by the widgets we have just created."
@@ -105,6 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "dddaf71e-1f62-46c8-a35f-48dd04d3006a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,6 +124,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "d660ad41-24bd-4236-8d57-268187bc7ac7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,6 +143,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "41a8c760-d2d2-4de4-91dd-7cb0bfc7bd86",
    "metadata": {},
    "source": [
     "You can see that now the pipeline when rendered doesn't only consist of its output, it also includes the widgets that control it. Change the widgets' values and observe how the output dynamically updates.\n",
@@ -144,6 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "b3ac92e6-4041-44e1-9997-6d36c10457b1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,6 +167,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0606ad0e-2917-4bfa-b8ab-152ce2b8b837",
    "metadata": {},
    "source": [
     "For information on using `.interactive()` take a look at the [User Guide](../user_guide/Interactive.ipynb)."

examples/reference/pandas/andrewscurves.ipynb

@@ -3,6 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "c3152ae5-a7e8-4a87-946c-0622095a1a1a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,6 +12,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3ff5961b-3576-4b7b-9b97-fcabc717aafa",
    "metadata": {},
    "source": [
     "Andrews curves provides a mechanism for visualising clusters of multivariate data.\n",
@@ -27,6 +29,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "36e7d95b-2810-4efa-ae16-291f63be1e0a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,6 +41,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "96e78c6a-92ee-4820-ae8a-13220e2f9870",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,6 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "27a21f83-4d3e-43a5-82e2-74f99cb7cfae",
    "metadata": {},
    "outputs": [],
    "source": [

examples/reference/pandas/lagplot.ipynb

@@ -3,6 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "0a497b8d-a35b-48b1-9e1a-e2acc907ff14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,13 +14,15 @@
   },
   {
    "cell_type": "markdown",
+   "id": "992bc493-914a-4dbb-80cd-6d7ed12a3076",
    "metadata": {},
    "source": [
     "Lag plots are most commonly used to look for patterns in time series data."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "f6d461a5-9c12-4042-8971-ad528af9977d",
    "metadata": {},
    "source": [
     "Given the following time series:"
@@ -28,6 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "16743185-fa7f-4854-90fc-f284907ad809",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,6 +44,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8ed87848-6a86-4025-bd7e-7900a69706c9",
    "metadata": {},
    "source": [
     "A lag plot with `lag=1` returns:"
@@ -48,6 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "d14609df-a88a-44fc-85b0-426e139d9b4b",
    "metadata": {},
    "outputs": [],
    "source": [

examples/reference/pandas/parallelcoordinates.ipynb

@@ -3,6 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "1289b546-07a6-49f9-a21f-b2bd891b10b1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,6 +12,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7c867b2b-d891-483d-8b80-12103ad72b91",
    "metadata": {},
    "source": [
     "Parallel coordinates are a common way of visualizing and analyzing high-dimensional datasets.\n",
@@ -21,6 +23,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "538eafff-24af-41ef-941d-c81499c88a39",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,6 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "7e25f890-5e01-4358-b4a9-f0183647dd58",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,6 +45,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "eb4a6aec-36e0-43f6-b776-8cad6d926428",
    "metadata": {},
    "outputs": [],
    "source": [