cleanup

Author: nicolaskruchten

doc/python/horizontal-legend.md

@@ -5,7 +5,7 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
+      format_version: "1.1"
       jupytext_version: 1.1.6
   kernelspec:
     display_name: Python 3
@@ -29,10 +29,12 @@ jupyter:
     name: Legends
     order: 14
     permalink: python/legend/
+    redirect_from: python/horizontal-legend/
     thumbnail: thumbnail/legends.gif
 ---
 
 #### Show Legend
+
 By default the legend is displayed on Plotly charts with multiple traces.
 
 ```python
@@ -217,9 +219,11 @@ fig.update_layout(
 
 fig.show()
 ```
+
 ### Size of Legend Items
 
-In this example [itemsizing](https://plot.ly/python/reference/#layout-legend-itemsizing) attribute determines the legend items symbols remain constant, regardless of how tiny/huge the bubbles would be in the graph.   
+In this example [itemsizing](https://plot.ly/python/reference/#layout-legend-itemsizing) attribute determines the legend items symbols remain constant, regardless of how tiny/huge the bubbles would be in the graph.
+
 ```python
 import plotly.graph_objects as go
 
@@ -243,6 +247,7 @@ fig.update_layout(legend= {'itemsizing': 'constant'})
 
 fig.show()
 ```
+
 #### Grouped Legend
 
 ```python
@@ -338,4 +343,5 @@ fig.show()
 ```
 
 #### Reference
+
 See https://plot.ly/python/reference/#layout-legend for more information!

doc/python/legend.md

@@ -5,7 +5,7 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
+      format_version: "1.1"
       jupytext_version: 1.1.1
   kernelspec:
     display_name: Python 3
@@ -23,7 +23,7 @@ jupyter:
     version: 3.6.7
   plotly:
     description: Learn how to find peaks and valleys on datasets in Python
-    display_as: peak-analysis
+    display_as: advanced_opt
     has_thumbnail: false
     language: python
     layout: base
@@ -35,6 +35,7 @@ jupyter:
 ---
 
 #### Imports
+
 The tutorial below imports [Pandas](https://plot.ly/pandas/intro-to-pandas-tutorial/), and [SciPy](https://www.scipy.org/).
 
 ```python
@@ -43,6 +44,7 @@ from scipy.signal import find_peaks
 ```
 
 #### Import Data
+
 To start detecting peaks, we will import some data on milk production by month:
 
 ```python
@@ -97,6 +99,7 @@ fig.show()
 ```
 
 #### Only Highest Peaks
+
 We can attempt to set our threshold so that we identify as many of the _highest peaks_ that we can.
 
 ```python

doc/python/peak-finding.md

@@ -5,7 +5,7 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
+      format_version: "1.1"
       jupytext_version: 1.2.0
   kernelspec:
     display_name: Python 3
@@ -23,7 +23,7 @@ jupyter:
     version: 3.6.8
   plotly:
     description: How to create charts from csv files with Plotly and Python
-    display_as: databases
+    display_as: advanced_opt
     has_thumbnail: false
     language: python
     layout: base
@@ -34,7 +34,6 @@ jupyter:
     thumbnail: thumbnail/csv.jpg
 ---
 
-
 CSV or comma-delimited-values is a very popular format for storing structured data. In this tutorial, we will see how to plot beautiful graphs using csv data, and Pandas. We will learn how to import csv data from an external source (a url), and plot it using Plotly and pandas.
 
 First we import the data and look at it.
@@ -57,7 +56,6 @@ fig = px.line(df, x = 'AAPL_x', y = 'AAPL_y', title='Apple Share Prices over tim
 fig.show()
 ```
 
-
 ### Plot from CSV with `graph_objects`
 
 ```python
@@ -76,6 +74,6 @@ fig.update_layout(title='Apple Share Prices over time (2014)',
 fig.show()
 ```
 
-
 #### Reference
+
 See https://plot.ly/python/getting-started for more information about Plotly's Python API!

doc/python/plot-data-from-csv.md

@@ -5,7 +5,7 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
+      format_version: "1.1"
       jupytext_version: 1.1.1
   kernelspec:
     display_name: Python 3
@@ -23,7 +23,7 @@ jupyter:
     version: 3.6.7
   plotly:
     description: Learn how to use Python to make a Random Walk
-    display_as: statistics
+    display_as: advanced_opt
     has_thumbnail: false
     language: python
     layout: base
@@ -36,10 +36,8 @@ jupyter:
 
 A [random walk](https://en.wikipedia.org/wiki/Random_walk) can be thought of as a random process in which a token or a marker is randomly moved around some space, that is, a space with a metric used to compute distance. It is more commonly conceptualized in one dimension ($\mathbb{Z}$), two dimensions ($\mathbb{Z}^2$) or three dimensions ($\mathbb{Z}^3$) in Cartesian space, where $\mathbb{Z}$ represents the set of integers. In the visualizations below, we will be using [scatter plots](https://plot.ly/python/line-and-scatter/) as well as a colorscale to denote the time sequence of the walk.
 
-
 #### Random Walk in 1D
 
-
 The jitter in the data points along the x and y axes are meant to illuminate where the points are being drawn and what the tendancy of the random walk is.
 
 ```python
@@ -137,6 +135,7 @@ fig.show()
 ```
 
 #### Advanced Tip
+
 We can formally think of a 1D random walk as a point jumping along the integer number line. Let $Z_i$ be a random variable that takes on the values +1 and -1. Let this random variable represent the steps we take in the random walk in 1D (where +1 means right and -1 means left). Also, as with the above visualizations, let us assume that the probability of moving left and right is just $\frac{1}{2}$. Then, consider the sum
 
 $$
@@ -164,4 +163,3 @@ $$
 $$
 
 Therefore, we expect our random walk to hover around $0$ regardless of how many steps we take in our walk.
-

doc/python/random-walk.md

@@ -5,7 +5,7 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
+      format_version: "1.1"
       jupytext_version: 1.1.1
   kernelspec:
     display_name: Python 3
@@ -23,7 +23,7 @@ jupyter:
     version: 3.6.7
   plotly:
     description: Learn how to perform smoothing using various methods in Python.
-    display_as: signal-analysis
+    display_as: advanced_opt
     has_thumbnail: false
     language: python
     layout: base
@@ -34,8 +34,8 @@ jupyter:
     thumbnail: /images/static-image
 ---
 
-
 #### Imports
+
 The tutorial below imports [NumPy](http://www.numpy.org/), [Pandas](https://plot.ly/pandas/intro-to-pandas-tutorial/), [SciPy](https://www.scipy.org/) and [Plotly](https://plot.ly/python/getting-started/).
 
 ```python
@@ -49,6 +49,7 @@ from scipy import signal
 ```
 
 #### Savitzky-Golay Filter
+
 `Smoothing` is a technique that is used to eliminate noise from a dataset. There are many algorithms and methods to accomplish this but all have the same general purpose of 'roughing out the edges' or 'smoothing' some data.
 
 There is reason to smooth data if there is little to no small-scale structure in the data. The danger to this thinking is that one may skew the representation of the data enough to change its percieved meaning, so for the sake of scientific honesty it is an imperative to at the very minimum explain one's reason's for using a smoothing algorithm to their dataset.