document corrections

Author: c-chaitanya

doc/python/ml-regression.md

@@ -174,7 +174,7 @@ fig.show()
 Notice how linear regression fits a straight line, but kNN can take non-linear shapes. Moreover, it is possible to extend linear regression to polynomial regression by using scikit-learn's `PolynomialFeatures`, which lets you fit a slope for your features raised to the power of `n`, where `n=1,2,3,4` in our example.
 
 
-With Plotly, it's easy to diplay latex equations in legend and titles by simply adding `$` before and after your equation. This way, you can see the coefficients that our polynomial regression fitted.
+With Plotly, it's easy to display latex equations in legend and titles by simply adding `$` before and after your equation. This way, you can see the coefficients that our polynomial regression fitted.
 <!-- #endregion -->
 
 ```python

doc/unconverted/python/3d-parametric-plots.md

@@ -12,7 +12,7 @@ jupyter:
     language: python
     name: python2
   plotly:
-    description: How to 3D Parameteric Plots in Python
+    description: How to 3D Parametric Plots in Python
     display_as: 3d_charts
     language: python
     layout: base
@@ -80,7 +80,7 @@ py.iplot(fig, filename='Parametric_plot')
 ```
 
 <!-- #region {"deletable": true, "editable": true} -->
-#### Parameteric Plot with Colorscale
+#### Parametric Plot with Colorscale
 <!-- #endregion -->
 
 ```python deletable=true editable=true

doc/unconverted/python/anova.md

@@ -25,7 +25,7 @@ jupyter:
 ---
 
 #### New to Plotly?
-Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/).
+Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
 <br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
 <br>We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!
 

doc/unconverted/python/average_multiple_curves.md

@@ -25,7 +25,7 @@ jupyter:
 ---
 
 #### New to Plotly?
-Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/).
+Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
 <br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
 <br>We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!
 

doc/unconverted/python/baseline-subtraction.md

@@ -60,7 +60,7 @@ py.iplot(table, filename='milk-production-dataframe')
 ```
 
 #### Plot with Baseline
-To subtact a baseline estimate from our data, it is a good idea to first we must first calculate the baseline values then plot the data with the baseline drawn in.
+To subtract a baseline estimate from our data, it is a good idea to first we must first calculate the baseline values then plot the data with the baseline drawn in.
 
 ```python
 baseline_values = peakutils.baseline(time_series)

doc/unconverted/python/basic-statistics.md

@@ -25,7 +25,7 @@ jupyter:
 ---
 
 #### New to Plotly?
-Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/).
+Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
 <br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
 <br>We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!
 

doc/unconverted/python/chord-diagram.md

@@ -45,10 +45,10 @@ To avoid cluttered edges we adopted the following procedure in choosing the inte
 - define a list,  `Dist`, having as elements the distances between the following pairs of points:
 $$(P_1, P_1), \:(P_1, P_2), \: (P_1, P_3),\: (P_1, P_4),\: (P_1, P_5)$$
 
-- In order to assign the control poligon to the Bézier curve that will be the edge between two connected
+- In order to assign the control polygon to the Bézier curve that will be the edge between two connected
 nodes, `V[i], V[j]`, we compute the distance between these nodes, and deduce the interval $k$, of two consecutive values in `Dist`, this distance  belongs to.
 
-- Since there are four such intervals indexed $k=0,1,2,3$, we  define the control poligon as follows: $${\bf b}_0=V[i],\:\: {\bf b}_1=V[i]/param,\:\: {\bf b}_2=V[j]/param, \:\:{\bf b}_3=V[j],$$ where `param` is chosen from the list: `params=[1.2, 1.5, 1.8, 2.1]`.
+- Since there are four such intervals indexed $k=0,1,2,3$, we  define the control polygon as follows: $${\bf b}_0=V[i],\:\: {\bf b}_1=V[i]/param,\:\: {\bf b}_2=V[j]/param, \:\:{\bf b}_3=V[j],$$ where `param` is chosen from the list: `params=[1.2, 1.5, 1.8, 2.1]`.
 
 Namely, if the distance(`V[i], V[j]`), belongs to the $K^{th}$ interval associated to `Dist`, then we choose `param= params[K]`.
 

doc/unconverted/python/density-plots.md

@@ -26,7 +26,7 @@ jupyter:
 
 <!-- #region {"deletable": true, "editable": true} -->
 #### New to Plotly?
-Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/).
+Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
 <br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
 <br>We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!
 <!-- #endregion -->

doc/unconverted/python/discrete-frequency.md

@@ -25,7 +25,7 @@ jupyter:
 ---
 
 #### New to Plotly?
-Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/).
+Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
 <br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
 <br>We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!
 
@@ -115,7 +115,7 @@ py.iplot(fig, filename='histogram-percentage')
 #### Cumulative Density Function
 
 
-We can also take the cumulatve sum of our dataset and then plot the cumulative density function, or `CDF`, as a scatter plot
+We can also take the cumulative sum of our dataset and then plot the cumulative density function, or `CDF`, as a scatter plot
 
 ```python
 cumsum = np.cumsum(x)

doc/unconverted/python/filled-chord-diagram.md

@@ -82,7 +82,7 @@ Let us denote by `total_comments` the total number of posts recorded in this com
 
 Theoretically the interval `[0, total_comments)` is mapped linearly onto the unit circle, identified with the interval $[0,2\pi)$.
 
-For a better looking plot one proceeds as follows: starting from the angular position $0$, in counter-clockwise direction, one draws succesively, around the unit circle,  two parallel arcs of length equal to a mapped row sum value, minus a fixed gap. Click the image below:
+For a better looking plot one proceeds as follows: starting from the angular position $0$, in counter-clockwise direction, one draws successively, around the unit circle,  two parallel arcs of length equal to a mapped row sum value, minus a fixed gap. Click the image below:
 
 
 
@@ -291,7 +291,7 @@ def control_pts(angle, radius):
 
 ```python
 def ctrl_rib_chords(l, r, radius):
-    # this function returns a 2-list containing control poligons of the two quadratic Bezier
+    # this function returns a 2-list containing control polygons of the two quadratic Bezier
     #curves that are opposite sides in a ribbon
     #l (r) the list of angular variables of the ribbon arc ends defining
     #the ribbon starting (ending) arc