Fixed warnings and errors

Author: daexs

doc/python/3d-mesh.md

@@ -42,7 +42,7 @@ import plotly.graph_objects as go
 import numpy as np
 
 # Download data set from plotly repo
-pts = np.loadtxt(np.DataSource().open('https://raw.githubusercontent.com/plotly/datasets/master/mesh_dataset.txt'))
+pts = np.loadtxt(np.lib.npyio.DataSource().open('https://raw.githubusercontent.com/plotly/datasets/master/mesh_dataset.txt'))
 x, y, z = pts.T
 
 fig = go.Figure(data=[go.Mesh3d(x=x, y=y, z=z, color='lightpink', opacity=0.50)])
@@ -58,7 +58,7 @@ The `alphahull` parameter sets the shape of the mesh. If the value is -1 (defaul
 import plotly.graph_objects as go
 import numpy as np
 
-pts = np.loadtxt(np.DataSource().open('https://raw.githubusercontent.com/plotly/datasets/master/mesh_dataset.txt'))
+pts = np.loadtxt(np.lib.npyio.DataSource().open('https://raw.githubusercontent.com/plotly/datasets/master/mesh_dataset.txt'))
 x, y, z = pts.T
 
 fig = go.Figure(data=[go.Mesh3d(x=x, y=y, z=z,

doc/python/bar-charts.md

@@ -68,6 +68,10 @@ fig.show()
 ```
 
 ```python
+import plotly.express as px
+
+long_df = px.data.medals_long()
+
 long_df
 ```
 
@@ -84,6 +88,10 @@ fig.show()
 ```
 
 ```python
+import plotly.express as px
+
+wide_df = px.data.medals_wide()
+
 wide_df
 ```
 

doc/python/ml-knn.md

@@ -264,6 +264,7 @@ import plotly.express as px
 import plotly.graph_objects as go
 import numpy as np
 from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
 
 mesh_size = .02
 margin = 1

doc/python/ml-regression.md

@@ -491,7 +491,7 @@ y = df['petal_width']
 # Define and fit the grid
 model = DecisionTreeRegressor()
 param_grid = {
-    'criterion': ['mse', 'friedman_mse', 'mae'],
+    'criterion': ['squared_error', 'friedman_mse', 'absolute_error'],
     'max_depth': range(2, 5)
 }
 grid = GridSearchCV(model, param_grid, cv=N_FOLD)

doc/python/smoothing.md

@@ -132,6 +132,16 @@ TMA_i = \frac{SMA_i + ... + SMA_{i+n}}{n}
 $$
 
 ```python
+import plotly.graph_objects as go
+import numpy as np
+
+np.random.seed(1)
+
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+noise = 2 * np.random.random(len(x)) - 1 # uniformly distributed between -1 and 1
+y_noise = y + noise
+
 def smoothTriangle(data, degree):
     triangle=np.concatenate((np.arange(degree + 1), np.arange(degree)[::-1])) # up then down
     smoothed=[]