GitBook: [master] 79 pages and 11 assets modified

plotly/6.2-basic-charts/6.2.5-tables.md

@@ -1,2 +1,125 @@
 # 6.2.6 Tables
 
+There are two methods to create a table in Plotly. One is `plotly.graph_objects,` the other is `plotly.figure_factory`
+
+## Graph Objects
+
+### 1. Basic Table
+
+```text
+import plotly.graph_objects as go
+
+fig = go.Figure(data=[go.Table(header=dict(values=['Jonas', 'Melo']),
+                 cells=dict(values=[[100, 90, 80, 85], [90, 85, 88, 65]]))
+                     ])
+fig.show()
+```
+
+![](../../.gitbook/assets/screenshot-2020-07-26-at-22.34.47.png)
+
+### 2. Customized Table
+
+```text
+fig = go.Figure(data=[go.Table(
+    header=dict(values=['Jonas', 'Melo'],
+                line_color='silver',
+                fill_color='navy',
+                align='center',
+                font=dict(color='white', size=14),
+                height=40),
+    cells=dict(values=[[100, 90, 80, 85], # 1st column
+                       [90, 85, 88, 65]], # 2nd column
+               line_color='silver',
+               fill_color='aliceblue',
+               align='left'))
+])
+
+fig.update_layout(width=500, height=300)
+fig.show()
+```
+
+![](../../.gitbook/assets/screenshot-2020-07-26-at-22.34.41.png)
+
+## Figure Factory
+
+### 3. Basic Table
+
+```text
+import plotly.figure_factory as ff
+data_matrix = [['Name', 'Semester', 'Score'],
+               ['Jonas', 1, 'A+'],
+               ['Melo', 1, 'B+'],
+               ['Jonas', 2, 'A'],
+               ['Melo', 2, 'A']]
+
+fig = ff.create_table(data_matrix)
+fig.show()
+```
+
+![](../../.gitbook/assets/newplot-3-%20%281%29.png)
+
+### 4. Table with LaTeX
+
+{% hint style="info" %}
+The default row height is 30 pixels. We can use \ **height\_constant**  to change the height of each row.
+{% endhint %}
+
+```text
+data_matrix = [['Name', 'Equation'],
+               ['Pythagorean Theorem', '$a^{2}+b^{2}=c^{2}$'],
+               ['Euler\'s Formula', '$F-E+V=2$'],
+               ['The Origin of Complex Numbers', '$i^{2}=-1$'],
+               ['Einstein\'s Theory of Relativity', '$E=m c^{2}$']]
+
+fig =  ff.create_table(data_matrix,height_constant=40)
+fig.show()
+```
+
+![](../../.gitbook/assets/newplot-2-%20%281%29.png)
+
+### 5. Customized Table
+
+{% hint style="info" %}
+**`colorscale` for the table :**
+
+*  value 0 is the header color
+* .5 is the first table color
+* 1 is the second table color
+{% endhint %}
+
+```text
+# load dataset and sample it
+df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv')
+df_sample = df[100: 110]
+
+colorscale = [[0, '#0080ff'],[.5, 'silver'],[1, '#ffffff']]
+fig =  ff.create_table(df_sample, colorscale=colorscale)
+
+fig.show()
+```
+
+![](../../.gitbook/assets/newplot-3-%20%282%29.png)
+
+{% hint style="info" %}
+* Customize font size
+* Customize height of each row.
+* Address index in the table
+{% endhint %}
+
+```text
+data_matrix = [['Name', 'Equation'],
+               ['Pythagorean Theorem', '$a^{2}+b^{2}=c^{2}$'],
+               ['Euler\'s Formula', '$F-E+V=2$'],
+               ['The Origin of Complex Numbers', '$i^{2}=-1$'],
+               ['Einstein\'s Theory of Relativity', '$E=m c^{2}$']]
+
+fig =  ff.create_table(data_matrix,height_constant=40,index=True)
+# Make text size larger
+for i in range(len(fig.layout.annotations)):
+    fig.layout.annotations[i].font.size = 16
+
+fig.show()
+```
+
+![](../../.gitbook/assets/newplot-4-%20%281%29.png)
+

plotly/6.3-statistical-charts/6.3.2-tree-charts.md

@@ -1,2 +1,47 @@
 # 6.3.2 Dendrograms
 
+The dendrogram is a tree diagram used to visualize and classify taxonomic relationships frequently used to illustrate the arrangement of the clusters produced by hierarchical clustering. The name dendrogram derives from the two ancient Greek words déndron and grámma, meaning “tree” and “drawing”. Dendrograms are frequently used in biology to show clustering between genes or samples, but they can represent any type of grouped data, i.e., used to illustrate the clustering of genes or samples.
+
+The dendrogram consists of stacked branches \(called clades\) that break down into further smaller branches. At the lowest level will be individual elements and then they are grouped according to attributes into clusters with fewer and fewer clusters on higher levels. The end of each clade \(called a leaf\) is the data.
+
+The arrangement of the clades reveals how similar they are to each other; two leaves in the same clade are more similar than two leaves in another clade. The y-axis \(the height of the branch\) shows how close data points or clusters are from one another. The taller the branch, the further and more different the clusters are.
+
+![](../../.gitbook/assets/dendrogram-explained.png)
+
+### 1. Basic Dendrogram
+
+```text
+import plotly.figure_factory as ff
+import numpy as np
+np.random.seed(1)
+
+X = np.random.rand(10, 7) # 10 samples, with 7 dimensions each
+fig = ff.create_dendrogram(X)
+fig.update_layout(width=800, height=400)
+fig.show()
+
+```
+
+![](../../.gitbook/assets/screenshot-2020-07-26-at-20.48.42.png)
+
+### 2. Dendrogram with color threshold
+
+```text
+fig = ff.create_dendrogram(X, color_threshold=1)
+fig.update_layout(width=800, height=500)
+fig.show()
+```
+
+![](../../.gitbook/assets/screenshot-2020-07-26-at-20.48.58.png)
+
+### 3. Categorical Dendrogram 
+
+```text
+labels = ['A','B','C','D','E','F','G','H','I','J']
+fig = ff.create_dendrogram(X, orientation='left', labels=labels)
+fig.update_layout(width=800, height=800)
+fig.show()
+```
+
+![](../../.gitbook/assets/screenshot-2020-07-26-at-20.49.06.png)
+