changed slide layout

3453a921 · wgallard · 7fb4d4ff · 3453a921
Commit 3453a921 authored 6 years ago by wgallard
--- a/visualization/DataVisualization1.ipynb
+++ b/visualization/DataVisualization1.ipynb
@@ -887,13 +887,6 @@
    "boxplot(flowers['sepal_length'], labels=['Sepal_length']);"
   ]
  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "execution_count": 22,
   "metadata": {
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   "execution_count": 23,
   "metadata": {
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+     "slide_type": "slide"
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   "execution_count": 26,
   "metadata": {
    "slideshow": {
-     "slide_type": "subslide"
+     "slide_type": "slide"
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   "outputs": [

 %% Cell type:markdown id: tags:
 # Data Visualization with Python
 ## Part 1: Python + Matplotlib
 ### [Guy Allard](mailto://w.g.allard@lumc.nl)
 %% Cell type:markdown id: tags:
 # Matplotlib
 - Plotting library for Python
 - High quality figures suitable for publication
 - Integrates with IPython, Jupyter and NumPy (in PyLab mode)
 - Established and robust
 - Large community / user base
 %% Cell type:markdown id: tags:
 # Interfaces
 1. Object Oriented
  - Best for larger development projects
  - Have to keep track of figures and axes
  - Steep learning curve
 <br><br>
 2. Pyplot State Machine
  - For interactive plotting
  - Takes care of many housekeeping tasks
  - Easier to learn than the OO interface
 <br><br>
 3. Pylab
  - Modelled on matlab
  - Imports common modules
  - Handles most housekeeping tasks
  - Easiest to learn
  - The one we will be using!
 %% Cell type:markdown id: tags:
 # Interfaces Example
 1. Object-oriented interface
 ```python
 import matplotlib.pyplot as plt
 import numpy as np
 x = np.arange(0, 10, 0.2)
 y = np.sin(x)
 fig = plt.figure()
 ax = fig.add_subplot(111)
 ax.plot(x, y)
 ```
 2. State-machine environment (pyplot)
 ```python
 import matplotlib.pyplot as plt
 import numpy as np
 x = np.arange(0, 10, 0.2)
 y = np.sin(x)
 plt.plot(x, y)
 ```
 3. PyLab mode
 ```python
 %pylab
 x = arange(0, 10, 0.2)
 y = sin(x)
 plot(x, y)
 ```
 %% Cell type:markdown id: tags:
 # Getting help
 Consult the built-in documentation, for example:
 ```
 >>> help(subplot)
 Help on function subplot in module matplotlib.pyplot:
 subplot(*args, **kwargs)
    Return a subplot axes positioned by the given grid definition.
 ...
 ```
 %% Cell type:markdown id: tags:
 # Useful Resources
 - Matplotlib Homepage
  - https://matplotlib.org/
 <br><br>
 - Gallery
  - https://matplotlib.org/gallery.html
  - Many examples with source code
 <br><br>
 - Online documentation
  - https://matplotlib.org/contents.html
  - Full API documentation
 %% Cell type:markdown id: tags:
 # First Steps
 ## Preparing the Jupyter Notebook
 1. Open a new Jupyter Notebook
 2. Run this code in the first empty cell:
 ```
 %pylab inline
 ```
 3. Now any pylab plotting commands will display in the notebook
 %% Cell type:code id: tags:
 ``` python
 %pylab inline
 ```
 %% Output
    Populating the interactive namespace from numpy and matplotlib
 %% Cell type:markdown id: tags:
 # Grab some data
 Use Pandas to load a dataset which contains population data for four countries
 %% Cell type:code id: tags:
 ``` python
 import pandas as pd
 populations = pd.read_csv(
    'https://git.lumc.nl/courses/programming-course/raw/visualization-2018/visualization/data/populations.csv'
 )
 ```
 %% Cell type:markdown id: tags:
 Take a quick look at the data
 %% Cell type:code id: tags:
 ``` python
 populations.head()
 ```
 %% Output
       Year  Belgium  Denmark  Netherlands   Sweden
    0  1950  8.63930  4.28135     10.11365  7.01660
    1  1951  8.67820  4.30370     10.26440  7.07040
    2  1952  8.73040  4.33380     10.38210  7.12445
    3  1953  8.77775  4.36930     10.49300  7.17145
    4  1954  8.81940  4.40570     10.61535  7.21360
 %% Cell type:markdown id: tags:
 # Plot it!
 Let's make a plot the population of the Netherlands on the y-axis, and the year on the x-axis
 %% Cell type:code id: tags:
 ``` python
 plot(populations['Year'], populations['Netherlands']);
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Add titles and label the axes
 %% Cell type:code id: tags:
 ``` python
 plot(populations['Year'], populations['Netherlands'])
 title('Historical Population of The Netherlands')
 xlabel('Year')
 ylabel('Population (Millions)');
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Change some properties of the line
 How about a 5px thick orange line?
 %% Cell type:code id: tags:
 ``` python
 plot(populations['Year'], populations['Netherlands'],
     linewidth=5, color='orange')
 title('Historical Population of The Netherlands')
 xlabel('Year')
 ylabel('Population (Millions)');
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Change some properties of the x-axis
 Label at five-year intervals
 Display the label vertically
 %% Cell type:code id: tags:
 ``` python
 plot(populations['Year'], populations['Netherlands'],
     linewidth=5, color='orange')
 title('Historical Population of The Netherlands')
 xlabel('Year')
 ylabel('Population (Millions)')
 xticks(range(1950, 2016, 5), rotation=90);
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Change which years are displayed
 %% Cell type:code id: tags:
 ``` python
 plot(populations['Year'], populations['Netherlands'],
     linewidth=5, color='orange')
 title('Historical Population of The Netherlands')
 xlabel('Year')
 ylabel('Population (Millions)')
 xticks(range(1950, 2016, 5), rotation=90)
 xlim(1970, 1990);
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Change the y-axis scale
 %% Cell type:code id: tags:
 ``` python
 plot(populations['Year'], populations['Netherlands'],
     linewidth=5, color='orange')
 title('Historical Population of The Netherlands')
 xlabel('Year')
 ylabel('Population (Millions)')
 xticks(range(1950, 2016, 5), rotation=90)
 xlim(1970, 1990)
 ylim(13,15);
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Clean up the number formatting on the y-axis
 Integer tick labels
 %% Cell type:code id: tags:
 ``` python
 plot(populations['Year'], populations['Netherlands'],
     linewidth=5, color='orange')
 title('Historical Population of The Netherlands')
 xlabel('Year')
 ylabel('Population (Millions)')
 xticks(range(1950, 2016, 5), rotation=90)
 xlim(1970, 1990)
 ylim(13,15)
 yticks(range(13,16));
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Plot multiple series
 Calling **plot** multiple times within the same cell will add multiple series to the chart
 Let's compare the Dutch with the Danes
 %% Cell type:code id: tags:
 ``` python
 plot(populations['Year'], populations['Netherlands'], color='orange')
 plot(populations['Year'], populations['Denmark'], color='red')
 title('Historical Populations of The Netherlands and Denmark')
 xlabel('Year')
 ylabel('Population (Millions)')
 xticks(range(1950, 2016, 5), rotation=90);
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Add a legend
 1. Give each plotted line a label
 2. Add a legend to the figure
 %% Cell type:code id: tags:
 ``` python
 plot(populations['Year'], populations['Netherlands'],
     color='orange', label='The Netherlands')
 plot(populations['Year'], populations['Denmark'],
     color='red', label='Denmark')
 legend(loc='upper left')
 title('Historical Populations of The Netherlands and Denmark')
 xlabel('Year')
 ylabel('Population (Millions)')
 xticks(range(1950, 2016, 5), rotation=90);
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Other plot types
 Let's load a different dataset and take a look at some different plot types
 %% Cell type:code id: tags:
 ``` python
 flowers = pd.read_csv('https://git.lumc.nl/courses/programming-course/raw/visualization-2018/visualization/data/iris.csv')
 flowers.head()
 ```
 %% Output
       sepal_length  sepal_width  petal_length  petal_width species
    0           5.1          3.5           1.4          0.2  setosa
    1           4.9          3.0           1.4          0.2  setosa
    2           4.7          3.2           1.3          0.2  setosa
    3           4.6          3.1           1.5          0.2  setosa
    4           5.0          3.6           1.4          0.2  setosa
 %% Cell type:markdown id: tags:
 # Boxplots
 A simple boxplot of the sepal-length distribution
 %% Cell type:code id: tags:
 ``` python
 boxplot(flowers['sepal_length'], labels=['Sepal_length']);
 ```
 %% Output
-%% Cell type:code id: tags:
-``` python
-```
 %% Cell type:markdown id: tags:
 # Boxplots
 Distributions of multiple features
 %% Cell type:code id: tags:
 ``` python
 # make a list containing the numeric feature column names
 features = list(flowers.columns[:-1])
 features
 ```
 %% Output
    ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
 %% Cell type:code id: tags:
 ``` python
 # plot the data
 boxplot([flowers[f] for f in features], labels=features);
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Controlling the size of the plot
 Let's change the shape of the boxplot
 %% Cell type:code id: tags:
 ``` python
 # make the figure 10 'units' wide and 5 'units' high
 figsize(10, 5)
 # plot the data
 boxplot([flowers[f] for f in features], labels=features);
 ```
 %% Output
 %% Cell type:code id: tags:
 ``` python
 figsize(7,4)
 ```
 %% Cell type:markdown id: tags:
 # Histogram
 %% Cell type:code id: tags:
 ``` python
 hist(flowers['petal_length'])
 title('Petal Length Distribution')
 xlabel('petal length')
 ylabel('count');
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Histogram
 change the number of 'bins'
 %% Cell type:code id: tags:
 ``` python
 hist(flowers['petal_length'], bins=20)
 title('Petal Length Distribution')
 xlabel('petal length')
 ylabel('count');
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Histogram
 Some formatting
 - Lines around the bars
 - color
 %% Cell type:code id: tags:
 ``` python
 hist(flowers['petal_length'], bins=20, facecolor='teal', edgecolor='black', alpha=0.7)
 title('Petal Length Distribution')
 xlabel('petal length')
 ylabel('count');
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Subplots
 Separate plots with their own axes within a single figure
 The syntax can be confusing!
 %% Cell type:code id: tags:
 ``` python
 for i in range(1, 5):
    subplot(2, 2, i)
    xticks([]), yticks([])
    text(0.5, 0.5, 'subplot(2, 2, %d)' % i, ha='center', size=18, alpha=0.75);
 ```
 %% Output
 %% Cell type:markdown id: tags:
 subplot(2, 2, 1) indicates the first cell of a 2 row x 2 column matrix
 subplot(2, 2, 4) indicates the fourth cell of a 2 column x 2 row matrix
 %% Cell type:markdown id: tags:
 # Subplots
 More complicated layouts
 %% Cell type:code id: tags:
 ``` python
 subplot(1, 3, 1)            # 1 row, 3 columns, cell 1
 xticks([]), yticks([])
 text(0.5, 0.5, '(1, 3, 1)', ha='center', size=18, alpha=0.75)
 subplot(2, 3, 3)            # 2 rows, 3 columns, cell 3
 xticks([]), yticks([])
 text(0.5, 0.5, '(2, 3, 3)', ha='center', size=18, alpha=0.75)
 subplot(3, 2, 6)            # 3 rows, 2 columns, cell 6
 xticks([]), yticks([])
 text(0.5, 0.5, '(3, 2, 6)', ha='center', size=18, alpha=0.75)
 subplot(3, 3, 5)            # 3 rows, 3 columns, cell 5
 xticks([]), yticks([])
 text(0.5, 0.5, '(3, 3, 5)', ha='center', size=18, alpha=0.75);
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Subplots and Boxplots
 Compare how the features are distributed by species
 %% Cell type:code id: tags:
 ``` python
 species = list(set(flowers.species))
 print(species)
 ```
 %% Output
    ['virginica', 'setosa', 'versicolor']
 %% Cell type:code id: tags:
 ``` python
 # make a dataset for each species
 setosa = flowers[flowers.species == 'setosa']
 versicolor = flowers[flowers.species == 'versicolor']
 virginica = flowers[flowers.species == 'virginica']
 ```
 %% Cell type:code id: tags:
 ``` python
 figsize(10, 8)
 for cell, feature in enumerate(features):
    subplot(2, 2, cell + 1)
    boxplot(
        [setosa[feature], versicolor[feature], virginica[feature]],
        labels=species
    )
    ylabel(feature)
 ```
 %% Output
 %% Cell type:code id: tags:
 ``` python
 figsize(7,4)
 ```
 %% Cell type:markdown id: tags:
 # Sketch-style drawing
 using xkcd mode
 %% Cell type:code id: tags:
 ``` python
 with xkcd():
    hist(flowers['petal_length'], bins=20, facecolor='teal', edgecolor='black')
    title('Petal Length Distribution')
    xlabel('petal length')
    ylabel('count');
 ```
 %% Output
 %% Cell type:markdown id: tags:
 # Saving to a file
 Images can be saved to a file using savefig after the plotting commands:
 ```
 savefig('myplot.pdf')
 ```
 The format of the saved image will be inferred from the given file extension.
 %% Cell type:markdown id: tags:
 # The End
 This lesson was based on previous work by [Jeroen Laros](mailto://j.f.j.laros@lumc.nl) and Martijn Vermaat
 License: [Creative Commons Attribution 3.0 License (CC-by)](http://creativecommons.org/licenses/by/3.0)
 %% Cell type:code id: tags:
 ``` python
 ```