Data Visualization (Lecture 4)

name: inverse
layout: true
class: center, middle, inverse

---
# Python for Data visualization
## Lecture 4

---
layout: false

## Plotting and Visualization

* Visualization is a key component of data analysis, allows you to explore data and communicate results
* Matplotlib is the most popular Python library for creating static, animated, and interactive visualizations in Python
* Pandas is built on top of Matplotlib and provides a high-level interface for plotting
* Seaborn is a Python data visualization library based on Matplotlib and provide same functionality as Matplotlib but with more attractive and informative statistical graphics

---
## Matplotlib primer

Matplotlib is a plotting library for the Python and its numerical mathematics extension, NumPy

```python
import matplotlib.pyplot as plt
```
The `pyplot` module provides a MATLAB-like interface for making plots

a simple plot example:

```python
mydata = range(1, 10) # create a list of numbers from 1 to 9
plt.plot(mydata) # plot the data
```

.cols[
.sixty[

<img src="https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/7639c971-697c-4751-a50e-85817c509ec7"
    alt="image" width="300" height="auto">
]
.fourty[
.small[though pandas's built-in plotting functions will deal with many of the mundane details of making plots,
Matplotlib goes beyond, for advanced plotting with its powerful and flexible functions.]
]
]

---

## Basic plotting with Matplotlib

### Line plot

.cols[
.sixty[

```python
import matplotlib.pyplot as plt
```

```python
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Line Plot')
plt.show()
```
]
.white[text] 
.fourty[
<img src="https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/a95011ae-1c06-4f4c-8dd9-9b9c2d54fcca" alt="image" width="400" height="auto">
]
]
---
## Basic plotting with Matplotlib

### Scatter plot

.cols[
.sixty[
```python
import matplotlib.pyplot as plt
```

```python
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.scatter(x, y, color='red', marker='o')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot')
plt.show()
```
]
.white[text]
.fourty[
<img src="https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/d837540e-a7b3-418e-9dc6-420379e2859f"
    alt="image" width="400" height="auto">
]
]
---
## Basic plotting with Matplotlib

### Bar plot

.cols[
.sixty[
```python
import matplotlib.pyplot as plt
```

```python
x = ['A', 'B', 'C', 'D', 'E']
y = [10, 20, 15, 25, 30]

plt.bar(x, y, color='green')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Plot')
plt.show()
```
]
.white[text]
.fourty[
<img src="https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/5cfe9848-6b0a-4983-8437-ca5b9418baee"
    alt="image" width="400" height="auto">
]
]
---
## Basic plotting with Matplotlib

### Histogram

.cols[
.sixty[

```python
import matplotlib.pyplot as plt
import random
```

```python
data = random.sample(range(1, 5000), 500)
plt.hist(data, bins=30)
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Histogram')
plt.show()
```
]
.white[text]
.fourty[
<img src="https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/13b97b80-0392-4f8a-8422-963d9dbc2dc7"
    alt="image" width="400" height="auto">
]
]
---
## Basic plotting with Matplotlib

### Pie chart

.cols[
.sixty[
```python
import matplotlib.pyplot as plt
```

```python
sizes = [15, 30, 45, 10]
labels = ['A', 'B', 'C', 'D']
plt.pie(sizes,
        labels=labels,
        autopct='%1.1f%%', 
        startangle=140)
plt.axis('equal') 
plt.title('Pie Chart')
plt.show()
```
]
.white[text]
.fourty[
<img src="https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/28b5c031-7dee-4e04-a4f4-239b670afe64"
    alt="image" width="400" height="auto">
]
]
---

## And so many more!

See [plot types](https://matplotlib.org/stable/plot_types/index.html) in Matplotlib documentation

.cols[
.fifty[
| Plot Command | Plot Name |
|-----------------------------|---------------------------|
| `plt.plot()` | Line Plot |
| `plt.scatter()` | Scatter Plot |
| `plt.bar()` | Bar Plot |
| `plt.barh()` | Horizontal Bar Plot |
| `plt.hist()` | Histogram |
| `plt.boxplot()` | Box Plot |
| `plt.violinplot()` | Violin Plot |
| `plt.pie()` | Pie Chart |
| `plt.polar()` | Polar Plot |
| `plt.contour()` | Contour Plot |
| `plt.contourf()` | Filled Contour Plot |
| `plt.imshow()` | Image Plot |
| `plt.hexbin()` | Hexbin Plot |
| `plt.stem()` | Stem Plot |
]
.fifty[
| Plot Command | Plot Name |
|-----------------------------|---------------------------|
| `plt.quiver()` | Quiver Plot |
| `plt.streamplot()` | Streamplot |
| `plt.errorbar()` | Errorbar Plot |
| `plt.fill_between()` | Fill Between Plot |
| `plt.step()` | Step Plot |
| `plt.scatter3D()` | 3D Scatter Plot |
| `plt.plot3D()` | 3D Line Plot |
| `plt.bar3d()` | 3D Bar Plot |
| `plt.contour3D()` | 3D Contour Plot |
| `plt.triplot()` | Triangular Plot |
| `plt.tricontour()` | Triangular Contour Plot |
| `plt.tricontourf()` | Filled Triangular Contour |
| `plt.spy()` | Spy Plot |
| `plt.broken_barh()` | Broken Bar Plot |
| `plt.matshow()` | Matrix Plot |
]
]

PS: run `help(plt.command)` to see more details about each plot command
---

## Figures and subplots

plots in Matplotlib reside within a `Figure` object. You can create a new figure with `plt.figure()`, and modify its properties

.cols[
.sixty[
```python
fig = plt.figure(figsize=(6, 4),
                 facecolor='lightskyblue',
                 layout='constrained'
                )
fig.suptitle('A nice Matplotlib Figure')
ax = fig.add_subplot()
ax.set_title('Axes',
             loc='left', 
             fontstyle='oblique',
             fontsize='medium'
            )
```
]
.white[text]
.fourty[
<img src="https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/e5c8d757-fc9c-45c8-a2d9-0d2987d59319"
    alt="image" width="400" height="auto">
]
]

PS: run `help(plt.figure)` to see all the available options
---

## Figures and subplots

To add subplots to a figure, you can use `add_subplot()`

so, for an empty matplotlib figure with three subplots:

.cols[
.sixty[

```python
fig = plt.figure()
ax1 = fig.add_subplot(2, 2, 1)
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2, 3)
```
]
.fourty[
<img src="https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/3285c2f0-ea66-47a7-8cf5-6b8834ca1260" alt="image" width="400" height="auto">
]
]

like `subplot` there is `subplots` to create multiple subplots at once:

```python
fig, axs = plt.subplots(2, 2, layout='constrained')
```
---

## Testing subplots

Let's first create 4 simple datasets:

```python
mydata1 = random.sample(range(1, 50), 10) # 10 random numbers between 1-50
mydata2 = random.sample(range(1, 5000), 500) # 500 random numbers between 1-5000
mydata3 = range(30) # numbers 0 to 29, ordered
# same as mydata3 but multiplied with random numbers 0 to 1 range
mydata4 = []
for num in mydata3:
    mydata4.append(num * random.random())
```

And then plot them in the subplots:

.cols[
.sixty[

```python
fig = plt.figure()
ax1 = fig.add_subplot(2, 2, 1)
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2, 3)
ax1.plot(mydata1, linestyle="dashed")
ax2.hist(mydata2, bins = 50, alpha=0.5)
ax3.scatter(mydata3, mydata4)
```
]
.fourty[
<img src="https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/e7a12979-f28d-4119-9fb4-22e5a322d3ee"
    alt="image" width="400" height="auto">
]
]

---

## Matplotlib Pros and Cons

.cols[
.fifty[

### Cons

* Steep Learning Curve
* Verbose Syntax
* Limited High-Level Plotting
* Default limited aesthetics
* Limited statistical plotting
]

.fifty[

### Pros

* Highly customizable
* Extensive documentation
* Active community
* Wide range of plot types
* Integration with Pandas
]
]

---

## Seaborn

### What is Seaborn?

* Seaborn is a Python data visualization library based on Matplotlib.
* It provides a high-level interface for drawing attractive statistical graphics.
* Seaborn is built on top of Matplotlib and integrates closely with Pandas data structures.

### Key Features of Seaborn

* Statistical Visualization: Seaborn simplifies the process of creating statistical plots such as histograms, box plots,
and scatter plots.

* Integration with Pandas: Seaborn seamlessly works with Pandas DataFrames, making it easy to visualize data directly from
data structures.

* Beautiful Aesthetics: Seaborn comes with beautiful default styles and color palettes, enhancing the visual appeal of
plots.

---

## Documentation

Great documentation is available for both Matplotlib and Seaborn. Seaborn particularly has a very detailed and well-organized documentation.

* [Matplotlib Documentation](https://matplotlib.org/stable/contents.html)
* [Seaborn Documentation](https://seaborn.pydata.org/)

---

## Seaborn getting Started

To use Seaborn, you need to install it first:

```python
conda install seaborn
```
 or
```python
pip install seaborn
```

Then, you can import it in your Python script:

```python
import seaborn as sns
```

---

## Test datasets

Seaborn comes with built-in datasets that you can use to test the library. To check the available datasets, you can use:

```python
sns.get_dataset_names()
```

To load a dataset, you can use:

```python
df = sns.load_dataset('iris')
```

---

## Seaborn plotting

Example scatter plot with Seaborn:

```python
df = sns.load_dataset('iris')
sns.scatterplot(x='sepal_length', y='sepal_width', data=df)
```

---

## Seaborn plotting

Example box plot with Seaborn:

```python
df = sns.load_dataset('iris')
sns.boxplot(x='species', y='sepal_length', data=df)
```

---

## Seaborn plotting

Example pair plot with Seaborn:
.cols[
.sixty[
```python
df = sns.load_dataset('iris')
sns.pairplot(df, hue='species')
```
]
.fourty[
<img src="https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/9fac17f9-15bb-494a-b788-dc0dc5010c07" alt="image" width="500" height="auto">
]
]

---
## Figure level and axes level plots

Seaborn has two types of functions for plotting:

---

---
## Seaborn Various Plots

.cols[
.fifty[
### Relational Plots

|  |  |
|--------------------------------|-----------------------|
| Scatter Plot | `sns.scatterplot()` |
| Line Plot | `sns.lineplot()` |
| Joint Plot | `sns.jointplot()` |
| Pair Plot | `sns.pairplot()` |
| Joint Kernel Density Estimate | `sns.jointplot(kind='kde')` |

### Categorical Plots

|  |  |
|--------------------------------|-----------------------|
| Bar Plot | `sns.barplot()` |
| Count Plot | `sns.countplot()` |
| Point Plot | `sns.pointplot()` |
| Box Plot | `sns.boxplot()` |
| Violin Plot | `sns.violinplot()` |
| Swarm Plot | `sns.swarmplot()` |
| Categorical Scatter Plot | `sns.catplot(kind='strip')` |

]

.fifty[
### Distribution Plots

|  |  |
|--------------------------------|-----------------------|
| Histogram | `sns.histplot(kind='hist')` |
| Kernel Density Estimate (KDE) | `sns.kdeplot()` |
| Rug Plot | `sns.rugplot()` |

### Matrix Plots

|  |  |
|--------------------------------|-----------------------|
| Heatmap | `sns.heatmap()` |
| Clustermap | `sns.clustermap()` |

### Regression Plots

|  |  |
|--------------------------------|-----------------------|
| Linear Regression Plot | `sns.regplot()` |
| Residual Plot | `sns.residplot()` |
| Lowess Smoothing Plot | `sns.lmplot()` |

]
]
---
## Seaborn Various Plots

### Time Series Plots

|  |  |
|--------------------------------|-----------------------|
| Time Series Plot | `sns.lineplot()` |
| Time Series Heatmap | `sns.heatmap()` |

### Multi-Plot Grids

|  |  |
|--------------------------------|-----------------------|
| FacetGrid | `sns.FacetGrid()` |
| PairGrid | `sns.PairGrid()` |
| JointGrid | `sns.JointGrid()` |

### Color Palettes

|  |  |
|--------------------------------|-----------------------|
| Color Palette | `sns.color_palette()` |
| Color Brewer Palettes | `sns.color_palette('colorbrewer')` |
| Cubehelix Palettes | `sns.color_palette('cubehelix')` |
| Xkcd Palettes | `sns.color_palette('xkcd')` |

---
## Seaborn Various Plots

### Plot Aesthetics

|  |  |
|--------------------------------|-----------------------|
| Set Theme | `sns.set_theme()` |
| Set Context | `sns.set_context()` |
| Set Style | `sns.set_style()` |
| Set Color Codes | `sns.set_color_codes()` |
| Set Palette | `sns.set_palette()` |
| Set Axis Labels | `sns.set_axis_labels()` |
| Set Title | `sns.set_title()` |
| Set Legend | `sns.set_legend()` |
| Set Ticks | `sns.set_ticks()` |
| Set Grid | `sns.set_grid()` |
| Set Spines | `sns.set_spines()` |
| Set Background | `sns.set_background()` |

PS: run `help(sns.command)` to see more details about each plot command

---

name: last-page
template: inverse
## That's all folks (for now)!