Python Graph Gallery (Part 1)

This essay is going to summarise the python scripts of common graphs in data exploration. The sample data used here are Boston House Data (sklearn.datasets.load_boston())and Singapore PSI data. We will cover the following groups of graph:

1. line graphs
2. bar graphs
3. dot graphs
4. area graphs

All the scripts are available: https://github.com/beverly0005/graph-gallery

Line Graphs

We are going to provide scripts for 3 types of line graphs: standard graph, time trend, density curve. Before going to detailed graphs, let’s get familiar with the following options.

Colormap

Markers

1. Standard graph

There are a number of common options. They should be applicable to other groups of graphs:

• figsize
• linewidth, linecolor, linetype
• xlabel, xtick, ylabel, ytick
• fontsize, fontfamily
• xlim, ylim
• legend
• grid
• background color
• highlight points
• Add: line, dot, line segment, text or other shape

Figure: What is the relationship between house price and crime rate?

Fig 1. Relation between House Price and Crime Rate

2. Time Trend

Figure: What is the trend of PSI over time

Figure 2. Trend of PSI in Singapore

3. Density Curve

Figure: What is the distribution of PSI

Figure 3. Distribution of PSI

Bar Graphs

1. Standard Graph

Figure: What is the average NOX regarding different accessibility to road

Figure 4. NOx in Different Accesibility to Radial Highways

2. Histogram

Figure: What is the distribution of accessibility to road

Figure 5. Distribution of Accessibility to Radial Highways

3. Horizontal Bar

Figure: What is the average NOX regarding different accessibility to road

Dot Graphs

1. Standard Graph

Figure: What is the relationship between Price and Crime per capita

2. Scatter wrt. Category

Figure: What is the relationship between house price and crime rate for different accessibility to road

Area Graphs

1. Standard Graph

Figure: What is the trend of PSI over time

2. Stacked Area Plot

Figure: What is the trend of PSI over time for different regions