1. 3D Scatter chart
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.scatter(1, 1, 1)
ax.scatter(2, 2, 1)
ax.scatter(2, 3, 0)
plt.show()
Where projection='3d' is used to set 3d mode for this chart, .scatter( plots a point chart.
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2. Table
Well, this is not a chart, but tables are still used sometimes.
import matplotlib.pyplot as plt
data = [[1, 2, 3, 4, 5],
[10,20,30,40,50],
[11,21,31,41,51]]
plt.table(data, loc='center', colLabels=['A','B','C','D','E'])
plt.show()
Here data is the data to use for table cells. Then we call .table( to plot a table and colLabels to define column titles.
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3. Boxplot chart
import matplotlib.pyplot as plt
plt.boxplot([2,3,6,2,4,5,1,10])
plt.show()
We just use .boxplot( to plot boxplot chart.
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4. Line chart
import matplotlib.pyplot as plt
plt.plot([1,2,10,6,15,3,4])
plt.show()
The .plot( is used to plot a line based on given data.
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5. Bar chart
import matplotlib.pyplot as plt
plt.bar(['UA', 'UK', 'USA'], [10, 11, 12])
plt.show()
The .bar method will plot bar chart, while ['UA', 'UK', 'USA'] is used as x-axis values and [10, 11, 12] as y-axis values.
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6. Heatmap
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
im = ax.imshow([[0.8, 2.4, 2.5], [1.3, 1.2, 0.0], [0.1, 2.0, 0.0]])
plt.show()
To draw heatmap we use .imshow(, which displays given data as an image.
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7. Histogram
import matplotlib.pyplot as plt
x = [1,2,5,1,2,3,5,6,7,4,2,2,4,5,6]
plt.hist(x,bins=5)
plt.show()
To build a histogram we use .hist( and set bins - number of histogram bins (bars) to group data into.
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8. World map
import matplotlib.pyplot as plt
import geopandas
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.plot()
plt.show()
First, we load geopandas - module to work with maps and geo charts. Then world.plot()` can be used to plot world map.
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