Planning a heatmap for 3 columns in python with marine
v1 v2 yy
15.25 44.34 100.00
83.05 59.78 100.00
96.61 65.09 100.00
100.00 75.47 100.00
100.00 50.00 100.00
100.00 68.87 100.00
100.00 79.35 100.00
100.00 100.00 100.00
100.00 63.21 100.00
100.00 100.00 100.00
100.00 68.87 100.00
0.00 56.52 92.86
10.17 52.83 92.86
23.73 46.23 92.86
In the above data window, I want to plot a heating diagram using v1 and v2 as the x and y axes and yy as the value. How can I do this in python? I tried the boat trip:
df = df.pivot('v1', 'v2', 'yy')
ax = sns.heatmap(df)
However, this doesn't work. Any other solution?
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Marine heatmap
contains categorical data. This means that each value that occurs will occupy the same space in the heatmap as any other value, no matter how numerically separated. This is usually not desirable for numeric data. Instead, one of the following methods can be selected.
Scatter
A color scatter plot can be as good as a heatmap. The point colors will represent the value yy
.
ax.scatter(df.v1, df.v2, c=df.yy, cmap="copper")
u = u"""v1 v2 yy
15.25 44.34 100.00
83.05 59.78 100.00
96.61 65.09 100.00
100.00 75.47 100.00
100.00 50.00 100.00
100.00 68.87 100.00
100.00 79.35 100.00
100.00 100.00 100.00
100.00 63.21 100.00
100.00 100.00 100.00
100.00 68.87 100.00
0.00 56.52 92.86
10.17 52.83 92.86
23.73 46.23 92.86"""
import pandas as pd
import matplotlib.pyplot as plt
import io
df = pd.read_csv(io.StringIO(u), delim_whitespace=True )
fig, ax = plt.subplots()
sc = ax.scatter(df.v1, df.v2, c=df.yy, cmap="copper")
fig.colorbar(sc, ax=ax)
ax.set_aspect("equal")
plt.show()
Hexbin
You can take a look Hexbin
. The data will be displayed in hexagonal cells and the data is aggregated as an average within each bin. The advantage here is that if you choose a large grid it will look like a scatter plot, and if you make it small it will look like a heat map, allowing you to easily adjust the graph to your desired resolution.
h1 = ax.hexbin(df.v1, df.v2, C=df.yy, gridsize=100, cmap="copper")
h2 = ax2.hexbin(df.v1, df.v2, C=df.yy, gridsize=10, cmap="copper")
u = u"""v1 v2 yy
15.25 44.34 100.00
83.05 59.78 100.00
96.61 65.09 100.00
100.00 75.47 100.00
100.00 50.00 100.00
100.00 68.87 100.00
100.00 79.35 100.00
100.00 100.00 100.00
100.00 63.21 100.00
100.00 100.00 100.00
100.00 68.87 100.00
0.00 56.52 92.86
10.17 52.83 92.86
23.73 46.23 92.86"""
import pandas as pd
import matplotlib.pyplot as plt
import io
df = pd.read_csv(io.StringIO(u), delim_whitespace=True )
fig, (ax, ax2) = plt.subplots(nrows=2)
h1 = ax.hexbin(df.v1, df.v2, C=df.yy, gridsize=100, cmap="copper")
h2 = ax2.hexbin(df.v1, df.v2, C=df.yy, gridsize=10, cmap="copper")
fig.colorbar(h1, ax=ax)
fig.colorbar(h2, ax=ax2)
ax.set_aspect("equal")
ax2.set_aspect("equal")
ax.set_title("gridsize=100")
ax2.set_title("gridsize=10")
fig.subplots_adjust(hspace=0.3)
plt.show()
Tripcolor
Schedule A Tripcolor
can be used to produce color responses on a graph according to the data, which is then interpreted as the edges of the triangles colored from the edge point data. Such a graph would require more data to provide meaningful insight.
ax.tripcolor(df.v1, df.v2, df.yy, cmap="copper")
u = u"""v1 v2 yy
15.25 44.34 100.00
83.05 59.78 100.00
96.61 65.09 100.00
100.00 75.47 100.00
100.00 50.00 100.00
100.00 68.87 100.00
100.00 79.35 100.00
100.00 100.00 100.00
100.00 63.21 100.00
100.00 100.00 100.00
100.00 68.87 100.00
0.00 56.52 92.86
10.17 52.83 92.86
23.73 46.23 92.86"""
import pandas as pd
import matplotlib.pyplot as plt
import io
df = pd.read_csv(io.StringIO(u), delim_whitespace=True )
fig, ax = plt.subplots()
tc = ax.tripcolor(df.v1, df.v2, df.yy, cmap="copper")
fig.colorbar(tc, ax=ax)
ax.set_aspect("equal")
ax.set_title("tripcolor")
plt.show()
Note that the graph tricontourf
may be the same if more points are available across the entire grid.
ax.tricontourf(df.v1, df.v2, df.yy, cmap="copper")
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The problem is your data has duplicate values like:
100.00 100.00 100.00
100.00 100.00 100.00
You need to reset the duplicate values, then expand and write like this:
import seaborn as sns
import pandas as pd
# fill data
df = pd.read_clipboard()
df.drop_duplicates(['v1','v2'], inplace=True)
pivot = df.pivot(index='v1', columns='v2', values='yy')
ax = sns.heatmap(pivot,annot=True)
plt.show()
print (pivot)
Pivot:
v2 44.34 46.23 50.00 52.83 56.52 59.78 63.21 65.09 \
v1
0.00 NaN NaN NaN NaN 92.86 NaN NaN NaN
10.17 NaN NaN NaN 92.86 NaN NaN NaN NaN
15.25 100.0 NaN NaN NaN NaN NaN NaN NaN
23.73 NaN 92.86 NaN NaN NaN NaN NaN NaN
83.05 NaN NaN NaN NaN NaN 100.0 NaN NaN
96.61 NaN NaN NaN NaN NaN NaN NaN 100.0
100.00 NaN NaN 100.0 NaN NaN NaN 100.0 NaN
v2 68.87 75.47 79.35 100.00
v1
0.00 NaN NaN NaN NaN
10.17 NaN NaN NaN NaN
15.25 NaN NaN NaN NaN
23.73 NaN NaN NaN NaN
83.05 NaN NaN NaN NaN
96.61 NaN NaN NaN NaN
100.00 100.0 100.0 100.0 100.0
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