Plotting Of 2d Data : Heatmap With Different Colormaps
I want to visualize 2D data that I have. For example following is the data with four attributes: att1 att2 att3 fun1 10 0 2 fun2 0 1 3 fun3
Solution 1:
With Python :
I found a better way :
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
# data loading
df = pd.read_csv("file.csv", index_col=0)
# plotting
fig,ax = plt.subplots()
ax.matshow(df.mask(((df == df) | df.isnull()) & (df.columns != "att1")),
cmap=cm.Reds) # You can change the colormap here
ax.matshow(df.mask(((df == df) | df.isnull()) & (df.columns != "att2")),
cmap=cm.Greens)
ax.matshow(df.mask(((df == df) | df.isnull()) & (df.columns != "att3")),
cmap=cm.Blues)
plt.xticks(range(3), df.columns)
plt.yticks(range(4), df.index)
plt.show()
some details :
df.mask(((df == df)| df.isnull())&(df.columns !="att1"))
att1 att2 att3
fun1 10NaNNaN
fun2 0NaNNaN
fun3 1NaNNaN
fun4 2NaNNaN
Older version, with numpy masked array :
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from numpy.ma import masked_array
import numpy as np
df = pd.read_clipboard() # just copied your example# define masked arrays to mask all but the given column
c1 = masked_array(df, mask=(np.ones_like(df)*(df.values[0]!=df.values[0][0])))
c2 = masked_array(df, mask=(np.ones_like(df)*(df.values[0]!=df.values[0][1])))
c3 = masked_array(df, mask=(np.ones_like(df)*(df.values[0]!=df.values[0][2])))
fig,ax = plt.subplots()
ax.matshow(c1,cmap=cm.Reds) # You can change the colormap here
ax.matshow(c2,cmap=cm.Greens)
ax.matshow(c3,cmap=cm.Blues)
plt.xticks(range(3), df.columns)
plt.yticks(range(4), df.index)
Some details :
df is a dataframe :
att1 att2 att3
fun1 10 0 2
fun2 0 1 3
fun3 1 10 5
fun4 2 3 10
c1, c2, c3 are masked arrays (for columns 1, 2 and 3):
>>> c1
masked_array(data =
[[10-- --]
[0-- --]
[1-- --]
[2-- --]],
mask =
[[FalseTrueTrue]
[FalseTrueTrue]
[FalseTrueTrue]
[FalseTrueTrue]],
fill_value =999999)
alternatively, you can start from a numpy 2D array :
>> data
array([[10, 0, 2],
[ 0, 1, 3],
[ 1, 10, 5],
[ 2, 3, 10]])
and replace all df
and df.values
with data
(the 2D array), except in the labeling part.
Solution 2:
Try:
ddf = structure(list(fn = structure(1:4, .Label = c("fun1", "fun2",
"fun3", "fun4"), class = "factor"), att1 = c(10L, 0L, 1L, 2L),
att2 = c(0L, 1L, 10L, 3L), att3 = c(2L, 3L, 5L, 10L)), .Names = c("fn",
"att1", "att2", "att3"), class = "data.frame", row.names = c(NA,
-4L))
ddffnatt1att2att3
1 fun1 10 0 2
2 fun2 0 1 3
3 fun3 1 10 5
4 fun4 2 3 10
ddfm = melt(ddf)
ddfmfnvariablevalue
1 fun1att1 10
2 fun2att1 0
3 fun3att1 1
4 fun4att1 2
5 fun1att2 0
6 fun2att2 1
7 fun3att2 10
8 fun4att2 3
9 fun1att3 2
10 fun2att3 3
11 fun3att3 5
12 fun4att3 10
>
ggplot(ddfm)+ geom_tile(aes(x=variable, y=fn, fill=value))
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