Корреляционная матрица Heatmap с несколькими наборами данных, которые имеют совпадающие столбцы

Question

Если у нас есть три набора данных:

X = pd.DataFrame({"t":[1,2,3,4,5],"A":[34,12,78,84,26], "B":[54,87,35,25,82], "C":[56,78,0,14,13], "D":[0,23,72,56,14], "E":[78,12,31,0,34]}) 
Y = pd.DataFrame({"t":[1,2,3,4,5],"A":[45,24,65,65,65], "B":[45,87,65,52,12], "C":[98,52,32,32,12], "D":[0,23,1,365,53], "E":[24,12,65,3,65]}) 
Z = pd.DataFrame({"t":[1,2,3,4,5],"A":[14,96,25,2,25], "B":[47,7,5,58,34], "C":[85,45,65,53,53], "D":[3,35,12,56,236], "E":[68,10,45,46,85]}) 

Где «т» является индекс.

Как мог один выход корреляционная матрица Heatmap похожа на Сиборн Например:

Только что ось будет выглядеть примерно так:

Answer 1

X = pd.DataFrame({"t":[1,2,3,4,5],"A":[34,12,78,84,26], "B":[54,87,35,25,82], "C":[56,78,0,14,13], "D":[0,23,72,56,14], "E":[78,12,31,0,34]}) 
Y = pd.DataFrame({"t":[1,2,3,4,5],"A":[45,24,65,65,65], "B":[45,87,65,52,12], "C":[98,52,32,32,12], "D":[0,23,1,365,53], "E":[24,12,65,3,65]}) 
Z = pd.DataFrame({"t":[1,2,3,4,5],"A":[14,96,25,2,25], "B":[47,7,5,58,34], "C":[85,45,65,53,53], "D":[3,35,12,56,236], "E":[68,10,45,46,85]}) 


catted = pd.concat([d.set_index('t') for d in [X, Y, Z]], axis=1, keys=['X', 'Y', 'Z']) 
catted = catted.rename_axis(['Source', 'Column'], axis=1) 

corrmat = catted.corr() 

f, ax = plt.subplots() 

sns.heatmap(corrmat, vmax=.8, square=True) 

sources = corrmat.columns.get_level_values(0) 
for i, source in enumerate(sources): 
    if i and source != sources[i - 1]: 
     ax.axhline(len(sources) - i, c="w") 
     ax.axvline(i, c="w") 
f.tight_layout() 

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ОТВЕТ НА ОТЗЫВ:
Я изменил t столбец в каждой из X, Y и Z

X = pd.DataFrame({"t":[1,2,3,4,5],"A":[34,12,78,84,26], "B":[54,87,35,25,82], "C":[56,78,0,14,13], "D":[0,23,72,56,14], "E":[78,12,31,0,34]}) 
Y = pd.DataFrame({"t":[6,7,8,9,10],"A":[45,24,65,65,65], "B":[45,87,65,52,12], "C":[98,52,32,32,12], "D":[0,23,1,365,53], "E":[24,12,65,3,65]}) 
Z = pd.DataFrame({"t":[11,12,13,14,15],"A":[14,96,25,2,25], "B":[47,7,5,58,34], "C":[85,45,65,53,53], "D":[3,35,12,56,236], "E":[68,10,45,46,85]}) 


catted = pd.concat([d.set_index('t') for d in [X, Y, Z]], axis=1, keys=['X', 'Y', 'Z']) 
catted = catted.rename_axis(['Source', 'Column'], axis=1) 

corrmat = catted.corr() 

f, ax = plt.subplots() 

sns.heatmap(corrmat, vmax=.8, square=True) 

sources = corrmat.columns.get_level_values(0) 
for i, source in enumerate(sources): 
    if i and source != sources[i - 1]: 
     ax.axhline(len(sources) - i, c="w") 
     ax.axvline(i, c="w") 
f.tight_layout() 

Теперь снова, но вместо этого я reset_index

X = pd.DataFrame({"t":[1,2,3,4,5],"A":[34,12,78,84,26], "B":[54,87,35,25,82], "C":[56,78,0,14,13], "D":[0,23,72,56,14], "E":[78,12,31,0,34]}) 
Y = pd.DataFrame({"t":[6,7,8,9,10],"A":[45,24,65,65,65], "B":[45,87,65,52,12], "C":[98,52,32,32,12], "D":[0,23,1,365,53], "E":[24,12,65,3,65]}) 
Z = pd.DataFrame({"t":[11,12,13,14,15],"A":[14,96,25,2,25], "B":[47,7,5,58,34], "C":[85,45,65,53,53], "D":[3,35,12,56,236], "E":[68,10,45,46,85]}) 


catted = pd.concat([d.reset_index(drop=True) for d in [X, Y, Z]], axis=1, keys=['X', 'Y', 'Z']) 
catted = catted.rename_axis(['Source', 'Column'], axis=1) 

corrmat = catted.corr() 

f, ax = plt.subplots() 

sns.heatmap(corrmat, vmax=.8, square=True) 

sources = corrmat.columns.get_level_values(0) 
for i, source in enumerate(sources): 
    if i and source != sources[i - 1]: 
     ax.axhline(len(sources) - i, c="w") 
     ax.axvline(i, c="w") 
f.tight_layout()