如何訂購在熊貓中繪製三維條形圖的數據框

我試圖用熊貓的三維條形圖創建圖表。回顧網絡上的一些例子，我看，要做到這一點的最好辦法是讓這樣的數據幀：如何訂購在熊貓中繪製三維條形圖的數據框

data 

Variable   A   B   C   D 
date 
2000-01-03 0.469112 -1.135632 0.119209 -2.104569 
2000-01-04 -0.282863 1.212112 -1.044236 -0.494929 
2000-01-05 -1.509059 -0.173215 -0.861849 1.071804

我的數據幀是：

df 

     Date_inicio    Date_Fin    Date_Max  Clase 
0 2004-04-09 23:00:00 2004-04-10 04:00:00 2004-04-10 02:00:00 MBCCM 
1 2004-04-12 23:00:00 2004-04-13 04:00:00 2004-04-13 00:00:00 MBSCL 
2 2004-04-24 04:00:00 2004-04-24 12:00:00 2004-04-24 09:00:00 SCL 
3 2004-05-02 07:00:00 2004-05-02 14:00:00 2004-05-02 11:00:00 SCL 
4 2004-05-30 05:00:00 2004-05-30 08:00:00 2004-05-30 07:00:00 MBCCM 
5 2004-05-31 03:00:00 2004-05-31 07:00:00 2004-05-31 05:00:00 MBCCM 
6 2004-06-08 00:00:00 2004-06-08 05:00:00 2004-06-08 03:00:00 MBSCL 
7 2004-06-12 22:00:00 2004-06-13 12:00:00 2004-06-13 06:00:00 CCM 
8 2004-06-13 03:00:00 2004-06-13 08:00:00 2004-06-13 06:00:00 MBCCM 
9 2004-06-14 00:00:00 2004-06-14 03:00:00 2004-06-14 02:00:00 MBSCL 
10 2004-06-14 03:00:00 2004-06-14 09:00:00 2004-06-14 07:00:00 MBSCL 
11 2004-06-17 08:00:00 2004-06-17 14:00:00 2004-06-17 11:00:00 MBCCM 
12 2004-06-17 12:00:00 2004-06-17 17:00:00 2004-06-17 14:00:00 MBCCM 
13 2004-06-22 00:00:00 2004-06-22 08:00:00 2004-06-22 06:00:00 SCL 
14 2004-06-22 08:00:00 2004-06-22 14:00:00 2004-06-22 11:00:00 MBCCM 
15 2004-06-22 23:00:00 2004-06-23 09:00:00 2004-06-23 06:00:00 CCM 
16 2004-07-01 05:00:00 2004-07-01 09:00:00 2004-07-01 06:00:00 MBCCM 
17 2004-07-02 00:00:00 2004-07-02 04:00:00 2004-07-02 02:00:00 MBSCL 
18 2004-07-04 12:00:00 2004-07-04 15:00:00 2004-07-04 13:00:00 MBCCM 
19 2004-07-06 04:00:00 2004-07-06 13:00:00 2004-07-06 07:00:00 SCL 
20 2004-07-07 04:00:00 2004-07-07 12:00:00 2004-07-07 10:00:00 CCM 
21 2004-07-08 03:00:00 2004-07-08 06:00:00 2004-07-08 05:00:00 MBCCM 
22 2004-07-08 12:00:00 2004-07-08 17:00:00 2004-07-08 13:00:00 MBCCM 
23 2004-07-08 02:00:00 2004-07-08 06:00:00 2004-07-08 04:00:00 MBCCM 
24 2004-07-09 05:00:00 2004-07-09 12:00:00 2004-07-09 08:00:00 CCM 
25 2004-07-11 18:00:00 2004-07-12 12:00:00 2004-07-11 21:00:00 MBSCL 
26 2004-07-11 23:00:00 2004-07-12 05:00:00 2004-07-12 02:00:00 MBSCL 
27 2004-07-15 11:00:00 2004-07-15 19:00:00 2004-07-15 12:00:00 CCM 
28 2004-07-16 12:00:00 2004-07-16 16:00:00 2004-07-16 14:00:00 MBCCM 
29 2004-07-17 02:00:00 2004-07-17 06:00:00 2004-07-17 05:00:00 MBCCM

現在我想的發生所有課程的小時。例如，在Date_inicio，Date_fin和Date_max的某個時間發生不同類別的次數。從DF我獲得下一個frecuencies表，

frec 

     Frec_Inicio Frec_Max Frec_Fin 
Horas       
1   2    0 1 
2   3    8 1 
3   5    3 2 
4   6    2 6 
5   6    6 5 
6   5    6 4 
7   5    7 2 
8   2    4 5 
9   1    6 6 
10   0    3 2 
11   2    5 5 
12   4    1 9 
13   2    4 2 
14   3    2 4 
15   0    2 3 
16   1    1 3 
17   0    2 3 
18   1    1 1 
19   0    0 3 
20   1    1 1 
21   1    1 0 
22   3    1 0 
23   9    1 0 
24   8    3 2

現在，我的目標是繪製3D欄像下面

enter image description here

圖中要做到這一點，我寫了下面的代碼

fig = plt.figure() 
ax = fig.add_subplot(111, projection='3d') 
xpos=np.arange(frec.shape[0]) 
ypos=np.arange(frec.shape[1]) 
xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25) 
xpos = xpos.flatten() 
ypos = ypos.flatten() 
zpos=np.zeros(frec.shape).flatten() 

dx=0.5 * np.ones_like(zpos) 
dy=0.5 * np.ones_like(zpos) 
dz=frec.values.ravel() 
dz[np.isnan(dz)]=0. 

ax.bar3d(xpos,ypos,zpos,dx,dy,dz,color='b', alpha=0.5) 
ax.set_xticks([.5,1.5,2.5]) 
ax.set_yticks([.5,1.5,2.5,3.5]) 
ax.w_yaxis.set_ticklabels(frec.columns) 
ax.w_xaxis.set_ticklabels(frec.index) 
ax.set_xlabel('Time') 
ax.set_ylabel('B') 
ax.set_zlabel('Occurrence') 
plt.show()

enter image description here

我如何得到一個更好的情節，類似於前面的數字？

來源

2014-05-21 user1345283

你能發佈數據？我不明白你爲什麼做'crosstab'並且從'Forma'中刪除所有的列，或許你想'Series.value_counts'。 matplotlib不能做真正的3D繪圖，試試'visvis'。 – HYRY

現在，我編輯更好的理解 – user1345283

下面是代碼做數：

import pandas as pd 
text="""Date_inicio,    Date_Fin,    Date_Max,  Clase 
2004-04-09 23:00:00, 2004-04-10 04:00:00, 2004-04-10 02:00:00, MBCCM 
2004-04-12 23:00:00, 2004-04-13 04:00:00, 2004-04-13 00:00:00, MBSCL 
2004-04-24 04:00:00, 2004-04-24 12:00:00, 2004-04-24 09:00:00, SCL 
2004-05-02 07:00:00, 2004-05-02 14:00:00, 2004-05-02 11:00:00, SCL 
2004-05-30 05:00:00, 2004-05-30 08:00:00, 2004-05-30 07:00:00, MBCCM 
2004-05-31 03:00:00, 2004-05-31 07:00:00, 2004-05-31 05:00:00, MBCCM 
2004-06-08 00:00:00, 2004-06-08 05:00:00, 2004-06-08 03:00:00, MBSCL 
2004-06-12 22:00:00, 2004-06-13 12:00:00, 2004-06-13 06:00:00, CCM 
2004-06-13 03:00:00, 2004-06-13 08:00:00, 2004-06-13 06:00:00, MBCCM 
2004-06-14 00:00:00, 2004-06-14 03:00:00, 2004-06-14 02:00:00, MBSCL 
2004-06-14 03:00:00, 2004-06-14 09:00:00, 2004-06-14 07:00:00, MBSCL 
2004-06-17 08:00:00, 2004-06-17 14:00:00, 2004-06-17 11:00:00, MBCCM 
2004-06-17 12:00:00, 2004-06-17 17:00:00, 2004-06-17 14:00:00, MBCCM 
2004-06-22 00:00:00, 2004-06-22 08:00:00, 2004-06-22 06:00:00, SCL 
2004-06-22 08:00:00, 2004-06-22 14:00:00, 2004-06-22 11:00:00, MBCCM 
2004-06-22 23:00:00, 2004-06-23 09:00:00, 2004-06-23 06:00:00, CCM 
2004-07-01 05:00:00, 2004-07-01 09:00:00, 2004-07-01 06:00:00, MBCCM 
2004-07-02 00:00:00, 2004-07-02 04:00:00, 2004-07-02 02:00:00, MBSCL 
2004-07-04 12:00:00, 2004-07-04 15:00:00, 2004-07-04 13:00:00, MBCCM 
2004-07-06 04:00:00, 2004-07-06 13:00:00, 2004-07-06 07:00:00, SCL 
2004-07-07 04:00:00, 2004-07-07 12:00:00, 2004-07-07 10:00:00, CCM 
2004-07-08 03:00:00, 2004-07-08 06:00:00, 2004-07-08 05:00:00, MBCCM 
2004-07-08 12:00:00, 2004-07-08 17:00:00, 2004-07-08 13:00:00, MBCCM 
2004-07-08 02:00:00, 2004-07-08 06:00:00, 2004-07-08 04:00:00, MBCCM 
2004-07-09 05:00:00, 2004-07-09 12:00:00, 2004-07-09 08:00:00, CCM 
2004-07-11 18:00:00, 2004-07-12 12:00:00, 2004-07-11 21:00:00, MBSCL 
2004-07-11 23:00:00, 2004-07-12 05:00:00, 2004-07-12 02:00:00, MBSCL 
2004-07-15 11:00:00, 2004-07-15 19:00:00, 2004-07-15 12:00:00, CCM 
2004-07-16 12:00:00, 2004-07-16 16:00:00, 2004-07-16 14:00:00, MBCCM 
2004-07-17 02:00:00, 2004-07-17 06:00:00, 2004-07-17 05:00:00, MBCCM""" 
import io 
df = pd.read_csv(io.BytesIO(text), skipinitialspace=True) 
df.drop(["Clase"], axis=1, inplace=True) 
df = df.apply(lambda s:s.str[11:13]).convert_objects(convert_numeric=True) 
df2 = df.apply(lambda s:s.value_counts()) 
print df2

這裏是繪製3D條代碼：

import pandas as pd 
text="""Horas Frec_Inicio Frec_Max Frec_Fin 
1   2    0 1 
2   3    8 1 
3   5    3 2 
4   6    2 6 
5   6    6 5 
6   5    6 4 
7   5    7 2 
8   2    4 5 
9   1    6 6 
10   0    3 2 
11   2    5 5 
12   4    1 9 
13   2    4 2 
14   3    2 4 
15   0    2 3 
16   1    1 3 
17   0    2 3 
18   1    1 1 
19   0    0 3 
20   1    1 1 
21   1    1 0 
22   3    1 0 
23   9    1 0 
24   8    3 2""" 
import io 
df = pd.read_csv(io.BytesIO(text), skipinitialspace=True, delim_whitespace=True) 
df.set_index("Horas", inplace=True) 
columns_name = [x.replace("_", " ") for x in df.columns] 
df.columns = [0, 2, 4] 
x, y, z = df.stack().reset_index().values.T 

import visvis as vv 
app = vv.use() 

f = vv.clf() 
a = vv.cla() 

bar =vv.bar3(x, y, z, width=0.8) 
bar.colors = ["r","g","b"] * 24 
a.axis.yTicks = dict(zip(df.columns, columns_name)) 
app.Run()

輸出：

enter image description here

來源

2014-05-22 07:03:46 HYRY

如何訂購在熊貓中繪製三維條形圖的數據框

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