teratail
回答率
85.30%
teratail header banner
teratail header banner
質問するログイン新規登録
トップに関する質問3D散布図でサンプルのグループ番号ごとにグラデーションで色分け

編集履歴

質問編集履歴

2

エラーが出るコードを追記

2020/03/16 00:42

投稿

teruque
teruque

スコア6

title CHANGED
File without changes
body CHANGED
@@ -53,7 +53,7 @@
53
53
  -0.1442105,-0.220104095,4.73E-02,18
54
54
  -0.1439502,-0.216082524,3.67E-02,18
55
55
 
56
- ##該当のソースコード
56
+ ##グラデーションになっていないがグラフはできるソースコード
57
57
 
58
58
  ```python
59
59
  import pandas as pd
@@ -80,7 +80,34 @@
80
80
  plt.legend()
81
81
  plt.show()
82
82
  ```
83
+ ##グラデーションにしようとしてエラーが出るソースコード
84
+ ```Python
85
+ import pandas as pd
86
+ import matplotlib.pyplot as plt
87
+ from mpl_toolkits.mplot3d import Axes3D
88
+ import matplotlib.colors as mcolors
83
89
 
90
+ df = pd.read_csv(r"test.csv", delimiter=",", sep='\s+', skipinitialspace=True)
91
+
92
+ fig = plt.figure()
93
+ ax = Axes3D(fig)
94
+
95
+ samples = sorted(set(df['sample']))
96
+ cmapval = df['sample']
97
+
98
+
99
+ # sample毎に描画
100
+ for idx, sample in enumerate(samples):
101
+ df2 = df[df['sample'] == sample]
102
+ X = df2["PC1"]
103
+ Y = df2["PC2"]
104
+ Z = df2["PC3"]
105
+ p=ax.scatter(X, Y, Z, c=cmapval, cmap='hsv', label=sample)
106
+
107
+ plt.legend()
108
+ plt.show()
109
+
110
+ ```
84
111
  ##補足情報（FW/ツールのバージョンなど）
85
112
  python 3.7
86
113
  seaborn 0.10.0

1

データをカンマ区切りに修正しました。

2020/03/16 00:42

投稿

teruque
teruque

スコア6

title CHANGED
File without changes
body CHANGED
@@ -4,63 +4,64 @@
4
4
  宜しくお願い致します。
5
5
 
6
6
  ###Data
7
- PC1 PC2 PC3 sample
7
+ PC1,PC2,PC3,sample
8
- -0.1413928 0.02173982 -4.15E-01 1
8
+ -0.1413928,0.02173982,-4.15E-01,1
9
- -0.1413101 0.017508996 -4.21E-01 1
9
+ -0.1413101,0.017508996,-4.21E-01,1
10
- -0.1417087 0.014979086 -4.08E-01 1
10
+ -0.1417087,0.014979086,-4.08E-01,1
11
- -0.1411702 0.008670829 -4.09E-01 1
11
+ -0.1411702,0.008670829,-4.09E-01,1
12
- -0.145876 0.123370613 -1.03E-01 2
12
+ -0.145876,0.123370613,-1.03E-01,2
13
- -0.1433736 0.124895934 -1.41E-01 2
13
+ -0.1433736,0.124895934,-1.41E-01,2
14
- -0.1456516 0.092020696 -1.09E-01 3
14
+ -0.1456516,0.092020696,-1.09E-01,3
15
- -0.1461915 0.094471875 -1.18E-01 3
15
+ -0.1461915,0.094471875,-1.18E-01,3
16
- -0.1461108 0.090837929 -1.21E-01 3
16
+ -0.1461108,0.090837929,-1.21E-01,3
17
- -0.1472447 0.119571378 -4.57E-02 4
17
+ -0.1472447,0.119571378,-4.57E-02,4
18
- -0.1472894 0.115520249 -4.68E-02 4
18
+ -0.1472894,0.115520249,-4.68E-02,4
19
- -0.1467504 0.121762527 -4.68E-02 4
19
+ -0.1467504,0.121762527,-4.68E-02,4
20
- -0.1491286 0.090455109 9.15E-02 5
20
+ -0.1491286,0.090455109,9.15E-02,5
21
- -0.1487338 0.09812073 9.12E-02 5
21
+ -0.1487338,0.09812073,9.12E-02,5
22
- -0.1489169 0.072974728 1.14E-01 6
22
+ -0.1489169,0.072974728,1.14E-01,6
23
- -0.1490467 0.085162278 1.12E-01 6
23
+ -0.1490467,0.085162278,1.12E-01,6
24
- -0.1489641 0.08360632 1.23E-01 7
24
+ -0.1489641,0.08360632,1.23E-01,7
25
- -0.148754 0.088689326 1.12E-01 7
25
+ -0.148754,0.088689326,1.12E-01,7
26
- -0.1487234 0.090154785 1.12E-01 7
26
+ -0.1487234,0.090154785,1.12E-01,7
27
- -0.1490534 0.096598206 9.36E-02 8
27
+ -0.1490534,0.096598206,9.36E-02,8
28
- -0.149099 0.096547726 8.85E-02 8
28
+ -0.149099,0.096547726,8.85E-02,8
29
- -0.1491893 0.096628792 1.14E-01 9
29
+ -0.1491893,0.096628792,1.14E-01,9
30
- -0.1490337 0.10319152 1.07E-01 9
30
+ -0.1490337,0.10319152,1.07E-01,9
31
- -0.1490627 0.102746721 1.06E-01 9
31
+ -0.1490627,0.102746721,1.06E-01,9
32
- -0.1479182 0.114529747 1.13E-01 10
32
+ -0.1479182,0.114529747,1.13E-01,10
33
- -0.1483447 0.106218026 1.26E-01 10
33
+ -0.1483447,0.106218026,1.26E-01,10
34
- -0.1483964 0.106148189 1.23E-01 11
34
+ -0.1483964,0.106148189,1.23E-01,11
35
- -0.1482859 0.103531061 1.23E-01 11
35
+ -0.1482859,0.103531061,1.23E-01,11
36
- -0.1487256 0.110975705 9.76E-02 12
36
+ -0.1487256,0.110975705,9.76E-02,12
37
- -0.1481255 0.126683827 9.22E-02 12
37
+ -0.1481255,0.126683827,9.22E-02,12
38
- -0.1479033 0.130060792 8.73E-02 12
38
+ -0.1479033,0.130060792,8.73E-02,12
39
- -0.1359165 0.061005493 -7.12E-02 13
39
+ -0.1359165,0.061005493,-7.12E-02,13
40
- -0.1372293 0.073245259 -4.54E-02 13
40
+ -0.1372293,0.073245259,-4.54E-02,13
41
- -0.1464516 -0.153001981 2.27E-03 14
41
+ -0.1464516,-0.153001981,2.27E-03,14
42
- -0.1449456 -0.204644258 9.43E-03 14
42
+ -0.1449456,-0.204644258,9.43E-03,14
43
- -0.1474867 -0.111179279 -4.29E-05 15
43
+ -0.1474867,-0.111179279,-4.29E-05,15
44
- -0.1463186 -0.170116339 1.78E-02 15
44
+ -0.1463186,-0.170116339,1.78E-02,15
45
- -0.1458789 -0.176175751 2.37E-02 15
45
+ -0.1458789,-0.176175751,2.37E-02,15
46
- -0.142477 -0.255583393 3.95E-02 16
46
+ -0.142477,-0.255583393,3.95E-02,16
47
- -0.143537 -0.25325231 3.06E-02 16
47
+ -0.143537,-0.25325231,3.06E-02,16
48
- -0.1428187 -0.278201657 2.85E-02 16
48
+ -0.1428187,-0.278201657,2.85E-02,16
49
- -0.1427046 -0.253661164 3.15E-02 17
49
+ -0.1427046,-0.253661164,3.15E-02,17
50
- -0.1432171 -0.270822563 2.90E-02 17
50
+ -0.1432171,-0.270822563,2.90E-02,17
51
- -0.1431351 -0.272426721 2.78E-02 17
51
+ -0.1431351,-0.272426721,2.78E-02,17
52
- -0.1442404 -0.212473411 5.68E-02 18
52
+ -0.1442404,-0.212473411,5.68E-02,18
53
- -0.1442105 -0.220104095 4.73E-02 18
53
+ -0.1442105,-0.220104095,4.73E-02,18
54
- -0.1439502 -0.216082524 3.67E-02 18
54
+ -0.1439502,-0.216082524,3.67E-02,18
55
55
 
56
56
  ##該当のソースコード
57
57
 
58
+ ```python
58
59
  import pandas as pd
59
60
  import matplotlib.pyplot as plt
60
61
  from mpl_toolkits.mplot3d import Axes3D
61
62
  import matplotlib.colors as mcolors
62
63
 
63
- df = pd.read_csv(r"ファイルパス", delimiter=",", sep='\s+', skipinitialspace=True)
64
+ df = pd.read_csv(r"test.csv", delimiter=",", sep='\s+', skipinitialspace=True)
64
65
 
65
66
  fig = plt.figure()
66
67
  ax = Axes3D(fig)
@@ -68,7 +69,7 @@
68
69
  samples = sorted(set(df['sample']))
69
70
  colors = list(mcolors.CSS4_COLORS.keys())
70
71
 
71
- sample毎に描画
72
+ # sample毎に描画
72
73
  for idx, sample in enumerate(samples):
73
74
  df2 = df[df['sample'] == sample]
74
75
  X = df2["PC1"]
@@ -78,6 +79,7 @@
78
79
 
79
80
  plt.legend()
80
81
  plt.show()
82
+ ```
81
83
 
82
84
  ##補足情報（FW/ツールのバージョンなど）
83
85
  python 3.7