plot a 3d plot using dataframe in matplotlib
0
I have following data and i am having trouble plotting a 3d Plot similar to the one showed in the examples of Matplotlib -> https://matplotlib.org/examples/mplot3d/custom_shaded_3d_surface.html
On the x axis i want to have the Residue column, on the y-axis the first row and the z axis should represent the values.
residue 0 1 2 3 4 5 6
0 0.0 0.0 1.671928 1.441439 0.808492 1.079337 1.186970 1.445275
1 1.0 0.0 1.348867 1.216174 1.324360 1.965453 2.121130 1.713321
2 2.0 0.0 1.281589 0.794236 1.083470 1.476939 2.011159 2.360246
3 3.0 0.0 0.798151 0.993858 1.020617 0.829792 1.280412 1.653299
4 4.0 0.0 0.789995 1.194215 1.407934 1.291384 1.555449 1.258266
5 5.0 0.0 0.653958 0.910582 1.585495 1.245847 1.620384 1.664490
6 6.0 0.0 0.782577 0.648373 1.284292 1.087762 1.523729 1.631152
7 7.0 0.0 1.094054 1.127248 0.958693 1.168483 0.897470 1.404080
8 8.0 0.0 0.433993 1.165169 0.925521 1.292363 1.075700 1.146139
9 9.0 0.0 1.114398 0.963963 1.062597 1.297358 1.412016 1.422071
10 10.0 0.0 0.706276 1.056272 1.381639 1.682080 1.779487 1.914487
11 11.0 0.0 1.059623 1.000653 1.152697 1.895022 1.562730 1.964862
Is it better not to use a Dataframe in this case?
this is the code im using:
z = df.iloc[1:,1:-1]
ff= [i for i in range(1,500)]
y=df["residue"]
print(len(z))
nrows, ncols = z.shape
x = np.linspace(min(ff),max(ff), ncols)
x, y = np.meshgrid(x, y)
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
plt.show()
python pandas matplotlib plot
asked Nov 25 '18 at 16:34
AmirAmir
113
add a comment |
0
I have following data and i am having trouble plotting a 3d Plot similar to the one showed in the examples of Matplotlib -> https://matplotlib.org/examples/mplot3d/custom_shaded_3d_surface.html
On the x axis i want to have the Residue column, on the y-axis the first row and the z axis should represent the values.
residue 0 1 2 3 4 5 6
0 0.0 0.0 1.671928 1.441439 0.808492 1.079337 1.186970 1.445275
1 1.0 0.0 1.348867 1.216174 1.324360 1.965453 2.121130 1.713321
2 2.0 0.0 1.281589 0.794236 1.083470 1.476939 2.011159 2.360246
3 3.0 0.0 0.798151 0.993858 1.020617 0.829792 1.280412 1.653299
4 4.0 0.0 0.789995 1.194215 1.407934 1.291384 1.555449 1.258266
5 5.0 0.0 0.653958 0.910582 1.585495 1.245847 1.620384 1.664490
6 6.0 0.0 0.782577 0.648373 1.284292 1.087762 1.523729 1.631152
7 7.0 0.0 1.094054 1.127248 0.958693 1.168483 0.897470 1.404080
8 8.0 0.0 0.433993 1.165169 0.925521 1.292363 1.075700 1.146139
9 9.0 0.0 1.114398 0.963963 1.062597 1.297358 1.412016 1.422071
10 10.0 0.0 0.706276 1.056272 1.381639 1.682080 1.779487 1.914487
11 11.0 0.0 1.059623 1.000653 1.152697 1.895022 1.562730 1.964862
Is it better not to use a Dataframe in this case?
this is the code im using:
z = df.iloc[1:,1:-1]
ff= [i for i in range(1,500)]
y=df["residue"]
print(len(z))
nrows, ncols = z.shape
x = np.linspace(min(ff),max(ff), ncols)
x, y = np.meshgrid(x, y)
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
plt.show()
python pandas matplotlib plot
asked Nov 25 '18 at 16:34
AmirAmir
113
You can sure use a dataframe. One problem is thatresidueis a column of the frame, so it would be included in the data. however, you would rather make it the dateframe index. Then you will need to create a meshgrid of the index and columns as shown in the many examples on this topic.
– ImportanceOfBeingErnest
Nov 25 '18 at 16:38
add a comment |
0
0
0
I have following data and i am having trouble plotting a 3d Plot similar to the one showed in the examples of Matplotlib -> https://matplotlib.org/examples/mplot3d/custom_shaded_3d_surface.html
On the x axis i want to have the Residue column, on the y-axis the first row and the z axis should represent the values.
residue 0 1 2 3 4 5 6
0 0.0 0.0 1.671928 1.441439 0.808492 1.079337 1.186970 1.445275
1 1.0 0.0 1.348867 1.216174 1.324360 1.965453 2.121130 1.713321
2 2.0 0.0 1.281589 0.794236 1.083470 1.476939 2.011159 2.360246
3 3.0 0.0 0.798151 0.993858 1.020617 0.829792 1.280412 1.653299
4 4.0 0.0 0.789995 1.194215 1.407934 1.291384 1.555449 1.258266
5 5.0 0.0 0.653958 0.910582 1.585495 1.245847 1.620384 1.664490
6 6.0 0.0 0.782577 0.648373 1.284292 1.087762 1.523729 1.631152
7 7.0 0.0 1.094054 1.127248 0.958693 1.168483 0.897470 1.404080
8 8.0 0.0 0.433993 1.165169 0.925521 1.292363 1.075700 1.146139
9 9.0 0.0 1.114398 0.963963 1.062597 1.297358 1.412016 1.422071
10 10.0 0.0 0.706276 1.056272 1.381639 1.682080 1.779487 1.914487
11 11.0 0.0 1.059623 1.000653 1.152697 1.895022 1.562730 1.964862
Is it better not to use a Dataframe in this case?
this is the code im using:
z = df.iloc[1:,1:-1]
ff= [i for i in range(1,500)]
y=df["residue"]
print(len(z))
nrows, ncols = z.shape
x = np.linspace(min(ff),max(ff), ncols)
x, y = np.meshgrid(x, y)
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
plt.show()
python pandas matplotlib plot
asked Nov 25 '18 at 16:34
AmirAmir
113
I have following data and i am having trouble plotting a 3d Plot similar to the one showed in the examples of Matplotlib -> https://matplotlib.org/examples/mplot3d/custom_shaded_3d_surface.html
On the x axis i want to have the Residue column, on the y-axis the first row and the z axis should represent the values.
residue 0 1 2 3 4 5 6
0 0.0 0.0 1.671928 1.441439 0.808492 1.079337 1.186970 1.445275
1 1.0 0.0 1.348867 1.216174 1.324360 1.965453 2.121130 1.713321
2 2.0 0.0 1.281589 0.794236 1.083470 1.476939 2.011159 2.360246
3 3.0 0.0 0.798151 0.993858 1.020617 0.829792 1.280412 1.653299
4 4.0 0.0 0.789995 1.194215 1.407934 1.291384 1.555449 1.258266
5 5.0 0.0 0.653958 0.910582 1.585495 1.245847 1.620384 1.664490
6 6.0 0.0 0.782577 0.648373 1.284292 1.087762 1.523729 1.631152
7 7.0 0.0 1.094054 1.127248 0.958693 1.168483 0.897470 1.404080
8 8.0 0.0 0.433993 1.165169 0.925521 1.292363 1.075700 1.146139
9 9.0 0.0 1.114398 0.963963 1.062597 1.297358 1.412016 1.422071
10 10.0 0.0 0.706276 1.056272 1.381639 1.682080 1.779487 1.914487
11 11.0 0.0 1.059623 1.000653 1.152697 1.895022 1.562730 1.964862
Is it better not to use a Dataframe in this case?
this is the code im using:
z = df.iloc[1:,1:-1]
ff= [i for i in range(1,500)]
y=df["residue"]
print(len(z))
nrows, ncols = z.shape
x = np.linspace(min(ff),max(ff), ncols)
x, y = np.meshgrid(x, y)
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
plt.show()
python pandas matplotlib plot
python pandas matplotlib plot
asked Nov 25 '18 at 16:34
AmirAmir
113
asked Nov 25 '18 at 16:34
AmirAmir
113
edited Nov 25 '18 at 16:40
Amir
asked Nov 25 '18 at 16:34
AmirAmir
113
asked Nov 25 '18 at 16:34
AmirAmir
113
asked Nov 25 '18 at 16:34
AmirAmir
113
113
You can sure use a dataframe. One problem is thatresidueis a column of the frame, so it would be included in the data. however, you would rather make it the dateframe index. Then you will need to create a meshgrid of the index and columns as shown in the many examples on this topic.
– ImportanceOfBeingErnest
Nov 25 '18 at 16:38
add a comment |
You can sure use a dataframe. One problem is thatresidueis a column of the frame, so it would be included in the data. however, you would rather make it the dateframe index. Then you will need to create a meshgrid of the index and columns as shown in the many examples on this topic.
– ImportanceOfBeingErnest
Nov 25 '18 at 16:38
You can sure use a dataframe. One problem is that
residue is a column of the frame, so it would be included in the data. however, you would rather make it the dateframe index. Then you will need to create a meshgrid of the index and columns as shown in the many examples on this topic.– ImportanceOfBeingErnest
Nov 25 '18 at 16:38
You can sure use a dataframe. One problem is that
residue is a column of the frame, so it would be included in the data. however, you would rather make it the dateframe index. Then you will need to create a meshgrid of the index and columns as shown in the many examples on this topic.– ImportanceOfBeingErnest
Nov 25 '18 at 16:38
add a comment |
1 Answer
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u = """ residue 0 1 2 3 4 5 6
0 0.0 0.0 1.671928 1.441439 0.808492 1.079337 1.186970 1.445275
1 1.0 0.0 1.348867 1.216174 1.324360 1.965453 2.121130 1.713321
2 2.0 0.0 1.281589 0.794236 1.083470 1.476939 2.011159 2.360246
3 3.0 0.0 0.798151 0.993858 1.020617 0.829792 1.280412 1.653299
4 4.0 0.0 0.789995 1.194215 1.407934 1.291384 1.555449 1.258266
5 5.0 0.0 0.653958 0.910582 1.585495 1.245847 1.620384 1.664490
6 6.0 0.0 0.782577 0.648373 1.284292 1.087762 1.523729 1.631152
7 7.0 0.0 1.094054 1.127248 0.958693 1.168483 0.897470 1.404080
8 8.0 0.0 0.433993 1.165169 0.925521 1.292363 1.075700 1.146139
9 9.0 0.0 1.114398 0.963963 1.062597 1.297358 1.412016 1.422071
10 10.0 0.0 0.706276 1.056272 1.381639 1.682080 1.779487 1.914487
11 11.0 0.0 1.059623 1.000653 1.152697 1.895022 1.562730 1.964862"""
import io
import pandas as pd
import numpy as np
df = pd.read_csv(io.StringIO(u), delim_whitespace=True)
df = df.set_index("residue")
Setting such that the residue column is not part of the data anymore.
Then you can create the meshgrid from the columns and the index and plot it according to the linked example.
x,y = np.meshgrid(df.columns.astype(float), df.index)
z = df.values
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import LightSource
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
rgb = LightSource(270, 45).shade(z, cmap=plt.cm.gist_earth, vert_exag=0.1, blend_mode='soft')
surf = ax.plot_surface(x, y, z, facecolors=rgb,
linewidth=0, antialiased=False, shade=False)
plt.show()
answered Nov 25 '18 at 17:11
ImportanceOfBeingErnestImportanceOfBeingErnest
130k13138215
thank you very much that was exactly what i was looking for, the df.value. I actually added the column name 8residue, 1 ,2,3, etc. manually but apperently it wasnt a smart strategy.
– Amir
Nov 25 '18 at 21:25
add a comment |
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1 Answer
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u = """ residue 0 1 2 3 4 5 6
0 0.0 0.0 1.671928 1.441439 0.808492 1.079337 1.186970 1.445275
1 1.0 0.0 1.348867 1.216174 1.324360 1.965453 2.121130 1.713321
2 2.0 0.0 1.281589 0.794236 1.083470 1.476939 2.011159 2.360246
3 3.0 0.0 0.798151 0.993858 1.020617 0.829792 1.280412 1.653299
4 4.0 0.0 0.789995 1.194215 1.407934 1.291384 1.555449 1.258266
5 5.0 0.0 0.653958 0.910582 1.585495 1.245847 1.620384 1.664490
6 6.0 0.0 0.782577 0.648373 1.284292 1.087762 1.523729 1.631152
7 7.0 0.0 1.094054 1.127248 0.958693 1.168483 0.897470 1.404080
8 8.0 0.0 0.433993 1.165169 0.925521 1.292363 1.075700 1.146139
9 9.0 0.0 1.114398 0.963963 1.062597 1.297358 1.412016 1.422071
10 10.0 0.0 0.706276 1.056272 1.381639 1.682080 1.779487 1.914487
11 11.0 0.0 1.059623 1.000653 1.152697 1.895022 1.562730 1.964862"""
import io
import pandas as pd
import numpy as np
df = pd.read_csv(io.StringIO(u), delim_whitespace=True)
df = df.set_index("residue")
Setting such that the residue column is not part of the data anymore.
Then you can create the meshgrid from the columns and the index and plot it according to the linked example.
x,y = np.meshgrid(df.columns.astype(float), df.index)
z = df.values
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import LightSource
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
rgb = LightSource(270, 45).shade(z, cmap=plt.cm.gist_earth, vert_exag=0.1, blend_mode='soft')
surf = ax.plot_surface(x, y, z, facecolors=rgb,
linewidth=0, antialiased=False, shade=False)
plt.show()
answered Nov 25 '18 at 17:11
ImportanceOfBeingErnestImportanceOfBeingErnest
130k13138215
thank you very much that was exactly what i was looking for, the df.value. I actually added the column name 8residue, 1 ,2,3, etc. manually but apperently it wasnt a smart strategy.
– Amir
Nov 25 '18 at 21:25
add a comment |
0
u = """ residue 0 1 2 3 4 5 6
0 0.0 0.0 1.671928 1.441439 0.808492 1.079337 1.186970 1.445275
1 1.0 0.0 1.348867 1.216174 1.324360 1.965453 2.121130 1.713321
2 2.0 0.0 1.281589 0.794236 1.083470 1.476939 2.011159 2.360246
3 3.0 0.0 0.798151 0.993858 1.020617 0.829792 1.280412 1.653299
4 4.0 0.0 0.789995 1.194215 1.407934 1.291384 1.555449 1.258266
5 5.0 0.0 0.653958 0.910582 1.585495 1.245847 1.620384 1.664490
6 6.0 0.0 0.782577 0.648373 1.284292 1.087762 1.523729 1.631152
7 7.0 0.0 1.094054 1.127248 0.958693 1.168483 0.897470 1.404080
8 8.0 0.0 0.433993 1.165169 0.925521 1.292363 1.075700 1.146139
9 9.0 0.0 1.114398 0.963963 1.062597 1.297358 1.412016 1.422071
10 10.0 0.0 0.706276 1.056272 1.381639 1.682080 1.779487 1.914487
11 11.0 0.0 1.059623 1.000653 1.152697 1.895022 1.562730 1.964862"""
import io
import pandas as pd
import numpy as np
df = pd.read_csv(io.StringIO(u), delim_whitespace=True)
df = df.set_index("residue")
Setting such that the residue column is not part of the data anymore.
Then you can create the meshgrid from the columns and the index and plot it according to the linked example.
x,y = np.meshgrid(df.columns.astype(float), df.index)
z = df.values
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import LightSource
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
rgb = LightSource(270, 45).shade(z, cmap=plt.cm.gist_earth, vert_exag=0.1, blend_mode='soft')
surf = ax.plot_surface(x, y, z, facecolors=rgb,
linewidth=0, antialiased=False, shade=False)
plt.show()
answered Nov 25 '18 at 17:11
ImportanceOfBeingErnestImportanceOfBeingErnest
130k13138215
thank you very much that was exactly what i was looking for, the df.value. I actually added the column name 8residue, 1 ,2,3, etc. manually but apperently it wasnt a smart strategy.
– Amir
Nov 25 '18 at 21:25
add a comment |
0
0
0
u = """ residue 0 1 2 3 4 5 6
0 0.0 0.0 1.671928 1.441439 0.808492 1.079337 1.186970 1.445275
1 1.0 0.0 1.348867 1.216174 1.324360 1.965453 2.121130 1.713321
2 2.0 0.0 1.281589 0.794236 1.083470 1.476939 2.011159 2.360246
3 3.0 0.0 0.798151 0.993858 1.020617 0.829792 1.280412 1.653299
4 4.0 0.0 0.789995 1.194215 1.407934 1.291384 1.555449 1.258266
5 5.0 0.0 0.653958 0.910582 1.585495 1.245847 1.620384 1.664490
6 6.0 0.0 0.782577 0.648373 1.284292 1.087762 1.523729 1.631152
7 7.0 0.0 1.094054 1.127248 0.958693 1.168483 0.897470 1.404080
8 8.0 0.0 0.433993 1.165169 0.925521 1.292363 1.075700 1.146139
9 9.0 0.0 1.114398 0.963963 1.062597 1.297358 1.412016 1.422071
10 10.0 0.0 0.706276 1.056272 1.381639 1.682080 1.779487 1.914487
11 11.0 0.0 1.059623 1.000653 1.152697 1.895022 1.562730 1.964862"""
import io
import pandas as pd
import numpy as np
df = pd.read_csv(io.StringIO(u), delim_whitespace=True)
df = df.set_index("residue")
Setting such that the residue column is not part of the data anymore.
Then you can create the meshgrid from the columns and the index and plot it according to the linked example.
x,y = np.meshgrid(df.columns.astype(float), df.index)
z = df.values
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import LightSource
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
rgb = LightSource(270, 45).shade(z, cmap=plt.cm.gist_earth, vert_exag=0.1, blend_mode='soft')
surf = ax.plot_surface(x, y, z, facecolors=rgb,
linewidth=0, antialiased=False, shade=False)
plt.show()
answered Nov 25 '18 at 17:11
ImportanceOfBeingErnestImportanceOfBeingErnest
130k13138215
u = """ residue 0 1 2 3 4 5 6
0 0.0 0.0 1.671928 1.441439 0.808492 1.079337 1.186970 1.445275
1 1.0 0.0 1.348867 1.216174 1.324360 1.965453 2.121130 1.713321
2 2.0 0.0 1.281589 0.794236 1.083470 1.476939 2.011159 2.360246
3 3.0 0.0 0.798151 0.993858 1.020617 0.829792 1.280412 1.653299
4 4.0 0.0 0.789995 1.194215 1.407934 1.291384 1.555449 1.258266
5 5.0 0.0 0.653958 0.910582 1.585495 1.245847 1.620384 1.664490
6 6.0 0.0 0.782577 0.648373 1.284292 1.087762 1.523729 1.631152
7 7.0 0.0 1.094054 1.127248 0.958693 1.168483 0.897470 1.404080
8 8.0 0.0 0.433993 1.165169 0.925521 1.292363 1.075700 1.146139
9 9.0 0.0 1.114398 0.963963 1.062597 1.297358 1.412016 1.422071
10 10.0 0.0 0.706276 1.056272 1.381639 1.682080 1.779487 1.914487
11 11.0 0.0 1.059623 1.000653 1.152697 1.895022 1.562730 1.964862"""
import io
import pandas as pd
import numpy as np
df = pd.read_csv(io.StringIO(u), delim_whitespace=True)
df = df.set_index("residue")
Setting such that the residue column is not part of the data anymore.
Then you can create the meshgrid from the columns and the index and plot it according to the linked example.
x,y = np.meshgrid(df.columns.astype(float), df.index)
z = df.values
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import LightSource
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
rgb = LightSource(270, 45).shade(z, cmap=plt.cm.gist_earth, vert_exag=0.1, blend_mode='soft')
surf = ax.plot_surface(x, y, z, facecolors=rgb,
linewidth=0, antialiased=False, shade=False)
plt.show()
answered Nov 25 '18 at 17:11
ImportanceOfBeingErnestImportanceOfBeingErnest
130k13138215
answered Nov 25 '18 at 17:11
ImportanceOfBeingErnestImportanceOfBeingErnest
130k13138215
answered Nov 25 '18 at 17:11
ImportanceOfBeingErnestImportanceOfBeingErnest
130k13138215
answered Nov 25 '18 at 17:11
ImportanceOfBeingErnestImportanceOfBeingErnest
130k13138215
130k13138215
thank you very much that was exactly what i was looking for, the df.value. I actually added the column name 8residue, 1 ,2,3, etc. manually but apperently it wasnt a smart strategy.
– Amir
Nov 25 '18 at 21:25
add a comment |
thank you very much that was exactly what i was looking for, the df.value. I actually added the column name 8residue, 1 ,2,3, etc. manually but apperently it wasnt a smart strategy.
– Amir
Nov 25 '18 at 21:25
thank you very much that was exactly what i was looking for, the df.value. I actually added the column name 8residue, 1 ,2,3, etc. manually but apperently it wasnt a smart strategy.
– Amir
Nov 25 '18 at 21:25
thank you very much that was exactly what i was looking for, the df.value. I actually added the column name 8residue, 1 ,2,3, etc. manually but apperently it wasnt a smart strategy.
– Amir
Nov 25 '18 at 21:25
add a comment |
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You can sure use a dataframe. One problem is that
residueis a column of the frame, so it would be included in the data. however, you would rather make it the dateframe index. Then you will need to create a meshgrid of the index and columns as shown in the many examples on this topic.– ImportanceOfBeingErnest
Nov 25 '18 at 16:38