问题

Is it possible to get something like this plot

from a pandas dataframe, in a a similar fashion as I would just simply do to do 2d-plots (df.plot())?

More precisely:

I have data that I read from csv files into pandas DataFrames with following structure:

1st level header        A        B       C       D        E         F 
2nd level header      2.0      1.0     0.2     0.4      0.6       0.8

        Index                                                      
     126.4348  -467048  -814795  301388  298430  -187654  -1903170 
     126.4310  -468329  -810060  304366  305343  -192035  -1881625 
     126.4272  -469209  -804697  305795  312472  -197013  -1854848 
     126.4234  -469685  -799604  305647  318936  -200957  -1827665 
     126.4195  -469795  -795708  304101  323922  -202192  -1805153 
     126.4157  -469610  -793795  301497  326780  -199323  -1791743 
     126.4119  -469213  -794362  298257  327092  -191547  -1790418 
     126.4081  -468687  -797499  294817  324717  -178875  -1802122 
     126.4043  -468097  -802853  291546  319800  -162225  -1825540 
     126.4005  -467486  -809663  288700  312745  -143334  -1857270 
     126.3967  -466863  -816878  286401  304170  -124505  -1892389 
     126.3929  -466210  -823335  284645  294827  -108228  -1925312 
     126.3890  -465485  -827966  283331  285520   -96733  -1950795 
     126.3852  -464637  -829997  282315  277018   -91559  -1964894 
     126.3814  -463617  -829104  281457  269965   -93242  -1965702 
     126.3776  -462399  -825487  280670  264824  -101170  -1953728 
     126.3738  -460982  -819857  279942  261819  -113660  -1931820 
     126.3700  -459408  -813317  279344  260927  -128242  -1904669 
     126.3662  -457757  -807177  279009  261885  -142112  -1877955 
     126.3624  -456143  -802715  279090  264233  -152667  -1857303 
     126.3585  -454700  -800940  279722  267380  -158023  -1847241 
     126.3547  -453566  -802397  280969  270692  -157406  -1850358 
     126.3509  -452862  -807050  282792  273579  -151350  -1866803 
     126.3471  -452672  -814262  285033  275591  -141627  -1894249 
     126.3433  -453030  -822898  287426  276486  -130942  -1928303 
     126.3395  -453910  -831501  289627  276273  -122426  -1963297 
     126.3357  -455223  -838544  291266  275222  -119021  -1993312 
     126.3319  -456834  -842695  292004  273824  -122882  -2013246 
     126.3280  -458571  -843048  291599  272725  -134907  -2019718 
     126.3242  -460252  -839292  289952  272620  -154497  -2011656 
          ...      ...      ...     ...     ...      ...       ...

What I would like to do with that

I would like to plot each of these columns (they are NMR spectra) against the index. In a 2D overlay, this is simple usage of the pandas wrapper around matplotlib. However, I would like to plot each spectrum in its own "line", along a third axis that has the second level headers as ticks. I tried to use matplotlib´s 3D plotting functionality, but it seems to only be suitable if you actually have three arrays of equal length, which in the case of my data does just not make sense, because each spectrum is recorded for one of the values from the second level header.

Am I maybe thinking too complicated when I try to make a 3D plot?

Is the figure I would like my plot to look like maybe not an actual 3D plot but rather some special version of overlaid 2D plots?

How I would prefer to do it

Bonus points for:

Using only python
Using only pandas and matplotlib
Already implemented functionality

If there is no obvious python way to do it, I would as well be happy about libraries of other languages that can do the same, such as R or Octave. I am just not as familiar with these, so I would probably not be able to adapt more hacky solutions in these languages to suit my requirements.

This question might be very similar, but as I understand it, it does not necessarily extend to software other than python and doesn't have an example of what the result should look like, so I am not sure if answers to that question might actually be helpful for this specific purpose.

What is wrong with matplotlib´s gallery examples

As lanery pointed out, polygon3D from the matplotlib gallery gets close to what I wish for. However it has some drawbacks some of which are not acceptable for most scientific publications:

With negative values, the whole plot gets shifted to what I would call "the middle of the screen", which looks kind of ugly, makes it hard to extract information from the figure and makes it different from the provided examples
You get that interactive plot window, which requires you to find an angle from which you can see everything you need to see. That might be good for some data exploration tasks, but if you use scripts for your visualization and a minor change to the graphic would force you to do some manual work again, this decreases the advantage you expect from scripting
If you have values that differ strongly and are not linear, something like [0,1,1.7,2.5,6.2], for your third dimension i.e. the second level header in this case, the 2d plots have very different distances from another, which is unacceptable, at least for any non-programming audience reading the publications
It is quite long and technical for a quite common plotting operation in spectroscopy. The amount of code would be fine if I wanted to build software that can make 3D plots in some context. For science it would be preferable to be able to accomplish something like this with a low amount of code.

回答1:

I gave you an example of plotting with the data from the continuous X and Y, and just hard-coded z based on your second level header.

from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib
%matplotlib inline

df = pd.read_csv("C:\Users\User\SkyDrive\Documents\import_data.tcsv.txt",header=None)

fig = plt.figure()
ax = fig.gca(projection='3d')

# Plot a sin curve using the x and y axes.
x = df[0]
ax.plot(x, df[1], zs=2, zdir='z', label='A')
ax.plot(x, df[2], zs=1, zdir='z', label='B')
ax.plot(x, df[3], zs=0.2, zdir='z', label='C')
ax.plot(x, df[4], zs=0.4, zdir='z', label='D')
ax.plot(x, df[5], zs=0.6, zdir='z', label='E')
ax.plot(x, df[6], zs=0.8, zdir='z', label='F')

# Customize the view angle so it's easier to see that the scatter points lie
# on the plane y=0
ax.view_init(elev=-150., azim=40)

plt.show()

Your going to have to play with the options on view_init to rotate around and get the axes where you want. I'm not really clear with what your end goal was, but this is the end plot.

来源：https://stackoverflow.com/questions/37336342/plot-spectroscopic-data-from-pandas-dataframe-in-3d-with-different-array-length

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python