Creating a 3D SMOOTH surface plot from pandas dataframe

Question

I have a dataframe z1 with entries as follows:

z1.ix[1:10,1:3]
            2017-04-01  2017-05-01
2017-01-04         NaN    0.993549
2017-01-05         NaN         NaN
2017-01-06    0.830973    0.978463
2017-01-09    0.926456         NaN
2017-01-10    0.998371    0.997590
2017-01-11    0.997539    0.999364
2017-01-12    NaN         0.989801
2017-01-13    0.999701    0.998526
2017-01-16    0.995119    0.998891

I am trying to create the surface plot using the row index as X-axis, column names as Y-axis and values on Z-axis.

I tried the following:

    import matplotlib as mpl
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D

    x = z1.columns
    y = z1.index
    X, Y = np.meshgrid(x, y)
    Z = z1
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    ax.plot_surface(X, Y, Z)

I get following error:

TypeError: ufunc multiply cannot use operands with types dtype('float64') and dtype('<M8[ns]')

Both the column names and row index for my dataframe are datetimes. How can I improve my code to make the 3D SMOOTH surface plot.

z1.shape
(182, 32)

Alex Ozerov · Accepted Answer · 2017-10-12 20:30:13Z

1

I think some of these libraries does not support datetime type.
You should convert your data to an appropriate format first. Here is the example (sorry for the io thing, it's just for simple copy-paste dataframe)

from io import StringIO
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

s = StringIO("""
            2017-04-01  2017-05-01
2017-01-04         NaN    0.993549
2017-01-05         NaN         NaN
2017-01-06    0.830973    0.978463
2017-01-09    0.926456         NaN
2017-01-10    0.998371    0.997590
2017-01-11    0.997539    0.999364
2017-01-12    NaN         0.989801
2017-01-13    0.999701    0.998526
2017-01-16    0.995119    0.998891""")
z1 = pd.DataFrame.from_csv(s, sep='\s+')


def string_date_to_numeric(li):
    series = pd.Series(li)
    datetime = pd.to_datetime(series)
    numeric = pd.to_numeric(datetime)
    return numeric


x = string_date_to_numeric(z1.columns)
y = string_date_to_numeric(z1.index)
X, Y = np.meshgrid(x, y)
print(X, Y)
Z = z1.values
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z)
fig.savefig('plot.png')

figure:

You can rename axis afterward.

ax.set_xticklabels(z1.columns)
ax.set_yticklabels(z1.index)

edited Oct 12, 2017 at 20:30

answered Oct 12, 2017 at 20:14

Alex Ozerov

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1 Comment

Alex Ozerov Over a year ago

added to code example, you can set size, formatting and everything else.

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