如何从数据框中显示绘图/3d 图？

Question

我是pandas 和matplotlib 的新手。无法获得准确的参考来绘制我的DataFrame，其架构如下

schema = StructType([
StructField("x", IntegerType(), True),
StructField("y", IntegerType(), True),
StructField("z", IntegerType(), True)])

喜欢绘制 3d 图 w.r.t。 x、y 和 z

这是我使用的示例代码

import matplotlib.pyplot as pltt

dfSpark = sqlContext.createDataFrame(tupleRangeRDD, schema) // reading as spark df
df = dfSpark.toPandas()
fig = pltt.figure();
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(df['x'], df['y'], df['z']) 

我得到一个空的图表。肯定错过了什么。有什么指点吗？

-谢谢

Request-1：打印 df

def print_full(x):
    pd.set_option('display.max_rows', len(x))
    print(x)
    pd.reset_option('display.max_rows')


print_full(df)

前 10 名的结果

         x    y       z
0      301  301      10
1      300  301      16
2      300  300       6
3      299  301      30
4      299  300      20
5      299  299      14
6      298  301      40
7      298  300      30
8      298  299      24
9      298  298      10
10     297  301      48

Answer 1

.plot_surface() 将 2D arrays 作为输入，而不是 1D DataFrame 列。 here 已经很好地解释了这一点，下面的代码说明了如何使用 DataFrame 输入获得所需的格式。转载如下，稍作修改，如附加 cmets。

但是，或者，.plot_trisurf() 使用 1D 输入。我在代码中间添加了一个示例。

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from mpl_toolkits.mplot3d import Axes3D

## Matplotlib Sample Code using 2D arrays via meshgrid
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X ** 2 + Y ** 2)
Z = np.sin(R)
fig = plt.figure()
ax = Axes3D(fig)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
                       linewidth=0, antialiased=False)
ax.set_zlim(-1.01, 1.01)

ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))

fig.colorbar(surf, shrink=0.5, aspect=5)
plt.title('Original Code')
plt.show()

## DataFrame from 2D-arrays
x = X.reshape(1600)
y = Y.reshape(1600)
z = Z.reshape(1600)
df = pd.DataFrame({'x': x, 'y': y, 'z': z}, index=range(len(x)))

# Plot using `.trisurf()`:

ax.plot_trisurf(df.x, df.y, df.z, cmap=cm.jet, linewidth=0.2)
plt.show()

# 2D-arrays from DataFrame
x1 = np.linspace(df['x'].min(), df['x'].max(), len(df['x'].unique()))
y1 = np.linspace(df['y'].min(), df['y'].max(), len(df['y'].unique()))

"""
x, y via meshgrid for vectorized evaluation of
2 scalar/vector fields over 2-D grids, given
one-dimensional coordinate arrays x1, x2,..., xn.
"""

x2, y2 = np.meshgrid(x1, y1)

# Interpolate unstructured D-dimensional data.
z2 = griddata((df['x'], df['y']), df['z'], (x2, y2), method='cubic')

# Ready to plot
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(x2, y2, z2, rstride=1, cstride=1, cmap=cm.coolwarm,
                       linewidth=0, antialiased=False)
ax.set_zlim(-1.01, 1.01)

ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))

fig.colorbar(surf, shrink=0.5, aspect=5)
plt.title('Meshgrid Created from 3 1D Arrays')

plt.show()

Answer 2

您可以以公式化的方式将DataFrame 转换为numpy 以将其渲染为表面。

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


def plottable_3d_info(df: pd.DataFrame):
    """
    Transform Pandas data into a format that's compatible with
    Matplotlib's surface and wireframe plotting.
    """
    index = df.index
    columns = df.columns

    x, y = np.meshgrid(np.arange(len(columns)), np.arange(len(index)))
    z = np.array([[df[c][i] for c in columns] for i in index])
    
    xticks = dict(ticks=np.arange(len(columns)), labels=columns)
    yticks = dict(ticks=np.arange(len(index)), labels=index)
    
    return x, y, z, xticks, yticks


### Compose your data.
the_data = pd.DataFrame(
    data=[
        [11.0, 1.20, 1.20, 0.90],
        [11.43, 1.31, 1.32, 0.95],
        [12.01, 1.45, 1.47, 1.11],
    ],
    index=['triangle', 'square', 'hexagon'],
    columns=['sodium', 'nylon 11', 'nylon 12', 'abs'],
)

### Transform to Matplotlib friendly format.
x, y, z, xticks, yticks = plottable_3d_info(the_data)

### Set up axes and put data on the surface.
axes = plt.figure().gca(projection='3d')
axes.plot_surface(x, y, z)

### Customize labels and ticks (only really necessary with
### non-numeric axes).
axes.set_xlabel('material')
axes.set_ylabel('shape')
axes.set_zlabel('USD per item')
axes.set_zlim3d(bottom=0)
plt.xticks(**xticks)
plt.yticks(**yticks)

plt.show()