Python 如何从数据框中显示绘图/3d 绘图？
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How to surface plot/3d plot from dataframe?
提问by mohan
I am new to pandasand matplotlib. Couldn't able to get exact reference to plot my DataFramewhose schema is as follows
我是pandas和 的新手matplotlib。无法获得准确的参考来绘制我DataFrame的架构如下
schema = StructType([
StructField("x", IntegerType(), True),
StructField("y", IntegerType(), True),
StructField("z", IntegerType(), True)])
Like to plot 3d graph w.r.t. x, y and z
喜欢绘制 x、y 和 z 的 3d 图形
Here is the sample code i used
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'])
I am getting a empty graph plot. definitely missing something. Any pointers?
我得到一个空图。肯定少了点什么。任何指针？
-Thx
-谢谢
Request-1: Print df
请求 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)
Result of top 10
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
回答by Stefan
.plot_surface()takes 2Darraysas inputs, not 1DDataFramecolumns. This has been explained quite well here, along with the below code that illustrates how one could arrive at the required format using DataFrameinput. Reproduced below with minor modifications like additional comments.
.plot_surface()将其2Darrays作为输入，而不是1DDataFrame列。这已经很好地解释了here，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，如附加评论。
Alternatively, however, there is .plot_trisurf()which uses 1Dinputs. I've added an example in the middle of the code.
然而，另一种方法是.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()
