Python Matplotlib：ValueError：x 和 y 必须具有相同的第一维
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Matplotlib: ValueError: x and y must have same first dimension
提问by Sam
I am trying to fit a linear line of best fit to my matplotlib graph. I keep getting the error that x and y do not have the same first dimension. But they both have lengths of 15. What am I doing wrong?
我正在尝试将最适合我的 matplotlib 图的线性线拟合。我不断收到 x 和 y 没有相同第一维的错误。但它们的长度都是 15。我做错了什么？
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
x = [0.46,0.59,0.68,0.99,0.39,0.31,1.09,0.77,0.72,0.49,0.55,0.62,0.58,0.88,0.78]
y = [0.315,0.383,0.452,0.650,0.279,0.215,0.727,0.512,0.478,0.335,0.365,0.424,0.390,0.585,0.511]
xerr = [0.01]*15
yerr = [0.001]*15
plt.rc('font', family='serif', size=13)
m, b = np.polyfit(x, y, 1)
plt.plot(x,y,'s',color='#0066FF')
plt.plot(x, m*x + b, 'r-') #BREAKS ON THIS LINE
plt.errorbar(x,y,xerr=xerr,yerr=0,linestyle="None",color='black')
plt.xlabel('$\Delta t$ $(s)$',fontsize=20)
plt.ylabel('$\Delta p$ $(hPa)$',fontsize=20)
plt.autoscale(enable=True, axis=u'both', tight=False)
plt.grid(False)
plt.xlim(0.2,1.2)
plt.ylim(0,0.8)
plt.show()
采纳答案by Bas Swinckels
You should make xand ynumpy arrays, not lists:
您应该制作x和ynumpy 数组，而不是列表：
x = np.array([0.46,0.59,0.68,0.99,0.39,0.31,1.09,
0.77,0.72,0.49,0.55,0.62,0.58,0.88,0.78])
y = np.array([0.315,0.383,0.452,0.650,0.279,0.215,0.727,0.512,
0.478,0.335,0.365,0.424,0.390,0.585,0.511])
With this change, it produces the expect plot. If they are lists, m * xwill not produce the result you expect, but an empty list. Note that mis anumpy.float64scalar, not a standard Python float.
通过此更改，它会生成期望图。如果它们是列表，m * x则不会产生您期望的结果，而是一个空列表。请注意，这m是一个numpy.float64标量，而不是标准的 Python float。
I actually consider this a bit dubious behavior of Numpy. In normal Python, multiplying a list with an integer just repeats the list:
我实际上认为这是 Numpy 的一个有点可疑的行为。在普通 Python 中，将列表与整数相乘只是重复列表：
In [42]: 2 * [1, 2, 3]
Out[42]: [1, 2, 3, 1, 2, 3]
while multiplying a list with a float gives an error (as I think it should):
将列表与浮点数相乘会产生错误（我认为应该如此）：
In [43]: 1.5 * [1, 2, 3]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-43-d710bb467cdd> in <module>()
----> 1 1.5 * [1, 2, 3]
TypeError: can't multiply sequence by non-int of type 'float'
The weird thing is that multiplying a Python list with a Numpy scalar apparently works:
奇怪的是，将 Python 列表与 Numpy 标量相乘显然有效：
In [45]: np.float64(0.5) * [1, 2, 3]
Out[45]: []
In [46]: np.float64(1.5) * [1, 2, 3]
Out[46]: [1, 2, 3]
In [47]: np.float64(2.5) * [1, 2, 3]
Out[47]: [1, 2, 3, 1, 2, 3]
So it seems that the float gets truncated to an int, after which you get the standard Python behavior of repeating the list, which is quite unexpected behavior. The best thing would have been to raise an error (so that you would have spotted the problem yourself instead of having to ask your question on Stackoverflow) or to just show the expected element-wise multiplication (in which your code would have just worked). Interestingly, addition between a list and a Numpy scalar does work:
因此，浮点数似乎被截断为整数，之后您将获得重复列表的标准 Python 行为，这是非常出乎意料的行为。最好的办法是提出一个错误（这样你就可以自己发现问题，而不必在 Stackoverflow 上提出你的问题）或者只显示预期的元素乘法（你的代码刚刚工作） . 有趣的是，列表和 Numpy 标量之间的加法确实有效：
In [69]: np.float64(0.123) + [1, 2, 3]
Out[69]: array([ 1.123, 2.123, 3.123])
回答by ThePredator
Changing your lists to numpyarrays will do the job!!
将您的列表更改为numpy数组即可！
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
x = np.array([0.46,0.59,0.68,0.99,0.39,0.31,1.09,0.77,0.72,0.49,0.55,0.62,0.58,0.88,0.78]) # x is a numpy array now
y = np.array([0.315,0.383,0.452,0.650,0.279,0.215,0.727,0.512,0.478,0.335,0.365,0.424,0.390,0.585,0.511]) # y is a numpy array now
xerr = [0.01]*15
yerr = [0.001]*15
plt.rc('font', family='serif', size=13)
m, b = np.polyfit(x, y, 1)
plt.plot(x,y,'s',color='#0066FF')
plt.plot(x, m*x + b, 'r-') #BREAKS ON THIS LINE
plt.errorbar(x,y,xerr=xerr,yerr=0,linestyle="None",color='black')
plt.xlabel('$\Delta t$ $(s)$',fontsize=20)
plt.ylabel('$\Delta p$ $(hPa)$',fontsize=20)
plt.autoscale(enable=True, axis=u'both', tight=False)
plt.grid(False)
plt.xlim(0.2,1.2)
plt.ylim(0,0.8)
plt.show()
