[英]Data science python error- ValueError: x and y must have same first dimension
I am working on doing some statistical analysis in python however I am new to the field and have been stuck on an error. 我正在使用python做一些统计分析,但是我是该领域的新手,并且一直陷入错误。
For background, I am computing a set of sample_means for each sample size, 200 times. 对于背景,我为每个样本大小计算了200个sample_means集合。 I am then calculating the mean and standard deviation for each sample size, which are then stored in arrays. 然后,我将计算每个样本量的平均值和标准差,然后将其存储在数组中。 This is my code: 这是我的代码:
in[] =
sample_sizes = np.arange(1,1001,1)
number_of_samples = 200
mean_of_sample_means = []
std_dev_of_sample_means = []
for x in range (number_of_samples):
mean_of_sample_means.append(np.mean(sample_sizes))
std_dev_of_sample_means.append(np.std(sample_sizes))
in[] = # mean and std of 200 means from 200 replications, each of size 10
trials[0], mean_of_sample_means[0], std_dev_of_sample_means[0]
out[] = (10, 500.5, 288.67499025720952)
I am now trying to plot the data with the following input: 我现在尝试使用以下输入来绘制数据:
plt.plot(sample_sizes, mean_of_sample_means);
plt.ylim([0.480,0.520]);
plt.xlabel("sample sizes")
plt.ylabel("mean probability of heads")
plt.title("Mean of sample means over 200 replications");
However when I do, I get thrown the following error: 但是,当我这样做时,会抛出以下错误:
242 if x.shape[0] != y.shape[0]:
243 raise ValueError("x and y must have same first dimension, but "
--> 244 "have shapes {} and {}".format(x.shape, y.shape))
245 if x.ndim > 2 or y.ndim > 2:
246 raise ValueError("x and y can be no greater than 2-D, but have "
ValueError: x and y must have same first dimension, but have shapes (1000,) and (200,)
Any thoughts on where I am going wrong? 对我要去哪里错有任何想法吗? I feel like its probably something obvious that im not seeing as I am new to this. 我觉得这很明显,因为我对此并不陌生。 Any help would be appreciated!! 任何帮助,将不胜感激!!
This line: 这行:
plt.plot(sample_sizes, mean_of_sample_means)
need both arguments to have the same shape (because you need x and y for your plot on some cartesian coordinate-system; to be more precise: the same size in regards to the first dimension as seen in the error: if x.shape[0] != y.shape[0]
). 需要两个参数都具有相同的形状(因为在某些笛卡尔坐标系上绘图需要x和y;更准确地说:关于第一维的大小与错误中所示的相同): if x.shape[0] != y.shape[0]
)。
But: 但:
sample_sizes = np.arange(1,1001,1) # 1000 !
and: 和:
number_of_samples = 200
mean_of_sample_means = []
for x in range (number_of_samples):
mean_of_sample_means.append(np.mean(sample_sizes)) # mean by default over flattened-structure
# so i assume: 1 element per iteration
# 200 !
And as expected, the error gives exactly this info: ValueError: x and y must have same first dimension, but have shapes (1000,) and (200,)
并如预期的那样,该错误完全提供了以下信息: ValueError: x and y must have same first dimension, but have shapes (1000,) and (200,)
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