将 numpy 数组转换为 Pandas 数据框 [英] Convert numpy array to pandas dataframe

问题描述

我有一个大小为 31x36 的 numpy 数组，我想转换为 Pandas 数据帧以进行处理.我正在尝试使用以下代码对其进行转换:

I have a numpy array of size 31x36 and i want to transform into pandas dataframe in order to process it. I am trying to convert it using the following code:

pd.DataFrame(data=matrix,
          index=np.array(range(1, 31)),
          columns=np.array(range(1, 36)))

但是，我收到以下错误:

However, I am receiving the following error:

ValueError: 传递值的形状是 (36, 31)，索引意味着 (35, 30)

ValueError: Shape of passed values is (36, 31), indices imply (35, 30)

如何解决问题并正确转换?

How can I solve the issue and transform it properly?

推荐答案

至于你尝试失败的原因，范围相差 1

As to why what you tried failed, the ranges are off by 1

pd.DataFrame(data=matrix,
          index=np.array(range(1, 32)),
          columns=np.array(range(1, 37)))

因为最后一个值不包括在范围内

As the last value isn't included in the range

实际上看看你在做什么，你本来可以做的:

Actually looking at what you're doing you could've just done:

pd.DataFrame(data=matrix,
          index=np.arange(1, 32)),
          columns=np.arange(1, 37)))

或者在纯 pandas 中:

pd.DataFrame(data=matrix,
          index=pd.RangeIndex(range(1, 32)),
          columns=pd.RangeIndex(range(1, 37)))

此外，如果您不指定索引和列参数，则会自动生成索引和列，该索引和列将从 0 开始.不清楚为什么需要它们从 1

Also if you don't specify the index and column params, an auto-generated index and columns is made, which will start from 0. Unclear why you need them to start from 1

你也可以没有传递索引和列参数，只是在构造后修改它们:

You could also have not passed the index and column params and just modified them after construction:

In[9]:
df = pd.DataFrame(adaption)
df.columns = df.columns+1
df.index = df.index + 1
df

Out[9]: 
          1         2         3         4         5         6
1 -2.219072 -1.637188  0.497752 -1.486244  1.702908  0.331697
2 -0.586996  0.040052  1.021568  0.783492 -1.263685 -0.192921
3 -0.605922  0.856685 -0.592779 -0.584826  1.196066  0.724332
4 -0.226160 -0.734373 -0.849138  0.776883 -0.160852  0.403073
5 -0.081573 -1.805827 -0.755215 -0.324553 -0.150827 -0.102148

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