将没有元组的列表转换为数据框

Question

通常，当您想要将一组数据转换为数据框时，您需要为每一列创建一个列表，从这些列表创建一个字典，然后从该字典创建一个数据框。我想要创建的数据框有 75 列，全部具有相同的行数。逐一定义列表是行不通的。相反，我决定创建一个列表，并迭代地将每行的某个块放入数据帧中。这里我将举一个例子，将列表转换为数据框：lst = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]# Example listdf =    a b c d e0  0 2 4 6 81  1 3 5 7 9# Result I want from the example list这是我的测试代码：import pandas as pdimport numpy as npdict = {'a':[], 'b':[], 'c':[], 'd':[], 'e':[]}df = pd.DataFrame(dict)# Here is my test data frame, it contains 5 columns and no rows.lst = np.arange(10).tolist()# This is my test list, it looks like this lst = [0, 2, …, 9]for i in range(len(lst)):    df.iloc[:, i] = df.iloc[:, i]\    .append(pd.Series(lst[2 * i:2 * i + 2]))# This code is supposed to put two entries per column for the whole data frame.# For the first column, i = 0, so [2 * (0):2 * (0) + 2] = [0:2]# df.iloc[:, 0] = lst[0:2], so df.iloc[:, 0] = [0, 1]# Second column i = 1, so [2 * (1):2 * (1) + 2] = [2:4]# df.iloc[:, 1] = lst[2:4], so df.iloc[:, 1] = [2, 3]# This is how the code was supposed to allocate lst to df.# However it outputs an error.当我运行此代码时，我收到此错误：ValueError: cannot reindex from a duplicate axis当我添加ignore_index = True这样的东西时for i in range(len(lst)):    df.iloc[:, i] = df.iloc[:, i]\    .append(pd.Series(lst[2 * i:2 * i + 2]), ignore_index = True)我收到此错误：IndexError: single positional indexer is out-of-bounds运行代码后，我检查了结果df。无论我是否忽略索引，输出都是相同的。In: dfOut:   a   b   c   d   e0  0 NaN NaN NaN NaN1  1 NaN NaN NaN NaN第一个循环似乎运行良好，但在尝试填充第二列时发生错误。有人知道如何让它发挥作用吗？谢谢。

Answer 1

lst = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

alst = np.array(lst)

df = pd.DataFrame(alst.reshape(2,-1, order='F'), columns = [*'abcde'])

print(df)

输出：

   a  b  c  d  e

0  0  2  4  6  8

1  1  3  5  7  9