通常，當(dāng)您想要將一組數(shù)據(jù)轉(zhuǎn)換為數(shù)據(jù)框時，您需要為每一列創(chuàng)建一個列表，從這些列表創(chuàng)建一個字典，然后從該字典創(chuàng)建一個數(shù)據(jù)框。我想要創(chuàng)建的數(shù)據(jù)框有 75 列，全部具有相同的行數(shù)。逐一定義列表是行不通的。相反，我決定創(chuàng)建一個列表，并迭代地將每行的某個塊放入數(shù)據(jù)幀中。這里我將舉一個例子，將列表轉(zhuǎn)換為數(shù)據(jù)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這是我的測試代碼：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.當(dāng)我運(yùn)行此代碼時，我收到此錯誤：ValueError: cannot reindex from a duplicate axis當(dāng)我添加ignore_index = True這樣的東西時for i in range(len(lst)):    df.iloc[:, i] = df.iloc[:, i]\    .append(pd.Series(lst[2 * i:2 * i + 2]), ignore_index = True)我收到此錯誤：IndexError: single positional indexer is out-of-bounds運(yùn)行代碼后，我檢查了結(jié)果df。無論我是否忽略索引，輸出都是相同的。In: dfOut:   a   b   c   d   e0  0 NaN NaN NaN NaN1  1 NaN NaN NaN NaN第一個循環(huán)似乎運(yùn)行良好，但在嘗試填充第二列時發(fā)生錯誤。有人知道如何讓它發(fā)揮作用嗎？謝謝。