我想我在嘗試保存包含一堆 Pandas 數(shù)據(jù)幀的 Pandas 系列時(shí)搞砸了。事實(shí)證明，每個(gè) DataFrame 都像我調(diào)用df.to_string()它們一樣被保存。從我目前的觀察來(lái)看，我的字符串在某些地方有額外的間距，\當(dāng) DataFrame 有太多列無(wú)法在同一行上顯示時(shí)，我的字符串也會(huì)有額外的間距。這是一個(gè)“更合適的數(shù)據(jù)幀：df = pd.DataFrame(columns=["really long name that goes on for a while", "another really long string", "c"]*6, data=[["some really long data",2,3]*6,[4,5,6]*6,[7,8,9]*6])我擁有并希望變成 DataFrame 的字符串如下所示：# str(df)' really long name that goes on for a while another really long string c \\\n0 some really long data 2 3 \n1 4 5 6 \n2 7 8 9 \n\n really long name that goes on for a while another really long string c \\\n0 some really long data 2 3 \n1 4 5 6 \n2 7 8 9 \n\n really long name that goes on for a while another really long string c \\\n0 some really long data 2 3 \n1 4 5 6 \n2 7 8 9 \n\n really long name that goes on for a while another really long string c \\\n0 some really long data 2 3 \n1 4 5 6 \n2 7 8 9 \n\n really long name that goes on for a while another really long string c \\\n0 some really long data 2 3 \n1 我如何將這樣的字符串還原回 DataFrame？謝謝
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守著一只汪
TA貢獻(xiàn)1872條經(jīng)驗(yàn) 獲得超4個(gè)贊
嘗試這個(gè)。更新為包含自動(dòng)計(jì)算行數(shù)的邏輯?；旧衔姨崛≡紨?shù)據(jù)幀索引（行號(hào)）的最大值，它在大字符串內(nèi)。
如果我們從使用您提供的示例轉(zhuǎn)換為字符串的數(shù)據(jù)幀開始：
df = pd.DataFrame(columns=["really long name that goes on for a while", "another really long string", "c"]*6,
data=[["some really long data",2,3]*6,[4,5,6]*6,[7,8,9]*6])
string = str(df)
首先，讓我們提取列名：
import re
import numpy as np
lst = re.split('\n', string)
num_rows = int(lst[lst.index('') -1][0]) + 1
col_names = []
lst = [i for i in lst if i != '']
for i in range(0,len(lst), num_rows + 1):
col_names.append(lst[i])
new_col_names = []
for i in col_names:
new_col_names.append(re.split(' ', i))
final_col_names = []
for i in new_col_names:
final_col_names += i
final_col_names = [i for i in final_col_names if i != '']
final_col_names = [i for i in final_col_names if i != '\\']
然后，讓我們獲取數(shù)據(jù)：
for i in col_names:
lst.remove(i)
new_lst = [re.split(r'\s{2,}', i) for i in lst]
new_lst = [i[1:-1] for i in new_lst]
newer_lst = []
for i in range(num_rows):
sub_lst = []
for j in range(i,len(final_col_names), num_rows):
sub_lst += new_lst[j]
newer_lst.append(sub_lst)
reshaped = np.reshape(newer_lst, (num_rows,len(final_col_names)))
最后，我們可以使用數(shù)據(jù)和列名創(chuàng)建重建的數(shù)據(jù)框：
fixed_df = pd.DataFrame(data=reshaped, columns = final_col_names)
我的代碼執(zhí)行了一些循環(huán)，因此如果您的原始數(shù)據(jù)幀有數(shù)十萬(wàn)行，這種方法可能需要一段時(shí)間。
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