Python 使用str（df）后如何返回数据帧？
我想我在保存一个包含一堆熊猫数据帧的熊猫系列时把事情搞砸了。事实证明，每个数据帧都被保存，就好像我对它们调用了Python 使用str（df）后如何返回数据帧？,python,pandas,dataframe,Python,Pandas,Dataframe,我想我在保存一个包含一堆熊猫数据帧的熊猫系列时把事情搞砸了。事实证明，每个数据帧都被保存，就好像我对它们调用了df.to\u string（） 根据我到目前为止的观察，我的字符串在某些地方有额外的间距，当数据帧有太多的列显示在同一行上时，也有额外的\ 以下是“更合适的数据帧： df = pd.DataFrame(columns=["really long name that goes on for a while", "another really long string", "c"]*6,
df.to\u string（）\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])
# 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 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 '
到_csv到_string到_string到_csv旧答案
在一个非常酷的扭曲中，这个答案也将帮助您读取StAdfExcel上数据粘贴输出的一般粘贴格式。考虑到我们可以从这样的字符串中读取<代码> df>代码>：
data = """ 0 20 30 40 50
1 5 NaN 3 5 NaN
2 2 3 4 NaN 4
3 6 1 3 1 NaN"""
import pandas as pd
from io import StringIO
data = StringIO(data)
df = pd.read_csv(data, sep="\\s+")
的输出读取到\u stringpd.read_csv(StringIO(df.to_string()), sep="\\s+")
df到_csv到_string到_string到_csv旧答案
在一个非常酷的扭曲中，这个答案也将帮助您读取StAdfExcel上数据粘贴输出的一般粘贴格式。考虑到我们可以从这样的字符串中读取<代码> df>代码>：
data = """ 0 20 30 40 50
1 5 NaN 3 5 NaN
2 2 3 4 NaN 4
3 6 1 3 1 NaN"""
import pandas as pd
from io import StringIO
data = StringIO(data)
df = pd.read_csv(data, sep="\\s+")
的输出读取到\u stringpd.read_csv(StringIO(df.to_string()), sep="\\s+")
结果是相同的。试试这个。更新后包含自动计算行数的逻辑。基本上我提取了原始数据帧索引（行数）的最大值，它位于大字符串内 如果我们使用您给出的示例从转换为字符串的数据帧开始：
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)
我的代码执行一些循环，因此，如果原始数据帧有数十万行，这种方法可能需要一段时间。尝试此方法。更新为包含自动计算行数的逻辑。基本上，我提取原始数据帧索引（行数）的最大值，它位于大字符串中 如果我们使用您给出的示例从转换为字符串的数据帧开始：
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)
我的代码执行一些循环，因此，如果原始数据帧有数十万行，这种方法可能需要一段时间。因此，我不确定这个问题对任何人有多大帮助，但我编写了一个函数（和一个帮助器），尝试将错误存储为pd.Series中嵌套的数据帧的数据带回 以下是功能：
def insertNan(substring):
rows = substring.split('\n')
headers = re.sub(" \s+", " ", rows[0].replace("\\","").strip()).split(" ")
# The [2] below is a placeholder for the index. (Look in str(df), may appear like "\\\n1")
# Notice that if your tables get past 100 rows, 2 needs to be 3, or be determined otherwise.
boundaries = [0] + [2] + [rows[0].find(header)+len(header) for header in headers]
values = []
for i, row in enumerate(rows):
values.append(row)
# First row is just column headers. If no headers then don't use these functions
if i==0:
continue
for j, bound in enumerate(boundaries[:-1]):
value = row[bound:boundaries[j+1]].strip()
if not value:
newstring = list(values[i])
newstring[boundaries[j+1]-3:boundaries[j+1]] = "NaN"
values[i] = ''.join(newstring)
if " " in value:
start = values[i].find(value)
newvalue = re.sub(" \s+", " ", value)
values[i] = values[i][:start]+newvalue+values[i][start+len(value)]
return '\n'.join(values)
def from_string(string):
string = string.replace("\\", "")
chunks = [insertNan(i).strip() for i in string.split("\n\n")]
frames = [pd.read_csv(StringIO(chunk), sep=" \\s+", engine='python')
for chunk in chunks]
return pd.concat(frames, axis=1)
# Read file and loop through series. These two lines might have to be modified.
corrupted_results = pd.read_excel(fileio, squeeze=True)
results = [from_string(result for result in corrupted_results.values
“…”.groupby特别感谢ColdSpeed、Charles Landau和JamesD，感谢他们的时间、帮助和善意！因此，我不确定这个问题对任何人有多大帮助，但我编写了一个函数（和一个助手）来尝试恢复我错误地存储在pd系列中的数据帧中的数据 以下是功能：
def insertNan(substring):
rows = substring.split('\n')
headers = re.sub(" \s+", " ", rows[0].replace("\\","").strip()).split(" ")
# The [2] below is a placeholder for the index. (Look in str(df), may appear like "\\\n1")
# Notice that if your tables get past 100 rows, 2 needs to be 3, or be determined otherwise.
boundaries = [0] + [2] + [rows[0].find(header)+len(header) for header in headers]
values = []
for i, row in enumerate(rows):
values.append(row)
# First row is just column headers. If no headers then don't use these functions
if i==0:
continue
for j, bound in enumerate(boundaries[:-1]):
value = row[bound:boundaries[j+1]].strip()
if not value:
newstring = list(values[i])
newstring[boundaries[j+1]-3:boundaries[j+1]] = "NaN"
values[i] = ''.join(newstring)
if " " in value:
start = values[i].find(value)
newvalue = re.sub(" \s+", " ", value)
values[i] = values[i][:start]+newvalue+values[i][start+len(value)]
return '\n'.join(values)
def from_string(string):
string = string.replace("\\", "")
chunks = [insertNan(i).strip() for i in string.split("\n\n")]
frames = [pd.read_csv(StringIO(chunk), sep=" \\s+", engine='python')
for chunk in chunks]
return pd.concat(frames, axis=1)
# Read file and loop through series. These two lines might have to be modified.
corrupted_results = pd.read_excel(fileio, squeeze=True)
results = [from_string(result for result in corrupted_results.values
“…”.groupby特别感谢ColdSpeed、Charles Landau和JamesD，感谢他们的时间、帮助和善良！@ColdSpeed事实上，当我尝试
读取我的字符串时，我遇到了一个解析错误。更新了问题，Thanks@coldspeed事实上，当我尝试对我拥有的字符串进行读取\u csv
时，我遇到了一个ParseError。更新了问题，谢谢。这项工作我完成了吗有包含空格的字符串吗？我当前的问题是我有一堆字符串（不是数据帧），我希望恢复数据帧。str（df）
只是我的字符串的一个示例。很抱歉confuction@AsheKetchum字符串是如何分隔的？如果可以在数据中找到未替换的分隔符，那么期望使用ab似乎有点不合理