标签归档:Python

在Python中减去两次

问题:在Python中减去两次

我有两个datetime.time值,exit并且enter想做类似的事情:

duration = exit - enter

但是,我收到此错误:

TypeError:-:“ datetime.time”和“ datetime.time”的不受支持的操作数类型

如何正确执行此操作?一种可能的解决方案是将time变量转换为datetime变量,然后进行推导,但是我敢肯定你们必须有一种更好,更清洁的方法。

I have two datetime.time values, exit and enter and I want to do something like:

duration = exit - enter

However, I get this error:

TypeError: unsupported operand type(s) for -: ‘datetime.time’ and ‘datetime.time

How do I do this correctly? One possible solution is converting the time variables to datetime variables and then subtruct, but I’m sure you guys must have a better and cleaner way.


回答 0

试试这个:

from datetime import datetime, date

datetime.combine(date.today(), exit) - datetime.combine(date.today(), enter)

combine 建立一个可以减去的日期时间。

Try this:

from datetime import datetime, date

datetime.combine(date.today(), exit) - datetime.combine(date.today(), enter)

combine builds a datetime, that can be subtracted.


回答 1

用:

from datetime import datetime, date

duration = datetime.combine(date.min, end) - datetime.combine(date.min, beginning)

使用起来date.min更加简洁,甚至可以在午夜使用。

date.today()如果第一个调用发生在23:59:59,而下一个调用发生在00:00:00,则可能不是这样,否则可能返回意外结果。

Use:

from datetime import datetime, date

duration = datetime.combine(date.min, end) - datetime.combine(date.min, beginning)

Using date.min is a bit more concise and works even at midnight.

This might not be the case with date.today() that might return unexpected results if the first call happens at 23:59:59 and the next one at 00:00:00.


回答 2

而不是使用时间尝试timedelta:

from datetime import timedelta

t1 = timedelta(hours=7, minutes=36)
t2 = timedelta(hours=11, minutes=32)
t3 = timedelta(hours=13, minutes=7)
t4 = timedelta(hours=21, minutes=0)

arrival = t2 - t1
lunch = (t3 - t2 - timedelta(hours=1))
departure = t4 - t3

print(arrival, lunch, departure)

instead of using time try timedelta:

from datetime import timedelta

t1 = timedelta(hours=7, minutes=36)
t2 = timedelta(hours=11, minutes=32)
t3 = timedelta(hours=13, minutes=7)
t4 = timedelta(hours=21, minutes=0)

arrival = t2 - t1
lunch = (t3 - t2 - timedelta(hours=1))
departure = t4 - t3

print(arrival, lunch, departure)

回答 3

datetime.time不支持这一点,因为以这种方式减去时间几乎没有意义。datetime.datetime如果要执行此操作,请使用完整的。

datetime.time does not support this, because it’s nigh meaningless to subtract times in this manner. Use a full datetime.datetime if you want to do this.


回答 4

您有两个datetime.time对象,因此您只需使用datetime.timedetla创建两个timedelta,然后像现在一样使用“-”操作数进行减法。以下是不使用datetime减去两次的示例方法。

enter = datetime.time(hour=1)  # Example enter time
exit = datetime.time(hour=2)  # Example start time
enter_delta = datetime.timedelta(hours=enter.hour, minutes=enter.minute, seconds=enter.second)
exit_delta = datetime.timedelta(hours=exit.hour, minutes=exit.minute, seconds=exit.second)
difference_delta = exit_delta - enter_delta

different_delta是您的差异,您可以出于自己的原因使用它。

You have two datetime.time objects so for that you just create two timedelta using datetime.timedetla and then substract as you do right now using “-” operand. Following is the example way to substract two times without using datetime.

enter = datetime.time(hour=1)  # Example enter time
exit = datetime.time(hour=2)  # Example start time
enter_delta = datetime.timedelta(hours=enter.hour, minutes=enter.minute, seconds=enter.second)
exit_delta = datetime.timedelta(hours=exit.hour, minutes=exit.minute, seconds=exit.second)
difference_delta = exit_delta - enter_delta

difference_delta is your difference which you can use for your reasons.


回答 5

python timedelta库应该可以满足您的需求。timedelta当减去两个datetime实例时,将返回A。

import datetime
dt_started = datetime.datetime.utcnow()

# do some stuff

dt_ended = datetime.datetime.utcnow()
print((dt_ended - dt_started).total_seconds())

The python timedelta library should do what you need. A timedelta is returned when you subtract two datetime instances.

import datetime
dt_started = datetime.datetime.utcnow()

# do some stuff

dt_ended = datetime.datetime.utcnow()
print((dt_ended - dt_started).total_seconds())

回答 6

import datetime

def diff_times_in_seconds(t1, t2):
    # caveat emptor - assumes t1 & t2 are python times, on the same day and t2 is after t1
    h1, m1, s1 = t1.hour, t1.minute, t1.second
    h2, m2, s2 = t2.hour, t2.minute, t2.second
    t1_secs = s1 + 60 * (m1 + 60*h1)
    t2_secs = s2 + 60 * (m2 + 60*h2)
    return( t2_secs - t1_secs)

# using it
diff_times_in_seconds( datetime.datetime.strptime( "13:23:34", '%H:%M:%S').time(),datetime.datetime.strptime( "14:02:39", '%H:%M:%S').time())
import datetime

def diff_times_in_seconds(t1, t2):
    # caveat emptor - assumes t1 & t2 are python times, on the same day and t2 is after t1
    h1, m1, s1 = t1.hour, t1.minute, t1.second
    h2, m2, s2 = t2.hour, t2.minute, t2.second
    t1_secs = s1 + 60 * (m1 + 60*h1)
    t2_secs = s2 + 60 * (m2 + 60*h2)
    return( t2_secs - t1_secs)

# using it
diff_times_in_seconds( datetime.datetime.strptime( "13:23:34", '%H:%M:%S').time(),datetime.datetime.strptime( "14:02:39", '%H:%M:%S').time())

回答 7

datetime.time 无法做到-但是您可以使用 datetime.datetime.now()

start = datetime.datetime.now()
sleep(10)
end = datetime.datetime.now()
duration = end - start

datetime.time can not do it – But you could use datetime.datetime.now()

start = datetime.datetime.now()
sleep(10)
end = datetime.datetime.now()
duration = end - start

回答 8

当您遇到类似的情况时,我最终使用了名为arrow的外部库。

看起来是这样的:

>>> import arrow
>>> enter = arrow.get('12:30:45', 'HH:mm:ss')
>>> exit = arrow.now()
>>> duration = exit - enter
>>> duration
datetime.timedelta(736225, 14377, 757451)

I had similar situation as you and I ended up with using external library called arrow.

Here is what it looks like:

>>> import arrow
>>> enter = arrow.get('12:30:45', 'HH:mm:ss')
>>> exit = arrow.now()
>>> duration = exit - enter
>>> duration
datetime.timedelta(736225, 14377, 757451)

使用Python读取/解析Excel(xls)文件

问题:使用Python读取/解析Excel(xls)文件

用Python读取Excel(XLS)文件(而不是CSV文件)的最佳方法是什么。

有默认情况下Python支持的内置程序包吗?

What is the best way to read Excel (XLS) files with Python (not CSV files).

Is there a built-in package which is supported by default in Python to do this task?


回答 0

我强烈建议使用xlrd读取.xls文件。

voyager提到了COM自动化的使用。几年前自己做过此事,请注意做这是真正的PITA。需要警告的数量巨大,并且缺少文档并且令人讨厌。我遇到了许多奇怪的错误和陷阱,其中一些花费了许多小时才能弄清楚。

更新:对于较新的.xlsx文件,推荐用于读写的库似乎是openpyxl(感谢IkarPohorský)。

I highly recommend xlrd for reading .xls files.

voyager mentioned the use of COM automation. Having done this myself a few years ago, be warned that doing this is a real PITA. The number of caveats is huge and the documentation is lacking and annoying. I ran into many weird bugs and gotchas, some of which took many hours to figure out.

UPDATE: For newer .xlsx files, the recommended library for reading and writing appears to be openpyxl (thanks, Ikar Pohorský).


回答 1

使用熊猫:

import pandas as pd

xls = pd.ExcelFile("yourfilename.xls")

sheetX = xls.parse(2) #2 is the sheet number

var1 = sheetX['ColumnName']

print(var1[1]) #1 is the row number...

Using pandas:

import pandas as pd

xls = pd.ExcelFile(r"yourfilename.xls") #use r before absolute file path 

sheetX = xls.parse(2) #2 is the sheet number+1 thus if the file has only 1 sheet write 0 in paranthesis

var1 = sheetX['ColumnName']

print(var1[1]) #1 is the row number...

回答 2

您可以选择其中任意一个http://www.python-excel.org/
我建议使用python xlrd库。

使用安装

pip install xlrd

导入使用

import xlrd

打开工作簿

workbook = xlrd.open_workbook('your_file_name.xlsx')

按名称打开工作表

worksheet = workbook.sheet_by_name('Name of the Sheet')

按索引打开工作表

worksheet = workbook.sheet_by_index(0)

读取单元格值

worksheet.cell(0, 0).value    

You can choose any one of them http://www.python-excel.org/
I would recommended python xlrd library.

install it using

pip install xlrd

import using

import xlrd

to open a workbook

workbook = xlrd.open_workbook('your_file_name.xlsx')

open sheet by name

worksheet = workbook.sheet_by_name('Name of the Sheet')

open sheet by index

worksheet = workbook.sheet_by_index(0)

read cell value

worksheet.cell(0, 0).value    

回答 3

我认为熊猫是最好的选择。已经有一个答案在这里使用与熊猫ExcelFile的功能,但它并没有为我正常工作。从这里我发现read_excel可以正常工作的函数:

import pandas as pd
dfs = pd.read_excel("your_file_name.xlsx", sheet_name="your_sheet_name")
print(dfs.head(10))

PS您需要为xlrd安装read_excel正常工作

更新21-03-2020:正如您可能在此处看到的那样,xlrd引擎存在问题,它将不推荐使用。该openpyxl是最好的替代品。因此,作为描述在这里,规范的语法应为:

dfs = pd.read_excel("your_file_name.xlsx", sheet_name="your_sheet_name", engine="openpyxl")

I think Pandas is the best way to go. There is already one answer here with Pandas using ExcelFile function, but it did not work properly for me. From here I found the read_excel function which works just fine:

import pandas as pd
dfs = pd.read_excel("your_file_name.xlsx", sheet_name="your_sheet_name")
print(dfs.head(10))

P.S. You need to have the xlrd installed for read_excel function to work

Update 21-03-2020: As you may see here, there are issues with the xlrd engine and it is going to be deprecated. The openpyxl is the best replacement. So as described here, the canonical syntax should be:

dfs = pd.read_excel("your_file_name.xlsx", sheet_name="your_sheet_name", engine="openpyxl")

回答 4

对于xlsx,我喜欢先前发布的解决方案https://web.archive.org/web/20180216070531//programming/4371163/reading-xlsx-files-using-python。我仅使用标准库中的模块。

def xlsx(fname):
    import zipfile
    from xml.etree.ElementTree import iterparse
    z = zipfile.ZipFile(fname)
    strings = [el.text for e, el in iterparse(z.open('xl/sharedStrings.xml')) if el.tag.endswith('}t')]
    rows = []
    row = {}
    value = ''
    for e, el in iterparse(z.open('xl/worksheets/sheet1.xml')):
        if el.tag.endswith('}v'):  # Example: <v>84</v>                            
            value = el.text
        if el.tag.endswith('}c'):  # Example: <c r="A3" t="s"><v>84</v></c>                                 
            if el.attrib.get('t') == 's':
                value = strings[int(value)]
            letter = el.attrib['r']  # Example: AZ22                         
            while letter[-1].isdigit():
                letter = letter[:-1]
            row[letter] = value
            value = ''
        if el.tag.endswith('}row'):
            rows.append(row)
            row = {}
    return rows

添加的改进包括按工作表名称获取内容,使用re获取列以及检查是否使用了共享字符串。

def xlsx(fname,sheet):
    import zipfile
    from xml.etree.ElementTree import iterparse
    import re
    z = zipfile.ZipFile(fname)
    if 'xl/sharedStrings.xml' in z.namelist():
        # Get shared strings
        strings = [element.text for event, element
                   in iterparse(z.open('xl/sharedStrings.xml')) 
                   if element.tag.endswith('}t')]
    sheetdict = { element.attrib['name']:element.attrib['sheetId'] for event,element in iterparse(z.open('xl/workbook.xml'))
                                      if element.tag.endswith('}sheet') }
    rows = []
    row = {}
    value = ''

    if sheet in sheets:
    sheetfile = 'xl/worksheets/sheet'+sheets[sheet]+'.xml'
    #print(sheet,sheetfile)
    for event, element in iterparse(z.open(sheetfile)):
        # get value or index to shared strings
        if element.tag.endswith('}v') or element.tag.endswith('}t'):
            value = element.text
        # If value is a shared string, use value as an index
        if element.tag.endswith('}c'):
            if element.attrib.get('t') == 's':
                value = strings[int(value)]
            # split the row/col information so that the row leter(s) can be separate
            letter = re.sub('\d','',element.attrib['r'])
            row[letter] = value
            value = ''
        if element.tag.endswith('}row'):
            rows.append(row)
            row = {}

    return rows

For xlsx I like the solution posted earlier as https://web.archive.org/web/20180216070531/https://stackoverflow.com/questions/4371163/reading-xlsx-files-using-python. I uses modules from the standard library only.

def xlsx(fname):
    import zipfile
    from xml.etree.ElementTree import iterparse
    z = zipfile.ZipFile(fname)
    strings = [el.text for e, el in iterparse(z.open('xl/sharedStrings.xml')) if el.tag.endswith('}t')]
    rows = []
    row = {}
    value = ''
    for e, el in iterparse(z.open('xl/worksheets/sheet1.xml')):
        if el.tag.endswith('}v'):  # Example: <v>84</v>                            
            value = el.text
        if el.tag.endswith('}c'):  # Example: <c r="A3" t="s"><v>84</v></c>                                 
            if el.attrib.get('t') == 's':
                value = strings[int(value)]
            letter = el.attrib['r']  # Example: AZ22                         
            while letter[-1].isdigit():
                letter = letter[:-1]
            row[letter] = value
            value = ''
        if el.tag.endswith('}row'):
            rows.append(row)
            row = {}
    return rows

Improvements added are fetching content by sheet name, using re to get the column and checking if sharedstrings are used.

def xlsx(fname,sheet):
    import zipfile
    from xml.etree.ElementTree import iterparse
    import re
    z = zipfile.ZipFile(fname)
    if 'xl/sharedStrings.xml' in z.namelist():
        # Get shared strings
        strings = [element.text for event, element
                   in iterparse(z.open('xl/sharedStrings.xml')) 
                   if element.tag.endswith('}t')]
    sheetdict = { element.attrib['name']:element.attrib['sheetId'] for event,element in iterparse(z.open('xl/workbook.xml'))
                                      if element.tag.endswith('}sheet') }
    rows = []
    row = {}
    value = ''

    if sheet in sheets:
    sheetfile = 'xl/worksheets/sheet'+sheets[sheet]+'.xml'
    #print(sheet,sheetfile)
    for event, element in iterparse(z.open(sheetfile)):
        # get value or index to shared strings
        if element.tag.endswith('}v') or element.tag.endswith('}t'):
            value = element.text
        # If value is a shared string, use value as an index
        if element.tag.endswith('}c'):
            if element.attrib.get('t') == 's':
                value = strings[int(value)]
            # split the row/col information so that the row leter(s) can be separate
            letter = re.sub('\d','',element.attrib['r'])
            row[letter] = value
            value = ''
        if element.tag.endswith('}row'):
            rows.append(row)
            row = {}

    return rows

回答 5

你可以使用任何的库列在这里(如Pyxlreader是基于JExcelApi的,或xlwt),加上COM自动化使用Excel本身的文件的阅读,但是对于您将引入厅认定为软件的依赖,这可能并不总是一个选择。

You can use any of the libraries listed here (like Pyxlreader that is based on JExcelApi, or xlwt), plus COM automation to use Excel itself for the reading of the files, but for that you are introducing Office as a dependency of your software, which might not be always an option.


回答 6

如果您需要旧的XLS格式。下面的代码为ansii’cp1251’。

import xlrd

file=u'C:/Landau/task/6200.xlsx'

try:
    book = xlrd.open_workbook(file,encoding_override="cp1251")  
except:
    book = xlrd.open_workbook(file)
print("The number of worksheets is {0}".format(book.nsheets))
print("Worksheet name(s): {0}".format(book.sheet_names()))
sh = book.sheet_by_index(0)
print("{0} {1} {2}".format(sh.name, sh.nrows, sh.ncols))
print("Cell D30 is {0}".format(sh.cell_value(rowx=29, colx=3)))
for rx in range(sh.nrows):
   print(sh.row(rx))

If you need old XLS format. Below code for ansii ‘cp1251’.

import xlrd

file=u'C:/Landau/task/6200.xlsx'

try:
    book = xlrd.open_workbook(file,encoding_override="cp1251")  
except:
    book = xlrd.open_workbook(file)
print("The number of worksheets is {0}".format(book.nsheets))
print("Worksheet name(s): {0}".format(book.sheet_names()))
sh = book.sheet_by_index(0)
print("{0} {1} {2}".format(sh.name, sh.nrows, sh.ncols))
print("Cell D30 is {0}".format(sh.cell_value(rowx=29, colx=3)))
for rx in range(sh.nrows):
   print(sh.row(rx))

回答 7

Python Excelerator也可以处理此任务。 http://ghantoos.org/2007/10/25/python-pyexcelerator-small-howto/

它在Debian和Ubuntu中也可用:

 sudo apt-get install python-excelerator

Python Excelerator handles this task as well. http://ghantoos.org/2007/10/25/python-pyexcelerator-small-howto/

It’s also available in Debian and Ubuntu:

 sudo apt-get install python-excelerator

回答 8

您可能还考虑运行(非python)程序xls2csv。将其输入xls文件,然后应返回一个csv。

You might also consider running the (non-python) program xls2csv. Feed it an xls file, and you should get back a csv.


回答 9

对于较早的Excel文件,有一个OleFileIO_PL模块可以读取所使用的OLE结构化存储格式。

For older Excel files there is the OleFileIO_PL module that can read the OLE structured storage format used.


回答 10

    with open(csv_filename) as file:
        data = file.read()

    with open(xl_file_name, 'w') as file:
        file.write(data)

您可以使用内置包将CSV转换为excel以上格式。CSV可以使用内置的dictreader和dictwriter程序包处理,其工作方式与python词典的工作方式相同。这很容易,我目前不知道任何内置的excel软件包,但是我遇到过openpyxl。这也非常简单明了。您可以在下面看到代码段,希望对您有所帮助

    import openpyxl
    book = openpyxl.load_workbook(filename)
    sheet = book.active 
    result =sheet['AP2']
    print(result.value)
    with open(csv_filename) as file:
        data = file.read()

    with open(xl_file_name, 'w') as file:
        file.write(data)

You can turn CSV to excel like above with inbuilt packages. CSV can be handled with an inbuilt package of dictreader and dictwriter which will work the same way as python dictionary works. which makes it a ton easy I am currently unaware of any inbuilt packages for excel but I had come across openpyxl. It was also pretty straight forward and simple You can see the code snippet below hope this helps

    import openpyxl
    book = openpyxl.load_workbook(filename)
    sheet = book.active 
    result =sheet['AP2']
    print(result.value)

递归子文件夹搜索并返回列表python中的文件

问题:递归子文件夹搜索并返回列表python中的文件

我正在编写一个脚本,以递归地遍历主文件夹中的子文件夹并根据某种文件类型构建一个列表。我的脚本有问题。当前设置如下

for root, subFolder, files in os.walk(PATH):
    for item in files:
        if item.endswith(".txt") :
            fileNamePath = str(os.path.join(root,subFolder,item))

问题是subFolder变量正在拉入子文件夹列表,而不是ITEM文件所在的文件夹。我曾考虑过为子文件夹运行一个for循环,然后加入路径的第一部分,但我想出了ID双重检查以了解在此之前是否有人提出建议。谢谢你的帮助!

I am working on a script to recursively go through subfolders in a mainfolder and build a list off a certain file type. I am having an issue with the script. Its currently set as follows

for root, subFolder, files in os.walk(PATH):
    for item in files:
        if item.endswith(".txt") :
            fileNamePath = str(os.path.join(root,subFolder,item))

the problem is that the subFolder variable is pulling in a list of subfolders rather than the folder that the ITEM file is located. I was thinking of running a for loop for the subfolder before and join the first part of the path but I figured Id double check to see if anyone has any suggestions before that. Thanks for your help!


回答 0

您应该使用dirpath调用的root。在dirnames供给这样你就可以进行清理,如果有文件夹,你不想os.walk递归到。

import os
result = [os.path.join(dp, f) for dp, dn, filenames in os.walk(PATH) for f in filenames if os.path.splitext(f)[1] == '.txt']

编辑:

经过最新一轮的投票之后,我发现这glob是一个用于扩展选择的更好工具。

import os
from glob import glob
result = [y for x in os.walk(PATH) for y in glob(os.path.join(x[0], '*.txt'))]

也是生成器版本

from itertools import chain
result = (chain.from_iterable(glob(os.path.join(x[0], '*.txt')) for x in os.walk('.')))

适用于Python的Edit2 3.4+

from pathlib import Path
result = list(Path(".").rglob("*.[tT][xX][tT]"))

You should be using the dirpath which you call root. The dirnames are supplied so you can prune it if there are folders that you don’t wish os.walk to recurse into.

import os
result = [os.path.join(dp, f) for dp, dn, filenames in os.walk(PATH) for f in filenames if os.path.splitext(f)[1] == '.txt']

Edit:

After the latest downvote, it occurred to me that glob is a better tool for selecting by extension.

import os
from glob import glob
result = [y for x in os.walk(PATH) for y in glob(os.path.join(x[0], '*.txt'))]

Also a generator version

from itertools import chain
result = (chain.from_iterable(glob(os.path.join(x[0], '*.txt')) for x in os.walk('.')))

Edit2 for Python 3.4+

from pathlib import Path
result = list(Path(".").rglob("*.[tT][xX][tT]"))

回答 1

Python 3.5中进行了更改:支持使用“ **”的递归glob。

glob.glob()有一个新的递归参数

如果要获取下面的每个.txt文件my_path(递归包括子目录):

import glob

files = glob.glob(my_path + '/**/*.txt', recursive=True)

# my_path/     the dir
# **/       every file and dir under my_path
# *.txt     every file that ends with '.txt'

如果需要迭代器,可以使用iglob作为替代方案:

for file in glob.iglob(my_path, recursive=False):
    # ...

Changed in Python 3.5: Support for recursive globs using “**”.

glob.glob() got a new recursive parameter.

If you want to get every .txt file under my_path (recursively including subdirs):

import glob

files = glob.glob(my_path + '/**/*.txt', recursive=True)

# my_path/     the dir
# **/       every file and dir under my_path
# *.txt     every file that ends with '.txt'

If you need an iterator you can use iglob as an alternative:

for file in glob.iglob(my_path, recursive=False):
    # ...

回答 2

我将把John La Rooy的列表理解理解为nest for的表达,以防万一其他人难以理解它。

result = [y for x in os.walk(PATH) for y in glob(os.path.join(x[0], '*.txt'))]

应等效于:

import glob

result = []

for x in os.walk(PATH):
    for y in glob.glob(os.path.join(x[0], '*.txt')):
        result.append(y)

这是列表理解os.walkglob.glob函数的文档。

I will translate John La Rooy’s list comprehension to nested for’s, just in case anyone else has trouble understanding it.

result = [y for x in os.walk(PATH) for y in glob(os.path.join(x[0], '*.txt'))]

Should be equivalent to:

import glob

result = []

for x in os.walk(PATH):
    for y in glob.glob(os.path.join(x[0], '*.txt')):
        result.append(y)

Here’s the documentation for list comprehension and the functions os.walk and glob.glob.


回答 3

这似乎是我能想到的最快的解决方案,并且比任何解决方案都快os.walk而且要快得多glob

  • 它也将免费为您提供所有嵌套子文件夹的列表。
  • 您可以搜索几个不同的扩展名。
  • 您也可以选择通过改变返回的文件不管是完整路径或只是名称f.pathf.name(不改变它的子文件夹!)。

Args :dir: str, ext: list
函数返回两个列表:subfolders, files

有关详细的速度分析,请参见下文。

def run_fast_scandir(dir, ext):    # dir: str, ext: list
    subfolders, files = [], []

    for f in os.scandir(dir):
        if f.is_dir():
            subfolders.append(f.path)
        if f.is_file():
            if os.path.splitext(f.name)[1].lower() in ext:
                files.append(f.path)


    for dir in list(subfolders):
        sf, f = run_fast_scandir(dir, ext)
        subfolders.extend(sf)
        files.extend(f)
    return subfolders, files


subfolders, files = run_fast_scandir(folder, [".jpg"])


速度分析

各种方法来获取所有子文件夹和主文件夹中具有特定文件扩展名的所有文件。

tl; dr:
fast_scandir显然是获胜的,并且是除os.walk之外所有其他解决方案的两倍。
os.walk落后第二名。
-使用glob会大大减慢该过程。
-没有任何结果使用自然排序。这意味着将对结果进行如下排序:1、10、2。要进行自然排序(1、2、10),请查看https://stackoverflow.com/a/48030307/2441026


结果:

fast_scandir    took  499 ms. Found files: 16596. Found subfolders: 439
os.walk         took  589 ms. Found files: 16596
find_files      took  919 ms. Found files: 16596
glob.iglob      took  998 ms. Found files: 16596
glob.glob       took 1002 ms. Found files: 16596
pathlib.rglob   took 1041 ms. Found files: 16596
os.walk-glob    took 1043 ms. Found files: 16596

测试使用W7x64,Python 3.8.1和20次运行完成。439个(部分嵌套的)子文件夹中的16596个文件。
find_files来自https://stackoverflow.com/a/45646357/2441026,可让您搜索多个扩展名。
fast_scandir由我自己编写,并且还将返回子文件夹列表。您可以给它提供扩展名列表以进行搜索(我测试了一个列表,其中一个条目很简单,if ... == ".jpg"并且没有显着差异)。


# -*- coding: utf-8 -*-
# Python 3


import time
import os
from glob import glob, iglob
from pathlib import Path


directory = r"<folder>"
RUNS = 20


def run_os_walk():
    a = time.time_ns()
    for i in range(RUNS):
        fu = [os.path.join(dp, f) for dp, dn, filenames in os.walk(directory) for f in filenames if
                  os.path.splitext(f)[1].lower() == '.jpg']
    print(f"os.walk\t\t\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(fu)}")


def run_os_walk_glob():
    a = time.time_ns()
    for i in range(RUNS):
        fu = [y for x in os.walk(directory) for y in glob(os.path.join(x[0], '*.jpg'))]
    print(f"os.walk-glob\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(fu)}")


def run_glob():
    a = time.time_ns()
    for i in range(RUNS):
        fu = glob(os.path.join(directory, '**', '*.jpg'), recursive=True)
    print(f"glob.glob\t\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(fu)}")


def run_iglob():
    a = time.time_ns()
    for i in range(RUNS):
        fu = list(iglob(os.path.join(directory, '**', '*.jpg'), recursive=True))
    print(f"glob.iglob\t\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(fu)}")


def run_pathlib_rglob():
    a = time.time_ns()
    for i in range(RUNS):
        fu = list(Path(directory).rglob("*.jpg"))
    print(f"pathlib.rglob\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(fu)}")


def find_files(files, dirs=[], extensions=[]):
    # https://stackoverflow.com/a/45646357/2441026

    new_dirs = []
    for d in dirs:
        try:
            new_dirs += [ os.path.join(d, f) for f in os.listdir(d) ]
        except OSError:
            if os.path.splitext(d)[1].lower() in extensions:
                files.append(d)

    if new_dirs:
        find_files(files, new_dirs, extensions )
    else:
        return


def run_fast_scandir(dir, ext):    # dir: str, ext: list
    # https://stackoverflow.com/a/59803793/2441026

    subfolders, files = [], []

    for f in os.scandir(dir):
        if f.is_dir():
            subfolders.append(f.path)
        if f.is_file():
            if os.path.splitext(f.name)[1].lower() in ext:
                files.append(f.path)


    for dir in list(subfolders):
        sf, f = run_fast_scandir(dir, ext)
        subfolders.extend(sf)
        files.extend(f)
    return subfolders, files



if __name__ == '__main__':
    run_os_walk()
    run_os_walk_glob()
    run_glob()
    run_iglob()
    run_pathlib_rglob()


    a = time.time_ns()
    for i in range(RUNS):
        files = []
        find_files(files, dirs=[directory], extensions=[".jpg"])
    print(f"find_files\t\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(files)}")


    a = time.time_ns()
    for i in range(RUNS):
        subf, files = run_fast_scandir(directory, [".jpg"])
    print(f"fast_scandir\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(files)}. Found subfolders: {len(subf)}")

This seems to be the fastest solution I could come up with, and is faster than os.walk and a lot faster than any glob solution.

  • It will also give you a list of all nested subfolders at basically no cost.
  • You can search for several different extensions.
  • You can also choose to return either full paths or just the names for the files by changing f.path to f.name (do not change it for subfolders!).

Args: dir: str, ext: list.
Function returns two lists: subfolders, files.

See below for a detailed speed anaylsis.

def run_fast_scandir(dir, ext):    # dir: str, ext: list
    subfolders, files = [], []

    for f in os.scandir(dir):
        if f.is_dir():
            subfolders.append(f.path)
        if f.is_file():
            if os.path.splitext(f.name)[1].lower() in ext:
                files.append(f.path)


    for dir in list(subfolders):
        sf, f = run_fast_scandir(dir, ext)
        subfolders.extend(sf)
        files.extend(f)
    return subfolders, files


subfolders, files = run_fast_scandir(folder, [".jpg"])


Speed analysis

for various methods to get all files with a specific file extension inside all subfolders and the main folder.

tl;dr:
fast_scandir clearly wins and is twice as fast as all other solutions, except os.walk.
os.walk is second place slighly slower.
– using glob will greatly slow down the process.
– None of the results use natural sorting. This means results will be sorted like this: 1, 10, 2. To get natural sorting (1, 2, 10), please have a look at https://stackoverflow.com/a/48030307/2441026


Results:

fast_scandir    took  499 ms. Found files: 16596. Found subfolders: 439
os.walk         took  589 ms. Found files: 16596
find_files      took  919 ms. Found files: 16596
glob.iglob      took  998 ms. Found files: 16596
glob.glob       took 1002 ms. Found files: 16596
pathlib.rglob   took 1041 ms. Found files: 16596
os.walk-glob    took 1043 ms. Found files: 16596

Tests were done with W7x64, Python 3.8.1, 20 runs. 16596 files in 439 (partially nested) subfolders.
find_files is from https://stackoverflow.com/a/45646357/2441026 and lets you search for several extensions.
fast_scandir was written by myself and will also return a list of subfolders. You can give it a list of extensions to search for (I tested a list with one entry to a simple if ... == ".jpg" and there was no significant difference).


# -*- coding: utf-8 -*-
# Python 3


import time
import os
from glob import glob, iglob
from pathlib import Path


directory = r"<folder>"
RUNS = 20


def run_os_walk():
    a = time.time_ns()
    for i in range(RUNS):
        fu = [os.path.join(dp, f) for dp, dn, filenames in os.walk(directory) for f in filenames if
                  os.path.splitext(f)[1].lower() == '.jpg']
    print(f"os.walk\t\t\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(fu)}")


def run_os_walk_glob():
    a = time.time_ns()
    for i in range(RUNS):
        fu = [y for x in os.walk(directory) for y in glob(os.path.join(x[0], '*.jpg'))]
    print(f"os.walk-glob\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(fu)}")


def run_glob():
    a = time.time_ns()
    for i in range(RUNS):
        fu = glob(os.path.join(directory, '**', '*.jpg'), recursive=True)
    print(f"glob.glob\t\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(fu)}")


def run_iglob():
    a = time.time_ns()
    for i in range(RUNS):
        fu = list(iglob(os.path.join(directory, '**', '*.jpg'), recursive=True))
    print(f"glob.iglob\t\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(fu)}")


def run_pathlib_rglob():
    a = time.time_ns()
    for i in range(RUNS):
        fu = list(Path(directory).rglob("*.jpg"))
    print(f"pathlib.rglob\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(fu)}")


def find_files(files, dirs=[], extensions=[]):
    # https://stackoverflow.com/a/45646357/2441026

    new_dirs = []
    for d in dirs:
        try:
            new_dirs += [ os.path.join(d, f) for f in os.listdir(d) ]
        except OSError:
            if os.path.splitext(d)[1].lower() in extensions:
                files.append(d)

    if new_dirs:
        find_files(files, new_dirs, extensions )
    else:
        return


def run_fast_scandir(dir, ext):    # dir: str, ext: list
    # https://stackoverflow.com/a/59803793/2441026

    subfolders, files = [], []

    for f in os.scandir(dir):
        if f.is_dir():
            subfolders.append(f.path)
        if f.is_file():
            if os.path.splitext(f.name)[1].lower() in ext:
                files.append(f.path)


    for dir in list(subfolders):
        sf, f = run_fast_scandir(dir, ext)
        subfolders.extend(sf)
        files.extend(f)
    return subfolders, files



if __name__ == '__main__':
    run_os_walk()
    run_os_walk_glob()
    run_glob()
    run_iglob()
    run_pathlib_rglob()


    a = time.time_ns()
    for i in range(RUNS):
        files = []
        find_files(files, dirs=[directory], extensions=[".jpg"])
    print(f"find_files\t\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(files)}")


    a = time.time_ns()
    for i in range(RUNS):
        subf, files = run_fast_scandir(directory, [".jpg"])
    print(f"fast_scandir\ttook {(time.time_ns() - a) / 1000 / 1000 / RUNS:.0f} ms. Found files: {len(files)}. Found subfolders: {len(subf)}")

回答 4

新的pathlib库将其简化为一行:

from pathlib import Path
result = list(Path(PATH).glob('**/*.txt'))

您还可以使用生成器版本:

from pathlib import Path
for file in Path(PATH).glob('**/*.txt'):
    pass

这将返回Path对象,您几乎可以将其用于任何对象,或者通过获取字符串形式的文件名file.name

The new pathlib library simplifies this to one line:

from pathlib import Path
result = list(Path(PATH).glob('**/*.txt'))

You can also use the generator version:

from pathlib import Path
for file in Path(PATH).glob('**/*.txt'):
    pass

This returns Path objects, which you can use for pretty much anything, or get the file name as a string by file.name.


回答 5

它不是最Python的答案,但我会把它放在这里很有趣,因为这是递归中的一个很好的类

def find_files( files, dirs=[], extensions=[]):
    new_dirs = []
    for d in dirs:
        try:
            new_dirs += [ os.path.join(d, f) for f in os.listdir(d) ]
        except OSError:
            if os.path.splitext(d)[1] in extensions:
                files.append(d)

    if new_dirs:
        find_files(files, new_dirs, extensions )
    else:
        return

在我的机器上,我有两个文件夹,root并且root2

mender@multivax ]ls -R root root2
root:
temp1 temp2

root/temp1:
temp1.1 temp1.2

root/temp1/temp1.1:
f1.mid

root/temp1/temp1.2:
f.mi  f.mid

root/temp2:
tmp.mid

root2:
dummie.txt temp3

root2/temp3:
song.mid

比方说,我想找到所有.txt和所有.mid文件在任何这些目录中,然后我可以做

files = []
find_files( files, dirs=['root','root2'], extensions=['.mid','.txt'] )
print(files)

#['root2/dummie.txt',
# 'root/temp2/tmp.mid',
# 'root2/temp3/song.mid',
# 'root/temp1/temp1.1/f1.mid',
# 'root/temp1/temp1.2/f.mid']

Its not the most pythonic answer, but I’ll put it here for fun because it’s a neat lesson in recursion

def find_files( files, dirs=[], extensions=[]):
    new_dirs = []
    for d in dirs:
        try:
            new_dirs += [ os.path.join(d, f) for f in os.listdir(d) ]
        except OSError:
            if os.path.splitext(d)[1] in extensions:
                files.append(d)

    if new_dirs:
        find_files(files, new_dirs, extensions )
    else:
        return

On my machine I have two folders, root and root2

mender@multivax ]ls -R root root2
root:
temp1 temp2

root/temp1:
temp1.1 temp1.2

root/temp1/temp1.1:
f1.mid

root/temp1/temp1.2:
f.mi  f.mid

root/temp2:
tmp.mid

root2:
dummie.txt temp3

root2/temp3:
song.mid

Lets say I want to find all .txt and all .mid files in either of these directories, then I can just do

files = []
find_files( files, dirs=['root','root2'], extensions=['.mid','.txt'] )
print(files)

#['root2/dummie.txt',
# 'root/temp2/tmp.mid',
# 'root2/temp3/song.mid',
# 'root/temp1/temp1.1/f1.mid',
# 'root/temp1/temp1.2/f.mid']

回答 6

递归是Python 3.5中的新增功能,因此它不适用于Python 2.7。这是使用r字符串的示例,因此您只需按Win,Lin,…上的路径提供路径即可。

import glob

mypath=r"C:\Users\dj\Desktop\nba"

files = glob.glob(mypath + r'\**\*.py', recursive=True)
# print(files) # as list
for f in files:
    print(f) # nice looking single line per file

注意:它将列出所有文件,无论文件应多深。

Recursive is new in Python 3.5, so it won’t work on Python 2.7. Here is the example that uses r strings so you just need to provide the path as is on either Win, Lin, …

import glob

mypath=r"C:\Users\dj\Desktop\nba"

files = glob.glob(mypath + r'\**\*.py', recursive=True)
# print(files) # as list
for f in files:
    print(f) # nice looking single line per file

Note: It will list all files, no matter how deep it should go.


回答 7

您可以通过这种方式返回绝对路径文件列表。

def list_files_recursive(path):
    """
    Function that receives as a parameter a directory path
    :return list_: File List and Its Absolute Paths
    """

    import os

    files = []

    # r = root, d = directories, f = files
    for r, d, f in os.walk(path):
        for file in f:
            files.append(os.path.join(r, file))

    lst = [file for file in files]
    return lst


if __name__ == '__main__':

    result = list_files_recursive('/tmp')
    print(result)

You can do it this way to return you a list of absolute path files.

def list_files_recursive(path):
    """
    Function that receives as a parameter a directory path
    :return list_: File List and Its Absolute Paths
    """

    import os

    files = []

    # r = root, d = directories, f = files
    for r, d, f in os.walk(path):
        for file in f:
            files.append(os.path.join(r, file))

    lst = [file for file in files]
    return lst


if __name__ == '__main__':

    result = list_files_recursive('/tmp')
    print(result)


回答 8

如果您不介意安装其他灯库,则可以执行以下操作:

pip install plazy

用法:

import plazy

txt_filter = lambda x : True if x.endswith('.txt') else False
files = plazy.list_files(root='data', filter_func=txt_filter, is_include_root=True)

结果应如下所示:

['data/a.txt', 'data/b.txt', 'data/sub_dir/c.txt']

它同时适用于Python 2.7和Python 3。

GitHub:https : //github.com/kyzas/plazy#list-files

免责声明:我是的作者plazy

If you don’t mind installing an additional light library, you can do this:

pip install plazy

Usage:

import plazy

txt_filter = lambda x : True if x.endswith('.txt') else False
files = plazy.list_files(root='data', filter_func=txt_filter, is_include_root=True)

The result should look something like this:

['data/a.txt', 'data/b.txt', 'data/sub_dir/c.txt']

It works on both Python 2.7 and Python 3.

Github: https://github.com/kyzas/plazy#list-files

Disclaimer: I’m an author of plazy.


回答 9

此功能将递归地仅将文件放入列表中。希望你能。

import os


def ls_files(dir):
    files = list()
    for item in os.listdir(dir):
        abspath = os.path.join(dir, item)
        try:
            if os.path.isdir(abspath):
                files = files + ls_files(abspath)
            else:
                files.append(abspath)
        except FileNotFoundError as err:
            print('invalid directory\n', 'Error: ', err)
    return files

This function will recursively put only files into a list.

import os


def ls_files(dir):
    files = list()
    for item in os.listdir(dir):
        abspath = os.path.join(dir, item)
        try:
            if os.path.isdir(abspath):
                files = files + ls_files(abspath)
            else:
                files.append(abspath)
        except FileNotFoundError as err:
            print('invalid directory\n', 'Error: ', err)
    return files

回答 10

您最初的解决方案几乎是正确的,但是变量“ root”是在递归路径周围动态更新的。os.walk()是一个递归生成器。每个元组集(根,subFolder,文件)都是按照设置方式针对特定根的。

root = 'C:\\'
subFolder = ['Users', 'ProgramFiles', 'ProgramFiles (x86)', 'Windows', ...]
files = ['foo1.txt', 'foo2.txt', 'foo3.txt', ...]

root = 'C:\\Users\\'
subFolder = ['UserAccount1', 'UserAccount2', ...]
files = ['bar1.txt', 'bar2.txt', 'bar3.txt', ...]

...

我对您的代码做了些微调整,以打印完整列表。

import os
for root, subFolder, files in os.walk(PATH):
    for item in files:
        if item.endswith(".txt") :
            fileNamePath = str(os.path.join(root,item))
            print(fileNamePath)

希望这可以帮助!

Your original solution was very nearly correct, but the variable “root” is dynamically updated as it recursively paths around. os.walk() is a recursive generator. Each tuple set of (root, subFolder, files) is for a specific root the way you have it setup.

i.e.

root = 'C:\\'
subFolder = ['Users', 'ProgramFiles', 'ProgramFiles (x86)', 'Windows', ...]
files = ['foo1.txt', 'foo2.txt', 'foo3.txt', ...]

root = 'C:\\Users\\'
subFolder = ['UserAccount1', 'UserAccount2', ...]
files = ['bar1.txt', 'bar2.txt', 'bar3.txt', ...]

...

I made a slight tweak to your code to print a full list.

import os
for root, subFolder, files in os.walk(PATH):
    for item in files:
        if item.endswith(".txt") :
            fileNamePath = str(os.path.join(root,item))
            print(fileNamePath)

Hope this helps!


当期望一维数组时,传递了列向量y

问题:当期望一维数组时,传递了列向量y

我需要适应RandomForestRegressorsklearn.ensemble

forest = ensemble.RandomForestRegressor(**RF_tuned_parameters)
model = forest.fit(train_fold, train_y)
yhat = model.predict(test_fold)

该代码始终有效,直到我对数据进行了一些预处理(train_y)。错误消息显示:

DataConversionWarning:当期望1d数组时,传递了列向量y。请将y的形状更改为(n_samples,),例如使用ravel()。

模型= forest.fit(train_fold,train_y)

以前train_y是一个Series,现在是numpy数组(它是列向量)。如果我应用train_y.ravel(),则它成为行向量,并且没有错误消息出现,通过预测步骤需要很长时间(实际上,它永远不会完成…)。

RandomForestRegressor我发现的文档中,train_y应将其定义为“ y : array-like, shape = [n_samples] or [n_samples, n_outputs] 如何解决此问题的想法?”。

I need to fit RandomForestRegressor from sklearn.ensemble.

forest = ensemble.RandomForestRegressor(**RF_tuned_parameters)
model = forest.fit(train_fold, train_y)
yhat = model.predict(test_fold)

This code always worked until I made some preprocessing of data (train_y). The error message says:

DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().

model = forest.fit(train_fold, train_y)

Previously train_y was a Series, now it’s numpy array (it is a column-vector). If I apply train_y.ravel(), then it becomes a row vector and no error message appears, through the prediction step takes very long time (actually it never finishes…).

In the docs of RandomForestRegressor I found that train_y should be defined as y : array-like, shape = [n_samples] or [n_samples, n_outputs] Any idea how to solve this issue?


回答 0

更改此行:

model = forest.fit(train_fold, train_y)

至:

model = forest.fit(train_fold, train_y.values.ravel())

编辑:

.values将给出数组中的值。(形状:[n,1)

.ravel 会将数组形状转换为(n,)

Change this line:

model = forest.fit(train_fold, train_y)

to:

model = forest.fit(train_fold, train_y.values.ravel())

Edit:

.values will give the values in an array. (shape: (n,1)

.ravel will convert that array shape to (n, )


回答 1

当我尝试训练KNN分类器时,我也遇到了这种情况。但似乎在警告不见了,我改变之后:
knn.fit(X_train,y_train)

knn.fit(X_train, np.ravel(y_train,order='C'))

在这行之前,我用过import numpy as np

I also encountered this situation when I was trying to train a KNN classifier. but it seems that the warning was gone after I changed:
knn.fit(X_train,y_train)
to
knn.fit(X_train, np.ravel(y_train,order='C'))

Ahead of this line I used import numpy as np.


回答 2

我有同样的问题。问题在于标签是按列格式的,而它却希望连续显示。用np.ravel()

knn.score(training_set, np.ravel(training_labels))

希望这能解决。

I had the same problem. The problem was that the labels were in a column format while it expected it in a row. use np.ravel()

knn.score(training_set, np.ravel(training_labels))

Hope this solves it.


回答 3

使用以下代码:

model = forest.fit(train_fold, train_y.ravel())

如果仍然像下面一样错误地打耳光?

Unknown label type: %r" % y

使用此代码:

y = train_y.ravel()
train_y = np.array(y).astype(int)
model = forest.fit(train_fold, train_y)

use below code:

model = forest.fit(train_fold, train_y.ravel())

if you are still getting slap by error as identical as below ?

Unknown label type: %r" % y

use this code:

y = train_y.ravel()
train_y = np.array(y).astype(int)
model = forest.fit(train_fold, train_y)

回答 4

另一种方法是使用 ravel

model = forest.fit(train_fold, train_y.values.reshape(-1,))

Another way of doing this is to use ravel

model = forest.fit(train_fold, train_y.values.reshape(-1,))

回答 5

使用neuraxle,您可以轻松解决此问题:

p = Pipeline([
   # expected outputs shape: (n, 1)
   OutputTransformerWrapper(NumpyRavel()), 
   # expected outputs shape: (n, )
   RandomForestRegressor(**RF_tuned_parameters)
])

p, outputs = p.fit_transform(data_inputs, expected_outputs)

Neuraxle是类似于sklearn的框架,用于深度学习项目中的超参数调整和AutoML!

With neuraxle, you can easily solve this :

p = Pipeline([
   # expected outputs shape: (n, 1)
   OutputTransformerWrapper(NumpyRavel()), 
   # expected outputs shape: (n, )
   RandomForestRegressor(**RF_tuned_parameters)
])

p, outputs = p.fit_transform(data_inputs, expected_outputs)

Neuraxle is a sklearn-like framework for hyperparameter tuning and AutoML in deep learning projects !


回答 6

format_train_y=[]
for n in train_y:
    format_train_y.append(n[0])
format_train_y=[]
for n in train_y:
    format_train_y.append(n[0])

回答 7

Y = y.values [:,0]

Y-formated_train_y

y-train_y

Y = y.values[:,0]

Y – formated_train_y

y – train_y


无法将“ Conda”识别为内部或外部命令

问题:无法将“ Conda”识别为内部或外部命令

我在Windows 7 Professional计算机上安装了Anaconda3 4.4.0(32位),并在Jupyter笔记本电脑上导入了NumPy和Pandas,因此我认为Python已正确安装。但是当我键入conda listconda --version在命令提示符下时,它说conda is not recognized as internal or external command.

我已经为Anaconda3设置了环境变量;Variable Name: PathVariable Value: C:\Users\dipanwita.neogy\Anaconda3

我该如何运作?

I installed Anaconda3 4.4.0 (32 bit) on my Windows 7 Professional machine and imported NumPy and Pandas on Jupyter notebook so I assume Python was installed correctly. But when I type conda list and conda --version in command prompt, it says conda is not recognized as internal or external command.

I have set environment variable for Anaconda3; Variable Name: Path, Variable Value: C:\Users\dipanwita.neogy\Anaconda3

How do I make it work?


回答 0

尽管其他人为您提供了很好的解决方案,但我认为指出实际情况会有所帮助。根据Anaconda 4.4更改日志,https : //docs.anaconda.com/anaconda/reference/release-notes/#what-s-new-in-anaconda-4-4 :

在Windows上,默认情况下不再更改PATH环境变量,因为这可能导致其他软件出现问题。建议的方法是,当您希望使用Anaconda软件时,改用Anaconda Navigator或Anaconda命令提示符(位于“ Anaconda”下的“开始”菜单中)。

(注意:最近的Win 10并不假定您具有安装或更新的特权。如果命令失败,请右键单击Anaconda命令提示符,选择“更多”,选择“以管理员身份运行”)

这是对先前安装的更改。尽管您也可以随时将其添加到PATH中,但建议使用Navigator或Anaconda Prompt。在安装过程中,现在没有选中将Anaconda添加到PATH的框,但是您可以选择它。

Although you were offered a good solution by others I think it is helpful to point out what is really happening. As per the Anaconda 4.4 changelog, https://docs.anaconda.com/anaconda/reference/release-notes/#what-s-new-in-anaconda-4-4:

On Windows, the PATH environment variable is no longer changed by default, as this can cause trouble with other software. The recommended approach is to instead use Anaconda Navigator or the Anaconda Command Prompt (located in the Start Menu under “Anaconda”) when you wish to use Anaconda software.

(Note: recent Win 10 does not assume you have privileges to install or update. If the command fails, right-click on the Anaconda Command Prompt, choose “More”, chose “Run as administrator”)

This is a change from previous installations. It is suggested to use Navigator or the Anaconda Prompt although you can always add it to your PATH as well. During the install the box to add Anaconda to the PATH is now unchecked but you can select it.


回答 1

我在Windows 10中遇到了同样的问题,请按照以下步骤更新环境变量,它可以正常工作。

我知道这对于简单的环境设置来说是一个冗长的答案,我认为这对于新窗口10用户可能有用。

1)打开Anaconda提示:

2)检查Conda安装位置。

where conda

3)打开高级系统设置

4)点击环境变量

5)编辑路径

6)添加新路径

 C:\Users\RajaRama\Anaconda3\Scripts

 C:\Users\RajaRama\Anaconda3

 C:\Users\RajaRama\Anaconda3\Library\bin

7)打开命令提示符并检查版本

8)在第7步键入conda之后,在cmd中安装anaconda-navigator,然后按y

I was faced with the same issue in windows 10, Updating the environment variable following steps, it’s working fine.

I know It is a lengthy answer for the simple environment setups, I thought it’s may be useful for the new window 10 users.

1) Open Anaconda Prompt:

2) Check Conda Installed Location.

where conda

3) Open Advanced System Settings

4) Click on Environment Variables

5) Edit Path

6) Add New Path

 C:\Users\RajaRama\Anaconda3\Scripts

 C:\Users\RajaRama\Anaconda3

 C:\Users\RajaRama\Anaconda3\Library\bin

7) Open Command Prompt and Check Versions

8) After 7th step type conda install anaconda-navigator in cmd then press y


回答 2

我找到了解决方案。可变值应为C:\Users\dipanwita.neogy\Anaconda3\Scripts

I found the solution. Variable value should be C:\Users\dipanwita.neogy\Anaconda3\Scripts


回答 3

现在在Windows上安装anaconda时,它不会自动将Python或Conda添加到您的路径中。

在安装过程中,您可以选中此框,也可以将python和/或python手动添加到路径中(如下面的图片所示)

如果您不知道您的conda和/或python在哪里,请在anaconda提示符下键入以下命令

where python
where conda

接下来,您可以通过在命令提示符下使用setx命令将Python和Conda添加到您的路径中(替换C:\Users\mgalarnyk\Anaconda2为运行时获得的结果where pythonwhere conda)。

SETX PATH "%PATH%;C:\Users\mgalarnyk\Anaconda2\Scripts;C:\Users\mgalarnyk\Anaconda2"

接下来,关闭该命令提示符并打开一个新命令。恭喜您现在可以使用conda和python

来源:https : //medium.com/@GalarnykMichael/install-python-on-windows-anaconda-c63c7c3d1444

When you install anaconda on windows now, it doesn’t automatically add Python or Conda to your path.

While during the installation process you can check this box, you can also add python and/or python to your path manually (as you can see below the image)

If you don’t know where your conda and/or python is, you type the following commands into your anaconda prompt

where python
where conda

Next, you can add Python and Conda to your path by using the setx command in your command prompt (replace C:\Users\mgalarnyk\Anaconda2 with the results you got when running where python and where conda).

SETX PATH "%PATH%;C:\Users\mgalarnyk\Anaconda2\Scripts;C:\Users\mgalarnyk\Anaconda2"

Next close that command prompt and open a new one. Congrats you can now use conda and python

Source: https://medium.com/@GalarnykMichael/install-python-on-windows-anaconda-c63c7c3d1444


回答 4

为了清楚起见,您需要转到controlpanel\System\Advanced system settings\Environment Variables\Path,然后点击编辑并添加:

C:Users\user.user\Anaconda3\Scripts

到最后并重新启动cmd行

Just to be clear, you need to go to the controlpanel\System\Advanced system settings\Environment Variables\Path, then hit edit and add:

C:Users\user.user\Anaconda3\Scripts

to the end and restart the cmd line


回答 5

如果您有较新版本的Anaconda Navigator,请打开安装中附带的Anaconda Prompt程序。在此处输入所有常用的conda update/ conda install命令。

我认为上面的答案可以解释这一点,但是我可以使用这样一个非常简单的指令。也许会帮助别人。

If you have a newer version of the Anaconda Navigator, open the Anaconda Prompt program that came in the install. Type all the usual conda update/conda install commands there.

I think the answers above explain this, but I could have used a very simple instruction like this. Perhaps it will help others.


回答 6

除了添加C:\Users\yourusername\Anaconda3和之外C:\Users\yourusername\Anaconda3\Scripts(如Raja所建议的那样),还将添加C:\Users\yourusername\Anaconda3\Library\bin到您的path变量中。如果您是在全新安装的Anaconda上执行此操作,则可以防止发生SSL错误。

In addition to adding C:\Users\yourusername\Anaconda3 and C:\Users\yourusername\Anaconda3\Scripts, as recommended by Raja (above), also add C:\Users\yourusername\Anaconda3\Library\bin to your path variable. This will prevent an SSL error that is bound to happen if you’re performing this on a fresh install of Anaconda.


回答 7

转到anaconda提示符(在笔记本电脑的搜索框中键入“ anaconda”)。输入以下命令

where conda

将该位置添加到您的环境路径变量中。关闭cmd,然后再次打开

Go To anaconda prompt(type “anaconda” in search box in your laptop). type following commands

where conda

add that location to your environment path variables. Close the cmd and open it again


回答 8

如果您不想将Anaconda添加到环境中。路径,并且您正在使用Windows,请尝试以下操作:

  • 打开cmd;
  • 键入文件夹安装的路径。就像这样:C:\ Users \ your_home文件夹\ Anaconda3 \ Scripts
  • 测试Anaconda,例如conda –version类型。
  • 更新Anaconda:conda更新conda或conda更新-全部或conda更新anaconda。

更新Spyder:

  • 康达更新qt pyqt
  • 康达更新间谍

If you don’t want to add Anaconda to env. path and you are using Windows try this:

  • Open cmd;
  • Type path to your folder instalation. It’s something like: C:\Users\your_home folder\Anaconda3\Scripts
  • Test Anaconda, for exemple type conda –version.
  • Update Anaconda: conda update conda or conda update –all or conda update anaconda.

Update Spyder:

  • conda update qt pyqt
  • conda update spyder

回答 9

我有Windows 10 64位,这对我有用此解决方案可以在两种(Anaconda / MiniConda)发行版中都可以使用。

  1. 首先,尝试卸载引起问题的anaconda / miniconda
  2. 之后,从“ C:\ Users \”删除“ .anaconda”和“ .conda”文件夹
  3. 如果您有任何杀毒软件装然后尝试排除所有文件夹,子文件夹内的“C:\ ProgramData \ Anaconda3 \”

    • 行为检测。
    • 病毒检测。
    • DNA扫描。
    • 可疑文件扫描。
    • 任何其他病毒防护模式。

    *(注意:“ C:\ ProgramData \ Anaconda3”此文件夹是默认安装文件夹,您可以在安装Anaconda时更改它,仅在安装目标位置提示处替换排除的路径)*

  4. 现在,以管理员权限安装Anaconda。
    • 将安装路径设置为“ C:\ ProgramData \ Anaconda3”或者您可以指定自定义路径,只是要记住该路径不应包含任何空格,并且应将其从病毒检测中排除。
    • 在“高级安装选项”中,您可以选中“将Anaconda添加到我的PATH环境变量(可选)”和“将Anaconda注册为我的默认Python 3.6”
    • 使用其他默认设置进行安装。完成后单击完成。
    • 重启你的电脑。

现在打开命令提示符或Anaconda提示符并使用以下命令检查安装

康达清单

如果您获得任何软件包列表,则表明anaconda / miniconda已成功安装。

I have Windows 10 64 bit, this worked for me, This solution can work for both (Anaconda/MiniConda) distributions.

  1. First of all try to uninstall anaconda/miniconda which is causing problem.
  2. After that delete ‘.anaconda’ and ‘.conda’ folders from ‘C:\Users\’
  3. If you have any antivirus software installed then try to exclude all the folders,subfolders inside ‘C:\ProgramData\Anaconda3\’ from

    • Behaviour detection.
    • Virus detection.
    • DNA scan.
    • Suspicious files scan.
    • Any other virus protection mode.

    *(Note: ‘C:\ProgramData\Anaconda3’ this folder is default installation folder, you can change it just replace your excluded path at installation destination prompt while installing Anaconda)*

  4. Now install Anaconda with admin privileges.
    • Set the installation path as ‘C:\ProgramData\Anaconda3’ or you can specify your custom path just remember it should not contain any white space and it should be excluded from virus detection.
    • At Advanced Installation Options you can check “Add Anaconda to my PATH environment variable(optional)” and “Register Anaconda as my default Python 3.6”
    • Install it with further default settings. Click on finish after done.
    • Restart your computer.

Now open Command prompt or Anaconda prompt and check installation using following command

conda list

If you get any package list then the anaconda/miniconda is successfully installed.


回答 10

当我多次安装Anaconda时,这个问题对我来说就出现了。我很小心地进行了卸载,但是有些事情卸载过程不会撤消。

就我而言,我需要删除一个文件Microsoft.PowerShell_profile.ps1~\Documents\WindowsPowerShell\。通过在文本编辑器中将其打开,我确定了该文件是罪魁祸首。我看到它引用了旧的安装位置C:\Anaconda3\

This problem arose for me when I installed Anaconda multiple times. I was careful to do an uninstall but there are some things that the uninstall process doesn’t undo.

In my case, I needed to remove a file Microsoft.PowerShell_profile.ps1 from ~\Documents\WindowsPowerShell\. I identified that this file was the culprit by opening it in a text editor. I saw that it referenced the old installation location C:\Anaconda3\.


回答 11

我刚刚启动了anaconda-navigator并从那里运行conda命令。

I have just launched anaconda-navigator and run the conda commands from there.


回答 12

我在Windows中遇到了这个问题。大多数答案都不是anaconda推荐的,您不应将路径添加到环境变量中,因为它可能会破坏其他内容。相反,您应该使用顶部答案中提到的anaconda提示符。

但是,这也可能会中断。在这种情况下,右键单击快捷方式,转到快捷方式选项卡,目标值应类似于:

%windir%\System32\cmd.exe "/K" C:\Users\myUser\Anaconda3\Scripts\activate.bat C:\Users\myUser\Anaconda3

I had this problem in windows. Most of the answers are not as recommended by anaconda, you should not add the path to the environment variables as it can break other things. Instead you should use anaconda prompt as mentioned in the top answer.

However, this may also break. In this case right click on the shortcut, go to shortcut tab, and the target value should read something like:

%windir%\System32\cmd.exe "/K" C:\Users\myUser\Anaconda3\Scripts\activate.bat C:\Users\myUser\Anaconda3

Flask-SQLAlchemy导入/上下文问题

问题:Flask-SQLAlchemy导入/上下文问题

我想构建我的Flask应用,例如:

./site.py
./apps/members/__init__.py
./apps/members/models.py

apps.members 是烧瓶蓝图。

现在,为了创建模型类,我需要拥有该应用程序,例如:

# apps.members.models
from flask import current_app
from flaskext.sqlalchemy import SQLAlchemy

db = SQLAlchemy(current_app)

class Member(db.Model):
    # fields here
    pass

但是,如果我尝试将该模型导入到我的Blueprint应用程序中,则会感到恐惧RuntimeError: working outside of request context。我如何在这里正确持有我的应用程序?相对导入可能有效,但它们很丑陋,并且有自己的上下文问题,例如:

from ...site import app

# ValueError: Attempted relative import beyond toplevel package

I want to structure my Flask app something like:

./site.py
./apps/members/__init__.py
./apps/members/models.py

apps.members is a Flask Blueprint.

Now, in order to create the model classes I need to have a hold of the app, something like:

# apps.members.models
from flask import current_app
from flaskext.sqlalchemy import SQLAlchemy

db = SQLAlchemy(current_app)

class Member(db.Model):
    # fields here
    pass

But if I try and import that model into my Blueprint app, I get the dreaded RuntimeError: working outside of request context. How can I get a hold of my app correctly here? Relative imports might work but they’re pretty ugly and have their own context issues, e.g:

from ...site import app

# ValueError: Attempted relative import beyond toplevel package

回答 0

flask_sqlalchemy模块没有要与应用程序马上初始化-你可以这样做,而不是:

# apps.members.models
from flask_sqlalchemy import SQLAlchemy

db = SQLAlchemy()

class Member(db.Model):
    # fields here
    pass

然后在应用程序设置中,您可以调用init_app

# apps.application.py
from flask import Flask
from apps.members.models import db

app = Flask(__name__)
# later on
db.init_app(app)

这样可以避免周期性导入。

这种模式并没有必要在你把你所有的车型在一个文件中。只需将db变量导入每个模型模块即可。

# apps.shared.models
from flask_sqlalchemy import SQLAlchemy

db = SQLAlchemy()

# apps.members.models
from apps.shared.models import db

class Member(db.Model):
    # TODO: Implement this.
    pass

# apps.reporting.members
from flask import render_template
from apps.members.models import Member

def report_on_members():
    # TODO: Actually use arguments
    members = Member.filter(1==1).all()
    return render_template("report.html", members=members)

# apps.reporting.routes
from flask import Blueprint
from apps.reporting.members import report_on_members

reporting = Blueprint("reporting", __name__)

reporting.route("/member-report", methods=["GET","POST"])(report_on_members)

# apps.application
from flask import Flask
from apps.shared import db
from apps.reporting.routes import reporting

app = Flask(__name__)
db.init_app(app)
app.register_blueprint(reporting)

注意:这是一些功能的草图 -显然,您可以做很多事来简化开发工作(使用create_app模式,在某些文件夹中自动注册蓝图等)。

The flask_sqlalchemy module does not have to be initialized with the app right away – you can do this instead:

# apps.members.models
from flask_sqlalchemy import SQLAlchemy

db = SQLAlchemy()

class Member(db.Model):
    # fields here
    pass

And then in your application setup you can call init_app:

# apps.application.py
from flask import Flask
from apps.members.models import db

app = Flask(__name__)
# later on
db.init_app(app)

This way you can avoid cyclical imports.

This pattern does not necessitate the you place all of your models in one file. Simply import the db variable into each of your model modules.

Example

# apps.shared.models
from flask_sqlalchemy import SQLAlchemy

db = SQLAlchemy()

# apps.members.models
from apps.shared.models import db

class Member(db.Model):
    # TODO: Implement this.
    pass

# apps.reporting.members
from flask import render_template
from apps.members.models import Member

def report_on_members():
    # TODO: Actually use arguments
    members = Member.filter(1==1).all()
    return render_template("report.html", members=members)

# apps.reporting.routes
from flask import Blueprint
from apps.reporting.members import report_on_members

reporting = Blueprint("reporting", __name__)

reporting.route("/member-report", methods=["GET","POST"])(report_on_members)

# apps.application
from flask import Flask
from apps.shared import db
from apps.reporting.routes import reporting

app = Flask(__name__)
db.init_app(app)
app.register_blueprint(reporting)

Note: this is a sketch of some of the power this gives you – there is obviously quite a bit more that you can do to make development even easier (using a create_app pattern, auto-registering blueprints in certain folders, etc.)


回答 1

原来app.pyhttps://flask-sqlalchemy.palletsprojects.com/en/2.x/quickstart/

...

app = flask.Flask(__name__)
app.config['DEBUG'] = True
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/test.db'
db = flask.ext.sqlalchemy.SQLAlchemy(app)

class Person(db.Model):
    id = db.Column(db.Integer, primary_key=True)
...

class Computer(db.Model):
    id = db.Column(db.Integer, primary_key=True)
...

# Create the database tables.
db.create_all()

...

# start the flask loop
app.run()

我只是将一个app.py拆分为app.py和model.py而不使用Blueprint。在这种情况下,以上答案无效。需要行代码才能工作。

之前

db.init_app(app)

之后

db.app = app
db.init_app(app)

并且,以下链接非常有用。

http://piotr.banaszkiewicz.org/blog/2012/06/29/flask-sqlalchemy-init_app/

an original app.py: https://flask-sqlalchemy.palletsprojects.com/en/2.x/quickstart/

...

app = flask.Flask(__name__)
app.config['DEBUG'] = True
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/test.db'
db = flask.ext.sqlalchemy.SQLAlchemy(app)

class Person(db.Model):
    id = db.Column(db.Integer, primary_key=True)
...

class Computer(db.Model):
    id = db.Column(db.Integer, primary_key=True)
...

# Create the database tables.
db.create_all()

...

# start the flask loop
app.run()

I just splitted one app.py to app.py and model.py without using Blueprint. In that case, the above answer dosen’t work. A line code is needed to work.

before:

db.init_app(app)

after:

db.app = app
db.init_app(app)

And, the following link is very useful.

http://piotr.banaszkiewicz.org/blog/2012/06/29/flask-sqlalchemy-init_app/


如何从Django 1.7的初始迁移迁移回去?

问题:如何从Django 1.7的初始迁移迁移回去?

我用一些模型创建了一个新的应用程序,现在我发现一些模型没有经过深思熟虑。由于我尚未提交代码,因此明智的做法是将数据库迁移到最后的良好状态,并使用更好的模型重新进行迁移。在这种情况下,最后的良好状态是新应用程序不存在的数据库。

如何从Django 1.7的初始迁移迁移回去?

South一个可以这样做:

python manage.py migrate <app> zero

<app>将从迁移历史记录中清除并删除的所有表<app>

如何在Django 1.7迁移中做到这一点?

I created a new app with some models and now I noticed that some of the models are poorly thought out. As I haven’t committed the code the sensible thing would be to migrate the database to last good state and redo the migration with better models. In this case the last good state is database where the new app doesn’t exist.

How can I migrate back from initial migration in Django 1.7?

In South one could do:

python manage.py migrate <app> zero

Which would clear <app> from migration history and drop all tables of <app>.

How to do this with Django 1.7 migrations?


回答 0

您也可以使用Django 1.7+进行相同操作:

python manage.py migrate <app> zero

<app>将从迁移历史记录中清除,并删除所有的表<app>

有关更多信息,请参见django文档

You can do the same with Django 1.7+ also:

python manage.py migrate <app> zero

This clears <app> from migration history and drops all tables of <app>

See django docs for more info.


回答 1

您还可以使用版本号:

python manage.py migrate <app> 0002

来源:https : //docs.djangoproject.com/en/1.7/ref/django-admin/#django-admin-migrate

you can also use the version number:

python manage.py migrate <app> 0002

Source: https://docs.djangoproject.com/en/1.7/ref/django-admin/#django-admin-migrate


正确的方法来反转pandas.DataFrame?

问题:正确的方法来反转pandas.DataFrame?

这是我的代码:

import pandas as pd

data = pd.DataFrame({'Odd':[1,3,5,6,7,9], 'Even':[0,2,4,6,8,10]})

for i in reversed(data):
    print(data['Odd'], data['Even'])

当我运行此代码时,出现以下错误:

Traceback (most recent call last):
  File "C:\Python33\lib\site-packages\pandas\core\generic.py", line 665, in _get_item_cache
    return cache[item]
KeyError: 5

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\*****\Documents\******\********\****.py", line 5, in <module>
    for i in reversed(data):
  File "C:\Python33\lib\site-packages\pandas\core\frame.py", line 2003, in __getitem__
    return self._get_item_cache(key)
  File "C:\Python33\lib\site-packages\pandas\core\generic.py", line 667, in _get_item_cache
    values = self._data.get(item)
  File "C:\Python33\lib\site-packages\pandas\core\internals.py", line 1656, in get
    _, block = self._find_block(item)
  File "C:\Python33\lib\site-packages\pandas\core\internals.py", line 1936, in _find_block
    self._check_have(item)
  File "C:\Python33\lib\site-packages\pandas\core\internals.py", line 1943, in _check_have
    raise KeyError('no item named %s' % com.pprint_thing(item))
KeyError: 'no item named 5'

为什么会出现此错误?
我该如何解决?
正确的逆转方法是pandas.DataFrame什么?

Here is my code:

import pandas as pd

data = pd.DataFrame({'Odd':[1,3,5,6,7,9], 'Even':[0,2,4,6,8,10]})

for i in reversed(data):
    print(data['Odd'], data['Even'])

When I run this code, i get the following error:

Traceback (most recent call last):
  File "C:\Python33\lib\site-packages\pandas\core\generic.py", line 665, in _get_item_cache
    return cache[item]
KeyError: 5

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\*****\Documents\******\********\****.py", line 5, in <module>
    for i in reversed(data):
  File "C:\Python33\lib\site-packages\pandas\core\frame.py", line 2003, in __getitem__
    return self._get_item_cache(key)
  File "C:\Python33\lib\site-packages\pandas\core\generic.py", line 667, in _get_item_cache
    values = self._data.get(item)
  File "C:\Python33\lib\site-packages\pandas\core\internals.py", line 1656, in get
    _, block = self._find_block(item)
  File "C:\Python33\lib\site-packages\pandas\core\internals.py", line 1936, in _find_block
    self._check_have(item)
  File "C:\Python33\lib\site-packages\pandas\core\internals.py", line 1943, in _check_have
    raise KeyError('no item named %s' % com.pprint_thing(item))
KeyError: 'no item named 5'

Why am I getting this error?
How can I fix that?
What is the right way to reverse pandas.DataFrame?


回答 0

data.reindex(index=data.index[::-1])

或者简单地:

data.iloc[::-1]

将反转您的数据帧,如果您想使for循环从下到上,则可以执行以下操作:

for idx in reversed(data.index):
    print(idx, data.loc[idx, 'Even'], data.loc[idx, 'Odd'])

要么

for idx in reversed(data.index):
    print(idx, data.Even[idx], data.Odd[idx])

因为你得到一个错误reversed首先调用data.__len__()返回6,然后试图调用data[j - 1]用于jrange(6, 0, -1)和第一个电话会data[5]; 但在pandas数据框中data[5]表示第5列,没有第5列,因此它将引发异常。(请参阅文档

data.reindex(index=data.index[::-1])

or simply:

data.iloc[::-1]

will reverse your data frame, if you want to have a for loop which goes from down to up you may do:

for idx in reversed(data.index):
    print(idx, data.loc[idx, 'Even'], data.loc[idx, 'Odd'])

or

for idx in reversed(data.index):
    print(idx, data.Even[idx], data.Odd[idx])

You are getting an error because reversed first calls data.__len__() which returns 6. Then it tries to call data[j - 1] for j in range(6, 0, -1), and the first call would be data[5]; but in pandas dataframe data[5] means column 5, and there is no column 5 so it will throw an exception. ( see docs )


回答 1

您可以以更简单的方式反转行:

df[::-1]

You can reverse the rows in an even simpler way:

df[::-1]

回答 2

反转数据帧后,现有答案都不会重置索引。

为此,请执行以下操作:

 data[::-1].reset_index()

这是一个实用程序函数,它也按照@Tim的注释删除了旧的索引列:

def reset_my_index(df):
  res = df[::-1].reset_index(drop=True)
  return(res)

只需将数据框传递给函数

None of the existing answers resets the index after reversing the dataframe.

For this, do the following:

 data[::-1].reset_index()

Here’s a utility function that also removes the old index column, as per @Tim’s comment:

def reset_my_index(df):
  res = df[::-1].reset_index(drop=True)
  return(res)

Simply pass your dataframe into the function


回答 3

这有效:

    for i,r in data[::-1].iterrows():
        print(r['Odd'], r['Even'])

This works:

    for i,r in data[::-1].iterrows():
        print(r['Odd'], r['Even'])

查找列的最大值,并使用Pandas返回相应的行值

问题:查找列的最大值,并使用Pandas返回相应的行值

我正在尝试使用Python Pandas查找具有最大值的CountryPlace

这将返回最大值:

data.groupby(['Country','Place'])['Value'].max()

但我怎么得到相应CountryPlace的名字吗?

Using Python Pandas I am trying to find the Country & Place with the maximum value.

This returns the maximum value:

data.groupby(['Country','Place'])['Value'].max()

But how do I get the corresponding Country and Place name?


回答 0

假设df有一个唯一的索引,则该行具有最大值:

In [34]: df.loc[df['Value'].idxmax()]
Out[34]: 
Country        US
Place      Kansas
Value         894
Name: 7

请注意,idxmax返回索引标签。因此,如果DataFrame在索引中有重复项,则标签可能不会唯一地标识该行,因此df.loc可能会返回多个行。

因此,如果df没有唯一索引,则必须按照上述步骤使索引唯一。取决于DataFrame,有时您可以使用stackset_index使索引唯一。或者,您可以简单地重置索引(这样行将被重新编号,从0开始):

df = df.reset_index()

Assuming df has a unique index, this gives the row with the maximum value:

In [34]: df.loc[df['Value'].idxmax()]
Out[34]: 
Country        US
Place      Kansas
Value         894
Name: 7

Note that idxmax returns index labels. So if the DataFrame has duplicates in the index, the label may not uniquely identify the row, so df.loc may return more than one row.

Therefore, if df does not have a unique index, you must make the index unique before proceeding as above. Depending on the DataFrame, sometimes you can use stack or set_index to make the index unique. Or, you can simply reset the index (so the rows become renumbered, starting at 0):

df = df.reset_index()

回答 1

df[df['Value']==df['Value'].max()]

这将返回整个行的最大值

df[df['Value']==df['Value'].max()]

This will return the entire row with max value


回答 2

国家和地方是该系列的索引,如果不需要该索引,则可以设置as_index=False

df.groupby(['country','place'], as_index=False)['value'].max()

编辑:

似乎您想让每个国家/地区的价值最大化,以下代码将满足您的要求:

df.groupby("country").apply(lambda df:df.irow(df.value.argmax()))

The country and place is the index of the series, if you don’t need the index, you can set as_index=False:

df.groupby(['country','place'], as_index=False)['value'].max()

Edit:

It seems that you want the place with max value for every country, following code will do what you want:

df.groupby("country").apply(lambda df:df.irow(df.value.argmax()))

回答 3

我认为返回具有最大值的行的最简单方法是获取其索引。argmax()可用于返回具有最大值的行的索引。

index = df.Value.argmax()

现在,索引可以用于获取该特定行的功能:

df.iloc[df.Value.argmax(), 0:2]

I think the easiest way to return a row with the maximum value is by getting its index. argmax() can be used to return the index of the row with the largest value.

index = df.Value.argmax()

Now the index could be used to get the features for that particular row:

df.iloc[df.Value.argmax(), 0:2]

回答 4

使用的index属性DataFrame。请注意,我没有在示例中键入所有行。

In [14]: df = data.groupby(['Country','Place'])['Value'].max()

In [15]: df.index
Out[15]: 
MultiIndex
[Spain  Manchester, UK     London    , US     Mchigan   ,        NewYork   ]

In [16]: df.index[0]
Out[16]: ('Spain', 'Manchester')

In [17]: df.index[1]
Out[17]: ('UK', 'London')

您还可以通过该索引获取值:

In [21]: for index in df.index:
    print index, df[index]
   ....:      
('Spain', 'Manchester') 512
('UK', 'London') 778
('US', 'Mchigan') 854
('US', 'NewYork') 562

编辑

很抱歉造成您的误解,请尝试以下操作:

In [52]: s=data.max()

In [53]: print '%s, %s, %s' % (s['Country'], s['Place'], s['Value'])
US, NewYork, 854

Use the index attribute of DataFrame. Note that I don’t type all the rows in the example.

In [14]: df = data.groupby(['Country','Place'])['Value'].max()

In [15]: df.index
Out[15]: 
MultiIndex
[Spain  Manchester, UK     London    , US     Mchigan   ,        NewYork   ]

In [16]: df.index[0]
Out[16]: ('Spain', 'Manchester')

In [17]: df.index[1]
Out[17]: ('UK', 'London')

You can also get the value by that index:

In [21]: for index in df.index:
    print index, df[index]
   ....:      
('Spain', 'Manchester') 512
('UK', 'London') 778
('US', 'Mchigan') 854
('US', 'NewYork') 562

Edit

Sorry for misunderstanding what you want, try followings:

In [52]: s=data.max()

In [53]: print '%s, %s, %s' % (s['Country'], s['Place'], s['Value'])
US, NewYork, 854

回答 5

为了以最大值打印“国家和地区”,请使用以下代码行。

print(df[['Country', 'Place']][df.Value == df.Value.max()])

In order to print the Country and Place with maximum value, use the following line of code.

print(df[['Country', 'Place']][df.Value == df.Value.max()])

回答 6

我在列中查找最大值的解决方案:

df.ix[df.idxmax()]

,也是最低要求:

df.ix[df.idxmin()]

My solution for finding maximum values in columns:

df.ix[df.idxmax()]

, also minimum:

df.ix[df.idxmin()]

回答 7

我建议使用nlargest以获得更好的性能和较短的代码。进口pandas

df[col_name].value_counts().nlargest(n=1)

I’d recommend using nlargest for better performance and shorter code. import pandas

df[col_name].value_counts().nlargest(n=1)

回答 8

您可以使用:

打印(df [df [‘Value’] == df [‘Value’]。max()])

You can use:

print(df[df['Value']==df['Value'].max()])

回答 9

import pandas
df是您创建的数据框。

使用命令:

df1=df[['Country','Place']][df.Value == df['Value'].max()]

这将显示其最大值的国家和地方。

import pandas
df is the data frame you create.

Use the command:

df1=df[['Country','Place']][df.Value == df['Value'].max()]

This will display the country and place whose value is maximum.


回答 10

尝试使用pandas导入数据时遇到类似的错误,数据集的第一列在单词开头之前有空格。我删除了空间,它就像一个魅力!

I encountered a similar error while trying to import data using pandas, The first column on my dataset had spaces before the start of the words. I removed the spaces and it worked like a charm!!


将base64中的字符串转换为图像并保存在Python中的文件系统中

问题:将base64中的字符串转换为图像并保存在Python中的文件系统中

我有一个base64格式的字符串,它表示PNG图像。有没有一种方法可以将该图像保存为PNG文件到文件系统?


我使用flex对图像进行编码。其实这就是我在服务器上得到的(在任何建议的方法后都看不到任何图像:()

iVBORw0KGgoAAAANSUhEUgAABoIAAAaCCAYAAAABZu+EAAAqOElEQVR42uzBAQEAAACAkP6v7ggK\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAA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I have a string in base64 format, which represents PNG image. Is there a way to save this image to the filesystem, as a PNG file?


I encoded the image using flex. Actually this is what I get on server (can’t see any image after any of proposed methods :( )

iVBORw0KGgoAAAANSUhEUgAABoIAAAaCCAYAAAABZu+EAAAqOElEQVR42uzBAQEAAACAkP6v7ggK\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAA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回答 0

从…开始

img_data = b'iVBORw0KGgoAAAANSUhEUgAABoIAAAaCCAYAAAABZu+EAAAqOElEQVR42uzBAQEAAACAkP6v7ggK\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACA2YMDAQAAAAAg\n/9dGUFVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVWkPDgkA\nAAAABP1/7QobAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAIcAeHkAAeLqlDIAAAAASUVORK5CYII='

使用base64编解码器解码数据,然后将其写入文件系统。

# In Python 2.7
fh = open("imageToSave.png", "wb")
fh.write(img_data.decode('base64'))
fh.close()

# or, more concisely using with statement
with open("imageToSave.png", "wb") as fh:
    fh.write(img_data.decode('base64'))

将该示例更新为Python 3,该Python 3从字符串/字节.encode().decode()函数中删除了对任意编解码器的支持:

# For both Python 2.7 and Python 3.x
import base64
with open("imageToSave.png", "wb") as fh:
    fh.write(base64.decodebytes(img_data))

Starting with

img_data = b'iVBORw0KGgoAAAANSUhEUgAABoIAAAaCCAYAAAABZu+EAAAqOElEQVR42uzBAQEAAACAkP6v7ggK\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACA2YMDAQAAAAAg\n/9dGUFVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\nVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVWkPDgkA\nAAAABP1/7QobAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAIcAeHkAAeLqlDIAAAAASUVORK5CYII='

Decoded the data using the base64 codec, and then write it to the filesystem.

# In Python 2.7
fh = open("imageToSave.png", "wb")
fh.write(img_data.decode('base64'))
fh.close()

# or, more concisely using with statement
with open("imageToSave.png", "wb") as fh:
    fh.write(img_data.decode('base64'))

Modernizing this example to Python 3, which removed arbitrary codec support from string/bytes .encode() and .decode() functions:

# For both Python 2.7 and Python 3.x
import base64
with open("imageToSave.png", "wb") as fh:
    fh.write(base64.decodebytes(img_data))

回答 1

如果imagestr是位图数据(我们现在知道不是),则可以使用它

imagestr是base64编码的字符串
width是图像的宽度是图像
height的高度

from PIL import Image
from base64 import decodestring

image = Image.fromstring('RGB',(width,height),decodestring(imagestr))
image.save("foo.png")

由于imagestr只是编码的png数据

from base64 import decodestring

with open("foo.png","wb") as f:
    f.write(decodestring(imagestr))

If the imagestr was bitmap data (which we now know it isn’t) you could use this

imagestr is the base64 encoded string
width is the width of the image
height is the height of the image

from PIL import Image
from base64 import decodestring

image = Image.fromstring('RGB',(width,height),decodestring(imagestr))
image.save("foo.png")

Since the imagestr is just the encoded png data

from base64 import decodestring

with open("foo.png","wb") as f:
    f.write(decodestring(imagestr))

回答 2

您也可以将其保存到字符串缓冲区,然后根据需要进行操作,

import cStringIO
data = json.loads(request.POST['imgData'])  # Getting the object from the post request
image_output = cStringIO.StringIO()
image_output.write(data.decode('base64'))   # Write decoded image to buffer
image_output.seek(0)  # seek beginning of the image string
# image_output.read()  # Do as you wish with it!

在django中,您可以将其另存为上传文件以保存到模型中:

from django.core.files.uploadedfile import SimpleUploadedFile
suf = SimpleUploadedFile('uploaded_file.png', image_output.read(), content_type='image/png')

或通过电子邮件发送:

email = EmailMessage('Hello', 'Body goes here', 'from@example.com',
                                     ['me@me.com', ])
                email.attach('design.png', image_output.read(), 'image/png')
                email.send()

You can also save it to a string buffer and then do as you wish with it,

import cStringIO
data = json.loads(request.POST['imgData'])  # Getting the object from the post request
image_output = cStringIO.StringIO()
image_output.write(data.decode('base64'))   # Write decoded image to buffer
image_output.seek(0)  # seek beginning of the image string
# image_output.read()  # Do as you wish with it!

In django, you can save it as an uploaded file to save to a model:

from django.core.files.uploadedfile import SimpleUploadedFile
suf = SimpleUploadedFile('uploaded_file.png', image_output.read(), content_type='image/png')

Or send it as an email:

email = EmailMessage('Hello', 'Body goes here', 'from@example.com',
                                     ['me@me.com', ])
                email.attach('design.png', image_output.read(), 'image/png')
                email.send()

回答 3

您可以使用枕头。

pip install Pillow



image = base64.b64decode(str(base64String))       
fileName = 'test.jpeg'

imagePath = FILE_UPLOAD_DIR + fileName

img = Image.open(io.BytesIO(image))
img.save(imagePath, 'jpeg')
return fileName

完整源代码的参考:https : //abhisheksharma.online/convert-base64-blob-to-image-file-in-python/

You can use Pillow.

pip install Pillow



image = base64.b64decode(str(base64String))       
fileName = 'test.jpeg'

imagePath = FILE_UPLOAD_DIR + fileName

img = Image.open(io.BytesIO(image))
img.save(imagePath, 'jpeg')
return fileName

reference for complete source code: https://abhisheksharma.online/convert-base64-blob-to-image-file-in-python/


回答 4

 import base64
 from PIL import Image
 import io
 image = base64.b64decode(str('stringdata'))       
 fileName = 'test.jpeg'

 imagePath = ('D:\\base64toImage\\'+"test.jpeg")
 img = Image.open(io.BytesIO(image))
 img.save(imagePath, 'jpeg')
 import base64
 from PIL import Image
 import io
 image = base64.b64decode(str('stringdata'))       
 fileName = 'test.jpeg'

 imagePath = ('D:\\base64toImage\\'+"test.jpeg")
 img = Image.open(io.BytesIO(image))
 img.save(imagePath, 'jpeg')

回答 5

您可能使用PyPNG包的png.Reader对象执行此操作-将base64字符串解码为常规字符串(通过base64标准库),然后将其传递给构造函数。

You could probably use the PyPNG package’s png.Reader object to do this – decode the base64 string into a regular string (via the base64 standard library), and pass it to the constructor.


回答 6

试试这个解决方案,

图像文件->二进制编码的字符串

二进制编码的字符串->图像文件

import base64

"""
1st step - convert image into binary
"""
with open("original_image.png", "rb") as original_file:
    encoded_string = base64.b64encode(original_file.read())

print(encoded_string)
# xmzWowsfJbpGwCe0DTveqwvos7Mf0lcVNe/Q+G1hO/p+UNPd/stUse8AhP/3fDixf8HI3No67nvhlYAAAAASUVORK5CYII='

print(type(encoded_string))
# <class 'bytes'>

"""
2nd step - create new image using the encoded string
"""
with open("new_image.png", "wb") as new_file:
    new_file.write(base64.decodebytes(encoded_string))

参考文献:

Try this solution,

image file –> binary encoded string

binary encoded string –> image file

import base64

"""
1st step - convert image into binary
"""
with open("original_image.png", "rb") as original_file:
    encoded_string = base64.b64encode(original_file.read())

print(encoded_string)
# xmzWowsfJbpGwCe0DTveqwvos7Mf0lcVNe/Q+G1hO/p+UNPd/stUse8AhP/3fDixf8HI3No67nvhlYAAAAASUVORK5CYII='

print(type(encoded_string))
# <class 'bytes'>

"""
2nd step - create new image using the encoded string
"""
with open("new_image.png", "wb") as new_file:
    new_file.write(base64.decodebytes(encoded_string))

References:


回答 7

如果您要解码网络图像,则可以使用以下代码:

import base64

with open("imageToSave.png", "wb") as fh:
  fh.write(base64.urlsafe_b64decode('data'))

数据=>是编码的字符串

它将解决填充错误

If you are trying to decode a web image you can simply use this :

import base64

with open("imageToSave.png", "wb") as fh:
  fh.write(base64.urlsafe_b64decode('data'))

data => is the encoded string

It will take care of the padding errors