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qs.stringify({ 'list':this.excelData },{ arrayFormat: 'brackets' })
arrayFormat 可以格式化你的數(shù)組參數(shù)

arrayformat選項(xiàng)輸出 指定數(shù)組的格式
　　qs.stringify({ id: ['b', 'c'] }, { arrayFormat: 'indices' })
　　// 'id[0]=b&id[1]=c'
　　qs.stringify({ id: ['b', 'c'] }, { arrayFormat: 'brackets' })
　　// 'id[]=b&id[]=c'
　　qs.stringify({ id: ['b', 'c'] }, { arrayFormat: 'repeat' })
　　// 'id=b&id=c'

Python2版本里的/運(yùn)算結(jié)果取整數(shù),例如：

5 / 2 = 2;
2 / 3 = 0;

到了Python3里就變了；

5 / 2 = 2.5;
2 / 3 = 0.66...;

再有就是Python2和Python3兩個(gè)版本有些地方差別還是比較大的，兩個(gè)版本不是兼容的，使用時(shí)注意區(qū)分Python的版本；

少量用游標(biāo)算,大量用服務(wù)器算,畢竟服務(wù)器計(jì)算能力比Python快

看起來(lái) 0:4, 4:7，... 可視為數(shù)組的下標(biāo)，可簡(jiǎn)化成元素個(gè)數(shù) 4, 3，...

那么問(wèn)題演變成“由同元素個(gè)數(shù)組成的序列，生成相應(yīng)的數(shù)組”，如下

def compute(*size_list):
    l2 = []
    for i, size in enumerate(size_list):
        l2.extend([str(i+1)] * size)
    return l2


def test_compute():
    assert compute(4, 3, 2, 2, 1) == [
        '1', '1', '1', '1',
        '2', '2', '2',
        '3', '3',
        '4', '4',
        '5',
    ]

pyenv + pyenv-virtualenv

可以曲線救國(guó)

for name, content in enumerate(globals()):

    if isinstance(content, list):
        print(name, content)

這樣可以獲取到全局的list變量的名字

在windows里環(huán)境變量的改變需要重啟電腦

你的文件應(yīng)該先 open, 然后再read. 我下面的這個(gè)例子能夠正常work

#!/usr/bin/python

import csv

filename = './a.csv'
  
duration_list = []
with open(filename) as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        duration_list.append(row['duration'])


print duration_list

你要做的是用jstack查看線程狀態(tài)，并且結(jié)合當(dāng)時(shí)的io與cpu情況做進(jìn)一步的分析。

經(jīng)實(shí)驗(yàn)及查看文檔發(fā)現(xiàn)，find()函數(shù)返回的是類(lèi)型為cursor的值，而find_one()返回的是數(shù)組或?qū)ο?，故要訪問(wèn)返回的文檔的某個(gè)字段時(shí)根據(jù)使用的查詢(xún)函數(shù)，若為resu = db.collection.find(),則可通過(guò)如下的方式訪問(wèn)：
resu = db[username + "fileinfo"].find()

historyfilelist = []
try:
    for ele in resu:
        global historyfilelist
        historyfilelist.append(ele["filename"])
    return HttpResponse(json.dumps(historyfilelist))

若是采用的find_one()函數(shù)，則可以直接通過(guò)字典訪問(wèn),(如下面的_id獲取的方式)
resu = db[fileinfo['username'] + "fileinfo"].find_one({"filename": filename})

if resu is None:
    db[fileinfo['username'] + "fileinfo"].insert(fileinfo)
    return HttpResponse(json.dumps({"Uploaded": []}))
elif resu["_id"] == md5:

上面方法親測(cè)可行。

兩次是相同的，例如兩個(gè)線程同時(shí)給total+1，如果不加鎖，就會(huì)兩個(gè)同時(shí)操作，導(dǎo)致total最終只+1，實(shí)際上是+2的

chrome調(diào)試面板看network，具體是哪個(gè)請(qǐng)求
本地和線上的環(huán)境對(duì)比

已解決，原因是文件名和模塊名重名導(dǎo)致的

注意:python3以后才支持yield from語(yǔ)法

import collections


def flatten(d, prefix="", sep="_"):
    def _take_prefix(k, v, p):
        if p:
            yield from flatten(v, "{}{}{}".format(p, sep, k))
        else:
            yield from flatten(v, str(k))

    if isinstance(d, dict):
        for k, v in d.items():
            if isinstance(v, str) or not isinstance(v, collections.Iterable):
                if prefix:
                    yield "{}{}{}".format(prefix, sep, k), v
                else:
                    yield k, v
            elif isinstance(v, dict):
                yield from _take_prefix(k, v, prefix)
            elif isinstance(v, list):
                for i in v:
                    yield from _take_prefix(k, i, prefix)
            else:
                pass
    else:
        pass

dic = {your dataset}
for key, value in flatten(dic):
    print("{}: {}".format(key, value))

結(jié)果如下,應(yīng)該能拍平了

status: changed
dataset_id: 5a4b463c855d783af4f5f695
dataset_name: AE_E
dataset_label: 1- ADVERSE EVENTS - Not Analyzed
details_variables_variable_id: 5a4b4647855d783b494f9d3f
details_variables_variable_name: CPEVENT
details_variables_variable_label: CPEVENT
details_variables_status: changed
details_variables_details_r_type_new_value: unary
details_variables_details_r_type_old_value: factor
details_variables_message: Variable with different R Type
details_variables_variable_id: 5a4b4647855d783b494f9d25
details_variables_variable_name: CPEVENT2
details_variables_variable_label: CPEVENT2
details_variables_status: changed
details_variables_details_r_type_new_value: unary
details_variables_details_r_type_old_value: binary
details_variables_message: Variable with different R Type
details_variables_variable_id: 5a4b4647855d783b494f9d26
details_variables_variable_name: CP_UNSCHEDULED
details_variables_variable_label: CP_UNSCHEDULED
details_variables_status: changed
details_variables_details_r_type_new_value: undefined
details_variables_details_r_type_old_value: unary
details_variables_message: Variable with different R Type
details_variables_variable_id: 5a4b4647855d783b494f9d02
details_variables_variable_name: VISIT_NUMBER
details_variables_variable_label: VISIT_NUMBER
details_variables_status: changed
details_variables_details_r_type_new_value: unary
details_variables_details_r_type_old_value: integer
details_variables_message: Variable with different R Type
details_variables_variable_id: 5a4b4647855d783b494f9ccf
details_variables_variable_name: VISIT_NUMBER2
details_variables_variable_label: VISIT_NUMBER2
details_variables_status: changed
details_variables_details_r_type_new_value: unary
details_variables_details_r_type_old_value: binary
details_variables_message: Variable with different R Type
details_many_visits: None

針對(duì)你修改后的問(wèn)題, 再加個(gè)函數(shù)就搞定:

# 這個(gè)fuck_all函數(shù)比較特例, 完全是針對(duì)你要區(qū)分的dataset下面的N個(gè)變量信息這種需求
def fuck_all(dic, prefix="details_variables"):
    lst = list(flatten(dic))  # flatten函數(shù)則比較通用,任何嵌套數(shù)據(jù)集都可以用它拍平
    lines = []
    top = {k: v for k, v in lst if not k.startswith(prefix)}
    index = 0
    for key, value in lst:
        if not key.startswith(prefix):
            continue
        else:
            if not lines:
                lines.append(top.copy())
        if key in lines[index].keys():
            index += 1
            lines.append(top.copy())
        lines[index][key] = value
    return lines

d = {your dataset}
for i in fuck_all(d):
    print(i)    

結(jié)果長(zhǎng)這樣,應(yīng)該是能滿(mǎn)足你需求了

{'status': 'changed', 'dataset_id': '5a4b463c855d783af4f5f695', 'dataset_name': 'AE_E', 'dataset_label': '1- ADVERSE EVENTS - Not Analyzed', 'details_many_visits': None, 'details_variables_variable_id': '5a4b4647855d783b494f9d3f', 'details_variables_variable_name': 'CPEVENT', 'details_variables_variable_label': 'CPEVENT', 'details_variables_status': 'changed', 'details_variables_details_r_type_new_value': 'unary', 'details_variables_details_r_type_old_value': 'factor', 'details_variables_message': 'Variable with different R Type'}
{'status': 'changed', 'dataset_id': '5a4b463c855d783af4f5f695', 'dataset_name': 'AE_E', 'dataset_label': '1- ADVERSE EVENTS - Not Analyzed', 'details_many_visits': None, 'details_variables_variable_id': '5a4b4647855d783b494f9d25', 'details_variables_variable_name': 'CPEVENT2', 'details_variables_variable_label': 'CPEVENT2', 'details_variables_status': 'changed', 'details_variables_details_r_type_new_value': 'unary', 'details_variables_details_r_type_old_value': 'binary', 'details_variables_message': 'Variable with different R Type'}
{'status': 'changed', 'dataset_id': '5a4b463c855d783af4f5f695', 'dataset_name': 'AE_E', 'dataset_label': '1- ADVERSE EVENTS - Not Analyzed', 'details_many_visits': None, 'details_variables_variable_id': '5a4b4647855d783b494f9d26', 'details_variables_variable_name': 'CP_UNSCHEDULED', 'details_variables_variable_label': 'CP_UNSCHEDULED', 'details_variables_status': 'changed', 'details_variables_details_r_type_new_value': 'undefined', 'details_variables_details_r_type_old_value': 'unary', 'details_variables_message': 'Variable with different R Type'}
{'status': 'changed', 'dataset_id': '5a4b463c855d783af4f5f695', 'dataset_name': 'AE_E', 'dataset_label': '1- ADVERSE EVENTS - Not Analyzed', 'details_many_visits': None, 'details_variables_variable_id': '5a4b4647855d783b494f9d02', 'details_variables_variable_name': 'VISIT_NUMBER', 'details_variables_variable_label': 'VISIT_NUMBER', 'details_variables_status': 'changed', 'details_variables_details_r_type_new_value': 'unary', 'details_variables_details_r_type_old_value': 'integer', 'details_variables_message': 'Variable with different R Type'}
{'status': 'changed', 'dataset_id': '5a4b463c855d783af4f5f695', 'dataset_name': 'AE_E', 'dataset_label': '1- ADVERSE EVENTS - Not Analyzed', 'details_many_visits': None, 'details_variables_variable_id': '5a4b4647855d783b494f9ccf', 'details_variables_variable_name': 'VISIT_NUMBER2', 'details_variables_variable_label': 'VISIT_NUMBER2', 'details_variables_status': 'changed', 'details_variables_details_r_type_new_value': 'unary', 'details_variables_details_r_type_old_value': 'binary', 'details_variables_message': 'Variable with different R Type'}

送佛送到西好了

from functools import reduce
import json

import pandas as pd


with open("your dataset file", "r") as fh:
    dic = json.load(fh)

df = pd.DataFrame(reduce(lambda x, y: x + y, (fuck_all(i) for i in dic)))
df.to_csv("out.csv", index=False)

成品

參考一下這個(gè)答案吧： https://segmentfault.com/q/10...

你這邊直接運(yùn)行pyc，它的默認(rèn)搜索路徑和預(yù)加載模塊中都沒(méi)有AAA，所以不可能識(shí)別出來(lái)。正常的流程是運(yùn)行一個(gè)頂層腳本，它的同級(jí)目錄中有多個(gè)依賴(lài)的package

pip install netifaces -i http://pypi.douban.com/simple/

大概思路：
crontab里面添加這個(gè)xxx.py的文件，或者用supervisor管理這個(gè)進(jìn)程
然后xxx.py里面執(zhí)行subprocess.call('http-server')
不是很?chē)?yán)謹(jǐn)，但這個(gè)思路能實(shí)現(xiàn)

看樣子你使用的是 python3 吧？
在 python2 里面可以直接使用 bytes(1)；而在 python3 里面 你還可以使用 bytes(str(1), 'utf8')。手動(dòng)滑稽：)

RewriteRule ^(.*)$ index.php/$1 [QSA,NU,PT,L]

在偽靜態(tài)規(guī)則后面加上 [QSA,NU,PT,L] 配置完成