需要用'detected'和bool3_res res的键is_doc1检查bool_res的{{1}}键
如果bool_2，那么我的bool3_res['detected'] == bool1_res['is_doc1'] == True必须返回
如果resp，那么我的bool3_res['detected'] == bool2_res['is_doc1'] == True必须返回\
3：否则返回“无效”
数据框
resp我的代码
user_uid,bool1,bool2,bool3,bool1_res,bool2_res,bool3_res
1001,27452.webp,981.webp,d92e.webp,"{'is_doc1': False, 'is_doc2': True}","{'is_doc1': True, 'is_doc2': True}","{'detected': True, 'count': 1}"
1002,27452.webp,981.webp,d92e.webp,"{'is_doc1': True, 'is_doc2': True}","{'is_doc1': False, 'is_doc2': True}","{'detected': True, 'count': 1}"
我的期望值
df ['new']
def new_func(x):
d1 = df['bool1_res'].to_dict()
d1 = eval(d1[0])
d2 = df['bool2_res'].to_dict()
d2 = eval(d2[0])
d3 = df['bool3_res'].to_dict()
d3 = eval(d3[0])
if d1['is_doc1'] == d3['detected'] == True:
resp = {
"task_id": "uid",
"group_id": "uid",
"data": {
"document1": df['bool1'],
"document2": df['bool3']
}
}
elif d2['is_doc1'] == d3['detected'] == True:
resp = {
"task_id": "user_uid",
"group_id": "uid",
"data": {
"document1": df['bool2'],
"document2": df['bool3']
}
}
elif d3['detected'] == False:
resp = 'Not valid'
else:
resp = 'Not valid'
return resp
df['new'] = df.apply(new_func, axis = 1)
#df['new'] = df[['bool1', 'bool2', 'bool3', 'bool1_res', 'bool2_res', 'bool3_res']].applymap(new_func)
我的出场 df ['new']
{'u_id': 'uid', 'group': 'uid', 'data': {'document1': ['981.webp'], 'document2': {'d92e.webp'}}}"
{'u_id': 'uid', 'group': 'uid', 'data': {'document1': ['27452.webp'], 'document2': {'d92e.webp'}}}"
答案 0 :(得分：0)
您应避免使用eval，而应将x与df一起使用，而将[]与x['bool1']一起使用，以对每一行进行处理，并为一个元素列表将x['bool2']添加到{{1 }}，x['bool3']和import ast
def new_func(x):
d1 = ast.literal_eval(x['bool1_res'])
d2 = ast.literal_eval(x['bool2_res'])
d3 = ast.literal_eval(x['bool3_res'])
if d1['is_doc1'] == d3['detected'] == True:
resp = {
"task_id": "uid",
"group_id": "uid",
"data": {
"document1": [x['bool1']],
"document2": [x['bool3']]
}
}
elif d2['is_doc1'] == d3['detected'] == True:
resp = {
"task_id": "user_uid",
"group_id": "uid",
"data": {
"document1": [x['bool2']],
"document2": [x['bool3']]
}
}
elif d3['detected'] == False:
resp = 'Not valid'
else:
resp = 'Not valid'
return resp
df['new'] = df.apply(new_func, axis = 1)
：
print (df['new'].iat[0])
{'task_id': 'user_uid', 'group_id': 'uid', 'data': {'document1': ['981.webp'], 'document2': ['d92e.webp']}}
print (df['new'].iat[1])
{'task_id': 'uid', 'group_id': 'uid', 'data': {'document1': ['27452.webp'], 'document2': ['d92e.webp']}}
function onLoad() {
logT("onLoad function called...");
document.body.style.background = 'green'
document.addEventListener("deviceready", onDeviceReady, false);
}
function onDeviceReady() {
logT("Device ready function called...");
loadUserTrackData();
}
function loadUserTrackData(){
cordova.exec(function(jsonObj) {
logT("Success -- loadUserTrackData");
if(jsonObj.length > 0){
var getContact = JSON.parse(jsonObj);
UserTrackData.CMI._id = getContact.id;
UserTrackData.CMI._name = getContact.name;
UserTrackData.CMI._lesson_status = getContact.lessonStatus;
UserTrackData.CMI._entry = getContact.entry;
UserTrackData.CMI._credit = getContact.credit;
UserTrackData.CMI._score_raw = getContact.scoreRaw;
UserTrackData.CMI._score_min = getContact.scoreMin;
UserTrackData.CMI._score_max = getContact.scoreMax;
UserTrackData.CMI._laucnh_data = getContact.launchData;
UserTrackData.CMI._lession_location = getContact.lessonLocation;
UserTrackData.CMI._lesson_mode = getContact.lessonMode;
UserTrackData.CMI._exit = getContact.exit;
UserTrackData.CMI._session_time = getContact.sessionTime;
UserTrackData.CMI._suspend_data = getContact.suspendData;
UserTrackData.CMI._total_time = getContact.totalTime;
UserTrackData.CMI._selected_position = getContact.position;
}
cordova.exec(function(url) {logT("Success");
var objTag = document.getElementById("serverData");
if(url.endsWith(".mp4") || url.endsWith(".3gp") || url.endsWith(".MOV")){
objTag.innerHTML = '<video width="100%" height="100%" autoplay controls controlsList="nodownload nofullscreen" poster="orange_rectangle.png"><source src=' + url + ' type="video/mp4">This browser does not support HTML5 video.</video>';
}
else if(url.toLowerCase().endsWith(".mp3") || url.endsWith(".m4a") || url.endsWith(".wav")){
objTag.innerHTML = '<audio controls><source src=' + url + '>Your browser does not support audio element.</video>';
}
else if(url.toLowerCase().endsWith(".pdf") || url.toLowerCase().endsWith(".doc")
|| url.toLowerCase().endsWith(".docx") || url.toLowerCase().endsWith(".xls")
|| url.toLowerCase().endsWith(".xlsx") || url.toLowerCase().endsWith(".ppt") || url.toLowerCase().endsWith(".pptx")){
objTag.innerHTML = '<object type="text/html" data=https://view.officeapps.live.com/op/view.aspx?src=' + url+ '></object>';
}
//scorm play path
else{
objTag.innerHTML = '<object type="text/html" data=' + url + '></object>';
}
},
function(e) {logT("Error: "+e);},
"JCommunicatorPlugin", "loadPackageURL",["", "file:///android_asset/www/samples/RuntimeBasicCalls_SCORM12/shared/launchpage.html", "" || {}]);},
function(e) {logT("Error: "+e);},
"JCommunicatorPlugin", "loadUserTrackData",["", "", "" || {}]);
}
//logT("Loading: "+url)
//file:///android_asset/www/samples/RuntimeBasicCalls_SCORM12/shared/launchpage.html
//iFrame height
//objTag.innerHTML = '<object type="text/html" data=' + url + '></object>';
//objTag.setAttribute('data', url);
function resizeIframe(obj) {
obj.style.height = obj.contentWindow.document.body.scrollHeight + 'px';
}
function logT(text){
cordova.exec(function() {},
function(e) {console.log("Error: "+e);},
"JCommunicatorPlugin", "log", ["", text, "" || {}]);
}
function insertScoElement(elementName, elementValue, position){
cordova.exec(function() {logT("got success result from insertScoElement");},
function(e){logT("Error: "+e);},
"JCommunicatorPlugin", "setSco", [elementName, elementValue, "" || {}]);
if (elementName == JSConstants.CMI.LessonStatus &&
(elementValue.toLowerCase() == "completed" ||
elementValue.toLowerCase() == "passed" )) {
logT("status track blocked as this is already completed");
cordova.exec(function() {logT("got success result from insertSco Completion status");},
function(e){logT("Error: "+e);},
"JCommunicatorPlugin", "setScoAsComplete", [position, elementValue, "" || {}]);
}
}
function getScoElementValue(elementName){
cordova.exec(function(elementVal) {logT("got success result from getScoElementValue");},
function(e) {logT("Error: "+e);},
"JCommunicatorPlugin", "getSco", [elementName, "", "" || {}]);
}
function scoInit(){
cordova.exec(function() {logT("got success result from scoInit");},
function(e){logT("Error: "+e);},
"JCommunicatorPlugin", "init", ["", "", "" || {}]);
}
function scoCommit(strEmpty){
cordova.exec(function() {logT("got success result from scoCommit");},
function(e){logT("Error: "+e);},
"JCommunicatorPlugin", "commit", [strEmpty, "", "" || {}]);
}
function scoFinish(){
cordova.exec(function() {logT("got success result from scoFinish");},
function(e) {logT("Error: "+e);},
"JCommunicatorPlugin", "finish", ["", "", "" || {}]);
}
var JSConstants = {
CMI:
{
Entry: "cmi.core.entry",
Id: "cmi.core.student_id",
Name: "cmi.core.student_name",
LessonStatus: "cmi.core.lesson_status",
None: "cmi.core.none",
Location: "cmi.core.lesson_location",
Credit: "cmi.core.credit",
SessionTime: "cmi.core.session_time",
TotalTime: "cmi.core.total_time",
ScoreRaw: "cmi.core.score.raw",
ScoreMin: "cmi.core.score.min",
ScoreMax: "cmi.core.score.max",
LessonMode: "cmi.core.lesson_mode",
Exit: "cmi.core.exit",
CmiObjectives: "cmi.objectives",
CmiInteractions: "cmi.interactions",
CmiInteractionsCount: "cmi.interactions._count",
CmiLDChildren: "cmi.launch_data._children",
CmiLDCount: "cmi.launch_data._count",
CmiLData: "cmi.launch_data",
CmiSuspendData: "cmi.suspend_data",
CmiLDCount: "cmi.launch_data._count",
CmiObjectiveChildren: "cmi.objectives._children",
CmiObjectiveCount: "cmi.objectives._count",
CmiInteractionsChildren: "cmi.interactions._children",
CmiInteractionsCount: "cmi.interactions._count",
CmiStudentDataMasteryScore: "cmi.student_data.mastery_score",
CmiSpAudio: "cmi.student_preference.audio",
CmiSpSpeed: "cmi.student_preference.speed",
CmiSpText: "cmi.student_preference.text",
CmiComments: "cmi.comments"
}
}
var UserTrackData = {
CMI:{
_id: "",
_name: "",
_credit: "",
_entry: "",
_exit: "",
_laucnh_data: "",
_lession_location: "",
_lesson_mode: "",
_lesson_status: "",
_score_max: "",
_score_min: "",
_score_raw: "",
_session_time: "",
_suspend_data: "",
_total_time: "",
_selected_position: 0
}
}
function printReturningData(strElementName){
if (strElementName == JSConstants.CMI.Id) { logT("sco input : " + UserTrackData.CMI._id);
} else if (strElementName == JSConstants.CMI.Name) { logT("sco input : " + UserTrackData.CMI._name);
} else if (strElementName == JSConstants.CMI.LessonStatus) { logT("sco input : " + UserTrackData.CMI._lesson_status);
} else if (strElementName == JSConstants.CMI.Entry){ logT("sco input : " + UserTrackData.CMI._entry);
} else if (strElementName == JSConstants.CMI.None){ logT("sco input : " + "");
} else if (strElementName == JSConstants.CMI.Credit){ logT("sco input : " + UserTrackData.CMI._credit);
} else if (strElementName == JSConstants.CMI.ScoreRaw){ logT("sco input : " + UserTrackData.CMI._score_raw);
} else if (strElementName == JSConstants.CMI.ScoreMin){ logT("sco input : " + UserTrackData.CMI._score_min);
} else if (strElementName == JSConstants.CMI.ScoreMax){ logT("sco input : " + UserTrackData.CMI._score_max);
} else if (strElementName == JSConstants.CMI.CmiLData){ logT("sco input : " + UserTrackData.CMI._laucnh_data);
} else if (strElementName == JSConstants.CMI.Location){ logT("sco input : " + UserTrackData.CMI._lession_location);
} else if (strElementName == JSConstants.CMI.LessonMode){ logT("sco input : " + UserTrackData.CMI._lesson_mode);
} else if (strElementName == JSConstants.CMI.Exit){ logT("sco input : " + UserTrackData.CMI._exit);
} else if (strElementName == JSConstants.CMI.SessionTime){ logT("sco input : " + UserTrackData.CMI._session_time);
} else if (strElementName == JSConstants.CMI.CmiSuspendData){ logT("sco input : " + UserTrackData.CMI._suspend_data);
} else if (strElementName == JSConstants.CMI.TotalTime){ logT("sco input : " + UserTrackData.CMI._total_time);
} else if (strElementName == JSConstants.CMI.CmiLDChildren){ logT("sco input : " + ""); // unknown.. copied from lms
} else if (strElementName == JSConstants.CMI.CmiLDCount){ logT("sco input : " + ""); // unknown.. copied from lms
} else if ((strElementName.indexOf(JSConstants.CMI.CmiObjectives) >= 0) ||
(strElementName.indexOf(JSConstants.CMI.CmiInteractions) >= 0)){
logT("sco input : " + ""); // unknown.. copied from lms
}else { logT("sco input : " + ""); // unknown.. copied from lms
}
}
if (!String.prototype.endsWith) {
String.prototype.endsWith = function(search, this_len) {
if (this_len === undefined || this_len > this.length) {
this_len = this.length;
}
return this.substring(this_len - search.length, this_len) === search;
};
}答案 1 :(得分：0)
我假设这是扩展代码行后数据的样子：（此外，即使您只添加一些空格也很容易阅读... ^ _ ^）
df = pd.DataFrame(
[
[1001, "27452.webp", "981.webp", "d92e.webp",
"{'is_doc1': False, 'is_doc2': True}",
"{'is_doc1': True, 'is_doc2': True}",
"{'detected': True, 'count': 1}"
],
[1002, "27452.webp", "981.webp", "d92e.webp",
"{'is_doc1': True, 'is_doc2': True}",
"{'is_doc1': False, 'is_doc2': True}",
"{'detected': True, 'count': 1}"
],
[1003, "27452.webp", "981.webp", "d92e.webp",
"{'is_doc1': True, 'is_doc2': True}",
"{'is_doc1': False, 'is_doc2': True}",
"{'detected': False, 'count': 1}"
],
],
columns=['user_uid', 'bool1', 'bool2', 'bool3', 'bool1_res', 'bool2_res',
'bool3_res'
]
)
执行过程分为两部分：（1）解析字符串和（2）处理/创建“新”列值。
# required packages
import ast
import pandas as pd
# for type suggestions
from typing import Any
此功能通过pd.DataFrame.applymap应用于数据框中的每个元素，并使用ast.literal_eval，如@jezrael正确建议的那样。
def str2dict(x: Any):
"""(Step 1) Parses argument using ast.literal_eval"""
try:
x = ast.literal_eval(x.strip())
# if x is not parsable, return x as-is
except ValueError as e:
pass
finally:
return x
此功能应用于数据框的每一行（通过pd.DataFrame.agg）：
根据您发布的函数中的逻辑，我：
检查bool3['detected']是否为False（您的前两个条件都已检测到== True）；如果是这样，请引发ValueError
检查bool1的is_doc1是否为True，否则bool2的is_doc1为True
如果is_doc1都不为True，请加注ValueError
def make_newcol_entry(x: pd.Series):
"""(Step 2) constructs "new" column value for pandas group"""
try:
if x.bool3_res['detected'] is False:
raise ValueError
# check is_doc1 properties
elif x.bool1_res['is_doc1'] is True:
document1 = x.bool1
elif x.bool2_res['is_doc1'] is True:
document1 = x.bool2
else:
raise ValueError
except ValueError:
entry = "not valid"
pass
# if there is `is_doc1` that is True, construct your entry.
else:
entry = {
"task_id": "uid",
"group_id": "uid",
"data": {"document1": document1, "document2": x.bool3}
}
return entry
df = df.assign(new=lambda x: x.applymap(str2dict) \
.agg(make_newcol_entry, axis=1))
请注意，这会解析数据框中的所有个元素。
要仅解析{em> bool_res列，可以分两步执行：
# select and parse only res cols ('bool#_res'), then apply
df.update(df.filter(regex=r'_res$', axis=1).applymap(str2dict))
df = df.assign(lambda x: x.agg(apply_make_newcol_entry, axis=1))
$ df
user_uid bool1 bool2 bool3 bool1_res bool2_res bool3_res new
0 1001 27452.webp 981.webp d92e.webp {'is_doc1': False, 'is_doc2': True} {'is_doc1': True, 'is_doc2': True} {'detected': True, 'count': 1} {'task_id': 'uid', 'group_id': 'uid', 'data': {'document1': '981.webp', 'document2': 'd92e.webp'}}
1 1002 27452.webp 981.webp d92e.webp {'is_doc1': True, 'is_doc2': True} {'is_doc1': False, 'is_doc2': True} {'detected': True, 'count': 1} {'task_id': 'uid', 'group_id': 'uid', 'data': {'document1': '27452.webp', 'document2': 'd92e.webp'}}
2 1003 27452.webp 981.webp d92e.webp {'is_doc1': True, 'is_doc2': True} {'is_doc1': False, 'is_doc2': True} {'detected': False, 'count': 1} not valid
$ df['new']
0 {'task_id': 'uid', 'group_id': 'uid', 'data': {'document1': '981.webp', 'document2': 'd92e.webp'}}
1 {'task_id': 'uid', 'group_id': 'uid', 'data': {'document1': '27452.webp', 'document2': 'd92e.webp'}}
2 not valid
Name: new, dtype: object