数据框如下所示：
0, 3710.968017578125, 2012-01-07T03:13:43.859Z
1, 3710.968017578125, 2012-01-07T03:13:48.890Z
2, 3712.472900390625, 2012-01-07T03:13:53.906Z
3, 3712.472900390625, 2012-01-07T03:13:58.921Z
4, 3713.110107421875, 2012-01-07T03:14:03.900Z
5, 3713.110107421875, 2012-01-07T03:14:03.937Z
6, 3713.89892578125, 2012-01-07T03:14:13.900Z
7, 3713.89892578125, 2012-01-07T03:14:13.968Z
8, 3713.89892578125, 2012-01-07T03:14:19.000Z
9, 3714.64990234375, 2012-01-07T03:14:24.000Z
10, 3714.64990234375, 2012-01-07T03:14:24.015Z
11, 3714.64990234375, 2012-01-07T03:14:29.000Z
12, 3714.64990234375, 2012-01-07T03:14:29.031Z
在某些行中，有些行具有不同的毫秒级时间戳，我想删除它们，只保留具有不同第二个时间戳的行。有些行具有相同的值（毫秒和秒），而不同的行（如第9到12行）因此，我不能使用a.loc[a.shift() != a]
所需的输出将是：
0, 3710.968017578125, 2012-01-07T03:13:43.859Z
1, 3710.968017578125, 2012-01-07T03:13:48.890Z
2, 3712.472900390625, 2012-01-07T03:13:53.906Z
3, 3712.472900390625, 2012-01-07T03:13:58.921Z
4, 3713.110107421875, 2012-01-07T03:14:03.900Z
6, 3713.89892578125, 2012-01-07T03:14:13.900Z
8, 3713.89892578125, 2012-01-07T03:14:19.000Z
9, 3714.64990234375, 2012-01-07T03:14:24.000Z
11, 3714.64990234375, 2012-01-07T03:14:29.000Z
答案 0 :(得分：1)
尝试：
$.ajax({
type: "POST",
url: 'wsQueryBuilder.asmx/Getdataset1',
data: '{"strQuery": "' + strQuery + '","strCon": "' + strCon + '"}',
contentType: "application/json; charset=utf-8",
dataType: "json",
async: false,
success: function(response) {
$("#spnGetdet").html(response.d);
var combo = eval(response.d);
var trHTML = "";
var rptHdrstr;
var counter = 0;
var col = [];
var colno = [];
var resultColumns = [];
var trhead = "";
var trfoot = "";
var alignColumns = [];
var totalColumns = [];
var warpColumns = [];
var strPrnTotal = "";
groupColumn = 0;
aggrgColumn = 0;
groupColumnName = "";
aggrgColumnName = "";
aggrgType = 0;
$('#tblrptviewhdr thead').find('tr').remove().end()
$('#tblrptviewhdr tbody').find('tr').remove().end()
$('#tblrptviewhdr tfoot').find('tr').remove().end()
var obj;
var alignobj;
var totobj;
obj = {
sWidth: "20px",
data: 'id',
sDefaultContent: '',
};
resultColumns.push(obj);
var cp = 0;
var selectCtrl = document.getElementById("cboRptGroupBy");
for (var key in combo[0]) {
if (col.indexOf(key) === -1) {
cp = cp + 1;
col.push(key);
colno.push(col.indexOf(key) + 1);
trhead += ('<th>' + key + '</th>')
trfoot += ('<th></th>')
if (typeof combo[0][key] === "number") {
alignobj = cp;
totobj = 1;
alignColumns.push(alignobj);
} else {
totobj = 0;
}
obj = { "mDataProp": key };
resultColumns.push(obj);
totalColumns.push(totobj);
warpColumns.push(cp);
if (selectCtrl.options[selectCtrl.selectedIndex].text == key) {
groupColumn = cp;
groupColumnName = selectCtrl.options[selectCtrl.selectedIndex].text;
}
if (document.getElementById("cboRptAggrgBy").value == key) {
aggrgColumn = cp;
aggrgColumnName = document.getElementById("cboRptAggrgBy").value;
}
}
if (alignColumns.length > 0) {
if ($('#chkResponsive').is(':checked')) {
var theadervalue = '<tr><th>S.No</th>' + trhead + '</tr>';
var tfootervalue = '<tr><th>Total </br></th>' + trfoot + '</tr>';
$("#trfooter").css("display", "");
} else {
var theadervalue = '<tr><th>S.No</th>' + trhead + '</tr>';
var tfootervalue = '<tr><th>Total </br></th>' + trfoot + '</tr>';
$("#trfooter").css("display", "");
}
} else {
var theadervalue = '<tr><th>S.No</th>' + trhead + '</tr>';
var tfootervalue = '<tr><th></th>' + trfoot + '</tr>';
$("#trfooter").css("display", "none");
}
$('#trheader').append(theadervalue)
$('#trfooter').append(tfootervalue)
$("#tblrptviewhdr").css("display", "");
var QbDTdata = {
data: combo,
columns: resultColumns,
"bDestroy": true,
"bFilter": true,
"oTableTools": true,
"bLengthChange": true,
"bInfo": true,
"oLanguage": true,
"paging": true,
"colReorder": true,
"bFooter": true,
"responsive": true,
"oLanguage": { "oPaginate": { "sPrevious": "<", "sNext": ">" } },
"pageLength": true,
//"iDisplayLength": tblPageLen,
"aLengthMenu": [
[10, 25, 50, 100, -1],
[10, 25, 50, 100, "All"]
],
dom: 'Bfrtip',
stateSaveParams: function(settings, data) {
for (var i = 0, ien = data.columns.length; i < ien; i++) {
delete data.columns[i].visible;
}
},
"stateLoadParams": function(settings, data) {
data.order = "";
},
"fnRowCallback": function(nRow, aData, iDisplayIndex, iDisplayIndexFull) {
var index = iDisplayIndexFull + 1;
$('td:first', nRow).html(index);
console.log(nRow)
return nRow;
},
"columnDefs": [
{ className: "dt-body-right", "targets": alignColumns },
{ className: "text-nowrap", "targets": warpColumns },
{ "searchable": false, "orderable": true, "targets": [0] }
],
"aDataSort": [groupColumn, 'asc'],
orderCellsTop: true,
initComplete: function() {
var info = this.api().page.info();
console.log('Total records', info.recordsTotal);
document.getElementById("lblTotalRecords").innerHTML = "Total Record(s) : " + info.recordsTotal;
if ($('#chkAddFilter').is(':checked')) {
if (groupColumn == 0) {
configFilter(this, warpColumns);
}
}
},
buttons: [{
extend: 'print',
text: '<i class="fa fa-print" style="color: #313131;"></i> <u>P</u>rint',
css: 'btn btn-primary button-input',
key: {
key: 'p',
altkey: true
},
message: '',
titleAttr: 'print',
title: '',
header: true,
footer: true,
autoPrint: true,
exportOptions: {
columns: ':visible',
grouped_array_index: groupColumnName,
aggrg_array_index: aggrgColumnName,
grouped_index: groupColumn,
aggrg_index: aggrgColumn,
aggrg_type: document.getElementById("cboRptAggrgType").value,
fileexp_type: 1
}
},
{
extend: 'pdfHtml5',
text: '<i class="fa fa-file-pdf-o" style="color: #ff2116;"></i> P<u>D</u>F',
titleAttr: 'PDF',
//download: 'open',
filename: frmQryView.hRptHdr.value,
key: {
key: 'd',
altkey: true
},
header: true,
footer: true,
orientation: PrnOrientation,
pageSize: PrnPaperSize,
exportOptions: {
columns: ':visible', //PdfExpClm,
stripHtml: true,
stripNewlines: false,
grouped_array_index: groupColumnName,
aggrg_array_index: aggrgColumnName,
grouped_index: groupColumn,
aggrg_index: aggrgColumn,
aggrg_type: document.getElementById("cboRptAggrgType").value,
fileexp_type: 2
}
}
],
"bStateSave": true,
"fixedHeader": tblFixHdr
};
}
},
failure: function(msg) {
alert("No records to display ");
}
});
我希望这是不言自明的。
答案 1 :(得分：0)
您可以使用以下脚本。我没有得到您的数据框列名称，因此我在列['x'，'date_time']下方发明了
df = pd.DataFrame([
(3710.968017578125, pd.to_datetime('2012-01-07T03:13:43.859Z')),
(3710.968017578125, pd.to_datetime('2012-01-07T03:13:48.890Z')),
(3712.472900390625, pd.to_datetime('2012-01-07T03:13:53.906Z')),
(3712.472900390625, pd.to_datetime('2012-01-07T03:13:58.921Z')),
(3713.110107421875, pd.to_datetime('2012-01-07T03:14:03.900Z')),
(3713.110107421875, pd.to_datetime('2012-01-07T03:14:03.937Z')),
(3713.89892578125, pd.to_datetime('2012-01-07T03:14:13.900Z')),
(3713.89892578125, pd.to_datetime('2012-01-07T03:14:13.968Z')),
(3713.89892578125, pd.to_datetime('2012-01-07T03:14:19.000Z')),
(3714.64990234375, pd.to_datetime('2012-01-07T03:14:24.000Z')),
(3714.64990234375, pd.to_datetime('2012-01-07T03:14:24.015Z')),
(3714.64990234375, pd.to_datetime('2012-01-07T03:14:29.000Z')),
(3714.64990234375, pd.to_datetime('2012-01-07T03:14:29.031Z'))],
columns=['x', 'date_time'])
df['time_diff'] = df.groupby('x')['date_time'].diff() df = df[(df['time_diff'].isnull()) | (df['time_diff'].map(lambda x: x.seconds > 1))] df = df.drop(['time_diff'], axis=1) df