我的csv数据看起来像这样：
$(document).ready(function(){
// scroll function
var mouseEvent = "DOMMouseScroll mousewheel wheel";
if( $('body').hasClass('mozilla') ){
mouseEvent = "DOMMouseScroll";
}
var isDoingStuff = false;
$(window).bind(mouseEvent, function(event){
console.log(event.originalEvent.deltaY);
$('body').find('.scroll-sec').show();
var windowWidth = $(window).width();
if( windowWidth > 767){
var headerHeight = parseInt( $('#cc-navbar').outerHeight() ) || 0;
if( $('body').hasClass('onepage-scroll') ){
event.preventDefault();
var currentdiv = $('body').find('.scroll-sec.active');
var current_id = $('body').find('.scroll-sec.active').attr('id');
var id_num = parseInt(current_id.split(/[-]+/).pop());
console.log(event.originalEvent.wheelDelta);
if (event.originalEvent.wheelDelta > 0 || event.originalEvent.detail < 0) {
//scrolled up
if( $('.site-footer').hasClass('at-bottom') ){
if(isDoingStuff) { return; }
isDoingStuff = true;
currentdiv.removeClass('active');
// currentdiv.prev('.scroll-sec').addClass('active');
$('body').find('.site-footer').removeClass('at-bottom');
$('body').find('.slier-menu-right a').removeClass('active');
id_num -= 1;
$('body').find('.slier-menu-right').find('a[href="#section-'+id_num+'"]').addClass('active');
currentdiv.css("transform", "translateY(100%)");
$('body').find('.site-footer').css("transform", "translateY(100%)");
$('body').find('#section-'+id_num).css("transform", "translateY(0)");
$('body').find('#section-'+id_num).addClass('active');
setTimeout(function() {isDoingStuff = false;}, 150);
}else if (!$('#section-0').hasClass("active")){
if(isDoingStuff) { return; }
isDoingStuff = true;
// console.log('scrolled up');
currentdiv.removeClass('active');
$('body').find('.site-footer').removeClass('at-bottom');
currentdiv.css("transform", "translateY(100%)");
$('body').find('.site-footer').css("transform", "translateY(100%)");
currentdiv.prevAll('.scroll-sec').css("transform", "translateY(-100%)");
$('body').find('.slier-menu-right a').removeClass('active');
id_num -= 1;
$('body').find('.slier-menu-right').find('a[href="#section-'+id_num+'"]').addClass('active');
$('body').find('#section-'+id_num).addClass('active');
$('body').find('#section-'+id_num).css("transform", "translateY(0)");
setTimeout(function() {isDoingStuff = false;}, 150);
$('.scroll-sec').find('.cc-scroll-down').remove();
$('.scroll-sec.active').append('<a class="cc-scroll-down" href="#"><span></span></a>');
if( id_num == 0 ){
if( $('nav.navbar-fixed-top').hasClass('shrink') ){
$('nav.navbar-fixed-top').removeClass('shrink');
}
}
}
}else{
//scrolled down
if ($('#section-0').hasClass("active")){
if( !$('nav.navbar-fixed-top').hasClass('shrink') ){
$('nav.navbar-fixed-top').addClass('shrink');
}
}
if (!$('.prefooter-section').hasClass("active")){
if(isDoingStuff) { return; }
isDoingStuff = true;
// console.log('scrolled down');
currentdiv.removeClass('active');
id_num += 1;
$('body').find('#section-'+id_num).addClass('active');
if( !$('.prefooter-section').hasClass('active') ){
currentdiv.css("transform", "translateY(-100%)");
currentdiv.nextAll('.scroll-sec').css("transform", "translateY(100%)");
$('body').find('.slier-menu-right a').removeClass('active');
$('body').find('.slier-menu-right').find('a[href="#section-'+id_num+'"]').addClass('active');
$('body').find('#section-'+id_num).css("transform", "translateY(0)");
$('.scroll-sec').find('.cc-scroll-down').remove();
$('.scroll-sec.active').append('<a class="cc-scroll-down" href="#"><span></span></a>');
}else{
$('body').find('.prefooter-section').css('height', 'auto');
var prefooterHeight = parseInt( $('body').find('.prefooter-section').outerHeight() ) || 0;
var footerHeight = parseInt( $('.site-footer').outerHeight() ) || 0;
var totalHeight = prefooterHeight+footerHeight;
$('body').find('.slier-menu-right a').removeClass('active');
$('body').find('.slier-menu-right').find('a[href="#section-'+id_num+'"]').addClass('active');
currentdiv.css("transform", "translateY(-"+totalHeight+"px)");
$('.prefooter-section').css("transform", "translateY(-"+footerHeight+"px)");
$('body').find('.site-footer').css("transform", "translateY(0)");
$('body').find('.site-footer').addClass('at-bottom');
}
setTimeout(function() {isDoingStuff = false;}, 150);
// if( !$('.prefooter-section').hasClass('active') ){}
}
}
}
}else{
console.log('scrolled just');
}
});
});
我想导入到DataFrame中，最左边的值是索引，中间是列标识符，最右边是该索引处列的值。
以前我将它们分开并手动导入，然后加入一个帧。我想知道是否有更快的方法可以一步到位。
答案 0 :(得分：3)
使用pd.read_csv，然后pivot将数据读入数据框：
import pandas as pd
df = pd.read_csv('data', header=None, names=['index','columns','value'])
df = df.pivot('index', 'columns', 'value')
print(df)
产量
columns SMState actuatorLowerFrontDuty ambientTemperature \
index
1471361094509 NaN NaN NaN
1471361094512 NaN 0.0 NaN
1471361094515 14.0 NaN NaN
1471361094516 NaN NaN NaN
1471361094519 NaN NaN 23.5
columns doorCtrlStatus doorLowerPosition doorUpperPosition \
index
1471361094509 NaN -73.334887 -3.29595
1471361094512 NaN NaN NaN
1471361094515 5.0 NaN NaN
1471361094516 NaN NaN NaN
1471361094519 NaN NaN NaN
columns lateralAccel longitudinalAccel sectionLowerCurrFiltered
index
1471361094509 NaN NaN NaN
1471361094512 NaN NaN -0.2
1471361094515 NaN NaN NaN
1471361094516 25.55 25.55 NaN
1471361094519 NaN NaN NaN