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Preprocessing Data

Data cleaning, smoothing, grouping

数据可以需要预处理技术，以确保准确，高效或有意义的分析。数据清洁是指查找，删除和替换不良数据的方法。检测当地极端变化和突然的变化可以帮助识别重要的数据趋势。平滑和下滑是从数据中消除噪声和多项式趋势的过程，同时缩放会改变数据的边界。分组和分组方法通过组识别数据特征。

应用程序

Data Cleaner Preprocess and organize column-oriented data

Attività di Live Editor

Clean Missing Data 发现,填充,或删除丢失的数据in the Live Editor
Clean Outlier Data Find, fill, or remove outliers in the Live Editor
Compute by Group Summarize, transform, or filter by group in the Live Editor
Find Change Points Find abrupt changes in data in the Live Editor
找到当地的极端 Find local maxima and minima in the Live Editor
标准化数据 Center and scale data in the Live Editor
流畅的数据 Smooth noisy data in the Live Editor
消除趋势 Remove polynomial trend from data in the Live Editor

Funzioni

Espandi Tutto

anymissing Determine if any array element is missing
ismissing Find missing values
rmmissing Remove missing entries
fillmissing 填写缺失值
missing Create missing values
standardizeMissing Insert standard missing values
isoutlier 查找数据中的离群值
filloutliers Detect and replace outliers in data
rmoutliers Detect and remove outliers in data
Movmad 移动中值绝对偏差
ischange Find abrupt changes in data
islocalmin 找到当地的最小值
islocalmax Find local maxima
smoothdata Smooth noisy data
movmean Moving mean
movmedian Moving median
detrend Remove polynomial trend
trenddecomp Find trends in data
normalize Normalize data
rescale Scale range of array elements
discretize Group data into bins or categories
groupcounts 组元素数量
集体窗 Filter by group
groupsummary 小组摘要计算
GroupTransform 小组转换
histcounts 直方图箱计数
histcounts2 Bivariate histogram bin counts
findgroups Find groups and return group numbers
splitapply Split data into groups and apply function
RowFun 应用程序ly function to table or timetable rows
varfun 应用程序ly function to table or timetable variables
accumarray Accumulate vector elements

Argomenti