Datar Preparation Feature
データ前処理加工も
ブラウザから思いのまま
ブラウザから思いのまま
VARISTA Data Editor
VARISTA Data Editorを使えば、データを整えるのもの思いのまま。直感的に操作できるデザイン。便利で豊富なフィルタ。パワフルなあなた専用のクラウド。こんなにデータの前処理が簡単だと思えるようなツールは他にありません。
Good bye coding again
You don't need to know anything about coding to edit data, and it's a great tool for busy people who normally use code to edit data. Or maybe you're just starting out in data science, or you're ready to take on Kaggle. VARISTA Data Editor is designed to help you get started with intuitive data editing.
A magical tool to work at a faster clip
VARISTA Data Editor allows you to edit your data smoothly and quickly, without stress, and when combined with VARISTA's AutoML, you can run the AI model's training and make further adjustments to your data in the meantime. Trial and error becomes incredibly easy to do. All you need is a browser and data.
On your way with 1-click
The edited data can be immediately put to machine learning seamlessly in VARISTA. Not only that, the data can be exported to various formats for use in external services. Even the tedious task of applying the data to test data can be completed in 1-click.
It's easy to find what you want to do
Data editing is just as easy as you think with more than 50 filters, including column deletion, column-to-column calculations, outlier removal, undersampling, target encoding, and more, not to mention the ability to combine data.
Commonly used separate data concatenation filters make merging separate data much easier than merging them in code with a simple interface.
Split the numbers
Box Cox Encoding
Column-to-column calculations
Replace Categories
Count Encoding
Frequency Encoding
Specify a range of values
Compare to fixed value
Compare columns
Concentrate
Change Datetime Format
Drop Outliers
Delete Column
Duplicate Column
Filling in missing with mean of the other category columns
Filling in missing with max
Filling in missing with mean
Filling in missing with median
Filling in missing with min
Filling in missing with mode
Filling in missing with other column values
Filling in missing with fixed value
GroupBy
Extract the first row
Label Ecoding
Merge
Get Dummies
Logical AND
Logical Nand
Logical NOR
Logical NOT
Logical OR
Logical XOR
To Lower
Max
Mean
Min
New Column
One Hot Encoding
Add Prefix
Rename the column
Round
Extracting rows at random
Search Text
Split
String Concat
String Length
Delete Text
Replace Text
Slice Text
Strip
Add Suffix
Sum
Extract the end of row
Target Encoding
As Boolean
As Number
As String
Undersampling
To Upper
Split the numbers
Box Cox Encoding
Column-to-column calculations
Replace Categories
Count Encoding
Frequency Encoding
Specify a range of values
Compare to fixed value
Compare columns
Concentrate
Change Datetime Format
Drop Outliers
Delete Column
Duplicate Column
Filling in missing with mean of the other category columns
Filling in missing with max
Filling in missing with mean
Filling in missing with median
Filling in missing with min
Filling in missing with mode
Filling in missing with other column values
Filling in missing with fixed value
GroupBy
Extract the first row
Label Ecoding
Merge
Get Dummies
Logical AND
Logical Nand
Logical NOR
Logical NOT
Logical OR
Logical XOR
To Lower
Max
Mean
Min
New Column
One Hot Encoding
Add Prefix
Rename the column
Round
Extracting rows at random
Search Text
Split
String Concat
String Length
Delete Text
Replace Text
Slice Text
Strip
Add Suffix
Sum
Extract the end of row
Target Encoding
As Boolean
As Number
As String
Undersampling
To Upper
Split the numbers
Box Cox Encoding
Column-to-column calculations
Replace Categories
Count Encoding
Frequency Encoding
Specify a range of values
Compare to fixed value
Compare columns
Concentrate
Change Datetime Format
Drop Outliers
Delete Column
Duplicate Column
Filling in missing with mean of the other category columns
Filling in missing with max
Filling in missing with mean
Filling in missing with median
Filling in missing with min
Filling in missing with mode
Filling in missing with other column values
Filling in missing with fixed value
GroupBy
Extract the first row
Label Ecoding
Merge
Get Dummies
Logical AND
Logical Nand
Logical NOR
Logical NOT
Logical OR
Logical XOR
To Lower
Max
Mean
Min
New Column
One Hot Encoding
Add Prefix
Rename the column
Round
Extracting rows at random
Search Text
Split
String Concat
String Length
Delete Text
Replace Text
Slice Text
Strip
Add Suffix
Sum
Extract the end of row
Target Encoding
As Boolean
As Number
As String
Undersampling
To Upper
Split the numbers
Box Cox Encoding
Column-to-column calculations
Replace Categories
Count Encoding
Frequency Encoding
Specify a range of values
Compare to fixed value
Compare columns
Concentrate
Change Datetime Format
Drop Outliers
Delete Column
Duplicate Column
Filling in missing with mean of the other category columns
Filling in missing with max
Filling in missing with mean
Filling in missing with median
Filling in missing with min
Filling in missing with mode
Filling in missing with other column values
Filling in missing with fixed value
GroupBy
Extract the first row
Label Ecoding
Merge
Get Dummies
Logical AND
Logical Nand
Logical NOR
Logical NOT
Logical OR
Logical XOR
To Lower
Max
Mean
Min
New Column
One Hot Encoding
Add Prefix
Rename the column
Round
Extracting rows at random
Search Text
Split
String Concat
String Length
Delete Text
Replace Text
Slice Text
Strip
Add Suffix
Sum
Extract the end of row
Target Encoding
As Boolean
As Number
As String
Undersampling
To Upper
Column details
With the VARISTA Data Editor, detailed information about the columns is displayed in 1-click. Not only does it display the number of data and missing values, but it also displays a histogram. In addition, if the column is a category, a list of categories is displayed. You can quickly check for category fluctuation.
Table Statistic
The entire data is presented in VARISTA's beautiful design, which is useful when you want to get a quick overview of your data. You can also see a snapshot of each step in the process of adding a filter, so you can see how the data changed at each step.
自動ビジュアライズ
データの分布、相関、ヒートマップもあっという間に表示します。
モデル構築前にデータ分布や相関を確認したい時にも面倒なコード記述は不要です。
モデル構築前にデータ分布や相関を確認したい時にも面倒なコード記述は不要です。