data-science-kit 0.0.1
Data Science Basic Functions
License: MIT
Requires Python: >=3.6
Keywords: data science,data,data kit,data science kit,science,science kit,data engineer,data engineering,exploratory data analysis,eda,measurement units,data operations,data preprocessing,preprocessing

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# Data Science Kit - Kitbag
KITBAG is a helpful library where you can find basic data science functions under three main headings. Three main headings are given below. The titles are given below;

* Measurement Units
  * Arithmetic Mean
  * Geometric Mean
  * Harmonic Mean
  * Median
  * Mode
  * Variance
  * Standart Deviation
  * Find Minimum Value In List
  * Find Maximum Value In List
  * Kurtosis
  * Skewnewss
  * Quantiles
  * Range Of Change
  * Covariance
  * Correlation
* Exploratory Data Analysis
  * Copy Dataset
  * Show Head & Tail Values
  * Structural Information
  * Variable Types
  * Object To Categorical
  * Observation Values Size
  * Variable Values Size
  * Size Of Dataset
  * Dimension Of DataFrame 
  * Dimension Of Series
  * Variable Names Of Dataset
  * Descriptive Statistics Of Dataset
  * Descriptive Statistics Of Numerical Variable 
  * Descriptive Statistics Of Categorical Variable  
  * Select Categorical Variables 
  * Select Numerical Variables 
  * Frequency Of Categorical Variables
  * Categorical Variables Percentage Of Dataset
  * Find Unique Categorical Variables
  * Frequency Unique Categorical Variables
  * Measure Operations Of Single Categorical Variables With All Numerical Variables
  * Measure Operations Of Single Categorical Variables Single Numerical Variables
  * Measure Operations Of Multiple Categorical Variables Single Numerical Variables
  * Create And Sort Rank Variables
  * Select Range Row By Index
* Data Operations
  * Missing Values
    * Is There Any NAN Values
    * Total NAN Values
    * Percentage NAN Values
    * Drop NAN Values In Row 
    * Drop NAN Values In Column
    * Fill NAN Values Column
    * Fill NAN Values All Dataset
    * Fil NAN Values KNN
    * Fil NAN Values EM
  * Outlier Values
    * Select Lower Outlier Values - According to the specified Column
    * Select Upper Outlier Values - According to the specified Column
    * Select All Outlier Values - According to the specified Column
    * Delete Outlier Values
    * Fill Mean Value All Outlier Values
    * Fill Suppression Method All Outlier Values
    * Local Outlier Factor
    * Local Outlier Factor Suppression Method
  * Variables Standartization
    * Numerical Variables Standartization  
    * Numerical Variables Normalization  
    * Numerical Variables Transformation  
    * Numerical Variables Binarize  
    * Numerical Variables To Categorical Variables
  * Encoding Operations
    * Ordinal Encoder
    * Label Encoder
    * Where Encoder
    * Onehot Encoder