dsx 0.9.4.0

The utilities pack for data science and analytics task. The core module ds_utils (Data Science Utilities) is designed to work with Pandas to simplify common tasks, such as generating metadata for the dataframe, validating merged dataframe, and visualizing dataframe.

<< Data Science Utilities (DSX)>>

The dsx package contains a collection of wrapper functions to simplify common operations in data analytics tasks. The core module ds_utils (data science utilities) is designed to work with DataFrame in Pandas to simplify common tasks.

The package can be can be used in the following setup:

• Jupyter Notebook
• Jupyter Lab
• PyCharm's Python Console
• iPython Console
• Python Script

Intallation

• Installation using Pip:

    pip install dsx

Documentation

Full Documentation Site: https://dataninja.ml/static/pages/docs_dsx/index.html

1. Core Module: "ds_utils"

The core module is "ds_utils". The module contains a list of functions that can accomplish common data analytics tasks with less codes. Basically, these functions are wrappers for commonly-used methods in Pandas, particularly methods of DataFrame object.

Some of the key features of the DataFrame utility functions are as following:

• Generate metadata of columns in a DataFrame
  • Number & percentage of missing values
  • Number & percentage of unique values
  • Data Type
• Generate accumulated percentage of values in a column
• Quick Rename of a single column
• Reorder columns of a DataFrame
• Standardize column names into iPython-friendly names
• Retrieve column name(s) by a partial keyword
• Expand concatenated string in a column into child table
• Visualize DataFrame object
  • DataGrid Viewer
  • Pivot Table Viewer
  • Quick Analyzer (Pivot table and visualizations)

1.1 Usage

Below is example codes for importing the module:

    from dsx.ds_utils import *

There are 2 categories of methods in dsx's classes, which are to be called in different ways:

• Methods: Dynamic functions of the class's instance
  • Invoke through the extended domain ('ds') of the native DataFrame object

    df = pd.read_excel(os.path.join(os.getcwd(), "data.xlsx"))
    df.ds.isnull("Column_Name")

• Static functions Static functions from the class's object
  • Invoke as a static function of pd_utils class

    df = pd.read_excel(os.path.join(os.getcwd(), "data.xlsx"))
    dsx.isnull(df, "Column_Name")

2. Data Science Workflow "ds_workflow" (Active Development / Work-In-Progress)

The "ml_utils" module contains the methods for simplifying common tasks in a data science workflow. The methods are built on top of the functions in the core module "pd_utils".

Some of the key features of the module are as the following:

• Get the column name of the features that are categorical
• Get the column name of the features that are numerical
• Create or merge the dummy variables created from categorical features with option to use k-1 dummification
• Data Exploration
  • Generate barplot and accumulated percentage report for all the categorical features
  • Generate distribution plot for all the numerical features
  • Generate heatmap of the the correlation matrix
• Preprocessing
  • Create a dataframe with all standardized features merged with other features
  • Generate features list
• Model Assessment
  • Generate Recall-Precision-Threshold Curve
  • Generate truepositive_falsepositive Curve

2.1 Usage

The methods in the module are only callable as the extended domain 'ml' in the native Pandas DataFrame object.

Calling a method in "ml_workflow":

    df = pd.read_excel(os.path.join(os.getcwd(), "data.xlsx"))

    cols_categorical = df.ml.get_features_categorical()