G-Accad
commented
1 year ago Is your feature request related to a problem? Please describe. When dealing with large volumes of raw data, it becomes challenging to quickly and effectively understand the characteristics and key insights from the data. Without a structured summary of the raw data, it's time-consuming and error-prone to make informed decisions or perform further analysis.
Describe the solution you'd like This workflow step should involve the following components:
- Data Quality Checks: Perform data quality checks to identify missing values, duplicates, outliers, and any other data anomalies. This step ensures that the data is clean and reliable for analysis.
- Data Profiling: Automatically generate descriptive statistics and metrics for the selected columns in the raw data, including measures like mean, median, standard deviation, and count. This will provide a high-level overview of the data's distribution and characteristics.
- Data Visualization: Create visualizations such as histograms, box plots, and scatter plots for numeric data, and bar charts for categorical data.
Describe alternatives you've considered
- Manual Data Summary: For example Excel (too time consuming)
Workflow:
graph LR
func["Functions Runs"]
input1("Type of Dataset") --> func
input2("Columns of interest") --> func
func --> output1("Generate Basic Descriptive Statistics")
output1--> output2("Visualizations: Histograms, Box Plots")
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