🌌 DataNova

DataNova — Quickly explore your data in a few lines of code!

🛠️ Installation

pip install DataNova

🚀 Features

• Data Profile: Assess the quality of your data profile(df)
• Bar Graph: Shows the top 5 most common values bar(df, 'Column_Name')
• Histogram: Plots the distribution hist(df, 'Column_Name')
• Exploratory Data Analysis: EDA(df)
• Simple Linear Regression: lm(df, x_var, y_var)

what's next?

• Logistic Regression (coming soon!)

Examples

Examples below use a dataset provided in the package called WINE_DF

from datanova import *
profile( WINE_DF )

• Most of our data is not blank, expect for 'price' (only 5% blank).

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bar( WINE_DF , 'province', top_n=7)

• California accounts for 32% of total wine sales, and the top five regions collectively contribute over half of all sales.

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hist( WINE_DF , 'price' , xlim = [0,105], n_bins = 25)

• On average, a bottle of wine costs $38. The price ranges from: $20-$48

$$\:$$ $$\:$$

eda( WINE_DF )

• Creates bar graphs and histograms for all columns in the dataset.

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Regression Modeling

figure, model = lm(WINE_DF, 'points' , 'price' ,  ylimit = [0,150] ,
                   xtitle = 'Points' , ytitle = 'Price ($)' , 
                   show_summary=False , alpha = 0.8 ) 

display( figure )  

• Creates a simple linear regression
• As the quality of wine increases (points), the price also increases.