<jupyter_start><jupyter_text>Example usageHere we will demonstrate how to use `snapedautility` package for your data science project.  Imports<jupyter_code>import snapedautility
from palmerpenguins import load_penguins
print(snapedautility.__version__)<jupyter_output>0.1.0<jupyter_text>Sample DataWe will be using the penguins data as an example.<jupyter_code>df = load_penguins()
df.head()<jupyter_output><empty_output><jupyter_text>Plot HistogramsTo generate histograms and bar plots for your data, import the `plot_histograms` function from the module `snapedautility.plot_histograms`.<jupyter_code>from snapedautility.plot_histograms import plot_histograms
plot_histograms(df, ["species", "bill_length_mm", "island"], 100, 100)<jupyter_output><empty_output><jupyter_text>Plot CorrelationTo generate the correlation plots for your data, import the `plot_corr` function from the module `snapedautility.plot_corr`.<jupyter_code>from snapedautility.plot_corr import plot_corr
plot_corr(df, ["bill_length_mm", "bill_depth_mm", 'species'])<jupyter_output><empty_output><jupyter_text>Detect OutliersTo detect the outliers in your data, import the `detect_outliers` function from the module `snapedautility.detect_outliers`.<jupyter_code>from snapedautility.detect_outliers import detect_outliers
[lq, hq], chart = detect_outliers(df["body_mass_g"], 250, 250)
print(f"The lower bound and the upper bound are {round(lq, 2)} and {round(hq, 2)}")
chart<jupyter_output>The lower bound and the upper bound are 1750.0 and 6550.0