blitzly 0.6.2

Description:

blitzly 0.6.2

blitzly ⚡️
Lightning-fast way to get plots with Plotly













Introduction 🎉
Plotly is great and powerful. But with great power comes great responsibility 🕸. And sometimes you just want to get a plot up and running as fast as possible. That's where blitzly ⚡️ comes in. It provides a set of functions that allow you to create plots with Plotly in a lightning-fast way. It's not meant to replace Plotly, but rather to complement it.
Check out some examples in the Jupyter notebook.

Install the package 📦
If you are using pip, you can install the package with the following command:
pip install blitzly

If you are using Poetry, you can install the package with the following command:
poetry add blitzly

installing dependencies 🧑‍🔧
With pip:
pip install -r requirements.txt

With Poetry:
poetry install

Available plots (so far 🚀)



Module
Method
Description




bar
model_feature_importances
Creates a bar chart with the feature importance of a model.


bar
multi_chart
Creates a bar chart with multiple groups.


dumbbell
simple_dumbbell
Plots a dumbbell plot. This can be used to compare two columns of data to visualize changes.


histogram
simple_histogram
Plots a histogram with one ore more distributions.


matrix
binary_confusion_matrix
Plots a confusion matrix for binary classification data.


matrix
cramers_v_corr_matrix
Cramer's V correlation for categorical features.


matrix
pearson_corr_matrix
Plots a Pearson product-moment correlation coefficients matrix.


scatter
scatter_matrix
Plots a scatter matrix.


scatter
multi_scatter
Create a multi scatter plot. It can be used to visualize the relationship between multiple variables from the same Pandas DataFrame.


scatter
dimensionality_reduction
Creates a plot to visualize higher dimensionality reduced data using matrix decomposition



Subplots 👩‍👩‍👧‍👦



Module
Method
Description




subplots
make_subplots
Create subplots using figure objects created with any of the above available plots.



Usage 🤌
Here are some examples. You can also open the playground notebook 📒.
dimensionality_reduction:
from blitzly.plots.scatter import dimensionality_reduction
import plotly.express as px

df = px.data.iris()
dimensionality_reduction(
df,
n_components=2,
target_column="species",
reduction_funcs=["PCA", "TNSE"],
)

Gives you this:

multi_bar:
from blitzly.plots.bar import multi_bar
import numpy as np

data = np.array([[8, 3, 6], [9, 7, 5]])
error_array = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])

multi_bar(
data,
x_labels=["Vienna", "Berlin", "Lisbon"],
group_labels=["Personal rating", "Global rating"],
errors=error_array,
title="City ratings 🏙",
mark_x_labels=["Lisbon"],
write_html_path="see_the_blitz.html",
)

Gives you this:

scatter matrix:
from blitzly.plots.scatter import scatter_matrix
import numpy as np
import pandas as pd

foo = np.random.randn(1000)
bar = np.random.randn(1000) + 1
blitz = np.random.randint(2, size=1000)
licht = np.random.randint(2, size=1000)
data = np.array([foo, bar, blitz, licht])
df = pd.DataFrame(data.T, columns=["foo", "bar", "blitz", "licht"])

scatter_matrix(
df,
dimensions=["foo", "bar", "blitz"],
color_dim=df["licht"],
title="My first scatter matrix 🙃",
show_upper_half=True,
diagonal_visible=False,
marker_color_scale="Rainbow",
marker_line_color="blue",
size=(500, 500),
)

Gives you this:

Contributing 👩‍💻
Please check out the guide on how to contribute to this project.