prda 1.0.0

Prda contains packages for data processing, analysis and visualization. The ultimate goal is to fill the “last mile” between analysts and packages.

prda

Prda contains packages for data processing, analysis and visualization.

Prda ultimate goal is to fill the “last mile” between analysts and packages. During my research practice, I have felt how “learning a package before utilizing” can be time-consuming and exhausting. The resulted inefficiency leads to the creation of prda.

To utilize prda, you only need to be familiar with pandas as most inputs is pd.DataFrame.

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For example：

import prda
import pandas as pd
import numpy as np
df = pd.DataFrame(data=np.array([np.arange(100) for i in range(5)]).T,columns=['a', 'b', 'c', 'd', 'e'])
prda.graphic.scatter_3d_html(df, x='a', y='b', z='c', color_hue='d', size_hue='e', title='demo_3d_scatter', filepath='demo_3d_scatter.html')

the above code will provide an interactive html figure that look like this:

demo_3d_scatter.html

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import prda
import pandas as pd
import numpy as np
datalen = 500
indices = np.arange(datalen)
col_a = np.arange(0, 10, 10/datalen)
col_b = np.random.randint(3, 8, datalen)
data = np.array([indices, col_a, col_b]).T
df = pd.DataFrame(data=data, columns=['idx', 'a', 'b'])

# draw
import random
point_markers = {
    'a': [(indices[i], col_a[i]) for i in random.sample(list(indices), 20)]
}
prda.graphic.lineplot_html(df, x='idx', y=['a', 'b'], markpoints=point_markers, filepath='demo_lineplot.html')

	idx	a	b
0	0.0	0.00	6.0
1	1.0	0.02	3.0
2	2.0	0.04	4.0
...	...	...	...
498	498.0	9.96	6.0
499	499.0	9.98	5.0

And code with the above DataFrame will draw anther plot look like this:

demo_lineplot.html

Although the current prda is far from completion, let along perfection. It is under improvement regularly.

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How to Use

You are welcome to clone prda for personal use (to use, simply add the folder to your system path) and pull request of your modification is super!! encouraged.

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2023.5.3 Major Updates

Add several easy-to-use functions, including **prep::**pca, select_continuous_variables, handle_missing_data, apply_linear_func(row-wisely), and **ml::**match_clusters, evaluate_param_combinations(optimal parameters searching, with base class::sklearn.base.BaseEstimator), etc.