Exploratory data analysis toolkit for Python.
Dora is a Python library designed to automate the painful parts of exploratory data analysis.
The library contains convenience functions for data cleaning, feature selection & extraction, visualization, partitioning data for model validation, and versioning transformations of data.
The library uses and is intended to be a helpful addition to common Python data analysis tools such as pandas, scikit-learn, and matplotlib.
To ensure latest code, install this library from the Github repo.
>>> from Dora import Dora
# without initial config
>>> dora = Dora()
>>> dora.configure(output = 'A', data = 'path/to/data.csv')
# is the same as
>>> import pandas as pd
>>> dataframe = pd.read_csv('path/to/data.csv')
>>> dora = Dora(output = 'A', data = dataframe)
>>> dora.data
A B C D useless_feature
0 1 2 0 left 1
1 4 NaN 1 right 1
2 7 8 2 left 1# read data with missing and poorly scaled values
>>> import pandas as pd
>>> df = pd.DataFrame([
... [1, 2, 100],
... [2, None, 200],
... [1, 6, None]
... ])
>>> dora = Dora(output = 0, data = df)
>>> dora.data
0 1 2
0 1 2 100
1 2 NaN 200
2 1 6 NaN
# impute the missing values (using the average of each column)
>>> dora.impute_missing_values()
>>> dora.data
0 1 2
0 1 2 100
1 2 4 200
2 1 6 150
# scale the values of the input variables (center to mean and scale to unit variance)
>>> dora.scale_input_values()
>>> dora.data
0 1 2
0 1 -1.224745 -1.224745
1 2 0.000000 1.224745
2 1 1.224745 0.000000# feature selection / removing a feature
>>> dora.data
A B C D useless_feature
0 1 2 0 left 1
1 4 NaN 1 right 1
2 7 8 2 left 1
>>> dora.remove_feature('useless_feature')
>>> dora.data
A B C D
0 1 2 0 left
1 4 NaN 1 right
2 7 8 2 left
# extract an ordinal feature through one-hot encoding
>>> dora.extract_ordinal_feature('D')
>>> dora.data
A B C D=left D=right
0 1 2 0 1 0
1 4 NaN 1 0 1
2 7 8 2 1 0
# extract a transformation of another feature
>>> dora.extract_feature('C', 'twoC', lambda x: x * 2)
>>> dora.data
A B C D=left D=right twoC
0 1 2 0 1 0 0
1 4 NaN 1 0 1 2
2 7 8 2 1 0 4# plot a single feature against the output variable
dora.plot_feature('column-name')
# render plots of each feature against the output variable
dora.explore()# create random partition of training / validation data (~ 80/20 split)
dora.set_training_and_validation()
# train a model on the data
X = dora.training_data[dora.input_columns()]
y = dora.training_data[dora.output]
some_model.fit(X, y)
# validate the model
X = dora.validation_data[dora.input_columns()]
y = dora.validation_data[dora.output]
some_model.score(X, y)# save a version of your data
>>> dora.data
A B C D useless_feature
0 1 2 0 left 1
1 4 NaN 1 right 1
2 7 8 2 left 1
>>> dora.snapshot('initial_data')
# keep track of changes to data
>>> dora.remove_feature('useless_feature')
>>> dora.extract_ordinal_feature('D')
>>> dora.impute_missing_values()
>>> dora.scale_input_values()
>>> dora.data
A B C D=left D=right
0 1 -1.224745 -1.224745 0.707107 -0.707107
1 4 0.000000 0.000000 -1.414214 1.414214
2 7 1.224745 1.224745 0.707107 -0.707107
>>> dora.logs
["self.remove_feature('useless_feature')", "self.extract_ordinal_feature('D')", 'self.impute_missing_values()', 'self.scale_input_values()']
# use a previous version of the data
>>> dora.snapshot('transform1')
>>> dora.use_snapshot('initial_data')
>>> dora.data
A B C D useless_feature
0 1 2 0 left 1
1 4 NaN 1 right 1
2 7 8 2 left 1
>>> dora.logs
[]
# switch back to your transformation
>>> dora.use_snapshot('transform1')
>>> dora.data
A B C D=left D=right
0 1 -1.224745 -1.224745 0.707107 -0.707107
1 4 0.000000 0.000000 -1.414214 1.414214
2 7 1.224745 1.224745 0.707107 -0.707107
>>> dora.logs
["self.remove_feature('useless_feature')", "self.extract_ordinal_feature('D')", 'self.impute_missing_values()', 'self.scale_input_values()']To run the test suite, simply run python3 spec.py from the Dora directory.
Pull requests welcome! Feature requests / bugs will be addressed through issues on this repository. While not every feature request will necessarily be handled by me, maintaining a record for interested contributors is useful.
Additionally, feel free to submit pull requests which add features or address bugs yourself.
The MIT License (MIT)
Copyright (c) 2016 Nathan Epstein
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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