 What’s for?¶

unipy is a toolkit for data scientists. This offers a number of scientific, statistical objects. This also contains many pythonic objects like generators, decorators and function wrappers, etc.

Some famous datasets embedded will make you easy to test.

Installation¶

pip install unipy

Usage¶

import unipy as up
import unipy.dataset.api as dm

Contents¶

Module contents¶

unipy¶

Provides
1. Data Handling Tools

2. Statistical Functions.

3. Function Wrappers to profile

4. Generally-used Plots

How to use¶

In terms of Data science, Data Preprocessing & Plotting is one of the most annoying parts of Data Analysis. unipy offers you many functions maybe once you have tried to search in google or stackoverflow.

The docstring examples assume that unipy has been imported as up::
>>> import unipy as up
Code snippets are indicated by three greater-than signs::
>>> x = 42
>>> x = x + 1
Use the built-in help function to view a function’s docstring::
>>> help(np.sort)
...

General-purpose documents like a glossary and help on the basic concepts of numpy are available under the docs sub-module:

>>> from unipy import docs
>>> help(docs)
...

Available subpackages¶

dataset

Some famous datasets like iris, titanic and adult

image

Image transformation tools.

math

Mathmatical core functions for unipy itself

plots

Most used plots

stats

Statistic tools

tools

Data handling tools

utils

High-level wrappers & Python function decorators

unipy_test

Test-codes of unipy

class unipy.Ellipse(diameter)[source]

Bases: object

Create an ellipse.

diameter
center
angle
coordinates()[source]
unipy.point_boxplot(data, groupby=None, value=None, rot=90, spread=0.2, dot_size=15.0, dot_color='b', dot_alpha=0.2, figsize=12, 9, *args, **kwargs)[source]

Boxplot with points.

Draw boxplots by given keys(groupby, value).

Parameters
• data (pandas.DataFrame) – a dataset.

• groupby (str or list-like (default: None)) – A key column to separate. (X-axis, categorical) When str, it should be a column name to groupby. When list-like, it contains a column name to groupby.

• value (str or list-like (default: None)) – A key column to get values. (Y-axis, numerical) When str, it should be a column name of values. When list-like, it contains a column name of values.

• rot (int (default: 90)) – A rotation angle to show X-axis labels.

• spread (float (default: .2)) – A spread ratio of points. The bigger, the pointing distribution width are broader.

• dot_size (float (default: 15.)) – A size of each points.

• dot_color (int (default: 'b')) – A color name of each points.

• dot_alpha (float (default: .2)) – A transparency value of each points.

Returns

• matplotlib.figure.Figure – A plot figure.

• Exceptions

• ———-

• AssertionError – It is raised when two or more names are given to groupby or value.

pandas.DataFrame.boxplot matplotlib.pyplot

Examples

>>> import unipy.dataset.api as dm
>>> from unipy.plots import point_boxplot
>>> dm.init()
Dataset : iris
>>> tmp = point_boxplot(data, groupby='species', value='sepal_length')
unipy.point_boxplot_axis(data, groupby=None, value=None, rot=90, spread=0.2, dot_size=15.0, dot_color='b', dot_alpha=0.2, share_yrange=True, figsize=12, 9, *args, **kwargs)[source]

Boxplot with points, horizontally seperated.

Draw boxplots by given keys(groupby, value).

Parameters
• data (pandas.DataFrame) – a dataset.

• groupby (str or list-like (default: None)) – A key column to separate. (X-axis, categorical) When str, it should be a column name to groupby. When list-like, it contains a column name to groupby.

• value (str or list-like (default: None)) – A key column to get values. (Y-axis, numerical) When str, it should be a column name of values. When list-like, it contains a column name of values.

• rot (int (default: 90)) – A rotation angle to show X-axis labels.

• spread (float (default: .2)) – A spread ratio of points. The bigger, the pointing distribution width are broader.

• dot_size (float (default: 15.)) – A size of each points.

• dot_color (int (default: 'b')) – A color name of each points.

• dot_alpha (float (default: .2)) – A transparency value of each points.

• share_yrange (Boolean (defalut: True)) – False then each Y-axis limit of boxplots will draw independent.

Returns

• matplotlib.figure.Figure – A plot figure.

• Exceptions

• ———-

• AssertionError – It is raised when two or more names are given to groupby or value.

pandas.DataFrame.boxplot matplotlib.pyplot

Examples

>>> import unipy.dataset.api as dm
>>> from unipy.plots import point_boxplot_axis
>>> dm.init()
Dataset : iris
>>> tmp = point_boxplot_axis(data,
...                          groupby='species',
...                          value='sepal_length',
...                          share_yrange=True)
unipy.mosaic_plot(data, groupby=None, col_list=None, show_values=True, rot=90, width=0.9, figsize=12, 9, *args, **kwargs)[source]

Mosaic Plot via Stacked bar plots.

Draw plots by given keys(groupby, value).

Parameters
• data (pandas.DataFrame) – a dataset.

• groupby (str or list-like (default: None)) – A key column to separate. (X-axis, categorical) When str, it should be a column name to groupby. When list-like, it contains a column name to groupby.

• col_list (str or list-like (default: None)) – A key column to get values. (Y-axis, numerical) When str, it should be column names of values. When list-like, it contains column names of values.

• rot (int (default: 90)) – A rotation angle to show X-axis labels.

• show_values (boolean (default: True)) – Choose If n is annotated.

Returns

• matplotlib.figure.Figure – A plot figure.

• Exceptions

• ———-

• AssertionError – It is raised when two or more names are given to groupby or value.

pandas.DataFrame.barplot matplotlib.pyplot

Examples

>>> import unipy.dataset.api as dm
>>> from unipy.plots import mosaic_plot
>>> dm.init()
Dataset : iris
>>> tmp = mosaic_plot(data, groupby='native_country',
... col_list=['workclass', 'education'])
unipy.rgb2gras(img_array)[source]
unipy.hough_transform(img_bin, theta_res=1, rho_res=1)[source]
unipy.deviation(container, method='mean', if_abs=True)[source]

Deviation.

unipy.vif(y, X)[source]

Variance inflation factor.

unipy.mean_absolute_percentage_error(measure, predict, thresh=3.0)[source]

Mean Absolute Percentage Error. It is a percent of errors. It measures the prediction accuracy of a forecasting method in Statistics with the real mesured values and the predicted values, for example in trend estimation. If MAPE is 5, it means this prediction method potentially has 5% error. It cannot be used if there are zero values, because there would be a division by zero.

unipy.average_absolute_deviation(measure, predict, thresh=2)[source]

Average Absolute Deviation. It is … It measures the prediction accuracy of a forecasting method in Statistics with the real mesured values and the predicted values, for example in trend estimation. If MAD is 5, it means this prediction method potentially has…

unipy.median_absolute_deviation(measure, predict, thresh=2)[source]

Median Absolute Deviation. It is … It measures the prediction accuracy of a forecasting method in Statistics with the real mesured values and the predicted values, for example in trend estimation. If MAD is 5, it means this prediction method potentially has…

unipy.calculate_interaction(rankTbl, pvTbl, target, ranknum=10)[source]

Feature interaction calculation.

unipy.f_test(a, b, scale=1, alternative='two-sided', conf_level=0.95, *args, **kwargs)[source]

F-Test.

unipy.f_test_formula(a, b, scale=1, alternative='two-sided', conf_level=0.95, *args, **kwargs)[source]

F-Test by formula.

unipy.anova_test(formula, data=None, typ=1)[source]

ANOVA Test.

unipy.anova_test_formula(formula, data=None, typ=1)[source]

ANOVA Test by formula.

unipy.chisq_test(data, x=None, y=None, correction=None, lambda_=None, margin=True, print_ok=True)[source]

Chi-square Test.

lambda_ gives the power in the Cressie-Read power divergence statistic. The default is 1. For convenience, lambda_ may be assigned one of the following strings, in which case the corresponding numerical value is used:

Parameters
• data (pandas.DataFrame) –

• x (str (default: None)) –

• y (str (default: None)) –

• correction ((default: None)) –

• lambda_ (lambda (default: None)) –

• margin (Boolean (default: True)) –

• print_ok (Boolean (default: True)) –

unipy.fisher_test(data, x=None, y=None, alternative='two-sided', margin=True, print_ok=True)[source]

Fisher’s Exact Test.

unipy.lasso_rank(formula=None, X=None, y=None, data=None, alpha=array([1e-05, 0.00011, 0.00021, 0.00031, 0.00041, 0.00051, 0.00061, 0.00071, 0.00081, 0.00091, 0.00101, 0.00111, 0.00121, 0.00131, 0.00141, 0.00151, 0.00161, 0.00171, 0.00181, 0.00191, 0.00201, 0.00211, 0.00221, 0.00231, 0.00241, 0.00251, 0.00261, 0.00271, 0.00281, 0.00291, 0.00301, 0.00311, 0.00321, 0.00331, 0.00341, 0.00351, 0.00361, 0.00371, 0.00381, 0.00391, 0.00401, 0.00411, 0.00421, 0.00431, 0.00441, 0.00451, 0.00461, 0.00471, 0.00481, 0.00491, 0.00501, 0.00511, 0.00521, 0.00531, 0.00541, 0.00551, 0.00561, 0.00571, 0.00581, 0.00591, 0.00601, 0.00611, 0.00621, 0.00631, 0.00641, 0.00651, 0.00661, 0.00671, 0.00681, 0.00691, 0.00701, 0.00711, 0.00721, 0.00731, 0.00741, 0.00751, 0.00761, 0.00771, 0.00781, 0.00791, 0.00801, 0.00811, 0.00821, 0.00831, 0.00841, 0.00851, 0.00861, 0.00871, 0.00881, 0.00891, 0.00901, 0.00911, 0.00921, 0.00931, 0.00941, 0.00951, 0.00961, 0.00971, 0.00981, 0.00991]), k=2, plot=False, *args, **kwargs)[source]

Feature selection by LASSO regression.

Parameters
• formula – R-style formula string

• X (list-like) – Column values for X.

• y (list-like) – A column value for y.

• data (pandas.DataFrame) – A DataFrame.

• alpha (Iterable) – An Iterable contains alpha values.

• k (int) – Threshold of coefficient matrix

• plot (Boolean (default: False)) – True if want to plot the result.

Returns

• rankTbl (pandas.DataFrame) – Feature ranking by given k.

• minIntercept (pandas.DataFrame) – The minimum intercept row in coefficient matrix.

• coefMatrix (pandas.DataFrame) – A coefficient matrix.

• kBest (pandas.DataFrame) – When Given k, The best intercept row in coefficient matrix.

• kBestPredY (dict) – A predicted Y with kBest alpha.

Example

>>> import unipy.dataset.api as dm
>>> dm.init()
['cars', 'anscombe', 'iris', 'nutrients', 'german_credit_scoring_fars2008', 'winequality_red', 'winequality_white', 'titanic', 'car90', 'diabetes', 'adult', 'tips', 'births_big', 'breast_cancer', 'air_quality', 'births_small']
Dataset : winequality_red
>>>
>>> ranked, best_by_intercept, coefTbl, kBest, kBestPred = lasso_rank(X=wine_red.columns.drop('quality'), y=['quality'], data=wine_red)
>>> ranked
rank  lasso_coef  abs_coef
volatile_acidity     1   -0.675725  0.675725
alcohol              2    0.194865  0.194865
>>> best_by_intercept
RSS  Intercept  fixed_acidity  volatile_acidity      alpha_0.00121  691.956364   3.134874       0.002374         -1.023793

citric_acid residual_sugar chlorides free_sulfur_dioxide alpha_0.00121 0.0 0.0 -0.272912 -0.0

total_sulfur_dioxide density pH sulphates alcohol alpha_0.00121 -0.000963 -0.0 -0.0 0.505956 0.264552

var_count

alpha_0.00121 6 >>>

unipy.feature_selection_vif(data, thresh=5.0)[source]

Stepwise Feature Selection for multivariate analysis.

It calculates OLS regressions and the variance inflation factors iterating all explanatory variables. If the maximum VIF of a variable is over the given threshold, It will be dropped. This process is repeated until all VIFs are lower than the given threshold.

Recommended threshold is lower than 5, because if VIF is greater than 5, then the explanatory variable selected is highly collinear with the other explanatory variables, and the parameter estimates will have large standard errors because of this.

Parameters
• data (DataFrame, (rows: observed values, columns: multivariate variables)) – design dataframe with all explanatory variables, as for example used in regression

• thresh (int, float) – A threshold of VIF

Returns

• Filtered_data (DataFrame) – A subset of the input DataFame

• dropped_List (DataFrame) – ‘var’ column : dropped variable names from input data columns ‘vif’ column : variance inflation factor of dropped variables

Notes

This function does not save the auxiliary regression.

statsmodels.stats.outliers_influence.variance_inflation_factor()

References

http://en.wikipedia.org/wiki/Variance_inflation_factor

unipy.from_formula(formula)[source]

R-style Formula Formatting.

unipy.exc(source, blacklist)[source]

Get items except the given list.

This function splits an Iterable into the given size of multiple chunks. The items of An iterable should be the same type.

Parameters
• source (Iterable) – An Iterable to filter.

• blacklist (Iterable) – A list contains items to eliminate.

Returns

A filtered list.

Return type

list

Infix Operator

Examples

>>> import unipy as up
>>> up.splitter(list(range(10)), how='equal', size=3)
[(0, 1, 2, 3), (4, 5, 6), (7, 8, 9)]
>>> up.splitter(list(range(10)), how='remaining', size=3)
[(0, 1, 2), (3, 4, 5), (6, 7, 8), (9,)]
unipy.splitter(iterable, how='equal', size=2)[source]

Split data with given size.

This function splits an Iterable into the given size of multiple chunks. The items of An iterable should be the same type.

Parameters
• iterable (Iterable) – An Iterable to split.

• how ({'equal', 'remaining'}) – The method to split. ‘equal’ is to split chunks with the approximate length within the given size. ‘remaining’ is to split chunks with the given size, and the remains are bound as the last chunk.

• size (int) – The number of chunks.

Returns

A list of chunks.

Return type

list

numpy.array_split(), itertools.islice()

Examples

>>> import unipy as up
>>> up.splitter(list(range(10)), how='equal', size=3)
[(0, 1, 2, 3), (4, 5, 6), (7, 8, 9)]
>>> up.splitter(list(range(10)), how='remaining', size=3)
[(0, 1, 2), (3, 4, 5), (6, 7, 8), (9,)]
unipy.even_chunk(iterable, chunk_size, *args, **kwargs)[source]

Split data into even size.

This function splits an Iterable into the given size of multiple chunks. The items of An iterable should be the same type.

Parameters
• iterable (Iterable) – An Iterable to split. If N-dimensional, It is chunked by 1st dimension.

• chunk_size (int) – The length of each chunks.

Returns

A generator yields a list of chunks. The data type of the elements in a list are equal to the source data type.

Return type

generator

itertools.islice yield from

Examples

>>> import numpy as np
>>> from unipy.tools.data_handler import even_chunk
>>> data = list(range(7))  # list, 1D
>>> print(data)
[0, 1, 2, 3, 4, 5, 6]
>>> chunked_gen = even_chunk(data, 3)
>>> print(chunked_gen)
<generator object even_chunk at 0x7fc4924897d8>
>>> next(chunked_gen)
[0, 1, 2]
>>> chunked = list(even_chunk(data, 3))
>>> print(chunked)
[[0, 1, 2], [3, 4, 5], ]
>>> data = np.arange(30).reshape(-1, 3)  # np.ndarray, 2D
>>> print(data)
array([[ 0,  1,  2],
[ 3,  4,  5],
[ 6,  7,  8],
[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17],
[18, 19, 20],
[21, 22, 23],
[24, 25, 26],
[27, 28, 29]])
>>> chunked_gen = even_chunk(data, 4)
>>> next(chunked_gen)
[array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8]), array([ 9, 10, 11])]
>>> next(chunked_gen)
[array([12, 13, 14]),
array([15, 16, 17]),
array([18, 19, 20]),
array([21, 22, 23])]
>>> next(chunked_gen)
[array([24, 25, 26]), array([27, 28, 29])]
>>> next(chunked_gen)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration
unipy.pair_unique(*args)[source]

Get Unique pairsets.

This function gets an unique pair-sets of given data.

Parameters

iterable (Iterable) – Iterables having an equal length.

Returns

A list of tuples. Each tuple is an unique pair of values.

Return type

list

Raises

ValueError – If the lengths of argments are not equal.

zip set

Examples

>>> from unipy.tools.data_handler import pair_unique
Dataset : titanic
Class     Sex    Age Survived  Freq
0   1st    Male  Child       No     0
1   2nd    Male  Child       No     0
2   3rd    Male  Child       No    35
3  Crew    Male  Child       No     0
4   1st  Female  Child       No     0
>>> pair_unique(data.iloc[:, 0], data.iloc[:, 1])
[(5, '1st'), (19, '3rd'), (29, '1st'), (20, 'Crew'),
(21, '1st'), (3, '3rd'), (16, 'Crew'), (26, '2nd'),
(23, '3rd'), (10, '2nd'), (24, 'Crew'), (7, '3rd'),
(4, 'Crew'), (27, '3rd'), (18, '2nd'), (28, 'Crew'),
(30, '2nd'), (11, '3rd'), (2, '2nd'), (1, '1st'),
(14, '2nd'), (31, '3rd'), (22, '2nd'), (17, '1st'),
(8, 'Crew'), (9, '1st'), (32, 'Crew'), (15, '3rd'),
(6, '2nd'), (12, 'Crew'), (13, '1st'), (25, '1st')]
>>> idx1 = [1, 2, 3]
>>> idx2 = [0, 9, 8, 4]
>>> pair_unique(idx1, idx2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: All argments should have the same length.
unipy.df_pair_unique(data_frame, col_list, to_frame=False)[source]

Get unique pairsets in pandas.DataFrame.

This function gets an unique pair-sets of given columns.

Parameters
• data_frame (pandas.DataFrame) – DataFrame to get unique-pairs.

• col_list (pandas.Index, list, tuple) – Column names of given DataFrame.

• to_frame (Boolean (default: False)) – Choose output type. If True, It returns pandas.DataFrame as an output. If False, It returns a list of tuples.

Returns

• list – If to_frame=False, A list of tuples is returned. Each tuple is an unique pair of values.

• pandas.DataFrame – If to_frame=True, pandas.DataFrame is returned. Each row is an unique pair of values.

pandas.DataFrame.itertuples()

Examples

>>> from unipy.tools.data_handler import df_pair_unique
Dataset : titanic
Class     Sex    Age Survived  Freq
0   1st    Male  Child       No     0
1   2nd    Male  Child       No     0
2   3rd    Male  Child       No    35
3  Crew    Male  Child       No     0
4   1st  Female  Child       No     0
>>> df_pair_unique(data, ['Class', 'Sex'])
[('3rd', 'Male'), ('2nd', 'Male'), ('2nd', 'Female'), ('1st', 'Female'),
('Crew', 'Male'), ('1st', 'Male'), ('Crew', 'Female'), ('3rd', 'Female')]
>>> df_pair_unique(data, ['Class', 'Sex'], to_frame=True)
Class     Sex
0   3rd    Male
1   2nd    Male
2   2nd  Female
3   1st  Female
4  Crew    Male
5   1st    Male
6  Crew  Female
7   3rd  Female
unipy.map_to_tuple(iterable)[source]

Only for some specific reason.

unipy.map_to_list(iterable)[source]

Only for some specific reason.

unipy.merge_csv(file_path, pattern='*.csv', sep=',', if_save=True, save_name=None, low_memory=True)[source]

Merge seperated csv type datasets into one dataset. Summary

This function get separated data files together. When merged, the file is sorted by its name in ascending order.

Parameters
• file_path (str) – A directory path of source files.

• pattern (str) – A File extension with conditional naming. (default: ‘*.csv’)

• sep (int) – A symbol seperating data columns.

• if_save (Boolean (Optional, default: True)) – False if you don’t want to save the result.

• save_name (str) – A filename to save the result. It should be given if if_save=True. If inappropriate name is given, the first name of file list is used.

• low_memory (Boolean (Optional, default: True)) – It is used for pandas.read_csv() option only.

Returns

A concatenated DataFrame.

Return type

pandas.DataFrame

Examples

>>> from unipy.tools.data_handler import merge_csv
Dataset : titanic
Class     Sex    Age Survived  Freq
0   1st    Male  Child       No     0
1   2nd    Male  Child       No     0
2   3rd    Male  Child       No    35
3  Crew    Male  Child       No     0
4   1st  Female  Child       No     0
5   2nd  Female  Child       No     0
6   3rd  Female  Child       No    17
7  Crew  Female  Child       No     0
8   1st    Male  Adult       No   118
>>> merged = merge_csv('./')
>>> merged
Class     Sex    Age Survived  Freq
0   1st    Male  Child       No     0
1   2nd    Male  Child       No     0
2   3rd    Male  Child       No    35
3  Crew    Male  Child       No     0
4   1st  Female  Child       No     0
5   2nd  Female  Child       No     0
6   3rd  Female  Child       No    17
7  Crew  Female  Child       No     0
8   1st    Male  Adult       No   118
unipy.nancumsum(iterable)[source]

A cumulative sum function.

A cumulative sum function.

Parameters

iterable (Iterable) – Iterables to calculate cumulative sum.

Yields

int – A cumulative summed value.

numpy.isnan()

Examples

>>> from unipy.tools.data_handler import nancumsum
>>> tmp = [1, 2, 4]
>>> nancumsum(tmp)
<generator object nancumsum at 0x1084553b8>
>>> list(nancumsum(tmp))
[1, 3, 7]
unipy.depth(iterable)[source]

Get dimension depth.

Get a dimension depth number of a nested iterable.

Parameters

iterable (iterable) – An Iterable to get a dimension depth number.

Returns

A dimension depth number.

Return type

int

collections.Iterable()

Examples

>>> from unipy.tools.data_handler import depth
>>> tmp = [(1, 3), (4, 6), (7, 9), (10, 12)]
>>> depth(tmp)
2
>>> tmp3d = [[np.arange(i) + i for i in range(2, j)]
...          for j in range(5, 10)]
>>> depth(tmp3d)
3
>>> # It can handle dict type (considering values only).
>>> tmp3d_dict = [{'key' + str(i): np.arange(i) + i for i in range(2, j)}
...               for j in range(5, 10)]
>>> depth(tmp3d_dict)
3

Zero-padding Function with nested sequence. Each elements of a given sequence is padded fixed-length.

Parameters
• arr (Iterable) – A nested sequence containing 1-Dimensional numpy.ndarray.

• max_len (int (default: None)) – A required fixed-length of each sequences. If None, It calculates the max length of elements as max_len.

• method ({'forward', 'backward'} (default: 'backward')) – where to pad.

Returns

A list containing 3-Dimensional numpy.ndarray with fixed-length 2D.

Return type

list

Examples

>>> tmp = [np.arange(i) + i for i in range(2, 5)]
>>> tmp
[array([2, 3]), array([3, 4, 5]), array([4, 5, 6, 7])]
array([[2, 3, 0, 0],
[3, 4, 5, 0],
[4, 5, 6, 7]])
array([[2, 3, 0, 0, 0, 0],
[3, 4, 5, 0, 0, 0],
[4, 5, 6, 7, 0, 0]])
array([[0, 0, 0, 2, 3],
[0, 0, 3, 4, 5],
[0, 4, 5, 6, 7]])

Zero-padding Function with nested sequence. Each elements of a given sequence is padded fixed-length.

Parameters
• arr (Iterable) – A nested sequence containing 2-Dimensional numpy.ndarray.

• max_len (int (default: None)) – A required fixed-length of each sequences. If None, It calculates the max length of elements as max_len.

• method ({'forward', 'backward'} (default: 'backward')) – where to pad.

Returns

A list containing 3-Dimensional numpy.ndarray with fixed-length 2D.

Return type

list

Raises

ValueError – All 3D shape of inner numpy.ndarray is not equal.

Examples

>>> tmp3d = [np.arange(i * 2).reshape(-1, 2) for i in range(1, 5)]
>>> tmp3d
[array([[0, 1]]),
array([[0, 1],
[2, 3]]),
array([[0, 1],
[2, 3],
[4, 5]]),
array([[0, 1],
[2, 3],
[4, 5],
[6, 7]])]
array([[[0, 1],
[0, 0],
[0, 0],
[0, 0]],
[[0, 1],

[2, 3], [0, 0], [0, 0]],

[[0, 1],

[2, 3], [4, 5], [0, 0]],

[[0, 1],

[2, 3], [4, 5], [6, 7]]])

>>> tmp3d_eye
[array([[1.]]),
array([[1., 0.],
[0., 1.]]),
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 24, in zero_padder_3d
ValueError: 3D shape should be equal.
unipy.multiprocessor(func, worker=2, arg_zip=None, *args, **kwargs)[source]

Use multiprocessing as a function.

Just for convenience.

Parameters
• func (Function) – Any function without lambda.

• worker (int (default: 2)) – A number of processes.

• arg_zip (zip (default: None)) – A zip instance.

Returns

A list contains results of each processes.

Return type

list

multiprocessing.pool

Examples

>>> from unipy.utils.wrapper import multiprocessor
>>> alist = [1, 2, 3]
>>> blist = [-1, -2, -3]
>>> def afunc(x, y):
...     return x + y
...
>>> multiprocessor(afunc, arg_zip=zip(alist, blist))
[0, 0, 0]
>>> def bfunc(x):
...     return x + 2
...
>>> multiprocessor(bfunc, arg_zip=zip(alist))
[3, 4, 5]
unipy.uprint(*args, print_ok=True, **kwargs)[source]

Print option interface.

This function is equal to print function but added print_ok option. This allows you to control printing in a function.

Parameters
• *args (whatever print allows.) – It is same as print does.

• print_ok (Boolean (default: True)) – An option whether you want to print something out or not.

• arg_zip (zip (default: None)) – A zip instance.

unipy.lprint(input_x, output, name=None)[source]

Print option interface.

This function is to stdout the shape of input layer & output layer in Deep Learning architecture.

Parameters
• input_x (numpy.ndarray) – A numpy.ndarray object of input source.

• output (numpy.ndarray) – A numpy.ndarray object of output target.

• name (str (default: None)) – An optional name you want to print out.

unipy.aprint(*arr, maxlen=None, name_list=None, decimals=None)[source]

Stdout the numpy.ndarray in pretty.

It prints the multiple numpy.ndarray out “Side by Side.”

Parameters
• arr (numpy.ndarray) – Any arrays you want to print out.

• maxlen (int (default: None)) – A length for each array to print out. It is automatically calculated in case of None.

• name_list (list (default: None)) – A list contains the names of each arrays. Upper Alphabet is given in case of None.

• decimals (int (default: None)) – A number to a specified number of digits to truncate.

Examples

>>> from unipy.utils.wrapper import aprint
>>> arr_x = np.array([
... [.6, .5, .1],
... [.4, .2, .8],
... ])
>>> arr_y = np.array([
... [.4, .6],
... [.7, .3,],
... ])
>>> aprint(arr_x, arr_y)
=========================================
|  A                 |    B             |
|  (2, 3)            |    (2, 2)        |
=========================================
|  [[0.6 0.5 0.1]    |    [[0.4 0.6]    |
|   [0.4 0.2 0.8]]   |     [0.7 0.3]]   |
=========================================
>>> aprint(arr_x, arr_y, name_list=['X', 'Y'])
=========================================
|  X                 |    Y             |
|  (2, 3)            |    (2, 2)        |
=========================================
|  [[0.6 0.5 0.1]    |    [[0.4 0.6]    |
|   [0.4 0.2 0.8]]   |     [0.7 0.3]]   |
=========================================
>>> aprint(arr_x, arr_y, arr_y[:1], name_list=['X', 'Y', 'Y_1'])
============================================================
|  X                 |    Y             |    Y_1           |
|  (2, 3)            |    (2, 2)        |    (1, 2)        |
============================================================
|  [[0.6 0.5 0.1]    |    [[0.4 0.6]    |    [[0.4 0.6]]   |
|   [0.4 0.2 0.8]]   |     [0.7 0.3]]   |                  |
============================================================
unipy.time_profiler(func)[source]

Print wrapper for time profiling.

This wrapper prints out start, end and elapsed time.

Parameters

func (Function) – A function to profile.

Returns

A wrapped function.

Return type

Function

functools.wraps decorator

Examples

>>> import unipy as up
>>> @up.time_profiler
... def afunc(i):
...     return len(list(range(i)))
...
>>> res = afunc(58)
(afunc) Start   : 2018-06-20 22:11:35.511374
(afunc) End     : 2018-06-20 22:11:35.511424
(afunc) Elapsed :             0:00:00.000050
>>> res
58
unipy.time_logger(func)[source]

Logging wrapper for time profiling.

This wrapper logs start, end and elapsed time.

Parameters

func (Function) – A function to profile.

Returns

A wrapped function.

Return type

Function

functools.wraps decorator

Examples

>>> import unipy as up
>>> @up.time_logger
... def afunc(i):
...     return len(list(range(i)))
...
>>> res = afunc(58)
(afunc) Start   : 2018-06-20 22:11:35.511374
(afunc) End     : 2018-06-20 22:11:35.511424
(afunc) Elapsed :             0:00:00.000050
>>> res
58
class unipy.profiler(type='logging')[source]

Bases: object

unipy.job_wrapper(func)[source]

Print wrapper for time profiling.

This wrapper prints out start & end line.

Parameters

func (Function) – A function to separate print-line job.

Returns

A wrapped function.

Return type

Function

functools.wraps decorator

Examples

>>> import unipy as up
>>> @up.job_wrapper
... def afunc(i):
...     return len(list(range(i)))
...
>>> afunc(458)
----------- [afunc] START -----------

———– [afunc] END ———–

afunc : 0:00:00.000023

458

class unipy.Infix(func)[source]

Bases: object

Wrapper for define an operator.

This wrapper translates a function to an operator.

Returns

A wrapped function.

Return type

Function

functools.partial decorator

Examples

>>> @Infix
...     return x + y
...
11
>>> instanceof = Infix(isinstance)
>>> 5 |instanceof| int
True
unipy.infix(func)[source]

A functional API for Infix decorator.

Returns

A wrapped function.

Return type

Function

unipy.utils.wrapper.infix

Examples

>>> @infix
...     return x + y
...
11
>>> instanceof = infix(isinstance)
>>> 5 |instanceof| int
True
class unipy.ReusableGenerator(generator)[source]

Bases: object

Temporary Interface to re-use generator for convenience.

Once assigned, It can be infinitely consumed **as long as an input generator remains un-exhausted.

_source

A source generator.

Type

generator

generator itertools.tee

Examples

>>> from unipy.utils.generator import ReusableGenerator
>>> gen = (i for i in range(10))
>>> gen
<generator object <genexpr> at 0x11120ebf8>
>>> regen = ReusableGenerator(gen)
>>> regen
<unipy.utils.generator.ReusableGenerator object at 0x1061a97f0>
>>> list(regen)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(regen)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(gen)  # If the source is used, copied one will be exhausted too.
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(gen)
[]
>>> list(regen)
[]
unipy.re_generator(generator)[source]

A functional API for unipy.ReusableGenerator.

Once assigned, It can be infinitely consumed **as long as an output generator is called at least one time.

Parameters

generator (generator) – An generator to copy. This original generator should not be used anywhere else, until the copied one consumed at least once.

Returns

A generator to be used infinitely.

Return type

generator

generator itertools.tee

Examples

>>> from unipy.utils.generator import re_generator
>>> gen = (i for i in range(10))
>>> gen
<generator object <genexpr> at 0x11120ebf8>
>>> regen = copy_generator(gen)
>>> regen
<unipy.utils.generator.ReusableGenerator object at 0x1061a97f0>
>>> list(regen)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(regen)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(gen)  # Once the copied one is used, the source will be exhausted.
[]
>>> list(gen)
[]
>>> list(regen)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(regen)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
unipy.split_generator(iterable, size)[source]
unipy.num_fromto_generator(start, end, term)[source]

A range function yields pair chunks.

It had made for time-formatting query. It yields a tuple of (start, start+(term-1)) pair, until start > end.

Parameters

*args (int) – end or start, end[, term] It works like range function.

Yields

tuple – A tuple of (start, start+(term-1)) pair, until start > end.

yield

Examples

>>> from unipy.utils.generator import num_fromto_generator
>>>
>>> query = 'BETWEEN {pre} AND {nxt};'
>>>
>>> q_list = [query.format(pre=item, nxt=item)
...           for item in num_fromto_generator(1, 100, 10)]
>>> print(q_list)
BETWEEN 1 AND 10;
>>> print(q_list)
BETWEEN 11 AND 20;
unipy.dt_fromto_generator(start, end, day_term, tm_format='%Y%m%d')[source]

A range function yields datetime formats by pair.

It had made for time-formatting query. It yields a tuple of (start, start+(term-1)) pair, until start > end.

Parameters
• start (str) – start datetime like ‘yyyymmdd’.

• end (str) – start datetime like ‘yyyymmdd’.

• day_term (int) – term of days.

• tm_format ((default: '%Y%m%d')) – datetime format string.

Yields

tuple – A tuple of (start, start+(term-1)) pair, until start > end.

yield

Examples

>>> from unipy.utils.generator import dt_fromto_generator
>>> dt_list = [item for item in
...            dt_fromto_generator('20170101','20170331', 10)]
>>> dt_list[:3]
[('20170101', '20170110'),
('20170111', '20170120'),
('20170121', '20170130')]
unipy.tm_fromto_generator(start, end, day_term, tm_string=['000000', '235959'], tm_format='%Y%m%d')[source]

A range function yields datetime formats by pair.

It had made for time-formatting query. It yields a tuple of (start, start+(term-1)) pair, until start > end.

Parameters
• start (str) – start datetime like ‘yyyymmdd’.

• end (str) – start datetime like ‘yyyymmdd’.

• day_term (int) – term of days.

• tm_string (list (default: ['000000', '235959'])) – time strings to concatenate.

• tm_format ((default: '%Y%m%d')) – datetime format string.

Yields

tuple – A tuple of (start, start+(term-1)) pair, until start > end.

yield

Examples

>>> from unipy.utils.generator import tm_fromto_generator
>>> tm_list = [item for item in
...            tm_fromto_generator('20170101','20170331', 10)]
>>> tm_list[:3]
[('20170101000000', '20170110235959'),
('20170111000000', '20170120235959'),
('20170121000000', '20170130235959')]
unipy.timestamp_generator(*args)[source]

A range function yields pair timestep strings.

It had made for time-formatting query. It yields a tuple of (start, start+(term-1)) pair, until start > end.

Parameters

*args (int) – end or start, end[, term] It works like range function.

Yields

tuple – A tuple of (start, start+(term-1)) pair, until start > end.

yield

Examples

>>> from unipy.utils.generator import timestamp_generator
>>> timestamp_generator(1, 10, 2)
<generator object timestamp_generator at 0x10f519678>
>>> list(timestamp_generator(1, 14, 5))
[(1, 5), (6, 10), (11, 15)]
>>> begin, fin, period = 1, 10, 3
>>> list(timestamp_generator(begin, fin, period))
[(1, 3), (4, 6), (7, 9), (10, 12)]
>>> time_sequence = timestamp_generator(begin, fin, period)
>>> time_msg = "{start:2} to {end:2}, {term:2} days."
>>> for time in time_sequence:
... b, f = time
... print(time_msg.format(start=b, end=f, term=period))
...
1 to  3,  3 days.
4 to  6,  3 days.
7 to  9,  3 days.
10 to 12,  3 days.

Parameters

• pattern (str (default: '*')) – A pattern of regular expression to filter file in the target directory.

Returns

Nothing is returned.

Return type

None

None()

Examples

>>> gdrive_path_id = '1LA5334-SZdizcFqkl4xO8Hty7w1q0e8h'

Parameters

• pattern (str (default: '*')) – A pattern of regular expression to filter file in the target directory.

• src_dir (str (default: './data')) – A source directory to upload files in given URL ID.

Returns

Nothing is returned.

Return type

None