spinesUtils

Dedicated to helping users do more in less time.

spinesUtils is a user-friendly toolkit for the machine learning ecosystem, offering ready-to-use features such as

Logging functionality
Type checking and parameter generation
CSV file reading acceleration
Classifiers for imbalanced data
Pandas Dataframe data compression
Pandas DataFrame insight tools
Large data training and testing set splitting functions
An intuitive timer.

It is currently undergoing rapid iteration. If you encounter any issues with its functionalities, feel free to raise an issue.

Installation

You can install spinesUtils from PyPI:

pip install spinesUtils

Logger

You can use the Logger class to print your logs without worrying about handler conflicts with the native Python logging module.

This class provides log/debug/info/warning/error/critical methods, where debug/info/warning/error/critical are partial versions of the log method, available for use as needed.

# load spinesUtils module
from spinesUtils.logging import Logger

# create a logger instance, with name "MyLogger", and no file handler, the default level is "INFO"
# You can specify a file path `fp` during instantiation. If not specified, logs will not be written to a file.
logger = Logger(name="MyLogger", fp=None, level="DEBUG")

logger.log("This is an info log emitted by the log function.", level='INFO')
logger.debug("This is an debug message")
logger.info("This is an info message.")
logger.warning("This is an warning message.")
logger.error("This is an error message.")
logger.critical("This is an critical message.")

2024-01-19 15:02:51 - MyLogger - INFO - This is an info log emitted by the log function.
2024-01-19 15:02:51 - MyLogger - DEBUG - This is an debug message
2024-01-19 15:02:51 - MyLogger - INFO - This is an info message.
2024-01-19 15:02:51 - MyLogger - WARNING - This is an warning message.
2024-01-19 15:02:51 - MyLogger - ERROR - This is an error message.
2024-01-19 15:02:51 - MyLogger - CRITICAL - This is an critical message.

Type checking and parameter generation

from spinesUtils.asserts import *

# check parameter type
@ParameterTypeAssert({
    'a': (int, float),
    'b': (int, float)
})
def add(a, b):
    pass

# try to pass a string to the function, and it will raise an ParametersTypeError error
add(a=1, b='2')

---------------------------------------------------------------------------

ParametersTypeError                       Traceback (most recent call last)

Cell In[2], line 12
      9     pass
     11 # try to pass a string to the function, and it will raise an ParametersTypeError error
---> 12 add(a=1, b='2')


File ~/projects/spinesUtils/spinesUtils/asserts/_inspect.py:196, in ParameterTypeAssert.__call__.<locals>.wrapper(*args, **kwargs)
    194 if mismatched_params:
    195     error_msg = self.build_type_error_msg(mismatched_params)
--> 196     raise ParametersTypeError(error_msg)
    198 return func(**kwargs)


ParametersTypeError: Function 'add' parameter(s) type mismatch: b only accept '['int', 'float']' type.

# check parameter value
@ParameterValuesAssert({
    'a': lambda x: x > 0,
    'b': lambda x: x > 0
})
def add(a, b):
    pass

# try to pass a negative number to the function, and it will raise an ParametersValueError error
add(a=1, b=-2)

---------------------------------------------------------------------------

ParametersValueError                      Traceback (most recent call last)

Cell In[3], line 10
      7     pass
      9 # try to pass a negative number to the function, and it will raise an ParametersValueError error
---> 10 add(a=1, b=-2)


File ~/projects/spinesUtils/spinesUtils/asserts/_inspect.py:258, in ParameterValuesAssert.__call__.<locals>.wrapper(*args, **kwargs)
    256 if mismatched_params:
    257     error_msg = self.build_values_error_msg(mismatched_params)
--> 258     raise ParametersValueError(error_msg)
    260 return func(**kwargs)


ParametersValueError: Function 'add' parameter(s) values mismatch: `b` must in or satisfy ''b': lambda x: x > 0' condition(s).

# generate a dictionary of keyword arguments for a given function using provided arguments
generate_function_kwargs(add, a=1, b=2)

{'a': 1, 'b': 2}

# isinstance function with support for None
augmented_isinstance(1, (int, float, None))

True

# raise_if and raise_if_not functions
raise_if(ValueError, 1 == 1, "test raise_if")

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

Cell In[6], line 2
      1 # raise_if and raise_if_not functions
----> 2 raise_if(ValueError, 1 == 1, "test raise_if")


File ~/projects/spinesUtils/spinesUtils/asserts/_type_and_exceptions.py:115, in raise_if(exception, condition, error_msg)
    112 assert issubclass(exception, BaseException), "Exception must be a subclass of BaseException."
    114 if condition:
--> 115     raise exception(error_msg)


ValueError: test raise_if

raise_if_not(ZeroDivisionError, 1 != 1, "test raise_if_not")

---------------------------------------------------------------------------

ZeroDivisionError                         Traceback (most recent call last)

Cell In[7], line 1
----> 1 raise_if_not(ZeroDivisionError, 1 != 1, "test raise_if_not")


File ~/projects/spinesUtils/spinesUtils/asserts/_type_and_exceptions.py:144, in raise_if_not(exception, condition, error_msg)
    141 assert issubclass(exception, BaseException), "Exception must be a subclass of BaseException."
    143 if not condition:
--> 144     raise exception(error_msg)


ZeroDivisionError: test raise_if_not

Faster csv reader

from spinesUtils import read_csv

your_df = read_csv(
    fp='/path/to/your/file.csv',
    sep=',',  # equal to pandas read_csv.sep
    turbo_method='polars',  # use turbo_method to speed up load time
    chunk_size=None,  # it can be integer if you want to use pandas backend
    transform2low_mem=True,  # it can compresses file to save more memory
    verbose=False
)

Classifiers for imbalanced data

from spinesUtils.models import MultiClassBalanceClassifier

# make a toy dataset
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

dataset = make_classification(
    n_samples=10000,
    n_features=2,
    n_informative=2,
    n_redundant=0,
    n_repeated=0,
    n_classes=3,
    n_clusters_per_class=1,
    weights=[0.01, 0.05, 0.94],
    class_sep=0.8,
    random_state=0
)

X, y = dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

from sklearn.ensemble import RandomForestClassifier

classifier = MultiClassBalanceClassifier(
    base_estimator=RandomForestClassifier(n_estimators=100),
    n_classes=3,
    random_state=0,
    verbose=0
)

# fit the classifier
classifier.fit(X_train, y_train)

# predict
y_pred = classifier.predict(X_test)

# print classification report
print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

           0       0.74      0.72      0.73        32
           1       0.91      0.71      0.80       111
           2       0.98      1.00      0.99      1857

    accuracy                           0.98      2000
   macro avg       0.88      0.81      0.84      2000
weighted avg       0.98      0.98      0.98      2000

Pandas dataframe data compression

# make a toy dataset
import pandas as pd
import numpy as np

df = pd.DataFrame({
    'a': np.random.randint(0, 100, 100000),
    'b': np.random.randint(0, 100, 100000),
    'c': np.random.randint(0, 100, 100000),
    'd': np.random.randint(0, 100, 100000),
    'e': np.random.randint(0, 100, 100000),
    'f': np.random.randint(0, 100, 100000),
    'g': np.random.randint(0, 100, 100000),
    'h': np.random.randint(0, 100, 100000),
    'i': np.random.randint(0, 100, 100000),
    'j': np.random.randint(0, 100, 100000),
    'k': np.random.randint(0, 100, 100000),
    'l': np.random.randint(0, 100, 100000),
    'm': np.random.randint(0, 100, 100000),
    'n': np.random.randint(0, 100, 100000),
    'o': np.random.randint(0, 100, 100000),
    'p': np.random.randint(0, 100, 100000),
    'q': np.random.randint(0, 100, 100000),
    'r': np.random.randint(0, 100, 100000),
    's': np.random.randint(0, 100, 100000),
    't': np.random.randint(0, 100, 100000),
    'u': np.random.randint(0, 100, 100000),
    'v': np.random.randint(0, 100, 100000),
    'w': np.random.randint(0, 100, 100000),
    'x': np.random.randint(0, 100, 100000),
    'y': np.random.randint(0, 100, 100000),
    'z': np.random.randint(0, 100, 100000),
})

# compress dataframe
from spinesUtils import transform_dtypes_low_mem

transform_dtypes_low_mem(df, verbose=True, inplace=True)

Converting ...:   0%|          | 0/26 [00:00<?, ?it/s]


[log] INFO - Memory usage before conversion is: 19.84 MB  
[log] INFO - Memory usage after conversion is: 2.48 MB  
[log] INFO - After conversion, the percentage of memory fluctuation is 87.5 %

# batch compress dataframes
from spinesUtils import transform_batch_dtypes_low_mem

# make some toy datasets
df1 = pd.DataFrame({
    'a': np.random.randint(0, 100, 100000),
    'b': np.random.randint(0, 100, 100000),
    'c': np.random.randint(0, 100, 100000),
    'd': np.random.randint(0, 100, 100000),
    'e': np.random.randint(0, 100, 100000),
    'f': np.random.randint(0, 100, 100000),
    'g': np.random.randint(0, 100, 100000),
    'h': np.random.randint(0, 100, 100000),
    'i': np.random.randint(0, 100, 100000),
    'j': np.random.randint(0, 100, 100000),
    'k': np.random.randint(0, 100, 100000),
    'l': np.random.randint(0, 100, 100000),
    'm': np.random.randint(0, 100, 100000),
    'n': np.random.randint(0, 100, 100000),
    'o': np.random.randint(0, 100, 100000),
    'p': np.random.randint(0, 100, 100000),
    'q': np.random.randint(0, 100, 100000),
    'r': np.random.randint(0, 100, 100000),
    's': np.random.randint(0, 100, 100000),
    't': np.random.randint(0, 100, 100000),
    'u': np.random.randint(0, 100, 100000),
    'v': np.random.randint(0, 100, 100000),
    'w': np.random.randint(0, 100, 100000),
    'x': np.random.randint(0, 100, 100000),
    'y': np.random.randint(0, 100, 100000),
    'z': np.random.randint(0, 100, 100000),
})

df2 = df1.copy()
df3 = df1.copy()
df4 = df1.copy()

# batch compress dataframes
transform_batch_dtypes_low_mem([df1, df2, df3, df4], verbose=True, inplace=True)

Batch converting ...:   0%|          | 0/4 [00:00<?, ?it/s]


[log] INFO - Memory usage before conversion is: 79.35 MB  
[log] INFO - Memory usage after conversion is: 9.92 MB  
[log] INFO - After conversion, the percentage of memory fluctuation is 87.5 %

Pandas DataFrame insight tools

from spinesUtils import df_preview, classify_samples_dist

# make a toy dataset
import pandas as pd
import numpy as np

df = pd.DataFrame({
    'a': np.random.randint(0, 100, 100000),
    'b': np.random.randint(0, 100, 100000),
    'c': np.random.randint(0, 100, 100000),
    'd': np.random.randint(0, 100, 100000),
    'e': np.random.randint(0, 100, 100000),
    'f': np.random.randint(0, 100, 100000),
    'g': np.random.randint(0, 100, 100000),
    'h': np.random.randint(0, 100, 100000),
    'i': np.random.randint(0, 100, 100000),
    'j': np.random.randint(0, 100, 100000),
    'k': np.random.randint(0, 100, 100000),
    'l': np.random.randint(0, 100, 100000),
    'm': np.random.randint(0, 100, 100000),
    'n': np.random.randint(0, 100, 100000),
    'o': np.random.randint(0, 100, 100000),
    'p': np.random.randint(0, 100, 100000),
    'q': np.random.randint(0, 100, 100000),
    'r': np.random.randint(0, 100, 100000),
    's': np.random.randint(0, 100, 100000),
    't': np.random.randint(0, 100, 100000),
    'u': np.random.randint(0, 100, 100000),
    'v': np.random.randint(0, 100, 100000),
    'w': np.random.randint(0, 100, 100000),
    'x': np.random.randint(0, 100, 100000),
    'y': np.random.randint(0, 100, 100000),
    'z': np.random.randint(0, 100, 100000),
})

df_insight = df_preview(df)

df_insight

	total	nunique	dtype	max	75%	median	25%	mean	mode	variation	std	skew	kurt	samples
a	100000	100	int64	99.0	74.0	50.0	25.0	49.53968	36	0.9892	28.848392	-0.000158	-1.196434	(32, 81)
b	100000	100	int64	99.0	75.0	49.0	24.0	49.41822	40	0.98928	28.937601	0.005974	-1.206987	(76, 28)
c	100000	100	int64	99.0	75.0	50.0	25.0	49.58261	82	0.98923	28.928019	-0.003537	-1.202994	(21, 68)
d	100000	100	int64	99.0	75.0	49.0	24.0	49.46308	9	0.98906	28.886459	0.003344	-1.200654	(42, 90)
e	100000	100	int64	99.0	75.0	49.0	25.0	49.55014	37	0.98911	28.834041	0.003987	-1.196103	(15, 59)
f	100000	100	int64	99.0	74.0	49.0	24.0	49.20195	4	0.98926	28.886463	0.009183	-1.203297	(72, 9)
g	100000	100	int64	99.0	75.0	50.0	25.0	49.62199	4	0.98919	28.849264	-0.012746	-1.199283	(69, 64)
h	100000	100	int64	99.0	75.0	50.0	25.0	49.58739	40	0.98917	28.83744	-0.004719	-1.193858	(30, 79)
i	100000	100	int64	99.0	75.0	49.0	24.0	49.41076	10	0.98939	28.910095	0.005218	-1.207459	(36, 54)
j	100000	100	int64	99.0	74.0	49.0	25.0	49.45686	46	0.98909	28.816681	0.004751	-1.190756	(29, 95)
k	100000	100	int64	99.0	74.0	50.0	25.0	49.54948	46	0.98914	28.806187	-0.003731	-1.196876	(32, 94)
l	100000	100	int64	99.0	74.0	49.0	24.0	49.45631	20	0.98923	28.921314	0.002344	-1.205342	(22, 91)
m	100000	100	int64	99.0	74.0	49.0	24.0	49.43142	49	0.98901	28.852962	0.002507	-1.198267	(94, 26)
n	100000	100	int64	99.0	75.0	50.0	24.0	49.49325	8	0.98931	28.899022	0.000698	-1.200786	(46, 50)
o	100000	100	int64	99.0	75.0	50.0	25.0	49.52091	4	0.98923	28.869563	-0.003987	-1.202426	(33, 13)
p	100000	100	int64	99.0	74.0	49.0	24.0	49.40997	61	0.98918	28.900207	0.007921	-1.204621	(58, 93)
q	100000	100	int64	99.0	75.0	50.0	25.0	49.62826	33	0.98936	28.831896	-0.003291	-1.201172	(82, 31)
r	100000	100	int64	99.0	75.0	50.0	24.0	49.47208	60	0.98925	28.873943	0.000515	-1.202925	(0, 26)
s	100000	100	int64	99.0	75.0	50.0	25.0	49.64847	48	0.9893	28.853741	-0.010258	-1.202701	(94, 37)
t	100000	100	int64	99.0	74.0	50.0	25.0	49.55305	32	0.98898	28.801028	-0.001721	-1.193403	(85, 10)
u	100000	100	int64	99.0	74.0	49.0	24.0	49.45428	80	0.98928	28.876812	0.002018	-1.201612	(56, 16)
v	100000	100	int64	99.0	75.0	50.0	25.0	49.59953	16	0.98945	28.891313	-0.006261	-1.199011	(60, 39)
w	100000	100	int64	99.0	74.0	49.0	24.0	49.34131	4	0.98915	28.925175	0.009523	-1.203308	(78, 96)
x	100000	100	int64	99.0	74.0	49.0	25.0	49.45791	95	0.98933	28.860322	0.007199	-1.198962	(93, 79)
y	100000	100	int64	99.0	74.0	50.0	25.0	49.58517	34	0.98929	28.765474	-0.000497	-1.193016	(80, 42)
z	100000	100	int64	99.0	74.0	50.0	24.0	49.44355	21	0.98876	28.85751	0.000819	-1.201063	(25, 25)

Large data training and testing set splitting functions

# make a toy dataset
import pandas as pd
import numpy as np

df = pd.DataFrame({
    'a': np.random.randint(0, 100, 100000),
    'b': np.random.randint(0, 100, 100000),
    'c': np.random.randint(0, 100, 100000),
    'd': np.random.randint(0, 100, 100000),
    'e': np.random.randint(0, 100, 100000),
    'f': np.random.randint(0, 100, 100000),
    'g': np.random.randint(0, 100, 100000),
    'h': np.random.randint(0, 100, 100000),
    'i': np.random.randint(0, 100, 100000),
    'j': np.random.randint(0, 100, 100000),
    'k': np.random.randint(0, 100, 100000),
    'l': np.random.randint(0, 100, 100000),
    'm': np.random.randint(0, 100, 100000),
    'n': np.random.randint(0, 100, 100000),
    'o': np.random.randint(0, 100, 100000),
    'p': np.random.randint(0, 100, 100000),
    'q': np.random.randint(0, 100, 100000),
    'r': np.random.randint(0, 100, 100000),
    's': np.random.randint(0, 100, 100000),
    't': np.random.randint(0, 100, 100000),
    'u': np.random.randint(0, 100, 100000),
    'v': np.random.randint(0, 100, 100000),
    'w': np.random.randint(0, 100, 100000),
    'x': np.random.randint(0, 100, 100000),
    'y': np.random.randint(0, 100, 100000),
    'z': np.random.randint(0, 100, 100000),
})

# split dataframe into training and testing sets

# return numpy.ndarray
from spinesUtils import train_test_split_bigdata
from spinesUtils.feature_tools import get_x_cols

X_train, X_valid, X_test, y_train, y_valid, y_test = train_test_split_bigdata(
    df=df, 
    x_cols=get_x_cols(df, y_col='a'),
    y_col='a', 
    shuffle=True,
    return_valid=True,
    train_size=0.8,
    valid_size=0.5
)

print(X_train.shape, X_valid.shape, X_test.shape, y_train.shape, y_valid.shape, y_test.shape)
X_train[:5]

(80000, 25) (80000,) (10000, 25) (10000,) (10000, 25) (10000,)





array([[45, 83, 43, 94,  1, 86, 56,  0, 78, 60, 79, 42, 24, 43, 94, 83,
        45, 50, 59, 50, 17, 99, 40, 95, 70],
       [ 4, 81,  9, 25, 54, 18, 14,  6, 17, 39,  0, 36, 82, 33, 11, 76,
        92, 29, 33, 50, 44, 11, 87, 86, 31],
       [72, 82, 52, 96, 55, 89, 35, 71, 48, 73, 34, 19, 53, 89, 46, 57,
        84, 67, 10, 40, 50, 61, 10, 76, 84],
       [46, 45, 79, 53, 80, 85, 58, 65, 26, 49, 46, 97, 83, 47, 77, 97,
        26,  4, 33, 79, 36, 65, 50, 94, 87],
       [36,  7, 46, 10, 11, 33,  3,  7, 82, 29, 28,  2, 42, 89, 42, 66,
        79, 51, 49, 43, 63, 14, 13, 74, 26]])

# return pandas.DataFrame
from spinesUtils import train_test_split_bigdata_df
from spinesUtils.feature_tools import get_x_cols

train_df, valid_df, test_df = train_test_split_bigdata_df(
    df=df, 
    x_cols=get_x_cols(df, y_col='a'),
    y_col='a', 
    shuffle=True,
    return_valid=True,
    train_size=0.8,
    valid_size=0.5
)

print(train_df.shape, valid_df.shape, test_df.shape)
train_df.head()

(8000000, 26) (1000000, 26) (1000000, 26)

	b	c	d	e	f	g	h	i	j	k	...	r	s	t	u	v	w	x	y	z	a
0	14	67	41	87	68	87	27	67	26	62	...	63	43	77	4	6	72	5	63	73	27
1	47	37	43	98	55	68	82	48	37	35	...	99	92	23	44	92	14	54	95	58	59
2	52	97	71	62	18	54	22	2	57	93	...	82	6	61	41	24	40	54	11	9	5
3	48	45	22	46	32	37	6	13	42	67	...	9	1	65	84	11	86	54	22	89	85
4	26	23	55	31	61	72	68	82	6	19	...	13	44	3	93	66	53	75	93	53	43

5 rows × 26 columns

# performances comparison
from sklearn.model_selection import train_test_split
from spinesUtils import train_test_split_bigdata, train_test_split_bigdata_df
from spinesUtils.feature_tools import get_x_cols

# make a toy dataset
import pandas as pd
import numpy as np

df = pd.DataFrame({
    'a': np.random.randint(0, 100, 10000),
    'b': np.random.randint(0, 100, 10000),
    'c': np.random.randint(0, 100, 10000),
    'd': np.random.randint(0, 100, 10000),
    'e': np.random.randint(0, 100, 10000),
    'f': np.random.randint(0, 100, 10000),
    'g': np.random.randint(0, 100, 10000),
    'h': np.random.randint(0, 100, 10000),
    'i': np.random.randint(0, 100, 10000),
    'j': np.random.randint(0, 100, 10000),
    'k': np.random.randint(0, 100, 10000),
    'l': np.random.randint(0, 100, 10000),
    'm': np.random.randint(0, 100, 10000),
    'n': np.random.randint(0, 100, 10000),
    'o': np.random.randint(0, 100, 10000),
    'p': np.random.randint(0, 100, 10000),
    'q': np.random.randint(0, 100, 10000),
    'r': np.random.randint(0, 100, 10000),
    's': np.random.randint(0, 100, 10000),
    't': np.random.randint(0, 100, 10000),
    'u': np.random.randint(0, 100, 10000),
    'v': np.random.randint(0, 100, 10000),
    'w': np.random.randint(0, 100, 10000),
    'x': np.random.randint(0, 100, 10000),
    'y': np.random.randint(0, 100, 10000),
    'z': np.random.randint(0, 100, 10000),
})

# define a function to split a valid set for sklearn train_test_split
def train_test_split_sklearn(df, x_cols, y_col, shuffle, train_size, valid_size):
    X_train, X_test, y_train, y_test = train_test_split(df[x_cols], df[y_col], test_size=1-train_size, random_state=0, shuffle=shuffle)
    X_valid, X_test, y_valid, y_test = train_test_split(X_test, y_test, test_size=valid_size, random_state=0, shuffle=shuffle)
    return X_train, X_valid, X_test, y_train, y_valid, y_test

%timeit X_train, X_valid, X_test, y_train, y_valid, y_test = train_test_split_sklearn(df=df, x_cols=get_x_cols(df, y_col='a'), y_col='a', shuffle=True, train_size=0.8, valid_size=0.5)
%timeit X_train, X_valid, X_test, y_train, y_valid, y_test = train_test_split_bigdata(df=df, x_cols=get_x_cols(df, y_col='a'), y_col='a', shuffle=True, return_valid=True, train_size=0.8, valid_size=0.5)
%timeit train_df, valid_df, test_df = train_test_split_bigdata_df(df=df, x_cols=get_x_cols(df, y_col='a'), y_col='a', shuffle=True, return_valid=True, train_size=0.8, valid_size=0.5)

1.28 ms ± 20.5 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
1.05 ms ± 14.1 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
1.36 ms ± 11.7 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)

An intuitive timer

from spinesUtils.timer import Timer

# create a timer instance
timer = Timer()

# start the timer
timer.start()

# do something
for i in range(10):
    # timer sleep for 1 second
    timer.sleep(1)
    # print the elapsed time from last sleep
    print("Elapsed time: {} seconds".format(timer.last_timestamp_diff()))

# print the elapsed time
print("Total elapsed time: {} seconds".format(timer.total_elapsed_time()))

# stop the timer
timer.end()

Elapsed time: 1.0117900371551514 seconds
Elapsed time: 2.016140937805176 seconds
Elapsed time: 3.0169479846954346 seconds
Elapsed time: 4.0224690437316895 seconds
Elapsed time: 5.027086019515991 seconds
Elapsed time: 6.0309507846832275 seconds
Elapsed time: 7.035104036331177 seconds
Elapsed time: 8.040709972381592 seconds
Elapsed time: 9.042311906814575 seconds
Elapsed time: 10.046867847442627 seconds
Total elapsed time: 10.047839879989624 seconds





10.047943830490112

from spinesUtils.timer import Timer

# you can also use the timer as a context manager
t = Timer()
with t.session():
    t.sleep(1)
    print("Last step elapsed time:", round(t.last_timestamp_diff(), 2), 'seconds')
    t.middle_point()
    t.sleep(2)
    print("Last step elapsed time:", round(t.last_timestamp_diff(), 2), 'seconds')
    
    total_elapsed_time = t.total_elapsed_time()
    
print("Total Time:", round(total_elapsed_time, 2), 'seconds')

Last step elapsed time: 1.01 seconds
Last step elapsed time: 2.01 seconds
Total Time: 3.01 seconds

spinesUtils
Release 0.4.1

Release 0.4.1

0.4.1

0.4.0

0.3.13

0.3.12

0.3.11

0.3.10

0.3.9

0.3.8

0.3.7

0.3.6

Documentation

spinesUtils

Installation

Logger

Type checking and parameter generation

Faster csv reader

Classifiers for imbalanced data

Pandas dataframe data compression

Pandas DataFrame insight tools

Large data training and testing set splitting functions

An intuitive timer

Stats

Development practices

Releases

Contributors

	b	c	d	e	f	g	h	i	j	k	...	r	s	t	u	v	w	x	y	z	a
0	14	67	41	87	68	87	27	67	26	62	...	63	43	77	4	6	72	5	63	73	27
1	47	37	43	98	55	68	82	48	37	35	...	99	92	23	44	92	14	54	95	58	59
2	52	97	71	62	18	54	22	2	57	93	...	82	6	61	41	24	40	54	11	9	5
3	48	45	22	46	32	37	6	13	42	67	...	9	1	65	84	11	86	54	22	89	85
4	26	23	55	31	61	72	68	82	6	19	...	13	44	3	93	66	53	75	93	53	43

	b	c	d	e	f	g	h	i	j	k	...	r	s	t	u	v	w	x	y	z	a
0	14	67	41	87	68	87	27	67	26	62	...	63	43	77	4	6	72	5	63	73	27
1	47	37	43	98	55	68	82	48	37	35	...	99	92	23	44	92	14	54	95	58	59
2	52	97	71	62	18	54	22	2	57	93	...	82	6	61	41	24	40	54	11	9	5
3	48	45	22	46	32	37	6	13	42	67	...	9	1	65	84	11	86	54	22	89	85
4	26	23	55	31	61	72	68	82	6	19	...	13	44	3	93	66	53	75	93	53	43

spinesUtils Release 0.4.1

Release 0.4.1 Toggle Dropdown 0.4.1 0.4.0 0.3.13 0.3.12 0.3.11 0.3.10 0.3.9 0.3.8 0.3.7 0.3.6

Documentation

spinesUtils

Installation

Logger

Type checking and parameter generation

Faster csv reader

Classifiers for imbalanced data

Pandas dataframe data compression

Pandas DataFrame insight tools

Large data training and testing set splitting functions

An intuitive timer

Stats

Development practices

Releases

Contributors

spinesUtils
Release 0.4.1

Release 0.4.1

0.4.1

0.4.0

0.3.13

0.3.12

0.3.11

0.3.10

0.3.9

0.3.8

0.3.7

0.3.6

	b	c	d	e	f	g	h	i	j	k	...	r	s	t	u	v	w	x	y	z	a
0	14	67	41	87	68	87	27	67	26	62	...	63	43	77	4	6	72	5	63	73	27
1	47	37	43	98	55	68	82	48	37	35	...	99	92	23	44	92	14	54	95	58	59
2	52	97	71	62	18	54	22	2	57	93	...	82	6	61	41	24	40	54	11	9	5
3	48	45	22	46	32	37	6	13	42	67	...	9	1	65	84	11	86	54	22	89	85
4	26	23	55	31	61	72	68	82	6	19	...	13	44	3	93	66	53	75	93	53	43