geniusnhu

>>> from sklearn.linear_model import SGDRegressor
>>> from sklearn.datasets import make_regression
>>> import numpy as np
>>> import pandas as pd

>>> ### Load original data
>>> original_data = pd.read_csv('sample.csv')
>>> print(f'Shape of original data {original_data.shape:.f02}')
Shape of original data (100000, 21)

geniusnhu

### Concatenate string using '+' operation
def add_string_with_plus(iters):
    s = ""
    for i in range(iters):
        s += "abc"
    assert len(s) == 3*iters
    
### Concatenate strings using join() function
def add_string_with_join(iters):
    l = []

geniusnhu

import numpy as np
import pandas as pd
import objgraph

### class without __slots__
class PointWithDict():
    def __init__(self, iters):
        self.iters = iters
    def convert(self):
        s = ["xyz"]*self.iters

geniusnhu

import numpy as np
import itertools
import sys

def append_matrix_with_itertools(X, Y):
  
    """ Loop matrix using itertools.product()
    """
    
    MTX = np.zeros((X, Y))

geniusnhu

>>> import tracemalloc
>>> import numpy as np

>>> def create_array(x, y):
>>>     x = x**2
>>>     y = y**3
>>>     return np.ones((x, y, 1024, 3), dtype=np.uint8)

>>> tracemalloc.start()
>>> ### Run application

geniusnhu

>>> import numpy as np
>>> import sys
>>> import objgraph
>>> import psutil
>>> import pandas as pd


>>> ob = np.ones((1024, 1024, 1024, 3), dtype=np.uint8)

### Check object 'ob' size

geniusnhu

>>> import sys
>>> my_generator_list = (i for i in range(100000))
>>> print(f"My generator is {sys.getsizeof(my_generator_list)} bytes")
My generator is 128 bytes

>>> timeit(my_generator_list)
10000000 loops, best of 5: 32 ns per loop
  
>>> my_list = [i for i in range(100000)]
>>> print(f"My list is {sys.getsizeof(my_list)} bytes")

geniusnhu

def data_optimize(df, object_option=False):
    """Reduce the size of the input dataframe

    Parameters
    ----------
    df: pd.DataFrame
        input DataFrame
    object_option : bool, default=False
        if true, try to convert object to category

geniusnhu

## Example of code annotation for a function

def your_function(X):
    """ Explanation of what the function does 
    
    Parameters
    ----------
    X: dtype
        explanation of X
    y: dtype

geniusnhu

## Example of the main code pipeline in .py format

import sys, os
import pandas as pd
import numpy as np

from your_classes.ClassOne import load_funtion, preprocess_function, training_function, save_result_function
import your_classes.PATH

def main():