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Python Syntax


Recommended Reading : 【Python】 Python Table of Contents


1. Overview

2. Syntax


a. R Overview



1. Overview

Advantage 1. Easy to implement backend in app development, etc.

Advantage 2. Well-developed deep learning tools

Advantage 3. Various applications possible based on open community

Advantage 4. Excellent data visualization



2. Syntax

⑴ Python can return multiple values simultaneously

⑵ yield returns values one at a time when needed, rather than all at once

⑶ You can write multi-line code by using backspace \ for line breaks

⑷ A single-line string is represented by “~” or ‘~’, and a multi-line string is represented by “””~””” or ‘’’~’’’

⑸ adata.var_names=[i.upper() for i in list(adata.var_names)]

① Meaning : In adata.var_names, each i in list(data.var_names) is stored in uppercase

⑹ if issparse(counts.X):counts.X=counts.X.A

① Meaning : If counts.X is a sparse matrix, the value of counts.X.A is assigned to counts.X

⑺ pd.DataFrame([(str(i),str(j)) for i in range(4) for j in range(i+1, 4)])


	0	1
0	0	1
1	0	2
2	0	3
3	1	2
4	1	3
5	2	3


Broadcasting

① A feature in NumPy that allows operations between arrays of mismatched dimensions

② A small array (as long as it has a dimension of 1) is automatically expanded to match the larger array’s shape, and then the operation is performed

③ Data is not physically copied to match the small array to the large array


# Shape of `exp_log_mu`: (2257, 6)
# Shape of `theta_mn`: (6, 13344)

exp_log_mu_expanded = exp_log_mu[:, :, np.newaxis]   # Shape: (2257, 6, 1)
theta_mn_expanded = theta_mn[np.newaxis, :, :]       # Shape: (1, 6, 13344)    
result = exp_log_mu_expanded * theta_mn_expanded     # Shape: (2257, 6, 13344)



Input: 2024.04.30 13:41

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