nqdm 0.1.6

NQDM -- An extension of TQDM which enables you to loop over multiple objects simultaneously, and specify the depth of iteration for each object. It is just pure Python magic, no extra libraries needed. It is customizable, minimal and open source.

NQDM

A Gentle Introduction

A more generalised implementation to TQDM-progress bars, which simulates a single loop for multiple loops and returns multiple elements at the same time. It can iterate over the deeper levels. It is compatible with many data types, is customizable and beginner-friendly.

Installing

Install it using the terminal

pip install nqdm

Getting Started

First of all, you need to import nqdm.

from nqdm import nqdm

Let's explore some use cases of nqdm:

Working With Different Data Types

There are three types of arguments:

1. constant: returns 0, ..., constant-1
2. iterable: returns iterable[0], ..., iterable[-1]
3. hashable: returns {keys[0]: values[0]}, ..., {keys[-1]: values[-1]}

A more detailled list of available arguments and return values:

Argument	Type of Argument	Returns
constant	int, float, double	range(int(constant))
iterable	numpy.ndarray, range, list, str	list(array)
hashable	pandas.core.series.Series	[{k: v} for k, v in zip(hashable.index, hashable.values)]
hashable	dict	[{k: v} for k, v in hashable.items()]

Length variables (int, float)

Input:

len_1 = 2.0 
len_2 = 3 

for i in nqdm(len_1, len_2):
  print(i)

Output:

Lists and NumPy arrays

Input:

arg_1 = [1, 2, 3]
arg_2 = np.array([4, 5, 6])

for i in nqdm(arg_1, arg_2):
  print(i)

Output:

Strings

Input:

arg_1 = list("abc")
arg_2 = "cde"

for i in nqdm(arg_1, arg_2):
  print(i)

Output:

Dictionaries and Pandas Series

Input:

arg_1 = {"key1": 4, "key2": 5}
arg_2 = pd.Series(["horse", "cat", "mouse"], index=[978, 979, 980])

for i in nqdm(arg_1, arg_2):
  print(i)

Output:

NEW: Usage of Depth Feature

Input:

list_of_list_of_lists = [[[0, 1], [2, 3]], [[4, 5], [6, 7]]]
for list_of_list in nqdm(list_of_list_of_lists, depth=0):
  print(list_of_list)

Output:

Input:

list_of_list_of_lists = np.arange(8).reshape(2, 2, 2)
for arr in nqdm(list_of_list_of_lists, depth=1):
  print(arr)

Output:

Input:

list_of_dict_of_lists = [{"a": [0, 1], "b": [2, 3]}, {"a": [4, 5], "b": [6, 7]}]
for elem in nqdm(list_of_dict_of_lists, depth=2):
  print(elem)

Output:

Input:

list_of_list_of_lists = np.arange(8).reshape(2, 2, 2)
list_of_list_of_dicts = [[{"a": 1, "b": 2}, {"c": 3, "d": 4}], [{"e": 5, "f": 6}, {"g": 7, "h": 8}]]
for elems in nqdm(list_of_list_of_lists, list_of_list_of_dicts, depth=[0, 1]):
  print(elems)

Output:

NEW: Usage of Order Feature

Input:

for i in nqdm(2, 2, 2, order = "last"):
    print(i)

Output:

Input:

for i in nqdm(2, 2, 2, order = "first"):
    print(i)

Output:

Input:

for i in nqdm(2, 2, 2, order = [1, 2, 0]):
    print(i)

Output:

NEW: Usage of Enum Feature

Input:

data = pd.DataFrame({"x1": [2, 3, 5], "x2": [9, 8, 2], "x3": [0, 0, 2]})

for i in nqdm(data, enum=True, depth=1):
    print(i)

Output:

Built With

• tqdm - The progress bar

Authors

• Yamac Eren Ay - Initial work - yamaceay

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Special Thanks to:

• Stanislav Kosorin - stano45
• Ori Toledo - Logo design - oritoledo

Acknowledgments

It's worth mentioning that this module is built on top of TQDM, I would like to thank them for making this process of creating progress bars easy and flexible.