【Python】记录九：显示进度条工具tqdm简介
python中有很多丰富的第三方库，tqdm就是一种非常简单又实用的工具，它能够显示当前程序的执行进度，这个功能非常适合于长时间执行程序时使用。
关于tqdm这个有点难记的名字的由来，作者在github上是这么解释的：
tqdm means “progress” in Arabic (taqadum, تقدّم) and is an abbreviation for “I love you so much” in Spanish (te quiero demasiado).
西班牙语中的<我是如此爱你>~
基本用法
它的用法很简单，一般在循环操作中使用：
from time import sleep
from tqdm import tqdm
for i in tqdm(range(1000)):
sleep(0.01)
执行代码的结果是：
0%| | 0/1000 [00:00<?, ?it/s]
1%| | 10/1000 [00:00<00:10, 91.98it/s]
2%|▏ | 19/1000 [00:00<00:10, 90.34it/s]
3%|▎ | 28/1000 [00:00<00:10, 89.22it/s]
4%|▎ | 37/1000 [00:00<00:10, 88.62it/s]
5%|▍ | 46/1000 [00:00<00:10, 88.05it/s]
6%|▌ | 55/1000 [00:00<00:10, 88.16it/s]
6%|▋ | 64/1000 [00:00<00:10, 88.03it/s]
7%|▋ | 73/1000 [00:00<00:10, 87.91it/s]
8%|▊ | 82/1000 [00:00<00:10, 88.07it/s]
9%|▉ | 91/1000 [00:01<00:10, 88.16it/s]
10%|█ | 100/1000 [00:01<00:10, 87.99it/s]
11%|█ | 109/1000 [00:01<00:10, 87.88it/s]
12%|█▏ | 118/1000 [00:01<00:10, 87.79it/s]
13%|█▎ | 127/1000 [00:01<00:09, 87.98it/s]
14%|█▎ | 136/1000 [00:01<00:09, 87.86it/s]
14%|█▍ | 145/1000 [00:01<00:09, 87.72it/s]
15%|█▌ | 154/1000 [00:01<00:09, 87.60it/s]
16%|█▋ | 163/1000 [00:01<00:09, 87.55it/s]
17%|█▋ | 172/1000 [00:01<00:09, 87.56it/s]
18%|█▊ | 181/1000 [00:02<00:09, 87.57it/s]
19%|█▉ | 190/1000 [00:02<00:09, 87.57it/s]
20%|█▉ | 199/1000 [00:02<00:09, 87.58it/s]
21%|██ | 208/1000 [00:02<00:09, 87.59it/s]
22%|██▏ | 217/1000 [00:02<00:08, 87.76it/s]
23%|██▎ | 226/1000 [00:02<00:08, 88.26it/s]
24%|██▎ | 235/1000 [00:02<00:08, 88.06it/s]
24%|██▍ | 244/1000 [00:02<00:08, 87.91it/s]
25%|██▌ | 253/1000 [00:02<00:08, 87.55it/s]
26%|██▌ | 262/1000 [00:02<00:08, 87.81it/s]
27%|██▋ | 271/1000 [00:03<00:08, 88.24it/s]
28%|██▊ | 280/1000 [00:03<00:08, 88.41it/s]
29%|██▉ | 289/1000 [00:03<00:08, 88.33it/s]
30%|██▉ | 298/1000 [00:03<00:07, 88.30it/s]
31%|███ | 307/1000 [00:03<00:07, 88.28it/s]
32%|███▏ | 316/1000 [00:03<00:07, 88.46it/s]
32%|███▎ | 325/1000 [00:03<00:07, 87.93it/s]
33%|███▎ | 334/1000 [00:03<00:07, 87.68it/s]
34%|███▍ | 343/1000 [00:03<00:07, 87.87it/s]
35%|███▌ | 352/1000 [00:04<00:07, 87.70it/s]
36%|███▌ | 361/1000 [00:04<00:07, 87.87it/s]
37%|███▋ | 370/1000 [00:04<00:07, 87.80it/s]
38%|███▊ | 379/1000 [00:04<00:07, 88.12it/s]
39%|███▉ | 388/1000 [00:04<00:06, 88.35it/s]
40%|███▉ | 397/1000 [00:04<00:06, 88.28it/s]
41%|████ | 406/1000 [00:04<00:06, 88.53it/s]
42%|████▏ | 415/1000 [00:04<00:06, 88.25it/s]
42%|████▏ | 424/1000 [00:04<00:06, 87.80it/s]
43%|████▎ | 433/1000 [00:04<00:06, 87.75it/s]
44%|████▍ | 442/1000 [00:05<00:06, 87.45it/s]
45%|████▌ | 451/1000 [00:05<00:06, 87.25it/s]
46%|████▌ | 460/1000 [00:05<00:06, 87.10it/s]
47%|████▋ | 469/1000 [00:05<00:06, 87.00it/s]
48%|████▊ | 478/1000 [00:05<00:05, 87.18it/s]
49%|████▊ | 487/1000 [00:05<00:05, 87.82it/s]
50%|████▉ | 496/1000 [00:05<00:05, 87.76it/s]
50%|█████ | 505/1000 [00:05<00:05, 87.46it/s]
51%|█████▏ | 514/1000 [00:05<00:05, 87.39it/s]
52%|█████▏ | 523/1000 [00:05<00:05, 87.38it/s]
53%|█████▎ | 532/1000 [00:06<00:05, 87.63it/s]
54%|█████▍ | 541/1000 [00:06<00:05, 87.55it/s]
55%|█████▌ | 550/1000 [00:06<00:05, 87.50it/s]
56%|█████▌ | 559/1000 [00:06<00:05, 87.49it/s]
57%|█████▋ | 568/1000 [00:06<00:04, 87.71it/s]
58%|█████▊ | 577/1000 [00:06<00:04, 87.86it/s]
59%|█████▊ | 586/1000 [00:06<00:04, 87.47it/s]
60%|█████▉ | 595/1000 [00:06<00:04, 87.71it/s]
60%|██████ | 604/1000 [00:06<00:04, 87.86it/s]
61%|██████▏ | 613/1000 [00:06<00:04, 87.96it/s]
62%|██████▏ | 622/1000 [00:07<00:04, 88.04it/s]
63%|██████▎ | 631/1000 [00:07<00:04, 87.87it/s]
64%|██████▍ | 640/1000 [00:07<00:04, 88.23it/s]
65%|██████▍ | 649/1000 [00:07<00:03, 88.24it/s]
66%|██████▌ | 658/1000 [00:07<00:03, 88.23it/s]
67%|██████▋ | 667/1000 [00:07<00:03, 87.70it/s]
68%|██████▊ | 676/1000 [00:07<00:03, 87.88it/s]
68%|██████▊ | 685/1000 [00:07<00:03, 87.41it/s]
69%|██████▉ | 694/1000 [00:07<00:03, 87.22it/s]
70%|███████ | 703/1000 [00:08<00:03, 87.34it/s]
71%|███████ | 712/1000 [00:08<00:03, 87.42it/s]
72%|███████▏ | 721/1000 [00:08<00:03, 86.97it/s]
73%|███████▎ | 730/1000 [00:08<00:03, 87.16it/s]
74%|███████▍ | 739/1000 [00:08<00:02, 87.30it/s]
75%|███████▍ | 748/1000 [00:08<00:02, 86.89it/s]
76%|███████▌ | 757/1000 [00:08<00:02, 87.10it/s]
77%|███████▋ | 766/1000 [00:08<00:02, 87.51it/s]
78%|███████▊ | 775/1000 [00:08<00:02, 87.54it/s]
78%|███████▊ | 784/1000 [00:08<00:02, 87.56it/s]
79%|███████▉ | 793/1000 [00:09<00:02, 87.58it/s]
80%|████████ | 802/1000 [00:09<00:02, 87.63it/s]
81%|████████ | 811/1000 [00:09<00:02, 87.91it/s]
82%|████████▏ | 820/1000 [00:09<00:02, 87.99it/s]
83%|████████▎ | 829/1000 [00:09<00:01, 88.06it/s]
84%|████████▍ | 838/1000 [00:09<00:01, 88.37it/s]
85%|████████▍ | 847/1000 [00:09<00:01, 88.85it/s]
86%|████████▌ | 856/1000 [00:09<00:01, 88.48it/s]
86%|████████▋ | 865/1000 [00:09<00:01, 88.21it/s]
87%|████████▋ | 874/1000 [00:09<00:01, 88.03it/s]
88%|████████▊ | 883/1000 [00:10<00:01, 88.16it/s]
89%|████████▉ | 892/1000 [00:10<00:01, 88.26it/s]
90%|█████████ | 901/1000 [00:10<00:01, 88.32it/s]
91%|█████████ | 910/1000 [00:10<00:01, 88.37it/s]
92%|█████████▏| 919/1000 [00:10<00:00, 88.66it/s]
93%|█████████▎| 928/1000 [00:10<00:00, 88.60it/s]
94%|█████████▎| 937/1000 [00:10<00:00, 88.56it/s]
95%|█████████▍| 946/1000 [00:10<00:00, 88.02it/s]
96%|█████████▌| 955/1000 [00:10<00:00, 88.15it/s]
96%|█████████▋| 964/1000 [00:10<00:00, 88.40it/s]
97%|█████████▋| 973/1000 [00:11<00:00, 88.07it/s]
98%|█████████▊| 982/1000 [00:11<00:00, 88.14it/s]
99%|█████████▉| 991/1000 [00:11<00:00, 87.85it/s]
100%|██████████| 1000/1000 [00:11<00:00, 87.78it/s]
参考
两个惊艳的python库：tqdm和retry - mdumpling - 博客园
https://www.cnblogs.com/mdumpling/p/8016741.html
tqdm developers
https://github.com/tqdm