tqdm学习-一个快速，可扩展的Python和CLI进度条

(base) userdeMacBook-Pro:~ user$ conda activate deeplearning
(deeplearning) userdeMacBook-Pro:~ user$ conda install -c conda-forge tqdm
 
Collecting package metadata: done
Solving environment: done

## Package Plan ##

  environment location: /anaconda3/envs/deeplearning

  added / updated specs:
    - tqdm

...
Downloading and Extracting Packages
python-1.6           | 3.7 MB    | ##################################### | 100% 
tqdm-4.35.0          | 42 KB     | ##################################### | 100% 
decorator-4.4.0      | 13 KB     | ##################################### | 100% 
ca-certificates-2019 | 143 KB    | ##################################### | 100% 
openssl-1.1.1c       | 1.9 MB    | ##################################### | 100% 
Preparing transaction: done
Verifying transaction: done
Executing transaction: done

(deeplearning) userdeMBP:bin user$ jupyter notebook
Traceback (most recent call last):
  File "/anaconda3/envs/deeplearning/bin/jupyter", line 7, in <module>
    from jupyter_core.command import main
ModuleNotFoundError: No module named 'jupyter_core'

(deeplearning) userdeMBP:bin user$ conda uninstall tqdm
Collecting package metadata: done
Solving environment: done

## Package Plan ##

  environment location: /anaconda3/envs/deeplearning

  removed specs:
    - tqdm


The following packages will be REMOVED:

  tqdm-4.35.0-py_0


Proceed ([y]/n)? y

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(deeplearning) userdeMBP:bin user$ conda update python
The following packages will be UPDATED:

  certifi                pkgs/main::certifi-2019.3.9-py36_0 --> conda-forge::certifi-2019.9.11-py37_0
  python                                              1.6-0 --> 3.7.3-h93065d6_1
  readline               pkgs/main::readline-7.0-h1de35cc_5 --> conda-forge::readline-8.0-hcfe32e1_0
  sqlite                pkgs/main::sqlite-3.27.2-ha441bb4_0 --> conda-forge::sqlite-3.29.0-hb7d70f7_1
  tk                         pkgs/main::tk-8.6.8-ha441bb4_0 --> conda-forge::tk-8.6.9-h2573ce8_1003
  wheel              anaconda/pkgs/main::wheel-0.33.1-py36~ --> conda-forge::wheel-0.33.6-py37_0

(base) userdembp:bin user$ conda activate deeplearning3.5
(deeplearning3.5) userdembp:bin user$ pip install -e git+https://github.com/tqdm/tqdm.git@master#egg=tqdm
Obtaining tqdm from git+https://github.com/tqdm/tqdm.git@master#egg=tqdm
  Cloning https://github.com/tqdm/tqdm.git (to revision master) to ./src/tqdm
Installing collected packages: tqdm
  Running setup.py develop for tqdm
Successfully installed tqdm
You are using pip version 19.0.3, however version 19.2.3 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.

# conding:utf-8
from tqdm import tqdm
import time

text = ""
for char in tqdm(["a", "b", "c", "d"]):
    time.sleep(0.25)
    text = text + char
print(text)

/anaconda3/envs/deeplearning3.5/bin/python3.5 /Users/user/PycharmProjects/new/learning.py
 75%|███████▌  | 3/4 [00:00<00:00,  3.97it/s]abcd
100%|██████████| 4/4 [00:01<00:00,  3.96it/s]

Process finished with exit code 0

# conding:utf-8
from tqdm import tqdm
import time

text = 0
for i in tqdm(range(10)):
    time.sleep(0.25)
    text += i
print(text)

/anaconda3/envs/deeplearning3.5/bin/python3.5 /Users/user/PycharmProjects/new/learning.py
100%|██████████| 10/10 [00:02<00:00,  3.96it/s]
45

Process finished with exit code 0

# conding:utf-8
from tqdm import trange
import time

text = 0
for i in trange(10):
    time.sleep(0.25)
    text += i
print(text)

/anaconda3/envs/deeplearning3.5/bin/python3.5 /Users/user/PycharmProjects/new/learning.py
 90%|█████████ | 9/10 [00:02<00:00,  3.96it/s]45
100%|██████████| 10/10 [00:02<00:00,  3.96it/s]

Process finished with exit code 0

# conding:utf-8
from tqdm import tqdm
import time

pbar = tqdm(["a", "b", "c", "d"])
for char in pbar:
    time.sleep(0.25)
    pbar.set_description("processing %s" % char)

/anaconda3/envs/deeplearning3.5/bin/python3.5 /Users/user/PycharmProjects/new/learning.py
processing d: 100%|██████████| 4/4 [00:01<00:00,  3.95it/s]

Process finished with exit code 0

# conding:utf-8
from tqdm import tqdm
import time

with tqdm(total=100) as pbar:
    for i in range(10):
        time.sleep(0.1)
        pbar.update(10)

/anaconda3/envs/deeplearning3.5/bin/python3.5 /Users/user/PycharmProjects/new/learning.py
100%|██████████| 100/100 [00:01<00:00, 97.06it/s]

Process finished with exit code 0

# conding:utf-8
from tqdm import tqdm
import time

pbar = tqdm(total=100)
for i in range(10):
    time.sleep(0.1)
    pbar.update(10)
pbar.close()

/anaconda3/envs/deeplearning3.5/bin/python3.5 /Users/user/PycharmProjects/new/learning.py
100%|██████████| 100/100 [00:01<00:00, 97.40it/s]

Process finished with exit code 0

(deeplearning3.5) userdembp:new user$ time find . -name '*.py' -type f -exec cat \{} \; | wc -l
       8

real    0m0.015s
user    0m0.004s
sys     0m0.009s
(deeplearning3.5) userdembp:new user$ time find . -name '*.py' -type f -exec cat \{} \; | tqdm | wc -l
9it [00:00, 49998.33it/s]
       8

real    0m0.273s
user    0m0.207s
sys     0m0.053s
(deeplearning3.5) userdembp:new user$ 

# conding:utf-8
from tqdm import tqdm
import time

pbar = tqdm(total=100)
for i in range(10):
    time.sleep(0.1)
    pbar.update(10)
pbar.close()

(deeplearning3.5) userdembp:new user$ time find . -name '*.py' -type f -exec cat \{} \; | tqdm -unit loc --unit_scale --total 8 >> out.log
9.00loc [00:00, 33.6kloc/s]                                                                                                                                              

real    0m0.383s
user    0m0.218s
sys     0m0.076s
(deeplearning3.5) userdembp:new user$ ls
learning.py     out.log
(deeplearning3.5) userdembp:new user$ cat out.log
# conding:utf-8
from tqdm import tqdm
import time

pbar = tqdm(total=100)
for i in range(10):
    time.sleep(0.1)
    pbar.update(10)
pbar.close()

class tqdm():
  """
  装饰一个迭代器对象，返回一个表现得就像原来可迭代的迭代器；但是在每次值被请求时就打印一个动态的更新进度条
  """

  def __init__(self, iterable=None, desc=None, total=None, leave=True,
               file=None, ncols=None, mininterval=0.1,
               maxinterval=10.0, miniters=None, ascii=None, disable=False,
               unit='it', unit_scale=False, dynamic_ncols=False,
               smoothing=0.3, bar_format=None, initial=0, position=None,
               postfix=None, unit_divisor=1000):

class tqdm():
  def update(self, n=1):
      """
      手动更新进度条，对流streams有用，比如读文件
      E.g.:
      >>> t = tqdm(total=filesize) # Initialise
      >>> for current_buffer in stream:
      ...    ...
      ...    t.update(len(current_buffer))
      >>> t.close()
      最后一行高度推荐使用，但是如果``t.update()`` 是在``filesize``即将完全到达和打印时调用的话可能就不需要

      Parameters
      ----------
      n  : int, optional
            添加到迭代内部计数器的增长数[default: 1]
      """

  def close(self):
      """清除(if leave=False)和关闭进度条"""

  def clear(self, nomove=False):
      """清除当前的进度条显示."""

  def refresh(self):
      """强迫更新该进度条的显示Force refresh the display of this bar."""

  def unpause(self):
      """从最新运行时间重启tqdm计时器"""

  def reset(self, total=None):
      """
      为了重复使用，重设为第0次迭代。考虑和``leave=True``设置一起使用

      Parameters
      ----------
      total  : int, optional. 用于新进度条的次数.
      """

  def set_description(self, desc=None, refresh=True):
      """
      设置/修改进度条的描述格式

      Parameters
      ----------
      desc  : str, optional
      refresh  : bool, optional
          Forces refresh [default: True].
      """

  def set_postfix(self, ordered_dict=None, refresh=True, **kwargs):
      """
      设置/修改后缀(additional stats)
      with automatic formatting based on datatype.

      Parameters
      ----------
      ordered_dict  : dict or OrderedDict, optional
      refresh  : bool, optional
          Forces refresh [default: True].
      kwargs  : dict, optional
      """

  @classmethod
  def write(cls, s, file=sys.stdout, end="\n"):
      """通过tqdm打印信息(不覆盖进度条)."""

  @property
  def format_dict(self):
      """给只读权限人员访问的公用API"""

  def display(self, msg=None, pos=None):
      """
      使用``self.sp`` 去展示指定``pos``中的``msg``.

      当继承使用时，要考虑重载该函数 e.g.:
      ``self.some_frontend(**self.format_dict)`` instead of ``self.sp``.

      Parameters
      ----------
      msg  : str, optional. What to display (default: ``repr(self)``).
      pos  : int, optional. Position to ``moveto``
        (default: ``abs(self.pos)``).
      """

def trange(*args, **kwargs):
    """
    tqdm(xrange(*args), **kwargs)函数的缩写
    Python3+版本中使用 range来替换 xrange.
    """

class tqdm_gui(tqdm):
    """Experimental GUI version"""

def tgrange(*args, **kwargs):
    """Experimental GUI version of trange"""

class tqdm_notebook(tqdm):
    """Experimental IPython/Jupyter Notebook widget"""

def tnrange(*args, **kwargs):
    """Experimental IPython/Jupyter Notebook widget version of trange"""

"""
# Simple tqdm examples and profiling
# Benchmark
for i in _range(int(1e8)):
    pass
# Basic demo
import tqdm
for i in tqdm.trange(int(1e8)):
    pass
# Some decorations
import tqdm
for i in tqdm.trange(int(1e8), miniters=int(1e6), ascii=True,
                     desc="cool", dynamic_ncols=True):
    pass
# Nested bars
from tqdm import trange
for i in trange(10):
    for j in trange(int(1e7), leave=False, unit_scale=True):
        pass
# Experimental GUI demo
import tqdm
for i in tqdm.tgrange(int(1e8)):
    pass
# Comparison to https://code.google.com/p/python-progressbar/
try:
    from progressbar.progressbar import ProgressBar
except ImportError:
    pass
else:
    for i in ProgressBar()(_range(int(1e8))):
        pass
# Dynamic miniters benchmark
from tqdm import trange
for i in trange(int(1e8), miniters=None, mininterval=0.1, smoothing=0):
    pass
# Fixed miniters benchmark
from tqdm import trange
for i in trange(int(1e8), miniters=4500000, mininterval=0.1, smoothing=0):
    pass
"""

from time import sleep
from timeit import timeit
import re

# Simple demo
from tqdm import trange
for i in trange(16, leave=True):
    sleep(0.1)

# Profiling/overhead tests
stmts = filter(None, re.split(r'\n\s*#.*?\n', __doc__))
for s in stmts:
    print(s.replace('import tqdm\n', ''))
    print(timeit(stmt='try:\n\t_range = xrange'
                 '\nexcept:\n\t_range = range\n' + s, number=1), 'seconds')

"""
# Simple tqdm examples and profiling
# Benchmark
for i in _range(int(1e8)):
    pass

"""

from time import sleep
from timeit import timeit
import re

# Simple demo
from tqdm import trange
for i in trange(16, leave=True):
    sleep(0.1)

# Profiling/overhead tests
stmts = filter(None, re.split(r'\n\s*#.*?\n', __doc__))
for s in stmts:
    print(s.replace('import tqdm\n', ''))
    print(timeit(stmt='try:\n\t_range = xrange'
                 '\nexcept:\n\t_range = range\n' + s, number=1), 'seconds')

100%|██████████| 16/16 [00:01<00:00,  9.59it/s]
# Benchmark
for i in _range(int(1e8)):
    pass


2.328720851000071 seconds

"""
# Simple tqdm examples and profiling
# Basic demo
import tqdm
for i in tqdm.trange(int(1e8)):
    pass

"""

100%|██████████| 16/16 [00:01<00:00,  9.65it/s]
  0%|          | 301598/100000000 [00:00<00:33, 3015975.69it/s]
# Basic demo
for i in tqdm.trange(int(1e8)):
    pass


100%|██████████| 100000000/100000000 [00:18<00:00, 5407942.99it/s]
18.4932925789999 seconds

"""
# Simple tqdm examples and profiling
# Some decorations
import tqdm
for i in tqdm.trange(int(1e8), miniters=int(1e6), ascii=True,
                     desc="cool", dynamic_ncols=True):
    pass

"""

100%|██████████| 16/16 [00:01<00:00,  9.71it/s]
cool:   0%|          | 0/100000000 [00:00<?, ?it/s]
# Some decorations
for i in tqdm.trange(int(1e8), miniters=int(1e6), ascii=True,
                     desc="cool", dynamic_ncols=True):
    pass


cool: 100%|##########| 100000000/100000000 [00:17<00:00, 5650878.97it/s]
17.699349756000174 seconds

"""
# Simple tqdm examples and profiling
# Nested bars
from tqdm import trange
for i in trange(2): 
    for j in trange(int(1e7), leave=False, unit_scale=True):
        pass

"""

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# Nested bars
from tqdm import trange
for i in trange(2):
    for j in trange(int(1e7), leave=False, unit_scale=True):
        pass



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# Nested bars
from tqdm import trange
for i in trange(1): 
    for j in trange(int(1e7), leave=False):
        pass



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2.264081185000123 seconds

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# Nested bars
from tqdm import trange
for i in trange(1): 
    for j in trange(int(1e7), leave=True):
        pass



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100%|██████████| 10000000/10000000 [00:02<00:00, 4552905.40it/s][A
100%|██████████| 1/1 [00:02<00:00,  2.20s/it]
2.2038861539999743 seconds

100%|██████████| 10000000/10000000 [00:02<00:00, 4552905.40it/s]

"""
# Simple tqdm examples and profiling
# Experimental GUI demo
import tqdm
for i in tqdm.tgrange(int(1e8)):
    pass

"""

100%|██████████| 16/16 [00:01<00:00,  9.69it/s]
# Experimental GUI demo
for i in tqdm.tgrange(int(1e8)):
    pass

19.253753004999908 seconds

"""
# Simple tqdm examples and profiling
# Comparison to https://code.google.com/p/python-progressbar/
try:
    from progressbar.progressbar import ProgressBar
except ImportError:
    pass
else:
    for i in ProgressBar()(_range(int(1e8))):
        pass

"""

100%|██████████| 16/16 [00:01<00:00,  9.64it/s]
# Comparison to https://code.google.com/p/python-progressbar/
try:
    from progressbar.progressbar import ProgressBar
except ImportError:
    pass
else:
    for i in ProgressBar()(_range(int(1e8))):
        pass


0.0015016719999039196 seconds

"""
# Simple tqdm examples and profiling
# Dynamic miniters benchmark
from tqdm import trange
for i in trange(int(1e8), miniters=None, mininterval=0.1, smoothing=0):
    pass

"""

100%|██████████| 16/16 [00:01<00:00,  9.65it/s]
  0%|          | 313552/100000000 [00:00<00:31, 3134484.22it/s]
# Dynamic miniters benchmark
from tqdm import trange
for i in trange(int(1e8), miniters=None, mininterval=0.1, smoothing=0):
    pass


100%|██████████| 100000000/100000000 [00:16<00:00, 5960460.94it/s]
16.779653078000138 seconds

"""
# Simple tqdm examples and profiling
# Dynamic miniters benchmark
from tqdm import trange
for i in trange(int(1e8), miniters=2, mininterval=0):
    pass

"""

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...

  0%|          | 0/100000000 [00:00<?, ?it/s]
# Dynamic miniters benchmark
from tqdm import trange
for i in trange(int(1e8), miniters=2, mininterval=1):
    pass




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...

  0%|          | 0/100000000 [00:00<?, ?it/s]
# Fixed miniters benchmark
from tqdm import trange
for i in trange(int(1e8), miniters=4500000, mininterval=0.1, smoothing=0):
    pass




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...

from tqdm import trange
from random import random, randint
from time import sleep

with trange(10) as t:
    for i in t:
        # 描述将显示在左边
        t.set_description('GEN %i' % i)
        # 后缀将显示在右边，根据参数的数据类型自动格式化
        t.set_postfix(loss=random(), gen=randint(1,999), str='h',
                      lst=[1, 2])
        sleep(0.1)

with tqdm(total=10, bar_format="{postfix[0]} {postfix[1][value]:>8.2g}",
          postfix=["Batch", dict(value=0)]) as t:
    for i in range(10):
        sleep(0.1)
        t.postfix[1]["value"] = i / 2
        t.update()

GEN 9: 100%|██████████| 10/10 [00:01<00:00,  9.42it/s, gen=356, loss=0.806, lst=[1, 2], str=h]
Batch      4.5

from tqdm import tqdm
class TqdmExtraFormat(tqdm):
    """Provides a `total_time` format parameter"""
    @property
    def format_dict(self):
        d = super(TqdmExtraFormat, self).format_dict
        #d["total"]表示总迭代数，d["n"]表示当前为第几轮迭代
        print(d["elapsed"], d["total"], d["n"])
        total_time = d["elapsed"] * (d["total"] or 0) / max(d["n"], 1)
        d.update(total_time=self.format_interval(total_time) + " in total")
        return d

for i in TqdmExtraFormat(
      range(10), ascii=" .oO0",
      bar_format="{total_time}: {percentage:.0f}%|{bar}{r_bar}"):
    sleep(0.25)
    print(i)

00:00 in total: 0%|          | 0/10 [00:00<?, ?it/s]
0 10 0
00:02 in total: 10%|0         | 1/10 [00:00<00:02,  3.92it/s]
0
0.2551310062408447 10 1
00:02 in total: 20%|00        | 2/10 [00:00<00:02,  3.92it/s]
1
0.5111739635467529 10 2
00:02 in total: 30%|000       | 3/10 [00:00<00:01,  3.91it/s]
2
0.7674551010131836 10 3
00:02 in total: 40%|0000      | 4/10 [00:01<00:01,  3.92it/s]
3
1.0219080448150635 10 4
00:02 in total: 50%|00000     | 5/10 [00:01<00:01,  3.93it/s]
4
1.2738640308380127 10 5
00:02 in total: 60%|000000    | 6/10 [00:01<00:01,  3.94it/s]
5
1.525794267654419 10 6
00:02 in total: 70%|0000000   | 7/10 [00:01<00:00,  3.94it/s]
6
1.7809131145477295 10 7
00:02 in total: 80%|00000000  | 8/10 [00:02<00:00,  3.94it/s]
7
2.035114049911499 10 8
00:02 in total: 90%|000000000 | 9/10 [00:02<00:00,  3.93it/s]
8
2.2901201248168945 10 9
00:02 in total: 100%|0000000000| 10/10 [00:02<00:00,  3.93it/s]
9
2.543692111968994 10 10
2.544473886489868 10 10

from tqdm import trange
from time import sleep

for i in trange(4, desc='1st loop'):
    for j in trange(5, desc='2nd loop'):
        for k in trange(20, desc='3nd loop', leave=False):
            sleep(0.01)

from time import sleep
from tqdm import trange, tqdm
from multiprocessing import Pool, freeze_support, RLock

L = list(range(3))

def progresser(n):
    interval = 0.001 / (n + 2)
    total = 5000
    # {:<04.2}表示左对齐，数字总共4位，其中小数两位
    text = "#{}, est. {:<04.2}s".format(n, interval * total)
    for i in trange(total, desc=text, position=n):
        sleep(interval)

if __name__ == '__main__':
    freeze_support()  # for Windows support
    p = Pool(len(L), # 同时开启3个进程
             # again, for Windows support
             initializer=tqdm.set_lock, initargs=(RLock(),))
    p.map(progresser, L)
    print("\n" * (len(L) - 2)) #换行

module 'urllib' has no attribute 'urlretrieve'

import urllib, os
from tqdm import tqdm

class TqdmUpTo(tqdm):
    """Provides `update_to(n)` which uses `tqdm.update(delta_n)`."""
    def update_to(self, b=1, bsize=1, tsize=None):
        """
        b  : int, optional
            Number of blocks transferred so far [default: 1].
        bsize  : int, optional
            Size of each block (in tqdm units) [default: 1].
        tsize  : int, optional
            Total size (in tqdm units). If [default: None] remains unchanged.
        """
        if tsize is not None:
            self.total = tsize
        self.update(b * bsize - self.n)  # will also set self.n = b * bsize

eg_link = "https://caspersci.uk.to/matryoshka.zip"
with TqdmUpTo(unit='B', unit_scale=True, miniters=1,
              desc=eg_link.split('/')[-1]) as t:  # all optional kwargs
    urllib.request.urlretrieve(eg_link, filename=os.devnull,
                       reporthook=t.update_to, data=None)

matryoshka.zip: 262kB [00:03, 77.4kB/s]  

ModuleNotFoundError: No module named 'pandas'

(deeplearning) userdeMacBook-Pro:~ user$ conda install -n deeplearning pandas

import pandas as pd
import numpy as np
from tqdm import tqdm

df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))

# Register `pandas.progress_apply` and `pandas.Series.map_apply` with `tqdm`
# (can use `tqdm_gui`, `tqdm_notebook`, optional kwargs, etc.)
tqdm.pandas(desc="my bar!")

# Now you can use `progress_apply` instead of `apply`
# and `progress_map` instead of `map`
df.progress_apply(lambda x: x**2)
# can also groupby:
# df.groupby(0).progress_apply(lambda x: x**2)

/anaconda3/envs/deeplearning/bin/src/tqdm/tqdm/_tqdm.py:634: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version
  from pandas import Panel
my bar!: 100%|██████████| 6/6 [00:00<00:00, 265.34it/s]

IntProgress not found. Please update jupyter and ipywidgets.

(deeplearning) userdeMacBook-Pro:~ user$ conda install -n deeplearning ipywidgets

from tqdm import tnrange, tqdm_notebook
from time import sleep

for i in tnrange(3, desc='1st loop'):
    for j in tqdm_notebook(range(100), desc='2nd loop'):
        sleep(0.01)

from tqdm import tnrange, tqdm_notebook
from time import sleep

for i in tqdm_notebook(range(3), desc='1st loop'):
    for j in tqdm_notebook(range(100), desc='2nd loop'):
        sleep(0.01)

from tqdm import tnrange, tqdm_notebook
from time import sleep

for i in tqdm_notebook(range(3), desc='1st loop'):
    for j in tqdm_notebook(range(100), desc='2nd loop', leave=False):
        sleep(0.01)

from tqdm.autonotebook import tqdm
tqdm.pandas()

/anaconda3/envs/deeplearning/bin/src/tqdm/tqdm/autonotebook/__init__.py:18: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
  " (e.g. in jupyter console)", TqdmExperimentalWarning)

from tqdm import tqdm
import os.path

def find_files_recursively(path, show_progress=True):
    files = []
    # total=1 assumes `path` is a file
    t = tqdm(total=1, unit="file", disable=not show_progress)
    if not os.path.exists(path):
        raise IOError("Cannot find:" + path)

    def append_found_file(f):
        files.append(f)
        t.update()

    def list_found_dir(path):
        """returns os.listdir(path) assuming os.path.isdir(path)"""
        listing = os.listdir(path)
        # subtract 1 since a "file" we found was actually this directory
        t.total += len(listing) - 1
        # fancy way to give info without forcing a refresh
        t.set_postfix(dir=path[-10:], refresh=False)
        t.update(0)  # may trigger a refresh
        return listing

    def recursively_search(path):
        if os.path.isdir(path):
            for f in list_found_dir(path):
                recursively_search(os.path.join(path, f))
        else:
            append_found_file(path)

    recursively_search(path)
    t.set_postfix(dir=path)
    t.close()
    return files

from tqdm import tqdm, trange
from time import sleep

bar = trange(10)
for i in bar:
    # Print using tqdm class method .write()
    sleep(0.1)
    if not (i % 3):
        tqdm.write("Done task %i" % i)
    # Can also use bar.write()

 20%|██        | 2/10 [00:00<00:00,  9.48it/s]
Done task 0
 50%|█████     | 5/10 [00:00<00:00,  9.53it/s]
Done task 3
 80%|████████  | 8/10 [00:00<00:00,  9.50it/s]
Done task 6
100%|██████████| 10/10 [00:01<00:00,  9.56it/s]
Done task 9

from time import sleep
import contextlib
import sys
from tqdm import tqdm

class DummyTqdmFile(object):
    """Dummy file-like that will write to tqdm"""
    file = None
    def __init__(self, file):
        self.file = file

    def write(self, x):
        # Avoid print() second call (useless \n)
        if len(x.rstrip()) > 0:
            tqdm.write(x, file=self.file)

    def flush(self):
        return getattr(self.file, "flush", lambda: None)()

@contextlib.contextmanager
def std_out_err_redirect_tqdm():
    orig_out_err = sys.stdout, sys.stderr
    try:
        sys.stdout, sys.stderr = map(DummyTqdmFile, orig_out_err)
        yield orig_out_err[0]
    # Relay exceptions
    except Exception as exc:
        raise exc
    # Always restore sys.stdout/err if necessary
    finally:
        sys.stdout, sys.stderr = orig_out_err

def some_fun(i):
    print("Fee, fi, fo,".split()[i])

# Redirect stdout to tqdm.write() (don't forget the `as save_stdout`)
with std_out_err_redirect_tqdm() as orig_stdout:
    # tqdm needs the original stdout
    # and dynamic_ncols=True to autodetect console width
    for i in tqdm(range(3), file=orig_stdout, dynamic_ncols=True):
        sleep(.5)
        some_fun(i)

# After the `with`, printing is restored
print("Done!")

Fee,                                 
fi,                                          
fo,                                          
100%|██████████| 3/3 [00:01<00:00,  1.97it/s]
Done!