The following is a one-liner solution utilizing tqdm: import pandas as pd from tqdm import tqdm df = pd.concat([chunk for chunk in tqdm(pd.read_csv(file_name, chunksize=1000), desc=”Loading data”)]) If you know the total lines to be loaded, you can add that information with the parameter total to the tqdm fuction, resulting in a percentage output.
tqdm needs to known how many iters will be performed (the total amount) to show a progress bar. You can try this: from tqdm import tqdm train_x = range(100) train_y = range(200) train_iter = zip(train_x, train_y) # Notice `train_iter` can only be iter over once, so i get `total` in this way. total = min(len(train_x), … Read more
Now you can just do: from tqdm.notebook import tqdm tqdm.pandas() df.progress_apply(…) My version of tqdm is 4.39.0
tqdm now has a built-in contextmanager for redirecting the logger: import logging from tqdm import trange from tqdm.contrib.logging import logging_redirect_tqdm LOG = logging.getLogger(__name__) if __name__ == ‘__main__’: logging.basicConfig(level=logging.INFO) with logging_redirect_tqdm(): for i in trange(9): if i == 4: LOG.info(“console logging redirected to `tqdm.write()`”) # logging restored Copied from tqdm documentation
In the source code [1] there is a comment about it in format_meter method, it refers to {elapsed}<{remaining} [1] https://github.com/tqdm/tqdm/blob/master/tqdm/std.py#L397
Try using the position parameter when initialising the bars: pbar1 = tqdm(total=100, position=1) pbar2 = tqdm(total=200, position=0) From the tqdm GitHub page: position : int, optional Specify the line offset to print this bar (starting from 0) Automatic if unspecified. Useful to manage multiple bars at once (eg, from threads).
Just put range(10) inside tqdm(…)! It probably seemed too good to be true for you, but it really works (on my machine): from math import sqrt from joblib import Parallel, delayed from tqdm import tqdm result = Parallel(n_jobs=2)(delayed(sqrt)(i ** 2) for i in tqdm(range(100000)))
Find length by getting shape for index, row in tqdm(df.iterrows(), total=df.shape[0]): print(“index”,index) print(“row”,row)
My working solution (copied from the documentation): from tqdm.auto import tqdm tqdm.pandas()
The simplest way would probably be to apply tqdm() around the inputs, rather than the mapping function. For example: inputs = zip(param1, param2, param3) with mp.Pool(8) as pool: results = pool.starmap(my_function, tqdm.tqdm(inputs, total=len(param1))) Note that the bar is updated when my_function is called, rather than when it returns. If that distinction matters, you can consider … Read more