progress-table 0.1.27

Display progress as a pretty table in the command line.

Progress Table

Lightweight utility to display the progress of your process as a pretty table in the command line.

Designed to monitor machine learning experiments, but can be used for anything. Allows you to quickly see what is going on. Increases readability and cuteness of your command line logging.

Features

• Displaying pretty table in the terminal
• Progress bar embedded into the table
• Exporting data as lists, numpy arrays or pandas dataframes
• Built-in basic data aggregation: sum and mean

Purpose

Change this:

Into this:

Example

Click here for examples of integration with PyTorch and Keras: integrations.md.

import random
import sys
import time

from progress_table import ProgressTable

# Define the columns at the beginning
table = ProgressTable(
    columns=["step", "x", "x squared"],

    # Default arguments:
    refresh_rate=10,
    num_decimal_places=4,
    default_column_width=8,
    default_column_alignment="center",
    print_row_on_update=True,
    reprint_header_every_n_rows=30,
    custom_format=None,
    embedded_progress_bar=False,
    table_style="normal",
    file=sys.stdout,
)
table.add_column("x", width=3)
table.add_column("x root", color="red")
table.add_column("random average", color=["bright", "red"], aggregate="mean")

for step in range(10):
    x = random.randint(0, 200)

    # There are two equivalent ways to add new values
    # First:
    table["step"] = step
    table["x"] = x
    # Second:
    table.update("x root", x ** 0.5)
    table.update("x squared", x ** 2)

    # Display the progress bar by wrapping the iterator
    for _ in table(10):  # -> Equivalent to `table(range(10))`
        # You can use weights for aggregated values
        table.update("random average", random.random(), weight=1)
        time.sleep(0.1)

    # Go to the next row when you're ready
    table.next_row()

# Close the table when it's ready
table.close()

# Export your data
data = table.to_list()
pandas_df = table.to_df()
np_array = table.to_numpy()

Installation

Install Progress Table easily with pip:

pip install progress-table

Links

• See on GitHub
• See on PyPI

Alternatives

• Progress bars: great for tracking progress, but they do not provide pretty CLI data display

  • tqdm
  • Keras.utils.Progbar

• Libraries displaying data: great for presenting data, but they lack the tracking progress element

  • tabulate
  • texttable