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Logging for pandas dataframes

Project description


pdlog provides logging for pandas dataframes, to better enable you to monitor and debug your data pipelines.

For example:

>>> import pdlog
>>> df = df.log.dropna()
2020-05-26 20:55:30,049 INFO <pdlog> dropna: dropped 1 row (17%), 5 rows remaining


The above assumes that the logging module has been configured and that data has been loaded into a pandas DataFrame. Let's walk through those steps with a simple example.

  1. Configure logging:

    >>> import logging
    >>> fmt = "{asctime} {levelname} <{name}> {message}"
    >>> logging.basicConfig(format=fmt, style="{", level=logging.INFO)
  2. Load data into a pandas.DataFrame:

    >>> import pandas as pd
    >>> df = pd.DataFrame([0, 1, 2, None, 4])
    >>> df.head()
    0  0.0
    1  1.0
    2  2.0
    3  NaN
    4  4.0
  3. Importing pdlog and call a method under the log accessor:

    >>> import pdlog
    >>> df = df.log.dropna()
    2020-05-26 20:55:30,049 INFO <pdlog> dropna: dropped 1 row (17%), 5 rows remaining

Supported methods

pdlog currently supports the following pandas.DataFrame methods:

  • Filter rows and select columns:
    • drop_duplicates
    • drop
    • dropna
    • head
    • query
    • sample
    • tail
  • (Re-)set indexes:
    • reset_index
    • set_index
  • Rename indexes:
    • rename
  • Reshape:
    • melt
    • pivot
  • Impute:
    • bfill
    • ffill
    • fillna

Related Work


pandas-log is aimed at interactive usage. Its messages are friendlier and more verbose than pdlog aims to be. Ideally, each pdlog message should be a single line of dense information to help you understand whether your production code is doing what you think it is, while not overflowing your logs. These don't tend to make particularly friendly messages.


pdlog can be considered a port of tidylog (R package) to pandas. Their goals align with ours, and we think they've done a great job at reaching those goals.

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