Usage

Installation

First make Pandas Check available in your environment.

pip install pandas-checks

Then import it in your code. It works in Jupyter notebooks, IPython, and Python scripts run from the command line.

import pandas_checks

After importing, you don't need to access the pandas_checks module directly.

💡 Tip:
You can import Pandas Checks either before or after your code imports Pandas. Just somewhere. 😁

Basic usage

Pandas Checks adds .check methods to Pandas DataFrames and Series.

Say you have a nice function.

def clean_iris_data(iris: pd.DataFrame) -> pd.DataFrame:
    """Preprocess data about pretty flowers.

    Args:
        iris: The raw iris dataset.

    Returns:
        The cleaned iris dataset.
    """

    return (
        iris
        .dropna()
        .rename(columns={"FLOWER_SPECIES": "species"})
        .query("species=='setosa'")
    )

But what if you want to make the chain more robust? Or see what's happening to the data as it flows down the pipeline? Or understand why your new iris CSV suddenly makes the cleaned data look weird?

You can add some .check steps.

(
    iris
    .dropna()
    .rename(columns={"FLOWER_SPECIES": "species"})

    # Validate assumptions
    .check.assert_positive(subset=["petal_length", "sepal_length"])

    # Plot the distribution of a column after cleaning
    .check.hist(column='petal_length') 

    .query("species=='setosa'")

    # Display the first few rows after cleaning
    .check.head(3)  
)

The .check methods will display the following results:

Sample output
The .check methods didn't modify how the iris data is processed by your code. They just let you check the data as it flows down the pipeline. That's the difference between Pandas .head() and Pandas Checks .check.head().

Methods available

Here's what's in the doctor's bag.

Describe data

Standard Pandas methods: - .check.columns() - DataFrame | Series - .check.dtype() - Series - .check.dtypes() - DataFrame - .check.describe() - DataFrame | Series - .check.head() - DataFrame | Series - .check.info() - DataFrame | Series - .check.memory_usage() - DataFrame | Series - .check.nunique() - DataFrame | Series - .check.shape() - DataFrame | Series - .check.tail() - DataFrame | Series - .check.unique() - DataFrame | Series - .check.value_counts() - DataFrame | Series

New methods in Pandas Checks: - .check.function(): Apply an arbitrary lambda function to your data and see the result - DataFrame | Series - .check.ncols(): Count columns - DataFrame | Series - .check.ndups(): Count rows with duplicate values - DataFrame | Series - .check.nnulls(): Count rows with null values - DataFrame | Series - .check.nrows() - DataFrame | Series - .check.print(): Print a string, a variable, or the current dataframe - DataFrame | Series

Export interim files

.check.write(): Export the current data, inferring file format from the name - DataFrame | Series

Time your code

.check.print_time_elapsed(start_time): Print the execution time since you called start_time = pdc.start_timer() - DataFrame | Series

💡 Tip: You can also use this stopwatch outside a method chain, anywhere in your Python code:

from pandas_checks import print_elapsed_time, start_timer

start_time = start_timer()
...
print_elapsed_time(start_time)

Turn Pandas Check on or off

These methods can be used to disable subsequent Pandas Checks methods, either temporarily for a single method chain or permanently such as in a production environment.

.check.disable_checks(): Don't run checks. By default, still runs assertions. - DataFrame | Series
.check.enable_checks(): Run checks again. - DataFrame | Series

Validate data

Custom: - .check.assert_data(): Check that data passes an arbitrary condition - DataFrame | Series

Values: - .check.assert_all_nulls() - DataFrame | Series - .check.assert_less_than() - DataFrame | Series - .check.assert_greater_than() - DataFrame | Series - .check.assert_negative() - DataFrame | Series - .check.assert_no_nulls() - DataFrame | Series - .check.assert_nrows() - DataFrame | Series - .check.assert_positive() - DataFrame | Series - .check.assert_same_nrows(): Check that this DataFrame/Series has same number of rows as another DataFrame/Series, for example to validate 1:1 joins - DataFrame | Series - .check.assert_unique() - DataFrame | Series

Visualize data

.check.hist(): A histogram - DataFrame | Series
.check.plot(): An arbitrary plot you can customize - DataFrame | Series

Customizing a check

You can use Pandas Checks methods like the regular Pandas methods. They accept the same arguments. For example, you can pass: * .check.head(7) * .check.value_counts(column="species", dropna=False, normalize=True) * .check.plot(kind="scatter", x="sepal_width", y="sepal_length")

Also, most Pandas Checks methods accept 3 additional arguments: 1. check_name: text to display before the result of the check 2. fn: a lambda function that modifies the data displayed by the check 3. subset: limit a check to certain columns

(
    iris
    .check.value_counts(column='species', check_name="Varieties after data cleaning")
    .assign(species=lambda df: df["species"].str.upper()) # Do your regular Pandas data processing, like upper-casing the values in one column
    .check.head(n=2, fn=lambda df: df["petal_width"]*2) # Modify the data that gets displayed in the check only
    .check.describe(subset=['sepal_width', 'sepal_length'])  # Only apply the check to certain columns
)

Power user output

Configuring Pandas Check

Global configuration

You can change how Pandas Checks works everywhere. For example:

import pandas_checks as pdc

# Set output precision and turn off the cute emojis
pdc.set_format(precision=3, use_emojis=False)

# Don't run any of the calls to Pandas Checks, globally. 
pdc.disable_checks()

Run pdc.describe_options() to see the arguments you can pass to .set_format().

💡 Tip:
By default, disable_checks() and enable_checks() do not change whether Pandas Checks will run assertion methods (.check.assert_*).

To turn off assertions too, add the argument enable_asserts=False, such as: disable_checks(enable_asserts=False).

Local configuration

You can also adjust settings within a method chain by bookending the chain, like this:

# Customize format during one method chain
(
    iris
    .check.set_format(precision=7, use_emojis=False)
    ... # Any .check methods in here will use the new format
    .check.reset_format() # Restore default format
)

# Turn off Pandas Checks during one method chain
(
    iris
    .check.disable_checks()
    ... # Any .check methods in here will not be run
    .check.enable_checks() # Turn it back on for the next code
)

Hybrid EDA-Production data processing

Exploratory Data Analysis is often taught as a one-time step we do to plan our production data processing. But sometimes EDA is a cyclical process we go back to for deeper inspection during debugging, code edits, or changes in the input data. If explorations were useful in EDA, they may be useful again.

Unfortunately, it's hard to go back to the original EDA code. It's too out of sync. The prod data processing pipeline has usually evolved too much, making the EDA code a historical artifact full of cobwebs that we can't easily fire up again.

But if you use Pandas Checks during EDA, you could roll your .check methods into your first production code. Then in prod mode, disable Pandas Checks when you don't need it, to save compute and streamline output. When you ever need to pull out those EDA tools, enable Pandas Checks globally or locally.

This can make your prod pipline more transparent and easier to inspect.