df-and-order 0.2.5

Using df-and-order your interactions with dataframes become very clean and predictable.

🗄️ df-and-order

Yeah, it's just like Law & Order, but Dataframe & Order!

pip install df_and_order

Using df-and-order your interactions with dataframes become very clean and predictable.

• Tired of absolute file paths to data in shared notebooks in your repository?
• Can't remember how your datasets were generated?
• Want to have safe and reproducible data transformations?
• Like declarative config-based solutions?

Good news for you!

How it looks in code?

Imagine the world where all you need to do for reading some dataframe you need just a few lines:

reader = MagicDfReader()
df = reader.read(df_id='user_activity_may_2020')

Maybe you are interested in some transformed version of that dataframe? No problem!

reader = MagicDfReader()
# ready to fit a model on!
model_input_df = reader.read(df_id='user_activity_may_2020', transform_id='model_input')

Wow. Is it really magic?

df-and-order works with yaml configs. Every config contains metadata about a dataset as well as all desired transfomations. Here's an example:

df_id: user_activity_may_2020  # here's the dataframe identifier
initial_df_format: csv
metadata:  # this section contains some useful information about the dataset
  author: Data Man
  data_collection_date: 2020-05-01
transforms:
  model_input:  # here's the transform identifier
    df_format: csv
    in_memory:  # means we want to perform transformations in memory every time we calling it, permanent transforms are supported as well
    - module_path: df_and_order.steps.pd.DropColsTransformStep  # file where to find class describing some transformation. this one drops columns
      params:  # init params for the transformation class
        cols:
        - redundant_col
    - module_path: df_and_order.steps.DatesTransformStep  # another transformation that converts str to datetime
      params:
        cols:
        - date_col

Okay, what exactly is a df-and-order's transform?

Every transformation is about changing an initial dataset in any way.

A transformation is made of one or many steps. Each step represents some operation. Here are examples of such operations:

• dropping cols
• adding cols
• transforming existing cols
• etc

df-and-order uses subclasses of DfTransformStepConfig to describe a step. It's possible and highly recommended to declare init parameters for any step in config. Using Single Responsibility principle we achieve a granular control over our entire transformation.

Just by looking at the config you can say how the transformed dataframe was created.

Take a look at the more detailed overview to find more exciting stuff.

I also wrote an article to describe the benefits, check it out! There are lemurs and stuff.

Hope the lib will help somebody to boost the productivity.