Data + Adapter

Dapter is a convenient tool that helps working with multiple data sources. It allows you to easily rename column names and transform your data in one go.

With Dapter, you can store a series of instructions for your data cleaning routines into custom objects. You can then reuse the object to any DataFrames at any part of your code. See the step-by-step example below.

📝 Example

Renaming columns and adding transformations can be "lazily" set-up in a tuple:

import pandas as pd
from dapter import accepts

def convert_to_eur(col: pd.Series) -> pd.Series:
    return col * 0.92

eur_col = (accepts("Amount USD", "amount_usd","USD"), convert_to_eur)

euro_col is a series of instructions that will tell dapter to:

• Consider any column that is named after one of the names in accepts
• Apply convert_to_eur to those columns

Once we have defined all the column "instructions" we can then store them together in a custom object that inherits from dapter.BaseMapper

from dapter import BaseMapper

class TransactionMapper(BaseMapper):
    amount_eur = euro_col

We have just defined that all instructions of euro_col will be assigned to a new column called amount_eur.

This object can then be used to apply all the renaming and transformations stored inside it to any DataFrame

mapper = TransactionMapper()

dfs = mapper.apply(df1, df2, df3)
df = pd.concat(dfs)

🧰 Installation

Using pip:

pip install dapter

🔄 Infinite DataFrame compatibility

Dapter uses narwhals in the background so it can accepts any (See supported¹) kind of DataFrame libraries.

Which means you can define Polars Series and Expr transformations for pandas' Series and vice-versa!

You can also feed any DataFrame to the apply method.

Full sample code

from dapter import BaseMapper, accepts, accepts_anycases
import pandas as pd

df1 = pd.DataFrame(
    [
        {
            "Date": "2023-02-01 10:00:01",
            "Vendor Name": "Golden Oil LLC",
            "Amount USD": 49.99,
            "Category": "Personal",
        }
    ]
)

df2 = pd.DataFrame(
    [
        {
            "transaction_date": "2023-03-01 10:00:01",
            "vendor_name": "Get Cars Inc.",
            "amount_usd": 2999.9,
            "category": "Transportation",
        }
    ]
)
df3 = pd.DataFrame(
    [
        {
            "DATE": "2023-04-01 10:00:01",
            "VENDOR_NAME": "Maintainers Exc.",
            "USD": 5249.0,
            "CAT": "Personal",
        }
    ]
)


def convert_to_eur(col: pd.Series) -> pd.Series:
    return col * 0.92

def clean_str(col:pd.Series) -> pd.Series:
    return col.str.to_lower().str.replace(" ","_")

class TransactionMapper(BaseMapper):
    transaction_date = accepts("transaction_date", "Date","DATE")
    vendor_name = accepts_anycases()    
    amount_eur = accepts("Amount USD", "amount_usd","USD"), convert_to_eur
    category = accepts("Category", "category","CAT"), clean_str

mapper = TransactionMapper()

dfs = mapper.apply(df1, df2, df3)
df = pd.concat(dfs)
df

transaction_date	vendor_name	amount_eur	category
2023-02-01 10:00:01	Golden Oil LLC	45.99	personal
2023-03-01 10:00:01	Get Cars Inc.	2999.9	transportation
2023-04-01 10:00:01	Maintainers Exc.	5249.0	personal

Footnotes

1. cuDF, Modin, pandas, Polars, PyArrow, Dask, Ibis, Vaex ↩