Pythonic type hints with pandas?

Question

Let's take a simple function that takes a str and returns a dataframe:

import pandas as pd
def csv_to_df(path):
    return pd.read_csv(path, skiprows=1, sep='\t', comment='#')

What is the recommended pythonic way of adding type hints to this function?

If I ask python for the type of a DataFrame it returns pandas.core.frame.DataFrame. The following won't work though, as it'll tell me that pandas is not defined.

 def csv_to_df(path: str) -> pandas.core.frame.DataFrame:
     return pd.read_csv(path, skiprows=1, sep='\t', comment='#')

Answer 1

Why not just use pd.DataFrame?

import pandas as pd
def csv_to_df(path: str) -> pd.DataFrame:
    return pd.read_csv(path, skiprows=1, sep='\t', comment='#')

Result is the same:

> help(csv_to_df)
Help on function csv_to_df in module __main__:
csv_to_df(path:str) -> pandas.core.frame.DataFrame

Answer 2

I'm currently doing the following:

from typing import TypeVar
PandasDataFrame = TypeVar('pandas.core.frame.DataFrame')
def csv_to_df(path: str) -> PandasDataFrame:
    return pd.read_csv(path, skiprows=1, sep='\t', comment='#')

Which gives:

> help(csv_to_df)
Help on function csv_to_df in module __main__:

csv_to_df(path:str) -> ~pandas.core.frame.DataFrame

Don't know how pythonic that is, but it's understandable enough as a type hint, I find.

Answer 3

Now there is a pip package that can help with this. https://github.com/CedricFR/dataenforce

You can install it with pip install dataenforce and use very pythonic type hints like:

def preprocess(dataset: Dataset["id", "name", "location"]) -> Dataset["location", "count"]:
    pass

Answer 4

Check out the answer given here which explains the usage of the package data-science-types.

pip install data-science-types

Demo

# program.py

import pandas as pd

df: pd.DataFrame = pd.DataFrame({'col1': [1,2,3], 'col2': [4,5,6]}) # OK
df1: pd.DataFrame = pd.Series([1,2,3]) # error: Incompatible types in assignment

Run using mypy the same way:

$ mypy program.py

Answer 5

Take a look at pandera.

pandera provides a flexible and expressive API for performing data validation on dataframe-like objects to make data processing pipelines more readable and robust. Dataframes contain information that pandera explicitly validates at runtime. This is useful in production-critical or reproducible research settings.

---

The advantage of pandera is that you can also specify dtypes of individual DataFrame columns. The following example uses pandera to run-time enforce a DataFrame containing a single column of integers:

import pandas as pd
import pandera
from pandera.typing import DataFrame, Series

class Integers(pandera.SchemaModel):
    number: Series[int] 

@pandera.check_types
def my_fn(a: DataFrame[Integers]) -> None:
    pass

# This works
df = pd.DataFrame({"number": [ 2002, 2003]})
my_fn(df)

# Raises an exception
df = pd.DataFrame({"number": [ 2002.0, 2003]})
my_fn(df)

# Raises an exception
df = pd.DataFrame({"number": [ '2002', 2003]})
my_fn(df)

Answer 6

This is straying from the original question but building off of @dangom's answer using TypeVar and @Georgy's comment that there is no way to specify datatypes for DataFrame columns in type hints, you could use a simple work-around like this to specify datatypes in a DataFrame:

from typing import TypeVar
DataFrameStr = TypeVar("pandas.core.frame.DataFrame(str)")
def csv_to_df(path: str) -> DataFrameStr:
    return pd.read_csv(path, skiprows=1, sep='\t', comment='#')