João Tiago Ascensão jtascensao

## keybase.md

      
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                jtascensao
                / keybase.md
            
            
              Created
              March 14, 2020 16:02
            
          
    Keybase proof

I hereby claim:

I am jtascensao on github.
I am ascensao (https://keybase.io/ascensao) on keybase.
I have a public key ASDgm32MsN--MNppFmo46uEL6iT93WJ67PARV68Lm_ce8Ao

To claim this, I am signing this object:

  
## typing_example_2.py
is_positive('no_number')
# TypeError: '>' not supported between instances of 'str' and 'int'

## typing_example.py
def is_positive(a):
    if a > 0:
        return True
    else:
        return "Error, not positive number: " + a

is_positive(2)
# True

is_positive(-1)

## df_variable.py
# With initial value
data: pd.DataFrame = pd.DataFrame()

# No initial value
data: pd.DataFrame

## no_type_checking.py
from typing import Union

@no_type_check
def data_transformer(data: Union[pd.DataFrame, pd.Series]) -> pd.DataFrame:
    if isistance(data, pd.Series):
        ...
...

## type_union.py
from typing import Union

def data_transformer(data: Union[pd.DataFrame, pd.Series]) -> pd.DataFrame:
    if isistance(data, pd.Series):
        ...
    ...

## expected_type_for_elements.py
from typing import Dict

def rename_columns(df: pd.DataFrame, columns: Dict[str, str]) -> pd.DataFrame:
    ...

## annotations.py
{'df': pd.DataFrame,
 'return': pd.DataFrame}

## function_annotations_with_types.py
def data_transformer(df: pd.DataFrame) -> pd.DataFrame:
    df = df.copy()
    # df = do something
    return df

## using_docstrings.py
def data_transformer(df):
  """
    Performs some transformation on the original data.

    Parameters
    ----------
        df: a pandas dataframe to be transformed

    Returns
    -------
	is_positive('no_number')
	# TypeError: '>' not supported between instances of 'str' and 'int'
	def is_positive(a):
	if a > 0:
	return True
	else:
	return "Error, not positive number: " + a

	is_positive(2)
	# True

	is_positive(-1)
	# With initial value
	data: pd.DataFrame = pd.DataFrame()

	# No initial value
	data: pd.DataFrame
	from typing import Union

	@no_type_check
	def data_transformer(data: Union[pd.DataFrame, pd.Series]) -> pd.DataFrame:
	if isistance(data, pd.Series):
	...
	...
	from typing import Dict

	def rename_columns(df: pd.DataFrame, columns: Dict[str, str]) -> pd.DataFrame:
	...
	def data_transformer(df: pd.DataFrame) -> pd.DataFrame:
	df = df.copy()
	# df = do something
	return df
	def data_transformer(df):
	"""
	Performs some transformation on the original data.

	Parameters
	----------
	df: a pandas dataframe to be transformed

	Returns
	-------