Python pandas.Int32Dtype() Examples

The following are 2 code examples of pandas.Int32Dtype(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas , or try the search function .
Example #1
Source File: filtering_fe_autotype.py    From dash-docs with MIT License 6 votes vote down vote up 
def table_type(df_column):
    # Note - this only works with Pandas >= 1.0.0

    if sys.version_info < (3, 0):  # Pandas 1.0.0 does not support Python 2
        return 'any'

    if isinstance(df_column.dtype, pd.DatetimeTZDtype):
        return 'datetime',
    elif (isinstance(df_column.dtype, pd.StringDtype) or
            isinstance(df_column.dtype, pd.BooleanDtype) or
            isinstance(df_column.dtype, pd.CategoricalDtype) or
            isinstance(df_column.dtype, pd.PeriodDtype)):
        return 'text'
    elif (isinstance(df_column.dtype, pd.SparseDtype) or
            isinstance(df_column.dtype, pd.IntervalDtype) or
            isinstance(df_column.dtype, pd.Int8Dtype) or
            isinstance(df_column.dtype, pd.Int16Dtype) or
            isinstance(df_column.dtype, pd.Int32Dtype) or
            isinstance(df_column.dtype, pd.Int64Dtype)):
        return 'numeric'
    else:
        return 'any'
Example #2
Source File: epacems_to_parquet.py    From pudl with MIT License 4 votes vote down vote up 
def create_in_dtypes():
    """
    Create a dictionary of input data types.

    This specifies the dtypes of the input columns, which is necessary for some
    cases where, e.g., a column is always NaN.

    Returns:
        dict: mapping columns names to :mod:`pandas` data types.

    """
    # These measurement codes are used by all four of our measurement variables
    common_codes = (
        "LME",
        "Measured",
        "Measured and Substitute",
        "Other",
        "Substitute",
        "Undetermined",
        "Unknown Code",
        "",
    )
    co2_so2_cats = pd.CategoricalDtype(categories=common_codes, ordered=False)
    nox_cats = pd.CategoricalDtype(
        categories=common_codes + ("Calculated",), ordered=False
    )
    state_cats = pd.CategoricalDtype(
        categories=pc.cems_states.keys(), ordered=False)
    in_dtypes = {
        "state": state_cats,
        "plant_id_eia": "int32",
        "unitid": pd.StringDtype(),
        # "operating_datetime_utc": "datetime",
        "operating_time_hours": "float32",
        "gross_load_mw": "float32",
        "steam_load_1000_lbs": "float32",
        "so2_mass_lbs": "float32",
        "so2_mass_measurement_code": co2_so2_cats,
        "nox_rate_lbs_mmbtu": "float32",
        "nox_rate_measurement_code": nox_cats,
        "nox_mass_lbs": "float32",
        "nox_mass_measurement_code": nox_cats,
        "co2_mass_tons": "float32",
        "co2_mass_measurement_code": co2_so2_cats,
        "heat_content_mmbtu": "float32",
        "facility_id": pd.Int32Dtype(),
        "unit_id_epa": pd.Int32Dtype(),
    }
    return in_dtypes