DataFrames

src.dx.datatypes.main.quick_random_dataframe(num_rows=5, num_cols=2, dtype='float', factor=1.0)

Convenience function wrapping pd.DataFrame(np.random.rand( num_rows, num_columns )) to create a dataframe of random 0.0-1.0 values.

Parameters:

Name	Type	Description	Default
num_rows	int	Number of rows to generate	5
num_cols	int	Number of columns to generate	2
dtype	str	Data type to use for the generated dataframe values.	'float'
factor	float	Factor to multiply the generated values by.	1.0

Source code in src/dx/datatypes/main.py

def quick_random_dataframe(
    num_rows: int = 5,
    num_cols: int = 2,
    dtype: str = "float",
    factor: float = 1.0,
) -> pd.DataFrame:
    """
    Convenience function wrapping `pd.DataFrame(np.random.rand( num_rows, num_columns ))`
    to create a dataframe of random 0.0-1.0 values.

    Parameters
    ----------
    num_rows : int
        Number of rows to generate
    num_cols : int
        Number of columns to generate
    dtype : str
        Data type to use for the generated dataframe values.
    factor : float
        Factor to multiply the generated values by.
    """
    data = np.random.rand(num_rows, num_cols) * factor
    df = pd.DataFrame(data)
    return df.astype(dtype, errors="ignore")

src.dx.datatypes.main.random_dataframe(num_rows=5, dtype_column=True, integer_column=True, float_column=True, bool_column=True, decimal_column=False, datetime_column=True, datetimetz_column=False, date_column=False, time_column=False, time_delta_column=False, time_period_column=False, time_interval_column=False, text_column=False, keyword_column=True, dict_column=False, list_column=False, nested_tabular_column=False, lat_float_column=False, lon_float_column=False, latlon_point_column=False, filled_geojson_column=False, exterior_geojson_column=False, bytes_column=True, ipv4_address_column=False, ipv6_address_column=False, complex_number_column=False, uuid4_column=False)

Convenience function to generate a dataframe of num_rows length with randomly-generated values of mixed data types.

Parameters:

Name	Type	Description	Default
num_rows	int	Number of rows to generate	5
dtype_column	bool	Whether to include a column of type values	True
integer_column	bool	Whether to include a column of integer values	True
float_column	bool	Whether to include a column of float values	True
bool_column	bool	Whether to include a column of bool values	True
decimal_column	bool	Whether to include a column of decimal.Decimal values	False
datetime_column	bool	Whether to include a column of datetime.datetime values	True
datetimetz_column	bool	Whether to include a column of datetime.datetime values with timezones	False
date_column	bool	Whether to include a column of datetime.date values	False
time_column	bool	Whether to include a column of datetime.time values	False
time_delta_column	bool	Whether to include a column of pd.Timedelta values	False
time_period_column	bool	Whether to include a column of pd.Period values	False
time_interval_column	bool	Whether to include a column of pd.Interval values	False
text_column	bool	Whether to include a column of long str values using faker	False
keyword_column	bool	Whether to include a column of short str values	True
dict_column	bool	Whether to include a column of dict values	False
list_column	bool	Whether to include a column of list values	False
nested_tabular_column	bool	Whether to include a column of list values with nested dictionaries	False
lat_float_column	bool	Whether to include a column of float values representing latitudes	False
lon_float_column	bool	Whether to include a column of float values representing longitudes	False
latlon_point_column	bool	Whether to include a geopandas.GeoSeries of shapely.geometry.Point values (requires geopandas to be installed)	False
filled_geojson_column	bool	Whether to include a geopandas.GeoSeries of circular shapely.geometry.Polygon values (requires geopandas to be installed)	False
exterior_geojson_column	bool	Whether to include a geopandas.GeoSeries of rectangular shapely.geometry.LineString values (requires geopandas to be installed)	False
bytes_column	bool	Whether to include a column of bytes values	True
ipv4_address_column	bool	Whether to include a column of ipaddress.IPv4Address values	False
ipv6_address_column	bool	Whether to include a column of ipaddress.IPv6Address values	False
complex_number_column	bool	Whether to include a column of complex values	False
uuid4_column	bool	Whether to include a column of uuid.UUID values	False

Source code in src/dx/datatypes/main.py

def random_dataframe(
    num_rows: int = 5,
    dtype_column: bool = True,
    integer_column: bool = True,
    float_column: bool = True,
    bool_column: bool = True,
    decimal_column: bool = False,
    datetime_column: bool = True,
    datetimetz_column: bool = False,
    date_column: bool = False,
    time_column: bool = False,
    time_delta_column: bool = False,
    time_period_column: bool = False,
    time_interval_column: bool = False,
    text_column: bool = False,
    keyword_column: bool = True,
    dict_column: bool = False,
    list_column: bool = False,
    nested_tabular_column: bool = False,
    lat_float_column: bool = False,
    lon_float_column: bool = False,
    latlon_point_column: bool = False,
    filled_geojson_column: bool = False,
    exterior_geojson_column: bool = False,
    bytes_column: bool = True,
    ipv4_address_column: bool = False,
    ipv6_address_column: bool = False,
    complex_number_column: bool = False,
    uuid4_column: bool = False,
):  # noqa: C901
    """
    Convenience function to generate a dataframe of `num_rows` length
    with randomly-generated values of mixed data types.

    Parameters
    ----------
    num_rows : int
        Number of rows to generate
    dtype_column : bool
        Whether to include a column of `type` values
    integer_column : bool
        Whether to include a column of `integer` values
    float_column : bool
        Whether to include a column of `float` values
    bool_column : bool
        Whether to include a column of `bool` values
    decimal_column : bool
        Whether to include a column of `decimal.Decimal` values
    datetime_column : bool
        Whether to include a column of `datetime.datetime` values
    datetimetz_column : bool
        Whether to include a column of `datetime.datetime` values with timezones
    date_column : bool
        Whether to include a column of `datetime.date` values
    time_column : bool
        Whether to include a column of `datetime.time` values
    time_delta_column : bool
        Whether to include a column of `pd.Timedelta` values
    time_period_column : bool
        Whether to include a column of `pd.Period` values
    time_interval_column : bool
        Whether to include a column of `pd.Interval` values
    text_column : bool
        Whether to include a column of long `str` values using `faker`
    keyword_column : bool
        Whether to include a column of short `str` values
    dict_column : bool
        Whether to include a column of `dict` values
    list_column : bool
        Whether to include a column of `list` values
    nested_tabular_column : bool
        Whether to include a column of `list` values with nested dictionaries
    lat_float_column : bool
        Whether to include a column of `float` values representing latitudes
    lon_float_column : bool
        Whether to include a column of `float` values representing longitudes
    latlon_point_column : bool
        Whether to include a `geopandas.GeoSeries` of `shapely.geometry.Point` values (requires `geopandas` to be installed)
    filled_geojson_column : bool
        Whether to include a `geopandas.GeoSeries` of circular `shapely.geometry.Polygon` values (requires `geopandas` to be installed)
    exterior_geojson_column : bool
        Whether to include a `geopandas.GeoSeries` of rectangular `shapely.geometry.LineString` values (requires `geopandas` to be installed)
    bytes_column : bool
        Whether to include a column of `bytes` values
    ipv4_address_column : bool
        Whether to include a column of `ipaddress.IPv4Address` values
    ipv6_address_column : bool
        Whether to include a column of `ipaddress.IPv6Address` values
    complex_number_column : bool
        Whether to include a column of `complex` values
    uuid4_column : bool
        Whether to include a column of `uuid.UUID` values
    """
    df = pd.DataFrame(index=list(range(num_rows)))

    if dtype_column:
        df["dtype_column"] = misc.generate_dtype_series(num_rows)

    if bool_column:
        df["bool_column"] = misc.generate_boolean_series(num_rows)

    # numeric columns
    if integer_column:
        df["integer_column"] = numeric.generate_integer_series(num_rows)

    if float_column:
        df["float_column"] = numeric.generate_float_series(num_rows)

    if decimal_column:
        df["decimal_column"] = numeric.generate_decimal_series(num_rows)

    if complex_number_column:
        df["complex_number_column"] = numeric.generate_complex_number_series(num_rows)

    # date/time columns
    if datetime_column:
        df["datetime_column"] = date_time.generate_datetime_series(num_rows)

    if datetimetz_column:
        df["datetimetz_column"] = date_time.generate_datetimetz_series(num_rows)

    if date_column:
        df["date_column"] = date_time.generate_date_series(num_rows)

    if time_column:
        df["time_column"] = date_time.generate_time_series(num_rows)

    if time_delta_column:
        df["time_delta_column"] = date_time.generate_time_delta_series(num_rows)

    if time_period_column:
        df["time_period_column"] = date_time.generate_time_period_series(num_rows)

    if time_interval_column:
        df["time_interval_column"] = date_time.generate_time_interval_series(num_rows)

    # string columns
    if text_column:
        df["text_column"] = text.generate_text_series(num_rows)

    if keyword_column:
        df["keyword_column"] = text.generate_keyword_series(num_rows)

    # container columns
    if dict_column:
        df["dict_column"] = misc.generate_dict_series(num_rows)

    if list_column:
        df["list_column"] = misc.generate_list_series(num_rows)

    if nested_tabular_column:
        df["nested_tabular_column"] = generate_nested_tabular_series(
            num_rows,
            float_column=True,
            keyword_column=True,
        )

    # geopandas/shapely columns
    if lat_float_column:
        df["lat_float_column"] = geometry.generate_lat_float_series(num_rows)

    if lon_float_column:
        df["lon_float_column"] = geometry.generate_lon_float_series(num_rows)

    if latlon_point_column:
        df["latlon_point_column"] = geometry.generate_latlon_series(num_rows)

    if filled_geojson_column:
        df["filled_geojson_column"] = geometry.generate_filled_geojson_series(num_rows)

    if exterior_geojson_column:
        df["exterior_geojson_column"] = geometry.generate_exterior_bounds_geojson_series(num_rows)

    # extras
    if bytes_column:
        df["bytes_column"] = misc.generate_bytes_series(num_rows)

    if ipv4_address_column:
        df["ipv4_address_column"] = misc.generate_ipv4_series(num_rows)

    if ipv6_address_column:
        df["ipv6_address_column"] = misc.generate_ipv6_series(num_rows)

    if uuid4_column:
        df["uuid4_column"] = misc.generate_uuid4_series(num_rows)

    return df