StringColumn

class getml.data.columns.StringColumn(name='', role='categorical', df_name='')

Handle for categorical data that is kept in the getML engine

Args:

name (str, optional):

Name of the categorical column.

role (str, optional):

Role that the column plays.

df_name (str, optional):

name instance variable of the DataFrame containing this column.

Examples:

import numpy as np

import getml.data as data
import getml.engine as engine
import getml.data.roles as roles

# ----------------

engine.set_project("examples")

# ----------------
# Create a data frame from a JSON string

json_str = """{
    "names": ["patrick", "alex", "phil", "ulrike"],
    "column_01": [2.4, 3.0, 1.2, 1.4],
    "join_key": ["0", "1", "2", "3"],
    "time_stamp": ["2019-01-01", "2019-01-02", "2019-01-03", "2019-01-04"]
}"""

my_df = data.DataFrame(
    "MY DF",
    roles={
        "unused_string": ["names", "join_key", "time_stamp"],
        "unused_float": ["column_01"]}
).read_json(
    json_str
)

# ----------------

col1 = my_df["names"]

# ----------------

col2 = col1.substr(4, 3)

my_df.add(col2, "short_names", roles.categorical)

# ----------------
# If you do not explicitly set a role,
# the assigned role will either be
# roles.unused_string.

col3 = "user-" + col1 + "-" + col2

my_df["new_names"] = col3
my_df.set_role("new_names", roles.categorical)

Methods

as_num()	Transforms a categorical column to a numerical column.
as_ts([time_formats])	Transforms a categorical column to a time stamp.
contains(other)	Returns a boolean column indicating whether a string or column entry is contained in the corresponding entry of the other column.
count([alias])	COUNT aggregation.
count_distinct([alias])	COUNT DISTINCT aggregation.
is_null()	Determine whether the value is NULL.
substr(begin, length)	Return a substring for every element in the column.
to_numpy()	Transform column to numpy.ndarray
unique()	Transform column to numpy array containing all distinct values.
update(condition, values)	Returns an updated version of this column.
with_subroles(subroles[, append])	Returns a new column with new subroles.
with_unit(unit)	Returns a new column with a new unit,

Attributes

last_change	The last time any of the underlying data frames has been changed.
length	The length of the column (number of rows in the data frame).
name	The role of this column.
role	The role of this column.
subroles	The subroles of this column.
unit	The unit of this column.