StringColumn¶

class getml.data.columns.StringColumn(name='', role='categorical', num=0, df_name='')¶

Bases: getml.data.columns._Column

Handle for categorical data that is kept in the getML engine

Parameters

name (str, optional) – Name of the categorical column.
role (str, optional) – Role that the column plays.
num (int, optional) – Number of the column.
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)

Attributes Summary

length

The length of the column (number of rows in the data frame).

Methods Summary

`alias`(alias)	Adds an alias to the column.
`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`([sock])	Transform column to numpy array
`update`(condition, values)	Returns an updated version of this column.

Attributes Documentation

length¶: The length of the column (number of rows in the data frame).

Methods Documentation

alias(alias)¶

Adds an alias to the column. This is useful for joins.

Parameters: alias (str) – The name of the column as it should appear in the new DataFrame.

as_num()¶: Transforms a categorical column to a numerical column.

as_ts(time_formats=None)¶

Transforms a categorical column to a time stamp.

Parameters: time_formats (str) – Formats to be used to parse the time stamps.

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='new_column')¶

COUNT aggregation.

Parameters: alias (str) – Name for the new column.

count_distinct(alias='new_column')¶

COUNT DISTINCT aggregation.

Parameters: alias (str) – Name for the new column.

is_null()¶: Determine whether the value is NULL.

substr(begin, length)¶

Return a substring for every element in the column.

Parameters

begin (int) – First position of the original string.
length (int) – Length of the extracted string.

to_numpy(sock=None)¶

Transform column to numpy array

Parameters: sock (optional) – Socket connecting the Python API with the getML engine.

update(condition, values)¶

Returns an updated version of this column.

All entries for which the corresponding condition is True, are updated using the corresponding entry in values.

Parameters

condition (Boolean column) – Condition according to which the update is done
values – Values to update with