atoti.experimental package

Module contents

Warning

Experimental features are subject to breaking changes (even removals) in minor and/or patch releases.

atoti.experimental.create_date_hierarchy(name, cube, column, *, levels={'Day': 'd', 'Month': 'M', 'Year': 'Y'})

Create a multilevel date hierarchy based on a date column.

The new levels are created by matching a date pattern. Here is a non-exhaustive list of patterns that can be used:

Pattern

Description

Type

Examples

Y

Year

Integer

2001, 2005, 2020

YY

2-digits year

String

"01", "05", "20"

YYYY

4-digits year

String

"2001", "2005", "2020"

M

Month of the year (1 based)

Integer

1, 5, 12

MM

2-digits month

String

"01", "05", "12"

MMM

3-letters month

String

"Jan", "May", "Dec"

MMMM

Full month

String

"January", "May", "December"

d

Day of the month

Integer

1, 15, 30

dd

2-digits day of the month

String

"01", "15", "30"

w

Week number

Integer

1, 12, 51

eeee

Day of the week

String

"Monday", "Tuesday", "Sunday"

Q

Quarter

Integer

1, 2, 3, 4

QQQ

Quarter prefixed with Q

String

"Q1", "Q2", "Q3", "Q4"

H

Hour of day (0-23)

Integer

0, 12, 23

HH

2-digits hour of day

String

"00", "12", "23"

m

Minute of hour

Integer

0, 30, 59

mm

2-digits minute of hour

String

"00", "30", "59"

s

Second of minute

Integer

0, 5, 55

ss

2-digits second of minute

String

"00", "05", "55"

Supported patterns are the ones of the DateTimeFormatter in Java.

Parameters

  • name (str) – The name of the hierarchy to create.

  • cube (Cube) – The cube of the new hierarchy.

  • column (Column) – A store column containing a date or a datetime.

  • levels (Mapping[str, str]) – The mapping from the names of the levels to the patterns from which they will be created.

Example

>>> df = pd.DataFrame(
...     columns=["Date", "Quantity"],
...     data=[
...         ("2020-01-10", 150.0),
...         ("2020-01-20", 240.0),
...         ("2019-03-17", 270.0),
...         ("2019-12-12", 200.0),
...     ],
... )
>>> store = session.read_pandas(
...     df, keys=["Date"], store_name="create_date_hierarchy example"
... )
>>> cube = session.create_cube(store)
>>> l, m = cube.levels, cube.measures
>>> tt.experimental.create_date_hierarchy(
...     "Date parts",
...     cube,
...     store["Date"],
...     levels={"Year": "Y", "Month": "MMMM", "Day": "d"},
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     include_totals=True,
...     levels=[l["Year"], l["Month"], l["Day"]],
... )
                   Quantity.SUM
Year  Month    Day
Total                    860.00
2019                     470.00
      December           200.00
               12        200.00
      March              270.00
               17        270.00
2020                     390.00
      January            390.00
               10        150.00
               20        240.00

The full date can also be added back as the last level of the hierarchy:

>>> h = cube.hierarchies
>>> h["Date parts"] = {**h["Date parts"].levels, "Date": store["Date"]}
>>> cube.query(
...     m["Quantity.SUM"],
...     include_totals=True,
...     levels=l["Date parts", "Date"],
... )
                              Quantity.SUM
Year  Month    Day Date
Total                               860.00
2019                                470.00
      December                      200.00
               12                   200.00
                   2019-12-12       200.00
      March                         270.00
               17                   270.00
                   2019-03-17       270.00
2020                                390.00
      January                       390.00
               10                   150.00
                   2020-01-10       150.00
               20                   240.00
                   2020-01-20       240.00