Teradata Package for Python Function Reference | 20.00 - count - Teradata Package for Python - Look here for syntax, methods and examples for the functions included in the Teradata Package for Python.
Teradata® Package for Python Function Reference - 20.00
- Deployment
- VantageCloud
- VantageCore
- Edition
- Enterprise
- IntelliFlex
- VMware
- Product
- Teradata Package for Python
- Release Number
- 20.00.00.03
- Published
- December 2024
- ft:locale
- en-US
- ft:lastEdition
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Enterprise_2000
- Product Category
- Teradata Vantage
- teradataml.dataframe.window.count = count(distinct=False, skipna=False)
- DESCRIPTION:
Function returns the total number of qualified rows in a teradataml
DataFrame or ColumnExpression over the specified window.
PARAMETERS:
distinct:
Optional Argument.
Specifies a flag that decides whether to consider duplicate values in
a column or not.
Default Values: False
Types: bool
skipna:
Optional Argument.
Specifies a flag that decides whether to skip null values or not.
Default Values: False
Types: bool
RETURNS:
* teradataml DataFrame - When aggregate is executed using window created
on teradataml DataFrame.
* ColumnExpression, also known as, teradataml DataFrameColumn - When aggregate is
executed using window created on ColumnExpression.
RAISES:
RuntimeError - If column does not support the aggregate operation.
EXAMPLES:
# Load the data to run the example.
>>> load_example_data("dataframe", "admissions_train")
>>>
# Create a teradataml DataFrame on 'admissions_train' table.
>>> admissions_train = DataFrame("admissions_train")
>>> admissions_train
masters gpa stats programming admitted
id
22 yes 3.46 Novice Beginner 0
36 no 3.00 Advanced Novice 0
15 yes 4.00 Advanced Advanced 1
38 yes 2.65 Advanced Beginner 1
5 no 3.44 Novice Novice 0
17 no 3.83 Advanced Advanced 1
34 yes 3.85 Advanced Beginner 0
13 no 4.00 Advanced Novice 1
26 yes 3.57 Advanced Advanced 1
19 yes 1.98 Advanced Advanced 0
>>>
# Example 1: Calculate the count of values for the 'gpa' column
# in a Rolling window, partitioned over 'programming'.
# Create a Rolling window on 'gpa'.
>>> window = admissions_train.gpa.window(partition_columns=admissions_train.programming,
... window_start_point=-2,
... window_end_point=0)
>>>
# Execute count() on the Rolling window and attach it to the teradataml DataFrame.
# Note: DataFrame.assign() allows combining multiple window aggregate operations
# in one single call. In this example, we are executing count() along with
# max() window aggregate operations.
>>> df = admissions_train.assign(count_gpa=window.count(), max_gpa=window.max())
>>> df
masters gpa stats programming admitted count_gpa max_gpa
id
11 no 3.13 Advanced Advanced 1 3 1.98
27 yes 3.96 Advanced Advanced 0 3 3.13
26 yes 3.57 Advanced Advanced 1 3 3.45
6 yes 3.50 Beginner Advanced 1 3 3.50
9 no 3.82 Advanced Advanced 1 3 3.50
25 no 3.96 Advanced Advanced 1 3 3.60
39 yes 3.75 Advanced Beginner 0 1 3.75
31 yes 3.50 Advanced Beginner 1 2 3.50
29 yes 4.00 Novice Beginner 0 3 3.50
21 no 3.87 Novice Beginner 1 3 3.50
>>>
# Example 2: Calculate the count of all the valid columns in teradataml
# DataFrame, in an Expanding window, partitioned over 'programming',
# and order by 'id'.
# Create an Expanding window on teradataml DataFrame.
>>> window = admissions_train.window(partition_columns=admissions_train.masters,
... order_columns=admissions_train.id,
... window_start_point=None,
... window_end_point=0)
>>>
# Execute count() on the Expanding window.
>>> df = window.count()
>>> df
masters gpa stats programming admitted admitted_count gpa_count id_count masters_count programming_count stats_count
id
4 yes 3.50 Beginner Novice 1 3 3 3 3 3 3
7 yes 2.33 Novice Novice 1 5 5 5 5 5 5
14 yes 3.45 Advanced Advanced 0 6 6 6 6 6 6
15 yes 4.00 Advanced Advanced 1 7 7 7 7 7 7
19 yes 1.98 Advanced Advanced 0 9 9 9 9 9 9
20 yes 3.90 Advanced Advanced 1 10 10 10 10 10 10
3 no 3.70 Novice Beginner 1 1 1 1 1 1 1
5 no 3.44 Novice Novice 0 2 2 2 2 2 2
8 no 3.60 Beginner Advanced 1 3 3 3 3 3 3
9 no 3.82 Advanced Advanced 1 4 4 4 4 4 4
>>>
# Example 3: Calculate the count of all the valid columns in teradataml
# DataFrame, which are grouped by 'masters' and 'gpa' in a
# Contracting window, partitioned over 'masters'.
# Perform group_by() operation on teradataml DataFrame.
>>> group_by_df = admissions_train.groupby(["masters", "gpa"])
# Create a Contracting window on teradataml DataFrameGroupBy object.
>>> window = group_by_df.window(partition_columns=group_by_df.masters,
... window_start_point=-5,
... window_end_point=None)
# Execute count() on the Contracting window.
>>> window.count()
masters gpa gpa_count masters_count
0 yes 3.79 8 8
1 yes 3.50 10 10
2 yes 3.96 11 11
3 yes 4.00 12 12
4 yes 3.90 14 14
5 yes 2.33 15 15
6 no 3.52 6 6
7 no 3.83 7 7
8 no 3.82 8 8
9 no 3.55 9 9
>>>