Teradata Package for Python Function Reference | 20.00 - isna - 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.sql.DataFrameColumn.isna = isna(self)
- Test for NA values
PARAMETERS:
None
RETURNS:
When used with assign() function, newly assigned column contains
A boolean Series of numeric values:
- 1 if value is NA (None)
- 0 if values is not NA
Otherwise returns ColumnExpression, also known as, teradataml DataFrameColumn.
EXAMPLES:
>>> load_example_data("dataframe", "sales")
>>> df = DataFrame("sales")
# Example 1: Filter out the NA values from 'Mar' column.
>>> df[df.Mar.isna() == 1]
Feb Jan Mar Apr datetime
accounts
Orange Inc 210.0 None None 250.0 04/01/2017
Yellow Inc 90.0 None None NaN 04/01/2017
# Filter out the non-NA values from 'Mar' column.
>>> df[df.Mar.isna() == 0]
Feb Jan Mar Apr datetime
accounts
Blue Inc 90.0 50 95 101.0 04/01/2017
Red Inc 200.0 150 140 NaN 04/01/2017
Jones LLC 200.0 150 140 180.0 04/01/2017
Alpha Co 210.0 200 215 250.0 04/01/2017
# Example 2: Filter out the NA values from 'Mar' column using boolean True.
>>> df[df.Mar.isna() == True]
Feb Jan Mar Apr datetime
accounts
Orange Inc 210.0 None None 250.0 04/01/2017
Yellow Inc 90.0 None None NaN 04/01/2017
# Filter out the non-NA values from 'Mar' column using boolean False.
>>> df[df.Mar.isna() == False]
Feb Jan Mar Apr datetime
accounts
Blue Inc 90.0 50 95 101.0 04/01/2017
Red Inc 200.0 150 140 NaN 04/01/2017
Jones LLC 200.0 150 140 180.0 04/01/2017
Alpha Co 210.0 200 215 250.0 04/01/2017
# Example 3: Assign the tested values to dataframe as a column.
>>> df.assign(isna_=df.Mar.isna())
Feb Jan Mar Apr datetime isna_
accounts
Blue Inc 90.0 50.0 95.0 101.0 04/01/2017 0
Orange Inc 210.0 NaN NaN 250.0 04/01/2017 1
Red Inc 200.0 150.0 140.0 NaN 04/01/2017 0
Yellow Inc 90.0 NaN NaN NaN 04/01/2017 1
Jones LLC 200.0 150.0 140.0 180.0 04/01/2017 0
Alpha Co 210.0 200.0 215.0 250.0 04/01/2017 0