final class DataFrameNaFunctions extends Logging
Provides functions for handling missing values in a DataFrame.
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0.2.0
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- def drop(minNonNullsPerRow: Int, cols: Seq[String]): DataFrame
Returns a new DataFrame that excludes all rows containing fewer than
minNonNullsPerRownon-null and non-NaN values in the specified columnscols.Returns a new DataFrame that excludes all rows containing fewer than
minNonNullsPerRownon-null and non-NaN values in the specified columnscols.- If
minNonNullsPerRowis greater than the number of the specified columns, the method returns an empty DataFrame. - If
minNonNullsPerRowis less than 1, the method returns the original DataFrame. - If
colsis empty, the method returns the original DataFrame.
- minNonNullsPerRow
The minimum number of non-null and non-NaN values that should be in the specified columns in order for the row to be included.
- cols
A sequence of the names of columns to check for null and NaN values.
- returns
- Since
0.2.0
- Exceptions thrown
SnowparkClientExceptionif cols contains any unrecognized column name
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- def fill(valueMap: Map[String, Any]): DataFrame
Returns a new DataFrame that replaces all null and NaN values in the specified columns with the values provided.
Returns a new DataFrame that replaces all null and NaN values in the specified columns with the values provided.
valueMapdescribes which columns will be replaced and what the replacement values are.- It only supports Long, Int, short, byte, String, Boolean, float, and Double values.
- If the type of the given value doesn't match the column type (e.g. a Long value for a StringType column), the replacement in this column will be skipped.
- valueMap
A Map that associates the names of columns with the values that should be used to replace null and NaN values in those columns.
- returns
- Since
0.2.0
- Exceptions thrown
SnowparkClientExceptionif valueMap contains unrecognized columns
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- def replace(colName: String, replacement: Map[Any, Any]): DataFrame
Returns a new DataFrame that replaces values in a specified column.
Returns a new DataFrame that replaces values in a specified column.
Use the
replacementparameter to specify a Map that associates the values to replace with new values. To replace a null value, use None as the key in the Map.For example, suppose that you pass
col1forcolNameandMap(2 -> 3, None -> 2, 4 -> null)forreplacement. Incol1, this function replaces:2with3- null with
2 4with null
- colName
The name of the column in which the values should be replaced.
- replacement
A Map that associates the original values with the replacement values.
- Since
0.2.0
- Exceptions thrown
SnowparkClientExceptionif colName is an unrecognized column name
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- def transformation(funcName: String)(func: => DataFrame): DataFrame
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- Deprecated
(Since version 9)