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Returns a new DataFrame without specified columns. This is a no-op if the schema doesn't contain the given column name(s).
Syntax
drop(*cols: "ColumnOrName")
Parameters
| Parameter | Type | Description |
|---|---|---|
cols |
str or Column | A name of the column, or the Column to be dropped. |
Returns
DataFrame: A new DataFrame without the specified columns.
Notes
When an input is a column name, it is treated literally without further interpretation. Otherwise, it will try to match the equivalent expression. So dropping a column by its name drop(colName) has a different semantic with directly dropping the column drop(col(colName)).
Examples
df = spark.createDataFrame(
[(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
df.drop('age').show()
# +-----+
# | name|
# +-----+
# | Tom|
# |Alice|
# | Bob|
# +-----+
df.drop(df.age).show()
# +-----+
# | name|
# +-----+
# | Tom|
# |Alice|
# | Bob|
# +-----+
df2 = spark.createDataFrame([(80, "Tom"), (85, "Bob")], ["height", "name"])
df.join(df2, df.name == df2.name).drop('name').sort('age').show()
# +---+------+
# |age|height|
# +---+------+
# | 14| 80|
# | 16| 85|
# +---+------+