sort a dataframe by a column valuepython
>>> df.sort_values(by=['col1'])
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4sort a dataframe by a column valuepython
>>> df.sort_values(by=['col1'])
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4Returns a new DataFrame sorted by the specified column(s)
# Returns a new DataFrame sorted by the specified column(s)
df.sort(df.age.desc()).collect()
# [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
df.sort("age", ascending=False).collect()
# [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
df.orderBy(df.age.desc()).collect()
# [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
from pyspark.sql.functions import *
df.sort(asc("age")).collect()
# [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
df.orderBy(desc("age"), "name").collect()
# [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
df.orderBy(["age", "name"], ascending=[0, 1]).collect()
# [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]Copyright © 2021 Codeinu
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