Answers for "pandas dataframe sort multiple columns"

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    4

sort by two columns in pandas

df.sort_values(['a', 'b'], ascending=[True, False])

sort dataframe by column

df.sort_values(by='col1', ascending=False, na_position='first', inplace=True)

Returns 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')]