How to Sort a Dataframe in Python Pandas by Two or More Columns

How to sort a dataFrame in python pandas by two or more columns?

As of the 0.17.0 release, the sort method was deprecated in favor of sort_values. sort was completely removed in the 0.20.0 release. The arguments (and results) remain the same:

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

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You can use the ascending argument of sort:

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

For example:

In [11]: df1 = pd.DataFrame(np.random.randint(1, 5, (10,2)), columns=['a','b'])

In [12]: df1.sort(['a', 'b'], ascending=[True, False])
Out[12]:
   a  b
2  1  4
7  1  3
1  1  2
3  1  2
4  3  2
6  4  4
0  4  3
9  4  3
5  4  1
8  4  1

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As commented by @renadeen

Sort isn't in place by default! So you should assign result of the sort method to a variable or add inplace=True to method call.

that is, if you want to reuse df1 as a sorted DataFrame:

df1 = df1.sort(['a', 'b'], ascending=[True, False])

or

df1.sort(['a', 'b'], ascending=[True, False], inplace=True)

How to sort a Pandas DataFrame with multiple columns, some in ascending order and others descending order?

The DataFrame.sort_values method can handle this very easily. Just use the ascending argument and provide a list of boolean values.

import pandas as pd

my_df = pd.DataFrame({'col1':['a','a','a','a','b','b','b','b','c','c','c','c'],
                      'col2':[1,1,2,2,1,1,2,2,1,1,2,2],
                      'col3':[1,2,1,2,1,2,1,2,1,2,1,2]})

my_df = my_df.sort_values(by=['col1','col2','col3'], 
                          ascending=[False, False, True])

Note that the list provided in the ascending argument must have the same length as the one provided in the by argument.

How to sort dataframe rows by multiple columns

Use sort_values, which can accept a list of sorting targets. In this case it sounds like you want to sort by S/N, then Dis, then Rate:

df = df.sort_values(['S/N', 'Dis', 'Rate'])

#     S/N      Dis       Rate
# 0   332   4.6030  91.204062
# 3   332   9.1985  76.212943
# 6   332  14.4405  77.664282
# 9   332  20.2005  76.725955
# 12  332  25.4780  31.597510
# 15  332  30.6670  74.096975
# 1   445   5.4280  60.233917
# 4   445   9.7345  31.902842
# 7   445  14.6015  36.261851
# 10  445  19.8630  40.705467
# 13  445  24.9050   4.897008
# 16  445  30.0550  35.217889
# ...

Pandas: Sort a dataframe based on multiple columns

You can swap columns in list and also values in ascending parameter:

Explanation:

Order of columns names is order of sorting, first sort descending by Employee_Count and if some duplicates in Employee_Count then sorting by Department only duplicates rows ascending.

df1 = df.sort_values(['Employee_Count', 'Department'], ascending=[False, True])
print (df1)
  Department  Employee_Count
4        xyz              15
2        bca              11
0        abc              10 <-
1        adc              10 <-
3        cde               9

Or for test if use second False then duplicated rows are sorting descending:

df2 = df.sort_values(['Employee_Count', 'Department',],ascending=[False, False])
print (df2)
  Department  Employee_Count
4        xyz              15
2        bca              11
1        adc              10 <-
0        abc              10 <-
3        cde               9

How to sort a Pandas DataFrame by multiple columns in Python with ordered number

There is an issue when you write:

ordered = trade_value_df.sort_values(by=['Trade Value'], ascending=False,ignore_index=True)

You are assigning something new to the name ordered, so you're effectively losing the dataframe you had previously assigned to that name.

A possibility is to do all the operations on the same dataframe, rather than have multiple dataframe:

import pandas as pd

df = pd.DataFrame({'Code':['Apple', 'Amazon', 'Facebook', 'Samsung'], 'Volume':[500, 1000, 250, 100], 'Trade Value': [1000, 500, 750, 1500]})

df = df.sort_values(by=['Volume'], ascending=False,ignore_index=True)
df['Volume Order'] = df.index + 1

df = df.sort_values(by=['Trade Value'], ascending=False,ignore_index=True)
df['Trade Order'] = df.index + 1

print(df)
#        Code  Volume  Trade Value  Volume Order  Trade Order
# 0   Samsung     100         1500             4            1
# 1     Apple     500         1000             2            2
# 2  Facebook     250          750             3            3
# 3    Amazon    1000          500             1            4

Pandas: Sort by sum of 2 columns

This does the trick:

data = {"Column 1": [1, 3, 1], "Column 2": [1, 2, 3]}
df = pd.DataFrame(data)

sorted_indices = (df["Column 1"] + df["Column 2"]).sort_values().index

df.loc[sorted_indices, :]

I just created a series that has the sum of both the columns, sorted it, got the sorted indices, and printed those indices out for the dataframe.

(I changed the data a little so you can see the sorting in action. Using the data you provided, you wouldn't have been able to see the sorted data as it would have been the same as the original one.)

how to sort multiple columns in an order in pandas

For me working if sorting in one sort_values method:

df = df.sort_values(by=["A", "C", 'B'], ascending=[True, False, True], ignore_index=True)
print (df)
   A   B    C
0  a  c0  170
1  b  c0  170
2  c  c0   88
3  d  c1   12
4  e  c1   28
5  f  c2  160
6  g  c2   37
7  h  c1   12
8  i  c2  160
9  j  c0   88

If change order of sorting ouput is correct:

df = df.sort_values(by=["B", "C", 'A'], ascending=[True, False, True], ignore_index=True)
print (df)
   A   B    C
0  a  c0  170
1  b  c0  170
2  c  c0   88
3  j  c0   88
4  e  c1   28
5  d  c1   12
6  h  c1   12
7  f  c2  160
8  i  c2  160
9  g  c2   37

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