1. sort_values
pandas in the sort_values () function is similar to the principle of SQL in the order by, the data set in accordance with a field in the data for sorting, the function can be based on the specified columns of data can also be based on the specified rows of data sorting.
official document
## Parameters
DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')
#### Parameter Description
axis:{0 or ‘index', 1 or ‘columns'}, default 0，Sort by index by default，i.e. vertical sorting，in case of1，Then it is sorted horizontally
by:str or list of str；in the event thataxis=0，in that caseby="Listing"；in the event thataxis=1，in that caseby="The name of the line."；
ascending:boolean，Truethen ascending order，It can be[True,False]，i.e. first field in ascending order，Second descending order
inplace:boolean，Whether to replace an existing data frame with a sorted data frame
kind:Sorting methods，{‘quicksort', ‘mergesort', ‘heapsort'}, default ‘quicksort'。Doesn't seem to matter too much.
na_position : {‘first', ‘last'}, default ‘last'，Default missing values come last
2. sort_values
Building a DataFrame
import pandas as pd
df = ([['a', 100, 'c'], ['a', 300, 'a'], ['a', 200, 'b'],
['c', 300, 'a'], ['c', 200, 'b'], ['c', 100, 'c'],
['b', 200, 'b'], ['b', 300, 'a'], ['b', 100, 'c']], columns=['X', 'Y', 'Z'])
X Y Z
0 a 100 c
1 a 300 a
2 a 200 b
3 c 300 a
4 c 200 b
5 c 100 c
6 b 200 b
7 b 300 a
8 b 100 c
Sort df by Y, X columns in descending order
df.sort_values(by=['Y', 'X'], ascending=False, inplace=True) print(df)
X Y Z
3 c 300 a
7 b 300 a
1 a 300 a
4 c 200 b
6 b 200 b
2 a 200 b
5 c 100 c
8 b 100 c
0 a 100 c
3. groupby|sort_values
Sort column Y in ascending order after grouping by column X.
res = ('X', sort=False).apply(lambda x: x.sort_values('Y', ascending=True)).reset_index(drop=True)
print(res)X Y Z
0 a 100 c
1 a 200 b
2 a 300 a
3 c 100 c
4 c 200 b
5 c 300 a
6 b 100 c
7 b 200 b
8 b 300 a
Example:
Creating Data Frames
#Creating a dataframe using a dictionary dict
import numpy as np
import pandas as pd
df=({'col1':['A','A','B',,'D','C'],
'col2':[2,1,9,8,7,7],
'col3':[0,1,9,4,2,8]
})
print(df)
>>>
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
3 NaN 8 4
4 D 7 2
5 C 7 8
Sort by the first column and place the null value of that column first.
# Sort based on the first column and put the null value of that column first print(df.sort_values(by=['col1'],na_position='first')) >>> col1 col2 col3 3 NaN 8 4 0 A 2 0 1 A 1 1 2 B 9 9 5 C 7 8 4 D 7 2
Based on the second and third columns, the values are sorted in descending order.
#Based on the second and third columns, values are sorted in descending order print(df.sort_values(by=['col2','col3'],ascending=False)) >>> col1 col2 col3 2 B 9 9 3 NaN 8 4 5 C 7 8 4 D 7 2 0 A 2 0 1 A 1 1
Sort the data according to the values in the first column, in descending order, and replace the original data
# Sort the data according to the values in the first column, in descending order, and replace the original data
df.sort_values(by=['col1'],ascending=False,inplace=True,
na_position='first')
print(df)
>>>
col1 col2 col3
3 NaN 8 4
4 D 7 2
5 C 7 8
2 B 9 9
1 A 1 1
0 A 2 0
Sort the rows with index value 0, i.e., the first row, in descending order.
x = ({'x1':[1,2,2,3],'x2':[4,3,2,1],'x3':[3,2,4,1]})
print(x)
# Sort the rows with index value 0, i.e. the first row, in descending order
print(x.sort_values(by =0,ascending=False,axis=1))
>>>
x1 x2 x3
0 1 4 3
1 2 3 2
2 2 2 4
3 3 1 1
x2 x3 x1
0 4 3 1
1 3 2 2
2 2 4 2
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