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This article mainly explains "how to sort ascending according to multiple columns by pandas". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn "how to sort ascending by multiple columns by pandas"!

Pandas sorts ascending by multiple columns

Code example:

Import pandas as pd # reads the file df = pd.read_csv ('. / TianQi.csv') # string replacement and type conversion df ['maximum temperature'] = df ['maximum temperature'] .str.replace ('℃','). Astype ('int32') df.loc [:,' minimum temperature'] = df ['minimum temperature'] .str.replace ('℃') ''). Astype ('int32') # sort and get the top 5 highest temperature print (df.sort_values (by=' highest temperature) Ascending=False) .head ())''print: date maximum temperature minimum temperature weather wind direction wind level air quality 2019-7-4 38 25 clear ~ cloudy southwest wind force 2 good 206 2019-7-26 37 27 clear southwest wind force 2 good 142 2019-5-23 37 21 fine southeast wind force 2 Good 183 2019-7-3 36 24 clear southeast wind 1 good 204 2019-7-24 36 27 cloudy ~ thunderstorm southwest wind 2 good'# sorted by multiple columns print (by= ['maximum temperature') 'minimum temperature'] Ascending= True) .head (10))''print: date maximum temperature minimum temperature weather wind direction wind level air quality 2019-12-30-5-12 clear northwest wind 4 excellent 364 2019-12-31-3-10 clear northwest wind 1 excellent 42 2019-2-12-3-8 Lesser Snow ~ cloudy northeast wind 2 you 44 2019-2-14-3-6 Lesser Snow ~ cloudy southeast wind 2 good 14 2019-1-15-2-10 clear northwest wind 3 good 37 2019-2-7-2-7 cloudy northeast wind 3 you 38 2019-2-8-1- 7 cloudy southwest wind 2 you 4 2019-1-5 0-8 cloudy northeast wind 2 excellent 39 2019-2-9 0-8 cloudy northeast wind 2 excellent 40 2019-2-10 0-8 cloudy southeasterly wind 1 excellent print (df.sort_values ['maximum temperature') 'minimum temperature'] Ascending= False) .head (10))''print: date maximum temperature minimum temperature weather wind direction wind level air quality 2019-7-4 38 25 clear ~ cloudy southwest wind 2 good 206 2019-7-26 37 27 clear southwest wind 2 good 142 2019-5-23 37 21 clear southeast Wind force 2 good 201 2019-7-21 36 27 clear ~ cloudy southwest wind 2 light pollution 2019-7-24 36 27 cloudy southwest shower southwest wind 2 good 207 2019-7-27 36 27 cloudy southeast wind 2 light pollution 17436 24 cloudy southeasterly wind 2 good 175 2019 / 6 am 25 36 24 cloudy southeasterly winds 2 good 183 2019-7-3 36 24 clear southeasterly winds 1 good 170 2019-6-20 36 23 cloudy ~ clear southeasterly winds 2 light pollution''print (df.sort_values [' maximum temperature' 'minimum temperature'], ascending= [True False]) .head (10))''print: date maximum temperature, minimum temperature weather wind direction wind level air quality 2019-12-30-5-12 clear northwest wind 4 you 44 2019-2-14-3-6 Lesser Snow ~ cloudy southeast wind 2 good 42 2019-2-12-3 -8 Lesser Snow ~ cloudy northeasterly wind 2 you 364 2019-12-31-3-10 clear northwest wind 1 you 37 2019-2-7-2-7 cloudy northeasterly wind 3 excellent 14 2019-1-15-2-10 fine northwest wind 3 good 38 2019-2-8-1- 7 cloudy southwest wind 2 you 4 2019-1-5 0-8 cloudy northeast wind 2 you 39 2019-2-9 0-8 cloudy northeast wind 2 you 40 2019-2-10 0-8 cloudy southeast wind 1 excellent''pandas, Sorting by the use of ranking function

Series

S.sort_index (ascending=False)

Sort the index of series, in ascending order by default

S.sort_values (ascending=False)

When sorting the values of series, whether in ascending or descending order, the missing value (NaN) will be the last.

DataFrame:

Dt.sort_index (ascending=False) # sort by column index dt.sort_inex (axis=1) # sort by row index dt.sort_values (by='columns_name') # sort by specified column value dt.sort_values (by='row_name', axis=1) # sort by specified row value

When using the by parameter to sort several columns (rows), the first in the list shall prevail, and the latter may not take effect, because sometimes it is impossible to sort both the first row (column) and the second row (column) in ascending order.

When you specify a row value for sorting, axis=1 must be set, otherwise an error will be reported, because the default is the column index, so the error can not be found. Axis=1 means to specify the row index.

Ranking

Series

S.rank (method='first')

The values of series are ranked in ascending order, and the output is ranked. When the ranking is the same, the output average ranking and method='first' ranking are sorted according to the order in which the values appear in the array.

In addition to the method parameter, first assigns rankings according to the order in which the values appear in the original data, and min uses the minimum ranking of the entire group, max is the largest ranking of the entire group, and average uses the average ranking, which is also the default ranking method. You can also set the ascending parameter, whether to sort descending or ascending.

DataFrame:

Dt.rank () # rank by column dt.rank (axis=1) # rank by row

The use of method and ascending parameters is the same as that of Series

At this point, I believe you have a deeper understanding of "how to sort ascending by multiple columns in pandas". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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