You’ll be able to worth please see syntax to type a pandas DataFrame via each index and column:
df = df.sort_values(via = ['column_name', 'index'], ascending = [False, True])
Refer to examples display tips on how to worth this syntax in follow.
Examples: Kind DataFrame via Each Index and Column
Refer to code presentations tips on how to type a pandas DataFrame via the column named issues and next via the index column:
import pandas as pd #manufacture DataFrame df = pd.DataFrame({'identity': [1, 2, 3, 4, 5, 6, 7, 8], 'issues': [25, 15, 15, 14, 20, 20, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}).set_index('identity') #view first few rows df.head() issues assists rebounds identity 1 25 5 11 2 15 7 8 3 15 7 10 4 14 9 6 5 20 12 6 #type via issues and next via index df.sort_values(via = ['points', 'id'], ascending = [False, True]) issues assists rebounds identity 8 29 4 12 1 25 5 11 7 25 9 9 5 20 12 6 6 20 9 5 2 15 7 8 3 15 7 10 4 14 9 6
The ensuing DataFrame is looked after via issues in descending line and next via the index in ascending line (if there occur to be two avid gamers who rating the similar collection of issues).
Be aware that if we don’t worth the ascending argument, next each and every column will worth ascending because the default sorting mode:
#type via issues and next via index df.sort_values(via = ['points', 'id']) issues assists rebounds identity 4 14 9 6 2 15 7 8 3 15 7 10 5 20 12 6 6 20 9 5 1 25 5 11 7 25 9 9 8 29 4 12
If the index column is lately no longer named, you’ll rename it and next type accordingly:
#type via issues and next via index df.rename_axis('index').sort_values(via = ['points', 'id']) issues assists rebounds identity 4 14 9 6 2 15 7 8 3 15 7 10 5 20 12 6 6 20 9 5 1 25 5 11 7 25 9 9 8 29 4 12
Spare Sources
Pandas: Tips on how to Kind Columns via Title
Pandas: Kind DataFrame via Past
Pandas: Tips on how to Loose Reproduction Rows
