In-depth understanding of pandas sorting: tips from single column sorting to multi-column sorting

Exploring pandas sorting methods: from basic sorting to multi-column sorting, specific code examples are required

Introduction:
In the process of data analysis and processing, sorting is A very basic and important operation. In Python's data analysis library, pandas provides a wealth of sorting methods to meet sorting needs in different scenarios. This article will introduce the sorting methods in pandas, from basic single-column sorting to multi-column sorting, and give specific code examples.

1. Basic sorting method

1. Sort by value: Use the sort_values() method
   The sort_values() method can sort the DataFrame or Series based on the value of the specified column. The default is ascending order. You can set the ascending parameter to False to sort in descending order.

The following is a sample code:

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie', 'David'],
        'age': [25, 30, 20, 35],
        'score': [80, 90, 85, 95]}

df = pd.DataFrame(data)

# 按照age列进行升序排序
df_sorted = df.sort_values('age')

print(df_sorted)

Output results:

      name  age  score
2  Charlie   20     85
0    Alice   25     80
1      Bob   30     90
3    David   35     95

2. Sort by index: use the sort_index() method
   sort_index() Methods can be sorted based on row or column index. The default is to sort by row index. You can set the axis parameter to 1 to sort by column index.

The following is a sample code:

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie', 'David'],
        'age': [25, 30, 20, 35],
        'score': [80, 90, 85, 95]}

df = pd.DataFrame(data)

# 按照行索引进行升序排序
df_sorted = df.sort_index()

print(df_sorted)

Output result:

      name  age  score
0    Alice   25     80
1      Bob   30     90
2  Charlie   20     85
3    David   35     95

2. Multi-column sorting method
Sometimes it is necessary to sort based on multiple columns . Pandas provides the multi-column sorting function of the sort_values() method, which can be implemented by passing the names of multiple sorting columns. Multi-column sorting will be sorted in the order of the columns passed, rows with the same first column will be sorted by the second column, and so on.

The following is a sample code:

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie', 'David'],
        'age': [25, 30, 20, 30],
        'score': [80, 90, 85, 95]}

df = pd.DataFrame(data)

# 按照age和score列进行升序排序
df_sorted = df.sort_values(['age', 'score'])

print(df_sorted)

Output result:

      name  age  score
2  Charlie   20     85
0    Alice   25     80
1      Bob   30     90
3    David   30     95

As shown above, first sort by the age column, and then sort rows with the same age column by the score column.

Conclusion:
This article introduces the sorting methods in pandas, from basic single-column sorting to multi-column sorting, and gives specific code examples. In the actual data analysis and processing process, the flexible application of these sorting methods can help us quickly process and analyze large amounts of data and improve work efficiency. I hope this article will help you understand and use pandas sorting methods.

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