DataFrame.sort() - ( Pandas DataFrame Basics )

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Syntax

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)

Example

import pandas as pd

# creating a sample dataframe
data = {'name': ['John', 'Alice', 'Bob', 'Mary'], 'age': [25, 20, 30, 27], 'salary': [50000, 30000, 70000, 45000]}
df = pd.DataFrame(data)

# sorting the dataframe by age
sorted_df = df.sort_values(by='age')

print(sorted_df)

Output

    name  age  salary
1  Alice   20   30000
0   John   25   50000
3   Mary   27   45000
2    Bob   30   70000

Explanation

DataFrame.sort_values() is a function in pandas that can be used to sort a dataframe by one or more column(s). It takes in various parameters, such as the column to sort by, the sort order, the sorting algorithm to use, whether to perform the sort operation in place or not, etc.

In the above example, we create a sample dataframe df and then sort it by the 'age' column using the sort_values() function. The resulting dataframe sorted_df is printed to the console.

Use

DataFrame.sort_values() is useful in sorting a dataframe by one or more columns. It is an essential tool in data analysis and manipulation, especially when dealing with large datasets.

Important Points

• DataFrame.sort_values() is used to sort a dataframe by one or more columns
• It takes in various parameters, such as the column to sort by, the sort order, the sorting algorithm to use, whether to perform the sort operation in place or not, etc.
• By default, it sorts the dataframe in ascending order
• It returns a new sorted dataframe and does not modify the original dataframe (unless specified with the inplace parameter)

Summary

In conclusion, DataFrame.sort_values() is a powerful function in pandas that can be used to sort a dataframe by one or more columns. It is essential in data analysis and manipulation tasks, especially when dealing with large datasets. It takes in various parameters and returns a new sorted dataframe while leaving the original dataframe intact (unless specified with the inplace parameter).