You can sort pandas DataFrame by one or more columns using the sort_values() method in ascending or descending order. To specify the order, you have to use the ascending boolean property; False for descending and True for ascending. By default, it is set to True.
In this article, I will explain how to sort pandas DataFrame with one or more columns. By default sort_values() returns a copy DataFrame with the result of the sort. To sort on current DataFrame use inplace=True.
1. Quick Examples of Sort by Multiple Columns in Pandas
If you are in a hurry, below are some quick examples of how to sort by multiple columns in Pandas DataFrame.
# Below are some quick examples
# Example 1: Sort multiple columns
df2 = df.sort_values(['Fee', 'Duration'],
ascending = [False, True])
# Example 2: Sort by two columns
df2 = df.sort_values(['Courses', 'Discount'],
ascending = [True, True])
# Example 3: Using the sorting function
df.sort_values(["Fee", "Courses"],
axis = 0, ascending = True,
inplace = True,
na_position = "first")
Let’s create a DataFrame with a few rows and columns and execute some examples to learn how sorting works.
# Create DataFrame
import pandas as pd
technologies = ({
'Courses':["Spark","Hadoop","pandas","Oracle","Java"],
'Fee' :[20000,25000,26000,22000,20000],
'Duration':['30days','35days','40days','50days','60days'],
'Discount':[1000,2300,1500,1200,2500]
})
df = pd.DataFrame(technologies, index = ['r1','r2','r3','r4','r5'])
print("Create DataFrame:\n", df)
Yields below output.
2. Sort Multiple Columns in Pandas DataFrame
By using the sort_values() method you can sort single column or multiple columns in DataFrame by ascending or descending order. When order is not specified, all specified columns are sorted in ascending order.
# Sort multiple columns
df2 = df.sort_values(['Fee', 'Discount'])
print("Get the DataFrame after sorting:\n", df2)
Yields below output.
In case, if you want to update the existing DataFrame use inplace=True. This function is used with the by parameter, which takes a list of column names you want to sort.
# Sort ascending order
df.sort_values(by=['Fee','Discount'], inplace=True)
print("Get the DataFrame after sorting:\n", df)
Yields the same output as above.
3. Sort in an Ascending Order
Use ascending param to sort the DataFrame in ascending or descending order. When you have multiple sorting columns. By default, it sorts in ascending order.
# Sort ascending order
df.sort_values(by=['Fee','Discount'], inplace=True,
ascending = [True, True])
print("Get the DataFrame after sorting:\n", df)
Yields below output.
# Output:
# Get the DataFrame after sorting:
Courses Fee Duration Discount
r1 Spark 20000 30days 1000
r5 Java 20000 60days 2500
r4 Oracle 22000 50days 1200
r2 Hadoop 25000 35days 2300
r3 pandas 26000 40days 1500
4. Sort Multiple Columns in Descending Order
If you want to sort in descending order, you can use the ascending parameter and set it to False. You can also specify different sorting orders for each input.
# Sort descending order
df.sort_values(by=['Fee','Discount'], inplace=True,
ascending = [True, False])
print("Get the DataFrame after sorting:\n", df)
Yields below output.
# Output:
Courses Fee Duration Discount
r0 Java 20000 60days 2500
r1 Spark 20000 30days 1000
r4 Oracle 22000 50days 1200
r2 Hadoop 25000 35days 2300
r3 pandas 26000 40days 1500
5. Conclusion
In this article, you have learned how to sort a DataFrame by multiple columns using Dataframe.sort_values() in ascending or descending order.
Happy Learning !!
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