You can sort a Pandas DataFrame by one or more columns using the sort_values() method, either in ascending or descending order. To specify the sort order, use the ascending parameter, which accepts boolean values: 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.
Quick Examples of Sort by Multiple Columns in Pandas
Following are quick examples of sorting by multiple columns.
# 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")
To run some examples of sorting multiple columns in Pandas DataFrame, let’s create Pandas DataFrame.
# 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.
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.
Sort in an Ascending Order
Use the ascending parameter to arrange the DataFrame either in ascending or descending order. When dealing with multiple sorting columns, the default behavior is to sort 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
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 also have the option to specify distinct 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
Conclusion
In this article, I have explained sorting a DataFrame by multiple columns using Dataframe.sort_values() in ascending or descending order.
Happy Learning !!
Related Articles
- Change the Order of DataFrame Columns in Pandas
- Convert Float to Integer in Pandas DataFrame
- Replace NaN with Blank/Empty String in Pandas
- Pandas groupby() Explained With Examples
- How to Create Pandas Pivot Multiple Columns
- Pandas Merge Multiple DataFrame
- DataFrame.sort_index() – Sort by Index
- Pandas Groupby Sort within Groups