In this short how-to article, we will learn how to sort the rows of a DataFrame by the value in a column in Pandas and PySpark.
Pandas
The sort_values function can be used for this task. We just need to give it the column name.
df.sort_values(by="Age")
By default, the index of the rows prior to sorting are kept, which is not an ideal situation. We can change this behavior by using the ignore_index parameter.
df.sort_values(by="Age", ignore_index=True)
By default, the values are sorted in ascending order and this can be changed using the ascending parameter.
df.sort_values(by="Age", ignore_index=True, ascending=False)
PySpark
The orderBy function can be used for this task. The syntax is similar to that of the sort_values function of Pandas.
df.orderBy("number")
The orderBy also sorts rows in ascending order. We can use the ascending parameter to sort in descending order.
df.orderBy("number", ascending=False)
This question is also being asked as:
- How to sort Pandas DataFrame by column?
