8 Things to Know to Master Value Sorting in Pandas

In the previous sorting, you may notice that the index goes with each sorted row, which puzzles me sometimes, when I want the sorted DataFrame has an ordered index. In this case, you can either reset the index after sorting, or simply take advantage of the ignore_index parameter, as shown below.

We don’t always need one column for sorting. In many cases, we need to sort the data frame by multiple columns. It’s also simple with sort_values because by doesn’t only take a single column but also a list of columns without any special syntax.

As we’ve seen so far, every sorting is done using the ascending order, which is the default behavior. However, we often want to have the data sorted by a descending order. We can take advantage of the ascending parameter.

What should we do if we sort by multiple columns and have different ascending requirements for these columns? In this case, we can pass a list of boolean values with each corresponding to one column.

Apparently, the sorted data isn’t something that we expect — the months are not in the desired order. To make this happen, we can take advantage of the sort_method taking a key parameter, to which we can pass a custom function for sorting, just like Python’s built-in sorted function. A possible solution is shown below.

• The key takes a callable, and we use a custom function here. Besides, this parameter is only available with pandas 1.1.0+.
• Unlike the key parameter used in sorted() , the key function applies to each of the sorting columns in the sort_values method.

The above sorting using the key parameter can be confusing to some people. Is there a cleaner way? Pandas is arguably the most versatile library for data processing, and you can expect that there is something neat to solve this relatively common problem — converting these lexicographically unordered columns to categorical data.

• We define a CategoricalDtype by specifying the order of the months.
• We cast the month column to the new defined category.
• When we sort the month, it will use the order of the months in the category data definition.

It’s important to remember that your datasets can always contain NANs. Unless you’ve examined your data quality and know that there are no NANs, you should pay attention to that. When we sort values, these NANs are placed behind all the other valid values, by default. If we want to change this default behavior, we set the na_position parameter.

• We first inject one NAN into the DataFrame object.
• When we do nothing with the na_position , the NAN value is placed at the end of the sorting group.
• When we set “first” to na_position , the NAN value appears at the top.