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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
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.
MORE IDEAS FROM THE SAME ARTICLE
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 ...
In the previous sorting, one thing you may have notices is that the
sort_values method will create a new
DataFrame object, 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.
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 lexicograph...
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 de...
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
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