Often you would like to get top N rows from a data frame such that you want the top values of a specific variable in each group defined by another variable. Note this is not the same as top N rows according to one variable in the whole dataframe. Let us say we have gapminder data frame that has life expectancy values for countries in five continents.
And we would like to see top 3 countries rows with large life expectancies descending order in each continent.
Basically, we need top N rows in each group. Getting top N rows with in each group involves multiple steps. First, let us see how to get top N rows within each group step by step and later we can combine some of the steps. Let us first load gapminder data frame from Carpentries site and filter the data frame to contain data for the year We save the resulting grouped dataframe into a new variable.
Remember, the resulting grouped dataframe has all the data, but for each group here continent separately. Next, we take the grouped dataframe and use the function apply in Pandas to sort each group within the grouped data frame. We have saved the resulting grouped and sorted dataframe into another variable. If you take a look at the content of this grouped and sorted dataframe, we can see that it has multi-index one for continent and the other index for row numbers. So, now we have a sorted dataframe.
If you examine the rows, we can see that first we have countries from Afria with lifeExp in descending order and the next are other continents sorted by lifeExp in descending order. Since the rows within each continent is sorted by lifeExp, we will get top N rows with high lifeExp for each continent. We got the top N rows within each group in multiple steps. We can combine these steps by chaining the commands. Here we have chained the steps so that there are just two steps to get top N rows within each group.
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How to Get Top N Rows with in Each Group in Pandas?
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I know that you explain it very well. And I hope that other readers will also experience how I feel after reading your article.One of the core libraries for preparing data is the Pandas library for Python. In a previous post, we explored the background of Pandas and the basic usage of a Pandas DataFramethe core data structure in Pandas.
Check out that post if you want to get up to speed with the basics of Pandas. These methods help you segment and review your DataFrames during your analysis. Pandas is typically used for exploring and organizing large volumes of tabular data, like a super-powered Excel spreadsheet. For example, perhaps you have stock ticker data in a DataFrame, as we explored in the last post.
Your Pandas DataFrame might look as follows:. This is where the Pandas groupby method is useful. You can use groupby to chunk up your data into subsets for further analysis. In your Python interpreterenter the following commands:.
We print our DataFrame to the console to see what we have. The easiest and most common way to use groupby is by passing one or more column names. Interpreting the output from the printed groups can be a little hard to understand. For each group, it includes an index to the rows in the original DataFrame that belong to each group. The input to groupby is quite flexible.
You can choose to group by multiple columns. For example, if we had a year column available, we could group by both stock symbol and year to perform year-over-year analysis on our stock data. In the previous example, we passed a column name to the groupby method. You can also pass your own function to the groupby method. This function will receive an index number for each row in the DataFrame and should return a value that will be used for grouping.
This can provide significant flexibility for grouping rows using complex logic. As an example, imagine we want to group our rows depending on whether the stock price increased on that particular day. We would use the following:. It returns True if the close value for that row in the DataFrame is higher than the open value; otherwise, it returns False.
In our example above, we created groups of our stock tickers by symbol. The result is the mean volume for each of the three symbols. Iteration is a core programming pattern, and few languages have nicer syntax for iteration than Python. Pandas groupby is no different, as it provides excellent support for iteration. You can loop over the groupby result object using a for loop:. Each iteration on the groupby object will return two values. The first value is the identifier of the group, which is the value for the column s on which they were grouped.
The second value is the group itself, which is a Pandas DataFrame object. This method returns a Pandas DataFrame, which we can manipulate as needed.
The count method will show you the number of values for each column in your DataFrame. Using our DataFrame from above, we get the following output:. However, this can be very useful where your data set is missing a large number of values. Using the count method can help to identify columns that are incomplete.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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Sign in to your account. The problem occurs when the data frame contains only one row see example above. In that case, the "animal" column disappears. A slight generalization on the recreate also exhibits the "group dropping" behavior despite having multiple rows in the output. Worth confirming any fix covers this situation too. Hi, jreback mroeschke. I looked into this a bit. I found the reason, why the column is dropped sometimes and sometimes not.
For functions like nlarges and apply not head the column is always dropped, if the input DataFrame equals the output DataFrame sorting too! It is checked, if the Index was changed. Depending on the result, different code is executed. While I tried to fix this, I ran into some issues with existing unittests. I would really appreciate an answert about the output format of these two functions. Depending on that I may have found a way to fix this issue and the issues related with this for example.
Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Note the tie-breaking in row But the goal, again, is to have row-specific N.
Looping through each row obviously doesn't count for performance reasons. And I've tried using. Based on ScottBoston's comment on the OP, it is possible to use the following mask based on rank to solve this problem:. Further boost : Bringing in numpy.
Thus, a numpy. Learn more. Pandas dataframe finding largest N elements of each row with row-specific N Ask Question. Asked 2 years, 11 months ago. Active 2 years, 11 months ago. Viewed 3k times. Zhang18 Zhang18 3, 5 5 gold badges 36 36 silver badges 56 56 bronze badges.
So, any reason why the second elem was picked? Does it matter if either of those were picked? It doesn't matter. That's exactly why I commented the behavior of. ScottBoston, thank you. That tip alone solved my problem. Since, we were looking for performance, did you check out this post - stackoverflow. Active Oldest Votes. DataFrame arr Series np.
Divakar Divakar k 14 14 gold badges silver badges bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. Socializing with co-workers while social distancing.This function is extremely useful for very quickly performing some basic data analysis on specific columns of data contained in a Pandas DataFrame.
For an introduction to pandas DataFrames please see last weeks post which can be found here. In the below article I am going to show you some tips for using this tool for data analysis. This post will show you how with a few additions to your code you can actually do quite a lot of analysis using this function.
In the examples shown in this article, I will be using a data set taken from the Kaggle website. It is designed for a machine learning classification task and contains information about medical appointments and a target variable which denotes whether or not the patient showed up to their appointment. It can be downloaded here. In the code below I have imported the data and the libraries that I will be using throughout the article.
The code below gives a count of each value in the Gender column. To sort values in ascending or descending order we can use the sort argument. One example is to combine with the groupby function. In the below example I am counting values in the Gender column and applying groupby to further understand the number of no-shows in each group. In the above example displaying the absolute values does not easily enable us to understand the differences between the two groups.
A better solution would be to show the relative frequencies of the unique values in each group. A good example of this would be the Age column which we displayed value counts for earlier in this post.
This parameter allows us to specificy the number of bins or groups we want to split the data into as an integer. We now have a count of values in each of these bins.What do I need to know about the pandas index? (Part 1)
Now we have a useful piece of analysis. There are other columns in our data set which have a large number of unique values where binning is still not going to provide us with a useful piece of analysis. A good example of this would be the Neighbourhood column. A better way to display this might be to view the top 10 neighbourhoods.
We can do this by combining with another Pandas function called nlargest as shown below. We can also use nsmallest to display the bottom 10 neighbourhoods which might also prove useful.Constant learning is what keeps me in the game, and after taking a look back knowing the functions from this article would be a huge time and nerve saver.
Some of them are purely a function, but some, on the other hand, refers to the way you use Pandas, and why one approach is better than the other. Wait, what? And also, we will measure the time.
How to Select Top N Rows with the Largest Values in a Column(s) in Pandas?
Remember this one next time when performing a loop. That was the first part of the problem — the second one was selecting top N records with the smallest distance. Enter — nsmallest.
As the name suggests, nlargest will return N largest values, and nsmallest will do just the opposite. One way to do so would be like this:.
Not an optimal solution, because it would result in this:. Now the results are much more satisfying:. You can implement almost the same logic to find 3 students who performed the worst — with the nsmallest function:. To recap, here it is:. The basic idea behind the cut function is binning values into discrete intervals. Not something too useful for now. But how about declaring the first bin going from 0 to 50, and the second one from 50 to ? Sounds like a plan.
If you are just starting out, this function will help you save both time and nerves. Thanks for reading. Sign in. Before you go If you are just starting out, this function will help you save both time and nerves. Towards Data Science A Medium publication sharing concepts, ideas, and codes. A year-old student of Data Science — also working in the field. Writer at Medium and Towards Data Science. Towards Data Science Follow. A Medium publication sharing concepts, ideas, and codes. See responses 3.
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