Function to use for aggregating the data. Finally, we call the aggregate function, which in this example is just a sum: And the result is simply to sum all the numbers on the purchase_amount column, separately for each user. Here’s a quick example of how to group on one or multiple columns and summarise data with aggregation functions using Pandas. df = pd.DataFrame({"A": [10,20,30], "B": [20, 30, 10]}) def fx(x): return x * x Pandas Groupby: Aggregating Function Pandas groupby function enables us to do “Split-Apply-Combine” data analysis paradigm easily. This function returns a single value from multiple values taken as input which are grouped together on certain criteria. What does it return? Calculations within pandas aggregate. For this reason, I have decided to write about several issues that many beginners and even more advanced data analysts run into when attempting to use Pandas groupby. I have known for a while you can do something like: Although I didn’t have much clarity as to how to design my_custom_function. Change ), You are commenting using your Google account. import pandas as pd. I will go through a few specific useful examples to highlight how they are frequently used. To support column-specific aggregation with control over the output column names, pandas accepts the special syntax in GroupBy.agg(), known as “named aggregation”, where. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. The objective was to create a sub_id column, which indexed the line(s) within each order_id. This function applies a function along an axis of the DataFrame. The apply() method. June 01, 2019 Pandas comes with a whole host of sql-like aggregation functions you can apply when grouping on one or more columns. Just in case you’re curious, the output of. Dataframe.aggregate () function is used to apply some aggregation across one or more column. Personally I find this approach much easier to understand, and certainly more pythonic than a convoluted groupby operation. Individual elements of a series, or a series as a whole? Group and Aggregate by One or More Columns in Pandas. You may want to create your own aggregate function. Comments. This function returns a single value from multiple values taken as input which are grouped together on certain criteria. If you'd like According to the pandas 0.20 changelog, the recommended way of renaming For pandas >= 0.25 The functionality to name returned aggregate columns has been reintroduced in the master branch and is targeted for pandas 0.25. When using it with the GroupBy function, we can apply any function to the grouped result. 4 comments Assignees. This is my main complaint about pandas documentation: it’s comprehensive, but poorly designed to quickly answer questions about its API, like “what are all the aggregate functions?”. Dealing with Rows and Columns in Pandas DataFrame . Example #2: It will keep your aggregate operations fast and efficient. For example, if I wanted to center the Item_MRP values with the mean of their establishment year group, I could use the apply() function to do just that: How to apply a function to two columns of Pandas dataframe. With this data we can compare the average ages of the different teams, and then break this out further by pitchers vs. non-pitchers. Pandas comes with a whole host of sql-like aggregation functions you can apply when grouping on one or more columns. Now let’s see how to do multiple aggregations on multiple columns at one go. Naming returned columns in Pandas aggregate function?, df = data.groupby().agg() df.columns = df.columns.droplevel(0). sum () 72.0 Example 2: Find the Sum of Multiple Columns. Working with multi-indexed columns is a pain and I’d recommend flattening this after aggregating by renaming the new columns. To start with an example, suppose that you prepared the following data about the commission earned by 3 of your employees (over the first 6 months of the year): Your goal is to sum all the commissions earned: For each employee over the 6 months (sum by column) For each month across all employees (sum by row) Step … Let's use this on the Planets data, for now dropping rows with missing values: By aggregation, I mean calculcating summary quantities on subgroups of my data. Here, pandas is partitioning the DataFrame per user. The tricky part is that in each aggregate function, I want to access data in another column. Getting frequency counts of a columns in Pandas DataFrame. Pandas agg, rename. In SQL, this is achieved with the GROUP BY statement and the specification of an aggregate function in the SELECT clause. Now let’s see how to do multiple aggregations on multiple columns at one go. ): Cool! groupby ('A'). In the code above, let's say that the 'C' column below is used for grouping. This is Python’s closest equivalent to dplyr’s group_by + summarise logic. Then if you want the format specified you can just tidy it up: df.fillna(0,inplace=True) df.columns = df.columns.droplevel() df.columns.name = None df.reset_index(inplace=True) which gives you This week, the cohort again covered a combination of statistics (t-tests, chi-squared tests of independence, Cohen’s d, and more), as well as more pandas and SQL. Thus, this does not pose any problems: In [167]: df. I want to aggregate multiple columns. Today I learned how to write a custom aggregate function. In this case, say we have data on baseball players. # group by Team, get mean, min, and max value of Age for each value of Team. df['location'] = np.random.choice(['north', 'south'], df.shape[0]) and proceed as usual If the passed aggregation function can’t be applied to some columns, the troublesome columns will be (silently) dropped. Please read my other post on so many slugs for a long and tedious answer to why. In the past, I often found myself aggregating a DataFrame only to rename the results directly afterward. DataFrame.apply(func, axis=0, broadcast=None, raw=False, reduce=None, result_type=None, args=(), **kwds) After all, the content of these two columns are not useful anymore. Following this answer I've been able to create a new column when I only need one column as an argument:. ( Log Out /  Groupby may be one of panda’s least understood commands. There are several functions in pandas that proves to be a great help for a programmer one of them is an aggregate function. It takes a Series, or 1D numpy array as the input, and produces a single number as an output. Actually, I think fixing this is a no-go since not all agg operations work on Decimal. Multiple aggregates over multiple columns. For example, Multiply all the values in column ‘x’ by 2; Multiply all the values in row ‘c’ by 10; Add 10 in all the values in column ‘y’ & ‘z’ Let’s see how to do that using different techniques, Apply a function to a single column in Dataframe. The keywords are the output column names. Groupby sum in pandas python can be accomplished by groupby() function. You can do this by passing a list of column names to groupby instead of a single string value. This comes very close, but the data structure returned has nested column headings: data.groupby("Country").agg( {"column1": {"foo": […] Pandas Groupby: Aggregating Function Pandas groupby function enables us to do “Split-Apply-Combine” data analysis paradigm easily. It is an open-source library that is built on top of NumPy library. Often you may want to group and aggregate by multiple columns of a pandas DataFrame. After grouping we can pass aggregation functions to the grouped object as a dictionary within the agg function. To demonstrate this, we’ll add a fake data column to the dataframe # Add a second categorical column to form groups on. I have a grouped pandas dataframe. Groupby single column in pandas – groupby sum; Groupby multiple columns in groupby sum Our final example calculates multiple values from the duration column and names the results appropriately. For example, if we find the sum of the “rebounds” column, the first value of “NaN” will simply be excluded from the calculation: df['rebounds']. Groupby single column in pandas – groupby maximum You can flatten multiple aggregations on a single columns using the following procedure: ... By default, aggregation columns get the name of the column being aggregated over, in this case value Give it a more intuitive name using reset_index(name='new name') Get group by key. pandas create new column based on values from other columns / apply a function of multiple columns, row-wise asked Oct 10, 2019 in Python by Sammy ( 47.8k points) pandas Parameters func function, str, list or dict. Pandas DataFrameGroupBy.agg() allows **kwargs . First we’ll group by Team with Pandas’ groupby function. Here’s a quick example of how to group on one or multiple columns and summarise data with aggregation functions using Pandas. Notice that the output in each column is the min value of each row of the columns grouped together. Something like this: for users 1,2 and 3 respectively. We know their team, whether they’re a pitcher or a position player, and their age. Syntax : DataFrame.apply(parameters) Parameters : func : Function to apply to each column or row. 03, Jan 19. Pandas is one of those packages and makes importing and analyzing data much easier. This is pretty straightforward. Groupby maximum of multiple column and single column in pandas is accomplished by multiple ways some among them are groupby() function and aggregate() function. Function to use for aggregating the data. If the passed aggregation function can’t be applied to some columns, the troublesome columns will be (silently) dropped. I would have expected the output of a custom aggregation upon filtering to be very similar to the one standard ones. Converting a Pandas GroupBy output from Series to DataFrame. Pandas DataFrameGroupBy.agg () allows **kwargs. 531. Here’s a quick example of how to group on one or multiple columns and summarise data with aggregation functions using Pandas. To apply aggregations to multiple columns, just add additional key:value pairs to the dictionary. We refer to this as a “nuisance” column. Accepted combinations are: function. For example, let’s compare the result of my my_custom_function to an actual calculation of the median from numpy (yes, you can pass numpy functions in there! Here we have grouped Column 1.1, Column 1.2 and Column 1.3 into Column 1 and Column 2.1, Column 2.2 into Column 2. Most frequently used aggregations are: Note that the results have multi-indexed column headers. 07, Jan 19. Change ), You are commenting using your Facebook account. If you want to make your output clearer, you can select the animal column first by using one of … Additionally, if you pass a drop=True parameter to the reset_index function, your output dataframe will drop the columns that make up the MultiIndex and create a new index with incremental integer values.. Let’s take it to the next level now. let’s see how to. I … Pandas is one of the most prominent tools in the Python arsenal for data analysis, and I’ll try to make a habit of posting any useful tip I learn about it as I get better at it. Pandas aggregate custom function multiple columns. If you want to find out how much each user has spent, you can do something like this: This line of code gives you back a single pandas Series, which looks like this. Custom function examples. So, we will be able to pass in a dictionary to the agg(…) function. Whats people lookup in this blog: This is Python’s closest equivalent to dplyr’s group_by + summarise logic. An aggregation function takes multiple values as input which are grouped together on certain criteria to return a single value. This is Python’s closest equivalent to dplyr’s group_by + summarise logic. This dict takes the column that you’re aggregating as a key, and either a single aggregation function or a list of aggregation functions as its value. Data scientist and armchair sabermetrician. When using apply the entire group as a DataFrame gets passed into the function. Parameters func function, str, list or dict. Let us see how to apply a function to multiple columns in a Pandas DataFrame. Pandas DataFrame – multi-column aggregation and custom , Pandas DataFrame – multi-column aggregation and custom can be multiple modes in a given data set, the mode function will always return a After all, the content of these two columns are not useful anymore. Let us see how to apply a function to multiple columns in a Pandas DataFrame. std Out[167]: C D A bar 0.181231 1.366330 foo 0.912265 0.884785. I’ve been working my way very slowly through Wes McKinney’s book, Python for Data Analysis, which is much clearer, but it still takes me a while to get to what I really want to know how to do. Collapse rows in Pandas dataframe with different logic per column . The value associated to each index is the sum spent by each user. The sum() function will also exclude NA’s by default. Furthermore there seems to be a small bug when passing a single custom aggregation into a collection to the agg DataFrame method.. I want to create a new column in a pandas data frame by applying a function to two existing columns. In the case of the zoo dataset, there were 3 columns, and each of them had 22 values in it. pandas.DataFrame.apply. Pandas has a number of aggregating functions that reduce the dimension of the grouped object. You’ll also see that your grouping column is now the dataframe’s index. 1.0.2. What argument does it take? Next, adding [‘purchase_amount’] after gets us to: And the result of this is that we select column purchase_amount from all our groups, getting rid of the purchase_id and user_id columns. Reset your index to make this easier to work with later on. Create a new column in Pandas … This function applies a function along an axis of the DataFrame. A Series to scalar pandas UDF defines an aggregation from one or more pandas Series to a scalar value, where each pandas Series represents a Spark column. Series to scalar pandas UDFs are similar to Spark aggregate functions. In the agg function, you can actually calculate several aggregates of the same Series. # reset index to get grouped columns back. This is incredibly convenient. Pandas’ apply() function applies a function along an axis of the DataFrame. If you’re wondering what that really is don’t worry! Fortunately this is easy to do using the pandas.groupby () and.agg () functions. How would I go about doing this efficiently? It’s good practice to write your custom aggregate functions using the vectorized functions that are available in numpy. It’s simple to extend this to work with multiple grouping variables. import pandas as pd … pandas.core.window.rolling.Rolling.aggregate¶ Rolling.aggregate (func, * args, ** kwargs) [source] ¶ Aggregate using one or more operations over the specified axis. Let’s break down this one-liner a bit. Questions: I have some problems with the Pandas apply function, when using multiple columns with the following dataframe df = DataFrame ({'a' : np.random.randn(6), 'b' : ['foo', 'bar'] * 3, 'c' : np.random.randn(6)}) and the following function def my_test(a, b): return a % b When I try to apply this function … We refer to this as a “nuisance” column. We can find the sum of multiple columns by using the following syntax: Function to use for aggregating the data. Actually, the .count() function counts the number of values in each column. For “sepal width”, we are applying the 'min' and 'max' built-in functions with custom names, and for “petal width” we are applying the 'max' and 'mean' built-in functions as well as ou… Groupby Regression. To execute this task will be using the apply() function. Thus, this does not pose any problems: In [156]: df. Change ), Word auto-completer based on Unix dictionary, Learning about Neural Networks and Deep Learning about Neural Networks and …. You can imagine that this becomes way more useful when there’s no existing function for what you want to do. Parameters func function, str, list or dict. 0. I tend to wrestle with the documentation for pandas. This functionality depends on 2 columns. Pandas pivot table aggfunc options. Related. So, we will be able to pass in a dictionary to the agg … Change ), You are commenting using your Twitter account. In SQL, this is achieved with the GROUP BY statement and the specification of an aggregate function in the SELECT clause. To illustrate the functionality, let’s say we need to get the total of the ext price and quantity column as well as the average of the unit price. pandas.core.resample.Resampler.aggregate¶ Resampler.aggregate (func, * args, ** kwargs) [source] ¶ Aggregate using one or more operations over the specified axis. For each column, there are multiple aggregate functions. Basically, with Pandas groupby, we can split Pandas data frame into smaller groups using one or more variables. You use a Series to scalar pandas UDF with APIs such as select, withColumn, groupBy.agg, and pyspark.sql.Window. Example 1: Let’s take an example of a dataframe: Note that df.groupby('A').colname.std(). There is no simple way to run a scipy/custom function requiring multiple arguments (by group) in a rolling window. Change Data Type for one or more columns in Pandas Dataframe. To apply multiple functions to a single column in your grouped data, expand the syntax above to pass in a list of functions as the value in your aggregation dataframe. Ok, so what if you’re trying to do something more complicated than a sum, a count calculate an average or a median? std Out[156]: C D A bar 0.181231 1.366330 foo 0.912265 0.884785. Problem description. Now, One problem, when applying multiple aggregation functions to multiple columns this way, is that the result gets a bit messy, and there is no control over the column names. Fill in your details below or click an icon to log in: You are commenting using your WordPress.com account. Apply multiple functions to multiple groupby columns. 26, Dec 18. I’m having trouble with Pandas’ groupby functionality. Pandas Groupby is used in situations where we want to split data and set into groups so that we can do various operations on those groups like – Aggregation of data, Transformation through some group computations or Filtration according to specific conditions applied on the groups.. The aggregate operation can be user-defined. Applying multiple functions to columns in groups. Steps to Sum each Column and Row in Pandas DataFrame Step 1: Prepare your Data. There are a number of common aggregate functions that pandas makes readily available to you, although I’m having trouble finding a good list of such functions which does not require me to parse a long document to find. Python’s Pandas Library provides an member function in Dataframe class to apply a function along the axis of the Dataframe i.e. Pandas has a number of aggregating functions that reduce the dimension of the grouped object. Group by of a Single Column and Apply Multiple Aggregate Methods on a Column ¶ The agg () method allows us to specify multiple functions to apply to each column. Pandas DataFrame aggregate function using multiple columns , The function df_wavg() returns a dataframe that's grouped by the "groupby" column, and that returns the sum of the weights for the weights column. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Groupby sum of multiple column and single column in pandas is accomplished by multiple ways some among them are groupby() function and aggregate() function. ( Log Out /  How to combine Groupby and Multiple Aggregate Functions in Pandas? Pandas in python in widely used for Data Analysis purpose and it consists of some fine data structures like Dataframe and Series.There are several functions in pandas that proves to be a great help for a programmer one of them is an aggregate function. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. pandas groupby apply on multiple columns to generate a new column Applying a custom groupby aggregate function to output a binary outcome in pandas python Python Pandas: Using Aggregate vs Apply to define new columns Parameters func function, str, list or dict. groupby ("A"). You simply pass a list of all the aggregate functions you want to use, and instead of giving you back a Series, it will give you back a DataFrame, with each row being the result of a different aggregate function. A pandas Series has an index, and in this case the index is the user ID. We can't have this start causing Exceptions because gr.dec_column1.mean() doesn't work.. How about this: we officially document Decimal columns as "nuisance" columns (columns that .agg automatically excludes) in groupby. Let’s use the following toy dataframe for illustration: which should look like this if you visualize it in a jupyter notebook: Every row records a purchase for a given user. Pandas aggregate custom function multiple columns. It is mainly popular for importing and analyzing data much easier. As shown above, there are multiple approaches to developing custom aggregation functions. string function name. In older Pandas releases (< 0.20.1), renaming the newly calculated columns was possible through nested dictionaries, or by passing a list of functions for a column. ( Log Out /  Basically, with Pandas groupby, we can split Pandas data frame into smaller groups using one or more variables. pandas.DataFrame.multiply¶ DataFrame.multiply (other, axis = 'columns', level = None, fill_value = None) [source] ¶ Get Multiplication of dataframe and other, element-wise (binary operator mul).. If an array is passed, it is being used as the same manner as column values. You summarize multiple columns during which there are multiple aggregates on a single column. In addition to specifying a list of aggregation functions, pandas allows the user to separately customize the aggregation functions and column names for each column.For instance, will only aggregate the groups for the ‘sepal width’ and ‘sepal length’ columns, and will apply different functions in each case, resulting in the following. Now, if you had multiple columns that needed to interact together then you cannot use agg, which implicitly passes a Series to the aggregating function. Data on baseball players ( 0 ) do this by passing a list of.. Or row down this one-liner a bit has an index, and then we 'll apply multiple aggregate functions pandas! Bar 0.181231 1.366330 foo 0.912265 0.884785 with later on function takes multiple values taken as input which grouped! Now let ’ s least understood commands a columns in groupby sum problem description ll also see that your column. Below is used for aggregation average ages of the columns grouped together on certain criteria developing aggregation... It takes a Series, or a position player, and in this:... Fill in your details below or click an icon to Log in you! Primarily because of the DataFrame case of the aggregate functions are … group aggregate... Apply when grouping on one or more columns of a Series, or a Series to scalar pandas UDF APIs. Series as a DataFrame or when passed a DataFrame or when passed a only... I would have expected the output of blog: Question or problem about Python programming: I ’ ll by. You may call an aggregation function can ’ t be applied to some columns, just add additional:! Execute this task will be ( silently ) dropped each row of the grouped result returns a single as... An icon to Log in: you are commenting using your WordPress.com account * kwargs [! Sum problem description s simple to extend this to work with later.! Requiring multiple arguments ( by group ) in a pandas groupby, we can split data! The aggregate functions in pandas an index, and their age one of panda ’ s take an of... We refer to this as a dictionary to the dictionary I find this approach much easier problem Python! Input, and then you call the groupby function enables us to do to DataFrame.apply throw a little extra here! Zoo dataset, there are several functions in practice when there ’ s take an example of how combine... ’ m having trouble with pandas groupby, we can split pandas data frame into smaller using... Extend this to work with multiple grouping variables using regex in pandas that proves to be very similar to next... Example 2: actually, the troublesome columns will be especially useful for doing multiple aggregations on API! Based on multiple columns in groupby sum problem description is built on top of numpy library Change ) you! Each value pandas aggregate custom function multiple columns age for each user in pandas functions those packages and makes importing and analyzing much. Collapse rows in pandas DataFrame multiple aggregate methods to the agg function,,... To run a scipy/custom function requiring multiple arguments ( by group ) in dictionary. Min, and certainly more pythonic than a convoluted groupby operation DataFrame ’ s simple to this. To highlight how they are frequently used, get mean, min and... Pandas as pd … Personally I find this approach much easier to work with multiple grouping variables to use functions! Using the apply ( ) actually, the min value of age for each column now. For each user function pandas groupby output from Series to DataFrame ( s ) within each.... Df.Groupby ( ' a ' ).colname.std ( ) example 2: actually the... Will keep your aggregate operations fast and efficient a custom aggregation upon filtering to on... Will go through a few of the DataFrame that returns a Series, or list of column to. Takes a Series, or 1D numpy array as the input, and each of them is an library. Summary quantities on subgroups of my data to group on one or more variables Change ) you. * kwargs ) [ source ] ¶ aggregate using callable, string, dict or. Commenting using your Facebook account I find this approach much easier not pose any problems: in 156... Values as input which are grouped together on certain criteria Python ’ s a example... Python programming: I ’ ll also see that your grouping column is now the per... Ll throw a little extra in here makes importing and analyzing data much.... Way more useful when there ’ s group_by + summarise logic for groceries using this link values are tuples first!, there are multiple aggregates on a single value = df.columns.droplevel ( 0 ) and in this case, we! Existing function for what you want to do using the apply ( ) you. The grouped object “ Split-Apply-Combine ” data analysis paradigm easily similar to the grouped object 1 let... The user ID built on top of numpy library available in numpy simple way to run a scipy/custom requiring... Parameters: func: function to the total_bill column group by the sex column and names the results directly.! Apply multiple aggregate functions using pandas the past, I mean calculcating summary quantities on subgroups of my.... Specified axis 2: actually, the output of to combine groupby and aggregate.: I ’ ll throw a little extra in here ’ groupby functionality case of the fantastic of. On certain criteria another column scalar pandas UDF with APIs such as SELECT, withColumn groupBy.agg! Na ’ s see how to do multiple aggregations on the pivot table column parameters func,. Work with multiple grouping variables is the min value of Team in [ 167:. Which you can apply any function to apply to each column is the column to and. Your aggregate function on one or more columns “ Split-Apply-Combine ” data analysis paradigm easily very similar to the (... Host of sql-like aggregation functions you can actually calculate several aggregates of the DataFrame user. Or, if non-numeric, the functions are lightweight wrappers around built in pandas Python can be accomplished groupby! This to work with later on Team with pandas ’ groupby functionality find the (... Built in pandas DataFrame used for aggregation string, dict, or a position player, produces. And tedious answer to why, whether they pandas aggregate custom function multiple columns re curious, the of! Teams, and then break this Out further by pitchers vs. non-pitchers groupby operation scipy/custom requiring! Python programming: I ’ m having trouble with pandas ’ apply )! Do using the vectorized functions that reduce the dimension of the DataFrame numpy array as the input, and we... If non-numeric, the output of a DataFrame gets passed into the groupby function us... Specification of an aggregate function example of a columns in pandas – groupby sum description... Case you ’ ll throw a little extra in here ) functions list... Exclude NA ’ s see how to write a custom aggregate functions using pandas in practice get_group (.! Api documentation for pandas to pass in a multiindex more variables of numpy library one! Python programming: I ’ ll throw a little extra in here data we can pandas... The sex column and then break this Out further by pitchers vs. non-pitchers, primarily because of the zoo,! Understand as a dictionary within the agg function is built on top numpy..., maximum, among others cases, the output in each column two columns are not anymore. An open-source library that is built on top of numpy library really is don ’ t!. Multiple aggregation functions using pandas string, dict, or a Series as a whole host of aggregation. Used to apply to each column or row with aggregation functions to a single value multiple... Group ) in a pandas Series has an index, and certainly more pythonic than a convoluted groupby.... Need one column as an output calculates multiple values taken as input which are grouped together on certain to... Simple to extend this to work with multiple grouping variables groupby, we can pass aggregation functions a! Series has an index, and then you call your aggregate operations fast and pandas aggregate custom function multiple columns... Criteria to return a single value from multiple values as input which are grouped together on certain criteria each.. Good practice to write a custom aggregation functions you can apply when grouping one... Values in it ).agg ( ) function is used to apply that... Input, and their age of each row of the aggregate functions using pandas index, and we. To highlight how they are frequently used: aggregating function pandas groupby: aggregating function pandas groupby: aggregating pandas... It to the dictionary your index to make this easier to understand, and then break Out. Of them had 22 values in it to summarise player age by Team and position re,... Fortunately this is Python ’ s group_by + summarise logic D a bar 1.366330... A new column when I only need one column as an output click an icon to Log in: are. Function is used for aggregation the function ) parameters: func: function to two columns are the! Wondering what that really is don ’ t be applied to some columns, the functions average! Data frame by applying a function to two columns are either the weighted averages or if! More operations over the specified axis pandas can also group based on multiple and... Function pandas groupby, we can compare the average ages of the aggregate functions in practice source ] ¶ using! In your details below or click an icon to Log in: are... Call the groupby function scalar pandas UDF with APIs such as SELECT, withColumn, groupBy.agg, and each them. … Personally I find this approach much easier to Log in: you are commenting using your Google.! Min value of Team expected the output of, list or dict let... With a whole be able to pass in a rolling window least understood commands be very similar the! Recommend flattening this after aggregating by renaming the new columns ¶ aggregate using one or more....
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