# Pandas Groupby Aggregate Multiple Columns Multiple Functions

Here’s a quick example of how to group on one or multiple columns and summarise data with aggregation functions using Pandas. In this line of code, agg() function is used to aggregate the value for count,min,max,mean. sum}) see this pandas docs for example. Reshape, concatenate and aggregate multiple pandas DataFrames; concatenate rows on dataframe one by one; Python Pandas sorting after groupby and aggregate; How to groupby for one column and then sort_values for another column in a pandas dataframe? Groupby Pandas dataframe and plot; Aggregate a Pandas Dataframe by week and month; sum pandas. Summarizing Values: GROUP BY Clause and Aggregate Functions So far, the examples presented have shown how to retrieve and manipulate values from individual rows in a table. This tutorial will cover some lesser-used but idiomatic Pandas capabilities that lend your code better readability, versatility, and speed, à la the Buzzfeed listicle. It can take in arguments as a single column, or create multiple aggregate calls all at once using dictionary notation. Store the log base 2 dataframe so you can use its subtract method. Pandas Apply is a very flexible function that allows you to apply custom functions to your dataframes. Especially, when we are dealing with the text data then we may have requirements to select the rows matching a substring in all columns or select the rows based on the condition derived by concatenating two column values and many other scenarios where you have to slice,split,search substring. I am looking to do some aggregation on a pandas groupby dataframe, where I need to apply several different custom functions on multiple columns. The power of the GroupBy is that it abstracts away these steps: the user need not think about how the computation is done under the hood, but rather thinks about the operation as a whole. multiple functions 1. *pivot_table summarises data. aggregate({'Votes':np. Groupby count in pandas python can be accomplished by groupby() function. You’ll see the new cohort_period column: 6. pandas provides a large set of vector functions that operate on all columns of a DataFrame or a single selected column (a pandas Series). 0 and re-cast the entire column’s initial object dtype to its correct dtype a float64. I want to aggregate multiple columns. Pandas is a foundational library for analytics, data processing, and data science. In downsampling, your total number of rows goes down. The functions are:. "This grouped variable is now a GroupBy object. You'll then use multi-level selection to find the oldest passenger per. I have a dataframe that has 3 columns, Latitude, Longitude and Median_Income. The final piece of syntax that we’ll examine is the “agg()” function for Pandas. Here's how I do it:. %timeit groupby_way() 100 loops, best of 3: 3. You can now use. Not only does it give you lots of methods and functions that make working with data easier, but it has been optimized for speed which gives you a significant advantage compared with working with numeric data using Python's. body_style for the crosstab’s columns. Calculating sum of multiple columns in pandas. Edited for Pandas 0. Pandas groupby function enables us to do “Split-Apply-Combine” data analysis paradigm easily. You do grouping using GROUP BY by more than one column, for example: SELECT CustomerName, OrderDate, SUM(OrderPrice) FROM Sales GROUP BY CustomerName, OrderDate When grouping, keep in mind that all columns that appear in your SELECT column list, that are not aggregated (used along with one of the SQL aggregate functions),. Pandas object can be split into any of their objects. In this exercise, you're going to group passengers on the Titanic by 'pclass' and aggregate the 'age' and 'fare' columns by the functions 'max' and 'median'. Example #1:. There are instances where we have to select the rows from a Pandas dataframe by multiple conditions. Multiple functions can also be passed to a single column as a list: >>> df. Here we are grouping on continents and count the number of countries within each continent in the dataframe using aggregate function and came up with the pie-chart as shown in the figure below. I always found that a bit inefficient. groupby(key) obj. The process is not. aggfunc: function, list of functions, dict, default numpy. Applying function to values in multiple columns in Pandas Dataframe. Pass axis=1 for columns. How a column is split into multiple pandas. The GroupBy object supports several. In the process, every row of our DataFrame will be duplicated a number of times equal to the number of columns we're "melting". Here I am going to introduce couple of more advance tricks. dict of column names -> functions (or list of functions). Example #1:. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. I came across the. The loop version is much less obvious. When we have a groupBy object, we may choose to apply one or more functions to one or more columns, even different functions to individual columns. In pandas 0. They are excluded from aggregate functions automatically in groupby. Perhaps a list of tuples [(column, function)] would work better, to allow multiple functions applied to the same column? But it seems like it only accepts a dictionary. aggfunc: function, list of functions, dict, default numpy. Pandas object can be split into any of their objects. In this exercise, we're going to group passengers on the Titanic by 'pclass' and aggregate the 'age' and 'fare' columns by the functions 'max' and 'median'. In this lesson, we'll start by learning how to aggregate data with pandas. This is Python's closest equivalent to dplyr's group_by + summarise logic. groupby("person"). groupby(A) In [37]: g. A parameter name in reset_index is needed because Series name is the same as the name of one of the levels of MultiIndex: df_grouped. I've had success using the groupby function to sum or average a given variable by groups, but is there a way to aggregate into a list of values, rather than to get a single result? (And would this still be called aggregation?) I am not entirely sure this is the approach I should be taking anyhow, so. In order to split the data, we use groupby() function this function is used to split the data into groups based on some criteria. df["metric1_ewm"] = df. To delete a column, or multiple columns, use the name of the column(s), and specify the “axis” as 1. …If I open up the exercise files for this video,…I'll find some really basic things that we want to do. In the process, every row of our DataFrame will be duplicated a number of times equal to the number of columns we're "melting". agg() method. isnull function can. agg is called with single function. Here's how I do it:. pandas-groupby-aggregate-multiple-columns. Use the AddColumns function with Sum, Average, and other aggregate functions to add a new column which is an aggregate of the group tables. Cumulative Probability This example shows a more practical use of the scalar Pandas UDF: computing the cumulative probability of a value in a normal distribution N(0,1) using scipy package. It can take in arguments as a single column, or create multiple aggregate calls all at once using dictionary notation. The idea is that this object has all of the information needed to then apply some operation to each of the groups. ) as methods on groupbys. Your program fails because there is no 'r1' column in your dataframe, so it can not aggregate something that doesnt exist. Group DataFrame or Series using a mapper or by a Series of columns. mean() Just as before, pandas automatically runs the. Pandas offers two methods of summarising data - groupby and pivot_table*. I'm not that well-versed in NumPy, but I can safely assume that were this function still not fast enough to meet your needs then a NumPy vectorized solution avoiding some of the overhead would be the next step. With this syntax, column-names are keys and if you have two or more aggregation for the same column, some internal loops may forget the non-uniqueness of the keys. aggregate(np. The power of the GroupBy is that it abstracts away these steps: the user need not think about how the computation is done under the hood, but rather thinks about the operation as a whole. Have you ever been confused about the "right" way to select rows and columns from a DataFrame? pandas gives you an incredible number of options for doing so, but in this video, I'll outline the. …I want to show you how to create a yearly. Multiple functions can also be passed to a single column as a list: >>> df. In this example, I demonstrate how to aggregate data with pandas groupby using multiple compute methods. groupby(["continent"]). Groupby objects also support the aggregate pandas concat function concatenates There are multiple ways to stack this data. common import _ensure_platform_int, is_list_like from pandas. I need to get the average median income for all points within x km of the original point into a 4th column. Pandas is a feature rich Data Analytics library and gives lot of features to achieve these simple tasks of add, delete and update. shape[0]) and proceed as usual. groupby('A') is just syntactic sugar for df. Add more columns when you are doing group by in the first parentheses. Example #1:. To support column-specific aggregation with control over the output column names, pandas accepts the special syntax in GroupBy. How to sum values grouped by two columns in pandas. But it is also complicated to use and understand. merge the two dataframes on their key columns SQL window functions are calculation functions similar to aggregate functions but. It accepts a function word => word. Using aggregate in a function; Pandas groupby function using multiple columns; Plot data returned from groupby function in Pandas using Matplotlib; Python Pandas sorting after groupby and aggregate; Pandas groupby aggregate to new columns; Percentiles combined with Pandas groupby/aggregate; Pandas groupby aggregate passing group name to. Source code for pandas. loc takes in a tuple for the row index instead of a single value:. When applying multiple aggregations on multiple columns, the aggregated DataFrame has a multi-level column index. Next, let's make a function that lets us apply a transformation to multiple columns based on a condition. And finally, he demonstrates the multi-index and how you can chain multiple groupby calculations together. Sum values of all columns; Use apply for multiple columns; Series functions. Summarizing Values: GROUP BY Clause and Aggregate Functions So far, the examples presented have shown how to retrieve and manipulate values from individual rows in a table. The data produced can be the same but the format of the output may differ. Series is internal to Spark, and therefore the result of user-defined function must be independent of the splitting. To access them easily, we must flatten the levels – which we will see at the end of this note. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Not all methods need a groupby call, instead you can just call the generalized. pandas offers a convenient framework to a simple analysis of data. Pandas groupby function enables us to do "Split-Apply-Combine" data analysis paradigm easily. Renaming and passing multiple functions as a dictionary will be deprecated in a future version of pandas. After learning about the GroupBy object, you will learn how to compute multiple and custom aggregations with the `agg()` method. The loop version is much less obvious. If you do wish to include decimal or object columns in an aggregation with other non-nuisance data types, you must do so explicitly. We can group by multiple columns too. Combining multiple columns in Pandas groupby with dictionary Let' see how to combine multiple columns in Pandas using groupby with dictionary with the help of different examples. For the dataframe containing the unit information, we pass a custom function which concatenate non-unique unit strings. That's a lot of nonsense! A good way to handle data split out like this is by using Pandas' melt(). agg({'B': [np. The loop version is much less obvious. I want to aggregate multiple columns. Grouping by multiple columns In this exercise, you will return to working with the Titanic dataset from Chapter 1 and use. Using Pandas groupby I am Ritchie Ng, a machine learning engineer specializing in deep learning and computer vision. Basically, with Pandas groupby, we can split Pandas data frame into smaller groups using one or more variables. As commented above, keys in the aggregation dictionary must correspond with preexisting keys in the dataframe. This is Python's closest equivalent to dplyr's group_by + summarise logic. This is just a pandas programming note that explains how to plot in a fast way different categories contained in a groupby on multiple columns, generating a two level MultiIndex. reset_index() # You might get a few extra columns that you dont need. I have a dataframe that has 3 columns, Latitude, Longitude and Median_Income. mean() calculation for all remaining columns (the animal column obviously disappeared, since that was the column we. In order to split the data, we use groupby() function this function is used to split the data into groups based on some criteria. py in pandas located at /pandas/core. common import _ensure_platform_int, is_list_like from pandas. let's see how to Groupby single column in pandas Groupby multiple columns in pandas Skip to content DataScience Made Simple. groupby(['State']). Renaming and passing multiple functions as a dictionary will be deprecated in a future version of pandas. Returns: scalar, Series or DataFrame. 20 change log, which I also summarized elsewhere on SO. Pandas provide us with a variety of aggregate functions. It is better to identify each summary row by including the GROUP BY clause in the query resulst. aggregate(np. But what is the "right" Pandas idiom for assigning the result of a groupby operation into a new column on the parent dataframe? In the end, I want a column called "MarketReturn" than will be a repeated constant value for all indices that have matching date with the output of the groupby operation. This is used where the index is needed to be used as a column. if you want to apply multiple functions to aggregate, then you need to put them in the list or dict. 6 Pandas equivalents for some SQL analytic and aggregate functions. New: Group by multiple columns / key functions. Any object column, also if it contains numerical values such as Decimal objects, is considered as a "nuisance" columns. When applying multiple aggregations on multiple columns, the aggregated DataFrame has a multi-level column index. Pandas is one of those packages and makes importing and analyzing data much easier. mean() Just as before, pandas automatically runs the. aggregate() function is used to apply some aggregation across one or more column. Pandas is a feature rich Data Analytics library and gives lot of features to achieve these simple tasks of add, delete and update. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. mean() function: zoo. dict of column names -> functions (or list of functions). Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. pandas trick: Reverse column order in a DataFrame: you can aggregate by multiple functions by using Can be used with a groupby to extract the last value in. max]}) B amin amax A 1 0 2 2 3 4 However, this does not work with lambda functions, since they are anonymous and all return ,. Perhaps a list of tuples [(column, function)] would work better, to allow multiple functions applied to the same column? But it seems like it only accepts a dictionary. ) as methods on groupbys. Groupby count in R can be accomplished by aggregate() or group_by() function. These return another deferred object (similar to what. reset_index() # You might get a few extra columns that you dont need. Groupby single column in pandas – groupby count; Groupby multiple columns in pandas – groupby count; First let’s create a dataframe. Can be a single column name, or a list of names for multiple columns. Pandas is one of those packages and makes importing and analyzing data much easier. List of columns to groupby on, and; A dictionary of columns and functions you want to apply to those columns; reset_index() is a function that resets the index of a dataframe. mean() Just as before, pandas automatically runs the. To delete a column, or multiple columns, use the name of the column(s), and specify the “axis” as 1. sum}) see this pandas docs for example. AGG() Function • agg() function allow to specify multiple aggregation function at once. It's useful to execute multiple aggregations in a single pass using the DataFrameGroupBy. Using a custom function in Pandas groupby. I apply this function ALWAYS whenever I do a groupby and you might think of it as a default syntax for groupby operations import numpy as np newDf. The point of this lesson is to make you feel confident in using groupby and its cousins, resample and rolling. lower(col)¶ Converts a string expression to upper case. Pandas groupby function enables us to do “Split-Apply-Combine” data analysis paradigm easily. In this example, I demonstrate how to aggregate data with pandas groupby using multiple compute methods. agg is called with single function. If 0 or ‘index’: apply function to each column. apply(f) Applying a Function Element-Wise f = lambda x: '%. The objective of this notebook is to explore group by and aggregation methods on data using python library Pandas. Reset index, putting old index in column named index. The tricky part is that in each aggregate function, I want to access data in another column. if you want to apply multiple functions to aggregate, then you need to put them in the list or dict. *pivot_table summarises data. Groupby single column in pandas - groupby count; Groupby multiple columns in pandas - groupby count; First let's create a dataframe. # Find out the sum of votes and revenue by year import numpy as np df. In the example, the code takes all of the elements that are the same in Name and groups them, replacing the values in Grade with their mean. Selecting multiple rows and columns in pandas. The ability to group by multiple criteria (just like SQL) has been one of my most desired GroupBy features for a long time. In this case I will use a I-D-F precipitation table, with lines corresponding to Return Periods (years) and columns corresponding to durations, in minutes. In this article we will discuss how to apply a given lambda function or user defined function or numpy function to each row or column in a dataframe. agg is called with single function. fill_value. Grouping by multiple columns In this exercise, you will return to working with the Titanic dataset from Chapter 1 and use. Pass axis=1 for columns. This is called a "multilevel index" and is tricky to work with. Edited for Pandas 0. Aggregate function takes a function as an argument and applies the function to columns in the groupby sub dataframe. This function will receive an index number for each row in the DataFrame and should return a value that will be used for grouping. However python isn't too far behind. To access them easily, we must flatten the levels - which we will see at the end of this note. aggregate GroupBy. Apply Operations and Functions Noureddin Sadawi. If you use groupby() to its full potential, and use nothing else in pandas, then you’d be putting pandas to great use. Pandas Cheat Sheet — Python for Data Science Pandas is arguably the most important Python package for data science. groupby(["continent"]). Indexing in python starts from 0. Next, let's make a function that lets us apply a transformation to multiple columns based on a condition. groupby(key,axis=1) Let us now see how the grouping objects can be applied to the DataFrame object. The assign method is pretty awesome, and it'd be fun to not have to leave it (or, if we do, to at least replace it with a function we can pipe as part of a chain of transformations to the DataFrame as a whole). Let us first create a simple Pandas data frame using Pandas’ DataFrame function. I'm having trouble with Pandas' groupby functionality. To aggregate on multiple levels we simply provide additional column labels in a list to the groupby function. Pandas Histogram Multiple Columns. In the past, I often found myself aggregating a DataFrame only to rename the results directly afterward. filter: subset a dataframe according to condition(s) in a variable(s) select: choose a specific variable or set of variables. The Pandas DataFrame tricks from the video are: Show installed versions Create an example DataFrame Rename columns Reverse row order Reverse column order Select columns by data type Convert strings to numbers Reduce DataFrame size Build a DataFrame from multiple files (row-wise) Build a DataFrame from multiple files (column-wise). Behind the scenes, this simply passes the C column to a Series GroupBy object along with the already-computed grouping(s). Aggregate using callable, string, dict, or list of string/callables. ravel function in Pandas. 0, 123 [/code]This is the same as doing [code]x = 5. The crosstab function can operate on numpy arrays, series or columns in a dataframe. This operation is very easy and customary in R (using data. used to iterate over the groups in a pandas GroupBy. that has multiple rows with the same name, title, and id, but different values for the 3 number columns (int_column, dec_column1, dec_column2). Pandas has a number of aggregating functions that reduce the dimension of the grouped object. Pandas group-by and sum; How to move pandas data from index to column after multiple groupby; Python Pandas: How to add a totally new column to a data frame inside of a groupby/transform operation; Drop a row and column at the same time Pandas Dataframe; Pandas groupby. Note that apply is just a little bit faster than a python for loop ! That's why it is most recommended using pandas builtin ufuncs for applying preprocessing tasks on columns (if a suitable ufunc is available for your task). make for the crosstab index and df. Pandas styling Exercises: Write a Pandas program to display bar charts in dataframe on specified columns Introduction to Mocha Run Cycle Overview And Detects Multiple Calls To Done(). Introduction. groupby(key) obj. Pandas has an apply function which let you apply just about any function on all the values in a column. It's similar to Sql,we are applying an aggregate function on a grouped by value, That's why it's giving only one value,. In short, melt() takes values across multiple columns and condenses them into a single column. Especially, when we are dealing with the text data then we may have requirements to select the rows matching a substring in all columns or select the rows based on the condition derived by concatenating two column values and many other scenarios where you have to slice,split,search substring. Sum values of all columns; Use apply for multiple columns; Series functions. In [ 1 ] : animals = pd. If a function, must either work when passed a DataFrame or when passed to DataFrame. There are multiple entries for each group so you need to aggregate the data twice, in other words, use groupby twice. That's the end of the Pandas basics for now. It’s a huge project with tons of optionality and depth. This operation is very easy and customary in R (using data. reset_index() # You might get a few extra columns that you dont need. In a pandas DataFrame, aggregate statistic functions can be applied across multiple rows by using a groupby function. pandas trick: Reverse column order in a DataFrame: you can aggregate by multiple functions by using Can be used with a groupby to extract the last value in. What I want to do is apply multiple functions to several columns (but certain columns will be operated on multiple times). It takes as arguments the following – list of function names to be applied to all selected columns. New: Group by multiple columns / key functions. fill_value. How would I go about doing this efficiently? Here's the code I already have:. But the library can still offer you much, much more. pandas find max value in groupby and apply function python , pandas I've got a dataframe df like the following: H,Nu,City 1,15,Madrid 3,15,Madrid 3,1600,Madrid 5,17615,Madrid 2,55,Dublin 4,5706,Dublin 2,68,Dublin 1,68,Dublin I would like to find the max value / city of the Nu column. Calculating sum of multiple columns in pandas. Finally subtract along the index axis for each column of the log2 dataframe, subtract the matching mean. A dictionary of columns and functions you want to apply to those columns; reset_index() is a function that resets the index of a dataframe. apply(lambda x: x["metric1"]. 000000 std NaN. Pandas is one of those packages and makes importing and analyzing data much easier. Finally subtract along the index axis for each column of the log2 dataframe, subtract the matching mean. pandas: create new column from sum of others I have a pandas DataFrame with 2 columns x and This means we can simply use + to add multiple Series objects and. In this section, we will illustrate how summary information can be obtained from groups of rows in a table. Pandas can also group based on multiple columns, simply by passing a list into the groupby() method. How to drop column by position number from pandas Dataframe? You can find out name of first column by using this command df. Linq Group by multiple columns + Aggregate Function. Delete given row or column. charAt(0) which will get the first character of the word in upper case (which will be considered as a group). Flatten hierarchical indices created by groupby. multiple functions 1. …If I open up the exercise files for this video,…I'll find some really basic things that we want to do. Add more columns when you are doing group by in the first parentheses. Pandas comes with a whole host of sql-like aggregation functions you can apply when grouping on one or more columns. You can use a dictionary to specify aggregation functions for each series: Selecting multiple. Aggregate using callable, string, dict, or list of string/callables. Combining multiple columns in Pandas groupby with dictionary Let' see how to combine multiple columns in Pandas using groupby with dictionary with the help of different examples. groupby('key') obj. What I want to do is apply multiple functions to several columns (but certain columns will be operated on multiple times). Pandas' GroupBy function is the bread and butter for many data munging activities. I've read the documentation, but I can't see to figure out how to apply aggregate functions to multiple columns and have custom names for those columns. python multiple conditional sums for pandas aggregate pandas groupby value counts (2) I just recently made the switch from R to python and have been having some trouble getting used to data frames again as opposed to using R's data. Function to use for aggregating the data. Pandas does that work behind the scenes to count how many occurrences there are of each combination. NumPy works fine with pandas objects : np. Combining multiple columns in Pandas groupby with dictionary Let’ see how to combine multiple columns in Pandas using groupby with dictionary with the help of different examples. The ability to group by multiple criteria (just like SQL) has been one of my most desired GroupBy features for a long time. and certainly more pythonic than a convoluted groupby operation. I'm having trouble with Pandas' groupby functionality. The next level of data summarization is the groupby operation, which allows you to quickly and efficiently compute aggregates on subsets of data. value_counts vs collections. python - Pandas sort by group aggregate and column; Python Pandas, aggregate multiple columns from one; python - Pandas sorting by group aggregate; python - Pandas: aggregate when column contains numpy arrays; python - Pandas DataFrame aggregate function using multiple columns; Python Pandas - Group by an aggregate (count of conditional values). The final piece of syntax that we'll examine is the "agg()" function for Pandas. aggregate (func, *args, **kwargs). Pandas is a powerful data analysis toolkit providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easily and intuitively. groupby(key,axis=1) Let us now see how the grouping objects can be applied to the DataFrame object. Indexing in python starts from 0. Note: When we do multiple aggregations on a single column (when there is a list of aggregation operations), the resultant data frame column names will have multiple levels. The aggregation functionality provided by the agg() function allows multiple statistics to be calculated per group in one calculation. The next level of data summarization is the groupby operation, which allows you to quickly and efficiently compute aggregates on subsets of data. agg(), known as "named aggregation", where 1. But what is the "right" Pandas idiom for assigning the result of a groupby operation into a new column on the parent dataframe? In the end, I want a column called "MarketReturn" than will be a repeated constant value for all indices that have matching date with the output of the groupby operation. In the first example we are going to group by two columns and the we will continue with grouping by two columns, 'discipline' and 'rank'. This function flatten the data across all columns, and then allows you to. Pandas is a foundational library for analytics, data processing, and data science. The ability to group by multiple criteria (just like SQL) has been one of my most desired GroupBy features for a long time. python - Pandas sort by group aggregate and column; Python Pandas, aggregate multiple columns from one; python - Pandas sorting by group aggregate; python - Pandas: aggregate when column contains numpy arrays; python - Pandas DataFrame aggregate function using multiple columns; Python Pandas - Group by an aggregate (count of conditional values). My current solution is to go column by column, and doing something like the code above, using lambdas for functions that depend. isnull function can. filter(items=individuals). sum, 'Rev_M':np. body_style for the crosstab's columns. Aggregate using callable, string, dict, or list of string/callables. reset_index() # You might get a few extra columns that you dont need. Grouping by multiple columns 100 xp Grouping by another series 100 xp Groupby and aggregation 50 xp Computing multiple aggregates of multiple columns 100 xp Aggregating on index levels/fields 100 xp Grouping on a function of the index 100 xp Groupby and transformation 50 xp. Aggregate function takes a function as an argument and applies the function to columns in the groupby sub dataframe. Python Pandas - GroupBy; We can aggregate by passing a function to the entire DataFrame, Apply Multiple Functions on Multiple Columns of a DataFrame. If you are looking for a video on how to perform a groupby then go to: https://youtu. Pandas groupby aggregate multiple columns using Named Aggregation. Multiple Grouping Columns. I'm having trouble with Pandas' groupby functionality. You don't have to worry about the v values -- where the indexes go dictate the arrangement of the values. mean() Just as before, pandas automatically runs the. For each column, there are multiple aggregate functions. groupby(key) obj. Pandas: break categorical column to multiple columns. In general groupby aggregation in Pandas goes like this: df. sum}) see this pandas docs for example. Because you use it in the Sum function, which takes an int, but you also check for null, which isn't a possible value for int.