pandas tricks pass multiple columns to lambda

Pandas tricks – pass multiple columns to lambda

Pandas is one of the most powerful tool for analyzing and manipulating data. In this article, I will be sharing with you the solutions for a very common issues you might have been facing with pandas when dealing with your data – how to pass multiple columns to lambda or self-defined functions.


You will have to install pandas on your working environment:

pip install pandas

When dealing with data, you will always have the scenario that you want to calculate something based on the value of a few columns, and you may need to use lambda or self-defined function to write the calculation logic, but how to pass multiple columns to lambda function as parameters?

Let me use some real world example, so that easier for you to understand the issue that I am talking about. Below table shows partial of the e-com delivery charges offered by some company, so the delivery charges are determined by package size (H+L+W), package weight and the delivery mode you are choosing.

Size (cm/kg) 3 hours express Next Day Delivery Same Day Delivery
<60 CM (H+L+W) & MAX 1KG 12 8 10
<80 CM (H+L+W) & MAX 5KG 15 9 11
<100 CM (H+L+W) & MAX 8KG 17 11 13
<120 CM (H+L+W) & MAX 10KG 19 14 16

And assuming we have the below order data, and we want to simulate the delivery charges. Let’s create the data in a pandas dataframe.

import pandas as pd

df = pd.DataFrame({
    "Order#" : ["1", "2", "3", "4"], 
    "Weight" : [5.0, 2.1, 8.1, 7.5], 
    "Package Size" : [80, 45, 110, 90],
    "Delivery Mode": ["Same Day", "Next Day", "Express", "Next Day"]})

If you view dataframe from Jupyter Notebook (you can sign up here to use it for free), you shall be able to see the data as per below.

Pandas pass multiple columns to lambda same data

Let’s also implement a calculate_rate function where we need to pass in the weight, package size, and delivery mode in order to calculate the delivery charges:

def calculate_rate(weight, package_size, delivery_mode):
    #set the charges as $20 since we do not have the complete rate card
    charges = 20
    if weight <=1 and package_size <60:
        if delivery_mode == "Express":
            charges = 13
        elif delivery_mode == "Next Day":
            charges = 8
            charges = 10
    elif weight <=5 and package_size <80:
        if delivery_mode == "Express":
            charges = 15
        elif delivery_mode == "Next Day":
            charges = 9
            charges = 11
    elif weight <=8 and package_size <100:
        if delivery_mode == "Express":
            charges = 17
        elif delivery_mode == "Next Day":
            charges = 11
            charges = 13
    return charges

Pass multiple columns to lambda

Here comes to the most important part. You probably already know data frame has the apply function where you can apply the lambda function to the selected dataframe. We will also use the apply function, and we have a few ways to pass the columns to our calculate_rate function.

 Option 1

We can select the columns that involved in our calculation as a subset of the original data frame, and use the apply function to it.

And in the apply function, we have the parameter axis=1 to indicate that the x in the lambda represents a row, so we can unpack the x with *x and pass it to calculate_rate.

df["Delivery Charges"] = df[["Weight", "Package Size", "Delivery Mode"]].apply(lambda x : calculate_rate(*x), axis=1)

If we check the df again in Jupyter Notebook, you should see the new column “Delivery Charges” with the figures calculated based on the logic we defined in calculate_rate function.

Pandas pass multiple columns to lambda

Option 2:

If you do not want to get a subset of the data frame and then apply the lambda, you can also directly use the apply function to the original data frame. In this case, you will need to select the columns before passing to the calculate_rate function. Same as above, we will need to specify the axis=1 to indicate it’s applying to each row.

df["Delivery Charges"] = df.apply(lambda x : calculate_rate(x["Weight"], x["Package Size"], x["Delivery Mode"]), axis=1)

This will produce the same result as option 1. And you can also use x.Weight instead of x[“Weight”] when passing in the parameter.



The two options we discussed to pass multiple columns to lambda are basically the same, and it’s either applying to the subset or the original data frame. I have not yet tested with a large set of data, so there might be some differences in terms of the performance, you may need to take a note if you are dealing with a lot of data.

You may also interested to read some other articles related to pandas.


pandas tricks calculate percentage within group

Pandas Tricks – Calculate Percentage Within Group

Pandas groupby probably is the most frequently used function whenever you need to analyse your data, as it is so powerful for summarizing and aggregating data. Often you still need to do some calculation on your summarized data, e.g. calculating the % of vs total within certain category. In this article, I will be sharing with you some tricks to calculate percentage within groups of your data.


You will need to install pandas if you have not yet installed:

pip install pandas
#or conda install pandas

I am going to use some real world example to demonstrate what kind of problems we are trying to solve. The sample data I am using is from this link , and you can also download it and try by yourself.

Let’s first read the data from this sample file:

import pandas as pd

# You can also replace the below file path to the URL of the file
df = pd.read_excel(r"C:\Sample Sales Data.xlsx", sheet_name="Sheet")

The data will be loaded into pandas dataframe, you will be able to see something as per below:

pandas tricks - calculate percentage within group

Let’s first calculate the sales amount for each transaction by multiplying the quantity and unit price columns.

df["Total Amount"] = df["Quantity"] * df["Price Per Unit"]

You can see the calculated result like below:

pandas tricks - calculate percentage within group

Calculate percentage within group

With the above details, you may want to group the data by sales person and the items they sold, so that you have a overall view of their performance for each person. You can do with the below :

#df.groupby(["Salesman","Item Desc"])["Total Amount"].sum()
df.groupby(["Salesman", "Item Desc"]).agg({"Total Amount" : "sum"})

And you will be able to see the total amount per each sales person:

pandas tricks - calculate percentage within group

This is good as you can see the total of the sales for each person and products within the given period.

Calculate the best performer

Now let’s see how we can get the % of the contribution to total revenue for each of the sales person, so that we can immediately see who is the best performer.

To achieve that, firstly we will need to group and sum up the “Total Amount” by “Salemans”, which we have already done previously.

df.groupby(["Salesman"]).agg({"Total Amount" : "sum"})

And then we calculate the sales amount against the total of the entire group. Here we can get the “Total Amount” as the subset of the original dataframe, and then use the apply function to calculate the current value vs the total. Take note, here the default value of axis is 0 for apply function.

[["Total Amount"]].apply(lambda x: 100*x/x.sum())

With the above, we should be able get the % of contribution to total sales for each sales person. And let’s also sort the % from largest to smallest:

sort_values(by="Total Amount", ascending=False)

Let’s put all together and run the below in Jupyter Notebook:

.agg({"Total Amount" : "sum"})[["Total Amount"]]\
.apply(lambda x: 100*x/x.sum())\
.sort_values(by="Total Amount", ascending=False)

You shall be able to see the below result with the sales contribution in descending order. (Do not confuse with the column name “Total Amount”, pandas uses the original column name for the aggregated data. You can rename it to whatever name you want later)

pandas tricks - calculate percentage within group for salesman


Calculate the most popular products

Similarly, we can follow the same logic to calculate what is the most popular products. This time we want to summarize the sales amount by product, and calculate the % vs total for both “Quantity” and “Total Amount”. And also we want to sort the data in descending order for both fields. e.g.:

df.groupby(["Item Desc"])\
.agg({"Quantity": "sum", "Total Amount" : "sum"})[["Quantity", "Total Amount"]]\
.apply(lambda x: 100*x/x.sum())\
.sort_values(by=["Quantity","Total Amount"], ascending=[False,False])

This will produce the below result, which shows “Whisky” is the most popular product in terms of number of quantity sold. But “Red Wine” contributes the most in terms of the total revenue probably because of the higher unit price.

pandas tricks - calculate percentage within group for products


Calculate best sales by product for each sales person

What if we still wants to understand within each sales person, what is the % of sales for each product vs his/her total sales amount?

In this case, we shall first group the “Salesman” and “Item Desc” to get the total sales amount for each group. And on top of it, we calculate the % within each “Salesman” group which is achieved with groupby(level=0).apply(lambda x: 100*x/x.sum()).

Note: After grouping, the original datafram becomes multiple index dataframe, hence the level = 0 here refers to the top level index which is “Salesman” in our case.

df.groupby(["Salesman", "Item Desc"])\
.agg({"Total Amount" : "sum"})\
.groupby(level=0).apply(lambda x: 100*x/x.sum())\
.sort_values(by=["Salesman", "Item Desc","Total Amount"], ascending=[True, True, False])

You will be able see the below result which already sorted by % of sales contribution for each sales person.

pandas tricks - calculate percentage within group - for salesman and product



This is just some simple use cases where we want to calculate percentage within group with the pandas apply function, you may also be interested to see what else the apply function can do from here.