- \n
- Introduction to Pandas Assign<\/a><\/li>\n
- The syntax of Pandas Assign<\/a><\/li>\n
- Pandas Assign examples<\/a><\/li>\n
- Pandas Assign FAQ<\/a><\/li>\n<\/ul>\n
Having said that, if you really want to understand Pandas assign, I recommend that you read the whole article.<\/p>\n
<\/a><\/p>\n
A quick introduction to Pandas Assign<\/h2>\n
So what does the assign method do?<\/p>\n
Put simply, the assign method adds new variables to Pandas dataframes.<\/p>\n
Quickly, I’ll explain that in a little more depth.<\/p>\n
Pandas is a toolkit for working with data in Python<\/h3>\n
You’re probably aware of this, but just to clarify: Pandas<\/a> is a toolkit for working with data in the Python<\/a> programming language.<\/p>\n
In Pandas, we typically work with a data structure called a dataframe.<\/p>\n
A dataframe is a collection of data stored in a rows and column format<\/a>.<\/p>\n
![\"A](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2020\/10\/pandas-dataframe_sales-example.png\")
<\/p>\nPandas gives us a toolkit for creating these Dataframes, and it also provides tools for modifying dataframes.<\/p>\n
Pandas has tools for sorting dataframes<\/a>, aggregating dataframes, reshaping dataframes, and a lot more.<\/p>\n
And one of the most important things we need to be able to do, is add new columns to a dataframe<\/em>. <\/p>\n
Pandas Assign Adds New Columns to a Dataframe<\/h3>\n
The Pandas assign method enables us to add new columns to a dataframe.<\/p>\n
We provide the input dataframe, tell assign how to calculate the new column, and it creates a new dataframe with the additional new column.<\/p>\n
![\"A](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2020\/10\/pandas-dataframe_sales-data_add-profit-variable_example.png\")
<\/p>\nIt’s fairly straightforward, but as the saying goes, the devil is in the details.<\/p>\n
So with that said, let’s take a look at the syntax so we can see how the assign method works.<\/p>\n
<\/a><\/p>\n
The syntax of the assign method<\/h2>\n
The syntax for the assign method is fairly simple.<\/p>\n
You type the name of your dataframe, then a “dot”, and then type
assign()<\/code>.<\/p>\nRemember, the assign method is a Python method that’s associated with dataframe objects, so we can use so-called “dot syntax” to call the method.<\/p>\n
Next, inside the parenthesis, we need to provide a “name value pair.”<\/p>\n
![\"An](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2020\/10\/pandas-assign_simple-syntax-explanation.png\")
<\/p>\nWhat does that mean?<\/p>\n
We simply provide the name of the new variable and the value that we want to assign to that variable. The value that we assign can be simple (like an integer constant), but it can also be a complicated value that we calculate.<\/p>\n
I’ll show you examples of exactly how we use it in the examples section of this tutorial<\/a>.<\/p>\n
Syntax to add multiple variables to a dataframe<\/h4>\n
One quick note on the syntax:<\/p>\n
If you want to add multiple variables, you can do this with a single call to the assign method.<\/p>\n
Just type the name of your dataframe, call the method, and then provide the name-value pairs for each new variable, separated by commas.<\/p>\n
![\"An](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2020\/10\/pandas-assign_syntax-add-multiple-variables_v2.png\")
<\/p>\nHonestly, adding multiple variables to a Pandas dataframe is really easy. I’ll show you how in the examples section<\/a>.<\/p>\n
The Output of the Assign Method<\/h3>\n
Before we look at the examples, let’s quickly talk about the output of the assign method.<\/p>\n
This is really important, so you need to pay attention …<\/p>\n
The output of the assign method is a new dataframe<\/em>.<\/p>\n
Read that again. It’s really important.<\/p>\n
The output of the assign method is a new dataframe<\/em><\/strong>.<\/p>\n
So if you use the assign method, you need to save the output in some way, or else the output will go to the console (if you’re working in an IDE).<\/p>\n
The implication of this, is that if you just run the method, your original dataframe will be left unchanged<\/em> unless you store the output to the original name.<\/p>\n
(You can obviously also store the output to a new name. This is safer, unless you’re positive that you want to overwrite your original data.)<\/p>\n
<\/a><\/p>\n
Examples: how to add a column to a dataframe in Pandas<\/h2>\n
Ok. Now that I’ve explained how the syntax works, let’s take a look at some examples of how to use assign to add new variables to a dataframe.<\/p>\n
Examples:<\/strong><\/p>\n
- \n
- Create a new variable and assign a constant<\/a><\/li>\n
- Add a variable that’s a computed value<\/a><\/li>\n
- Add multiple variables to your dataframe<\/a><\/li>\n
- Store the output of assign to a new name<\/a><\/li>\n
- Assign a specific value conditionally, based on another column<\/a><\/li>\n<\/ul>\n
Obviously, you can click on any of the above links, and it will take you to that example in the tutorial.<\/p>\n
Run this code first<\/h4>\n
Before you run any of these examples, you need to do two things:<\/p>\n
- \n
- import pandas<\/li>\n
- create the dataframe we’ll use<\/li>\n<\/ul>\n
Import Pandas<\/h5>\n
You can run this code to import Pandas:<\/p>\n
\r\nimport pandas as pd\r\n<\/pre>\n
Create DataFrame<\/h5>\n
Next, let’s create our dataframe.<\/p>\n
\r\nsales_data = pd.DataFrame({\r\n\"name\":[\"William\",\"Emma\",\"Sofia\",\"Markus\",\"Edward\",\"Thomas\",\"Ethan\",\"Olivia\",\"Arun\",\"Anika\",\"Paulo\"]\r\n,\"region\":[\"East\",\"North\",\"East\",\"South\",\"West\",\"West\",\"South\",\"West\",\"West\",\"East\",\"South\"]\r\n,\"sales\":[50000,52000,90000,34000,42000,72000,49000,55000,67000,65000,67000]\r\n,\"expenses\":[42000,43000,50000,44000,38000,39000,42000,60000,39000,44000,45000]})\r\n<\/pre>\nWe\u2019ve called this DataFrame
sales_data<\/code>. <\/p>\nThis dataframe contains mock sales data for 11 people and it has variables for both
sales<\/code> andexpenses<\/code>.<\/p>\nFrom here, we can use the assign() method to add some new variables.<\/p>\n
<\/a><\/p>\n
EXAMPLE 1: Create a new variable and assign a constant<\/h3>\n
In this first example, we’re going to add a new variable to the datafame and assign a constant value for every row.<\/p>\n
Let’s think about something specific.<\/p>\n
Say that you’re working with this dataset, and all of these people work for the same company. You might have some other dataframes that have records for salespeople who work for different<\/em> companies, but everyone in
sales_data<\/code> works for the same company.<\/p>\nWhat if we want to create a variable that contains the company name for the people in this dataframe?<\/p>\n
We can do that with assign as follows:<\/p>\n
\r\nsales_data.assign(company = \"Vandelay Industries\")\r\n<\/pre>\n
OUT:<\/p>\n
\r\n name region sales expenses company\r\n0 William East 50000 42000 Vandelay Industries\r\n1 Emma North 52000 43000 Vandelay Industries\r\n2 Sofia East 90000 50000 Vandelay Industries\r\n3 Markus South 34000 44000 Vandelay Industries\r\n4 Edward West 42000 38000 Vandelay Industries\r\n5 Thomas West 72000 39000 Vandelay Industries\r\n6 Ethan South 49000 42000 Vandelay Industries\r\n7 Olivia West 55000 60000 Vandelay Industries\r\n8 Arun West 67000 39000 Vandelay Industries\r\n9 Anika East 65000 44000 Vandelay Industries\r\n10 Paulo South 67000 45000 Vandelay Industries\r\n<\/pre>\n
Explanation<\/h6>\n
So what did we do in this example?<\/p>\n
Here, we created a new variable called
company<\/code>.<\/p>\nFor every row in the data, the value for the
company<\/code> variable is the same. The value is “Vandelay Industries.”<\/p>\nIn technical terms, the value is a constant for every row. More specifically, it’s a string value. <\/p>\n
Having said that, when we create variables with constant values, we can add string values like this example, but we can also assign a new variable with a constant numeric value. For example, try the code
sales_data.assign(newvar = 1)<\/code>.<\/p>\n<\/a><\/p>\n
EXAMPLE 2: Add a variable that’s a computed value<\/h3>\n
Here, we’re going to assign a new variable that’s a computed value.<\/p>\n
Specifically, we’re going to create a new variable called
profit<\/code> that equals sales minus expenses. (Finance and accounting geeks will know that this is not a precise way to compute profit, but we’ll use this simplified calculation for purposes of example.)<\/p>\nLet’s run the code, and I’ll explain below.<\/p>\n
\r\nsales_data.assign(profit = sales_data.sales - sales_data.expenses)\r\n<\/pre>\n
OUT:<\/p>\n
\r\n name region sales expenses profit\r\n0 William East 50000 42000 8000\r\n1 Emma North 52000 43000 9000\r\n2 Sofia East 90000 50000 40000\r\n3 Markus South 34000 44000 -10000\r\n4 Edward West 42000 38000 4000\r\n5 Thomas West 72000 39000 33000\r\n6 Ethan South 49000 42000 7000\r\n7 Olivia West 55000 60000 -5000\r\n8 Arun West 67000 39000 28000\r\n9 Anika East 65000 44000 21000\r\n10 Paulo South 67000 45000 22000\r\n<\/pre>\n
Explanation<\/h6>\n
Here, we created a new computed column called
profit<\/code>.<\/p>\nAs you can see,
profit<\/code> is simplysales<\/code> minusexpenses<\/code>.<\/p>\nNotice though, that when we reference the
sales<\/code> andexpenses<\/code> variables inside ofassign()<\/code>, we need to call them assales_data.sales<\/code> andsales_data.expenses<\/code>.<\/p>\nAlternatively, we could call them as
sales_data['sales']<\/code> andsales_data['expenses']<\/code>.<\/p>\nI prefer the former because they’re much easier to read, but you can choose.<\/p>\n
<\/a><\/p>\n
EXAMPLE 3: Add multiple variables to your dataframe<\/h3>\n
In the previous two examples, we were adding only one new variable at a time.<\/p>\n
Here in this example, we’ll add two variables at the same time. <\/p>\n
We’re going to add the
profit<\/code> variable and thecompany<\/code> variable.<\/p>\nLet’s take a look.<\/p>\n
\r\nsales_data.assign(profit = sales_data.sales - sales_data.expenses\r\n ,company = \"Vandelay Industries\"\r\n )\r\n<\/pre>\n
OUT:<\/p>\n
\r\n name region sales expenses profit company\r\n0 William East 50000 42000 8000 Vandelay Industries\r\n1 Emma North 52000 43000 9000 Vandelay Industries\r\n2 Sofia East 90000 50000 40000 Vandelay Industries\r\n3 Markus South 34000 44000 -10000 Vandelay Industries\r\n4 Edward West 42000 38000 4000 Vandelay Industries\r\n5 Thomas West 72000 39000 33000 Vandelay Industries\r\n6 Ethan South 49000 42000 7000 Vandelay Industries\r\n7 Olivia West 55000 60000 -5000 Vandelay Industries\r\n8 Arun West 67000 39000 28000 Vandelay Industries\r\n9 Anika East 65000 44000 21000 Vandelay Industries\r\n10 Paulo South 67000 45000 22000 Vandelay Industries\r\n<\/pre>\n
Explanation<\/h6>\n
Here in this example, we added two variables at the same time:
profit<\/code> andcompany<\/code>.<\/p>\nNotice that syntactically, I actually put the second variable on a new line of code. This is mostly for readability. If you want, you can keep all of your code on the same line, but I don’t necessarily recommend it. I personally think that your code is much easier to read and debug if each different variable assignment is on a separate line.<\/p>\n
That said, the two new variable assignments must<\/em> be separated by a comma. Here, the comma that separates the two variable assignments comes before the assignment of the
company<\/code> variable. This is important, so don’t forget the comma.<\/p>\n<\/a><\/p>\n
EXAMPLE 4: Store the output of assign to a new name<\/h3>\n
Finally, let’s do one more example. <\/p>\n
Here, we’re going to store the output to a new name.<\/p>\n
Notice that in the previous examples, the code did not<\/em> modify the original dataframe. <\/p>\n
When we use assign, it produces a new<\/em> dataframe as an output and leaves your original dataframe unchanged. This is very important to remember! Many beginner data science students get frustrated when they first use this technique, because they can’t figure out why their dataframe stays the same, even after they run
assign()<\/code>. Always remember: assign produces a new<\/em> dataframe.<\/p>\nHaving said that, we can store<\/em> the new output dataframe to a new name.<\/p>\n
If we want, we can store it to a new name, like
sales_data_revised<\/code>.<\/p>\nOr, we can store it to the original dataframe name,
sales_data<\/code>, and overwrite the original! <\/p>\nSo it is possible to directly modify your original dataframe, but you need to do it with an equal sign to store the output of the assign method.<\/p>\n
Ok, with all that said, let’s look at an example.<\/p>\n
Here, we’ll take the output of assign and store it to a new name called
sales_data_revised<\/code>.<\/p>\n\r\nsales_data_revised = sales_data.assign(profit = sales_data.sales - sales_data.expenses\r\n ,company = \"Vandelay Industries\"\r\n )\r\n<\/pre>\n
Now, the new dataframe is stored in
sales_data_revised<\/code>.<\/p>\nLet’s print it out.<\/p>\n
\r\nprint(sales_data_revised)\r\n<\/pre>\n
OUT:<\/p>\n
\r\n name region sales expenses profit company\r\n0 William East 50000 42000 8000 Vandelay Industries\r\n1 Emma North 52000 43000 9000 Vandelay Industries\r\n2 Sofia East 90000 50000 40000 Vandelay Industries\r\n3 Markus South 34000 44000 -10000 Vandelay Industries\r\n4 Edward West 42000 38000 4000 Vandelay Industries\r\n5 Thomas West 72000 39000 33000 Vandelay Industries\r\n6 Ethan South 49000 42000 7000 Vandelay Industries\r\n7 Olivia West 55000 60000 -5000 Vandelay Industries\r\n8 Arun West 67000 39000 28000 Vandelay Industries\r\n9 Anika East 65000 44000 21000 Vandelay Industries\r\n10 Paulo South 67000 45000 22000 Vandelay Industries\r\n<\/pre>\n
Explanation<\/h6>\n
When we run the code in this example, assign() is creating a new dataframe with the newly assigned variables,
profit<\/code> andcompany<\/code>.<\/p>\nBut instead of letting that new output be passed to the console, we’re storing it with a new name so we can access it later.<\/p>\n
Remember: assign produces a new<\/em> dataframe as an output and leaves the original unchanged. If you want to store the output, you need to use the equal sign to pass the output to a new name.<\/p>\n
How to Overwrite your Original Data<\/h4>\n
One last comment on this.<\/p>\n
You can actually overwrite your original data directly. To do this, just run the assign method and pass the output to the original dataframe name,
sales_data<\/code>.<\/p>\n\r\nsales_data = sales_data.assign(profit = sales_data.sales - sales_data.expenses\r\n ,company = \"Vandelay Industries\"\r\n )\r\n<\/pre>\n
This is totally appropriate to do in some circumstances. Sometimes, you really do want to overwrite your data.<\/p>\n
But be careful!<\/p>\n
Test your code before you do this, otherwise you might overwrite your data with incorrect values!<\/p>\n
<\/a><\/p>\n
EXAMPLE 5: Assign a specific value conditionally, based on another column<\/h3>\n
Ok, one more example to answer a new question in the comments section.<\/p>\n
Here, we’re going to answer the question “What if the company name is different for each individual, how do you assign that?”<\/p>\n
So, we’re going to assume that every person is from a different company, and we’re going to assign a specific company name based on the “name” of the sales agent.<\/p>\n
To do this, we’re going to create a mapping from name to company name, and then create the new
company<\/code> variable based on that mapping.<\/p>\nCreate Mapping from Name to Company<\/h4>\n
First, we’re going to create a mapping from the
name<\/code> variable to thecompany<\/code> name we want to assign.<\/p>\nTo do this, we’ll simply use a dictionary. The “keys” of the dictionary will be the names of our sales reps, and the corresponding “values” of our dictionary will be the company name we want to assign.<\/p>\n
\r\ncompany_mapping = {'William':'Alpha Company'\r\n ,'Emma':'Beta Company'\r\n ,'Markus':'Gamma Company'\r\n ,'Sofia':'Delta Company'\r\n ,'Markus':'Epsilon Company'\r\n ,'Edward':'Zeta Company'\r\n ,'Thomas':'Eta Company'\r\n ,'Ethan':'Theta Company'\r\n ,'Olivia':'Iota Company'\r\n ,'Arun':'Kappa Company'\r\n ,'Anika':'Lambda Company'\r\n ,'Paulo':'Mu Company'\r\n }\r\n<\/pre>\nAssign Company Name<\/h4>\n
Next, we’ll use the
map()<\/code> method to map from thename<\/code> variable to company name, and assign the output to a new variable calledcompany<\/code>.<\/p>\n\r\nsales_data.assign(company = sales_data.name.map(company_mapping))\r\n<\/pre>\n
OUT:<\/p>\n
\r\n name region sales expenses company\r\n0 William East 50000 42000 Alpha Company\r\n1 Emma North 52000 43000 Beta Company\r\n2 Sofia East 90000 50000 Delta Company\r\n3 Markus South 34000 44000 Epsilon Company\r\n4 Edward West 42000 38000 Zeta Company\r\n5 Thomas West 72000 39000 Eta Company\r\n6 Ethan South 49000 42000 Theta Company\r\n7 Olivia West 55000 60000 Iota Company\r\n8 Arun West 67000 39000 Kappa Company\r\n9 Anika East 65000 44000 Lambda Company\r\n10 Paulo South 67000 45000 Mu Company\r\n<\/pre>\n
Explanation<\/h5>\n
Here, we’re using the Pandas series method,
map()<\/code>.<\/p>\nTo do this, we’re retrieving the
name<\/code> variable and callingmap()<\/code>.<\/p>\nWe’re sending the output of this operation to a variable called
company<\/code>, and this whole expression is happening inside of a call to Pandas assign. <\/p>\nThe output creates a new variable called
company<\/code>, where the value of company is conditionally assigned according to the mapping we created in our dictionary,company_mapping<\/code>.<\/p>\n<\/a><\/p>\n
Frequently Asked Questions about the Pandas Assign Method<\/h2>\n
Let’s very quickly address one common question about the Pandas assign method.<\/p>\n
<\/a><\/p>\n
Question 1: Why is my dataframe unchanged, after using assign?<\/h3>\n
This is a very common question, and the answer is very straightforward.<\/p>\n
As I mentioned several times in this tutorial, the assign method returns a new<\/em> dataframe that contains the newly assigned variables, and it leaves your input dataframe unchanged.<\/p>\n
If you want to overwrite your dataframe, and add the new variables, you need to take the output and use the equal sign to re-store the output into the original name.<\/p>\n
So you need to set
sales_data = sales_data.assign(...)<\/code>, like this:<\/p>\n\r\nsales_data = sales_data.assign(profit = sales_data.sales - sales_data.expenses\r\n ,company = \"Vandelay Industries\"\r\n )\r\n<\/pre>\n
Keep in mind that this will overwrite your data! So you need to be very careful when you do this. Test your code and make sure that it’s working exactly<\/em> as expected before you do this. If you don’t you might overwrite your original data with an incorrect dataset, and you’ll have to re-start your data retrieval and data wrangling from scratch. This is sometimes a huge pain in the a**, so be careful.<\/p>\n
Alternatively, you can store the output of assign with a new name, like this:<\/p>\n
\r\nsales_data_revised = sales_data.assign(profit = sales_data.sales - sales_data.expenses\r\n ,company = \"Vandelay Industries\"\r\n )\r\n<\/pre>\n
Storing the output with a new name, like
sales_data_revised<\/code>, is safer because it doesn’t overwrite the original.<\/p>\nYou may actually want to overwrite the original, just make sure that your code works before you do. <\/p>\n
Leave your other questions in the comments below<\/h5>\n
Do you have other questions about the assign method?<\/p>\n
Leave your questions in the comments section near the bottom of the page.<\/p>\n
Discover how to master data wrangling with Pandas<\/h2>\n
This tutorial should give you a taste of how to use Pandas to manipulate your data, but there’s a lot<\/em> more to learn.<\/p>\n
If you really want to master data wrangling with Pandas, you should join our premium online course, Pandas Mastery<\/a>.<\/p>\n
Pandas Mastery is our online course that will teach you these critical data manipulation tools.<\/p>\n
Inside the course, you’ll learn all of the essentials of data manipulation in pandas, like:<\/p>\n
- \n
- adding new variables<\/li>\n
- filtering data by logical conditions<\/li>\n
- subsetting data<\/li>\n
- working with Pandas indexes<\/li>\n
- reshaping data<\/li>\n
- and much more …<\/li>\n<\/ul>\n
Additionally, you’ll discover our unique practice system that will enable you to memorize all of the syntax you learn. <\/p>\n
And, it will only take a few weeks.<\/p>\n
Find out more here:<\/p>\n
\nLearn More About Pandas Mastery<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
In this tutorial, I’ll explain how to use the Pandas assign method to add new variables to a Pandas dataframe. In this tutorial, I’ll explain what the assign method does and how it works. I’ll explain the syntax, and I’ll show you step-by-step examples of how to use it. If you need something specific, you … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":5592,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","om_disable_all_campaigns":false,"footnotes":""},"categories":[31,79,68,58],"tags":[],"publishpress_future_action":{"enabled":false,"date":"2024-05-04 18:23:52","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category"},"_links":{"self":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts\/5585"}],"collection":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/comments?post=5585"}],"version-history":[{"count":0,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts\/5585\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/media\/5592"}],"wp:attachment":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/media?parent=5585"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/categories?post=5585"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/tags?post=5585"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- Add a variable that’s a computed value<\/a><\/li>\n
- The syntax of Pandas Assign<\/a><\/li>\n