Pandas: Assign New Columns with assign()

Assign new columns to a DataFrame using the Pandas assign() method. Create derived calculations, enable method chaining, use lambda expressions for sequential updates, and return new objects without mutating the original DataFrame.

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Introduction to assign() 🔝

The assign() method creates and adds new columns to a DataFrame or overwrites existing ones. Unlike standard column assignment (e.g., df["new"] = ...), assign() returns a fresh DataFrame copy, making it indispensable for functional programming and method chaining pipelines.

Create numerical sample dataset 🔝

We start with a product inventory DataFrame containing quantities and unit prices.

import pandas as pd

df = pd.DataFrame({
    "PRODUCT": ["Pen", "Pencil", "Notebook", "Eraser"],
    "QTY": [10, 25, 5, 40],
    "PRICE": [15.0, 5.0, 80.0, 3.0]
})
print(df)
assert df.shape == (4, 3)

Expected output

    PRODUCT  QTY  PRICE
0       Pen   10   15.0
1    Pencil   25    5.0
2  Notebook    5   80.0
3    Eraser   40    3.0

Basic column assignment 🔝

Pass keyword arguments to assign() where the keyword name specifies the new column name and the value supplies the calculated Series.

df_total = df.assign(TOTAL_VALUE=df["QTY"] * df["PRICE"])
print(df_total)
assert df_total.loc[0, "TOTAL_VALUE"] == 150.0 and "TOTAL_VALUE" not in df.columns

Expected output

    PRODUCT  QTY  PRICE  TOTAL_VALUE
0       Pen   10   15.0        150.0
1    Pencil   25    5.0        125.0
2  Notebook    5   80.0        400.0
3    Eraser   40    3.0        120.0

Method chaining with lambda functions 🔝

Using callable lambda functions inside assign() lets you reference the dynamically updated DataFrame within a method chain.

df_chained = (
    df.query("QTY > 8")
      .assign(TOTAL=lambda x: x["QTY"] * x["PRICE"])
      .assign(TAX=lambda x: x["TOTAL"] * 0.1)
)
print(df_chained)
assert df_chained.loc[0, "TAX"] == 15.0

Expected output

  PRODUCT  QTY  PRICE  TOTAL   TAX
0     Pen   10   15.0  150.0  15.0
1  Pencil   25    5.0  125.0  12.5
3  Eraser   40    3.0  120.0  12.0

Assigning multiple columns sequentially 🔝

Pass multiple keyword arguments inside a single assign() call. In Python 3.6+, keyword arguments are evaluated in order, allowing subsequent lambdas to access newly created columns.

df_multi = df.assign(
    DISCOUNT_PRICE=lambda x: x["PRICE"] * 0.9,
    FINAL_TOTAL=lambda x: x["QTY"] * x["DISCOUNT_PRICE"]
)
print(df_multi)
assert df_multi.loc[0, "FINAL_TOTAL"] == 135.0

Expected output

    PRODUCT  QTY  PRICE  DISCOUNT_PRICE  FINAL_TOTAL
0       Pen   10   15.0           13.5        135.0
1    Pencil   25    5.0            4.5        112.5
2  Notebook    5   80.0           72.0        360.0
3    Eraser   40    3.0            2.7        108.0

Exercise: compute discounted prices 🔝

Create a DataFrame with MARKS = [80, 90, 70]. Use assign() with a lambda expression to create a column PERCENTAGE dividing MARKS by 100.0 and scaling to 100. Verify that the max percentage equals 90.0.

Exercise solution 🔝

Solution constructing the DataFrame and applying assign().

ex_df = pd.DataFrame({"MARKS": [80, 90, 70]})
answer = ex_df.assign(PERCENTAGE=lambda x: (x["MARKS"] / 100.0) * 100)
print(answer)
assert answer["PERCENTAGE"].max() == 90.0

Expected output

   MARKS  PERCENTAGE
0     80        80.0
1     90        90.0
2     70        70.0

Practice in Google Colab 🔝

Interactive Colab Notebook

Practice assign() examples interactively directly in your browser:

View on GitHub

Reference: Pandas assign() documentation.

Subhendu Mohapatra

Author & Instructor at plus2net

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