# Pandas: Add a New Column to a DataFrame Using assign()

> Add a new column to a Pandas DataFrame using the assign() method, with manually specified values or values computed from other columns

**URL:** https://sentry.io/answers/add-a-new-column-to-a-dataframe-in-python-pandas/

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## The Problem

How do I add a new column to an existing Pandas DataFrame? The new column should have the same number of rows as existing columns.

## The Solution

We can do this using the [`DataFrame.assign`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.assign.html) method. When called on a given DataFrame, this method creates a copy of that DataFrame with one or more additional columns added.

The new columns can contain manually specified values, or values can be computed for each row based on other columns. Look at the following example code:

```python
import pandas

# Create a dataframe with products and their cost prices
products = pandas.DataFrame([["apple", 2.0], ["orange", 3.0], ["pear", 4.0]],
                 columns=["product", "cost_price"])

print(products)

# Add a stock count column with manual data
products = products.assign(stock_count=[50, 40, 30])

print(products)

# Add a sale price column that calculates a 50% markup
products = products.assign(sale_price=lambda row: row.cost_price * 1.5)

print(products)
```

This code will print the `products` DataFrame with two, three and then four columns. Note the use of a [lambda expression](https://docs.python.org/3/reference/expressions.html#lambda) in the creation of the `sale_price` column – the values for this column are created by running the function on each row.

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*Source: [sentry.io/answers/add-a-new-column-to-a-dataframe-in-python-pandas/](https://sentry.io/answers/add-a-new-column-to-a-dataframe-in-python-pandas/)*