assign Method¶

The assign() method adds new columns to a DataFrame, returning a new DataFrame with the additions.

Basic Usage¶

Add new columns.

1. Single Column¶

```python import pandas as pd

df = pd.DataFrame({ 'name': ['Alice', 'Bob', 'Charlie'], 'salary': [50000, 60000, 70000] })

df = df.assign(bonus=5000) print(df) ```

name salary bonus 0 Alice 50000 5000 1 Bob 60000 5000 2 Charlie 70000 5000

2. Multiple Columns¶

python df = df.assign( bonus=5000, tax_rate=0.25 )

3. Returns New DataFrame¶

```python

Original unchanged unless reassigned¶

new_df = df.assign(bonus=5000) ```

Computed Columns¶

Create columns based on existing data.

1. From Other Columns¶

python df = df.assign( total_comp=df['salary'] + 5000 )

2. Using Lambda¶

python df = df.assign( total_comp=lambda x: x['salary'] + 5000 )

3. Multiple Computed¶

python df = df.assign( bonus=lambda x: x['salary'] * 0.1, tax=lambda x: x['salary'] * 0.25, net=lambda x: x['salary'] - x['salary'] * 0.25 )

Lambda Advantage¶

Lambda functions access the DataFrame being created.

1. Chain Dependencies¶

python df = df.assign( bonus=lambda x: x['salary'] * 0.1, total=lambda x: x['salary'] + x['bonus'] # Uses bonus just created )

2. Order Matters¶

```python

This works because bonus is created first¶

df.assign( bonus=df['salary'] * 0.1, total=lambda x: x['salary'] + x['bonus'] ) ```

3. Without Lambda Issue¶

```python

This fails if bonus column doesn't exist yet¶

df.assign(¶

bonus=df['salary'] * 0.1,¶

total=df['salary'] + df['bonus'] # Error!¶

)¶

```

LeetCode Example: Restaurant Growth¶

Calculate rolling sums and averages.

1. Sample Data¶

python df = pd.DataFrame({ 'visited_on': pd.date_range('2024-07-15', periods=7), 'amount': [30.0, 20.0, 40.0, 10.0, 50.0, 20.0, 60.0] }) df = df.set_index('visited_on')

2. Rolling Calculation¶

python rolling_sum = df['amount'].rolling('7D').sum()

3. Assign Multiple Columns¶

python df = df.assign( amount=rolling_sum, average_amount=round(rolling_sum / 7, 2) ) print(df)

amount average_amount visited_on 2024-07-15 30.0 4.29 2024-07-16 50.0 7.14 2024-07-17 90.0 12.86 2024-07-18 100.0 14.29 2024-07-19 150.0 21.43 2024-07-20 170.0 24.29 2024-07-21 230.0 32.86

Method Chaining¶

assign works well in method chains.

1. Chain Operations¶

python result = ( df .assign(bonus=lambda x: x['salary'] * 0.1) .assign(total=lambda x: x['salary'] + x['bonus']) .query('total > 60000') )

2. Multiple assigns¶

python result = ( df .assign(year=lambda x: x['date'].dt.year) .assign(month=lambda x: x['date'].dt.month) .groupby(['year', 'month']) .sum() )

3. With Other Methods¶

python result = ( df .dropna() .assign(calculated=lambda x: x['a'] + x['b']) .sort_values('calculated') )

Overwriting Columns¶

assign can replace existing columns.

1. Replace Column¶

python df = df.assign(salary=df['salary'] * 1.1) # 10% raise

2. Transform Column¶

python df = df.assign(name=df['name'].str.upper())

3. Multiple Transforms¶

python df = df.assign( salary=lambda x: x['salary'] * 1.1, name=lambda x: x['name'].str.title() )

vs Direct Assignment¶

Compare assign to direct column assignment.

1. Direct Assignment¶

python df['bonus'] = 5000 # Modifies df in place

2. assign Method¶

python df = df.assign(bonus=5000) # Returns new DataFrame

3. When to Use Each¶

```python

Direct: quick modifications¶

assign: method chaining, functional style¶

```

Exercises¶

Exercise 1. Create a DataFrame with a 'price' column. Use .assign() to add a 'tax' column (10% of price) and a 'total' column (price + tax) in a single call.

import pandas as pd

df = pd.DataFrame({'product': ['A', 'B', 'C'], 'price': [10.0, 25.0, 15.0]})
result = df.assign(
    tax=lambda x: x['price'] * 0.10,
    total=lambda x: x['price'] * 1.10
)
print(result)

Exercise 2. Use .assign() with a lambda that references a previously created column in the same call. For example, create 'bonus' as 10% of salary, then 'total_pay' as salary + bonus, all in one .assign() chain.

import pandas as pd

df = pd.DataFrame({'name': ['Alice', 'Bob'], 'salary': [50000, 60000]})
result = df.assign(
    bonus=lambda x: x['salary'] * 0.10,
    total_pay=lambda x: x['salary'] + x['salary'] * 0.10
)
print(result)

Exercise 3. Create a DataFrame and use .assign() to overwrite an existing column (e.g., round a float column to 2 decimal places). Verify that the original DataFrame is unchanged and only the returned DataFrame has the modification.

import pandas as pd

df = pd.DataFrame({'value': [3.14159, 2.71828, 1.41421]})
result = df.assign(value=lambda x: x['value'].round(2))
print("Original:", df['value'].tolist())
print("Assigned:", result['value'].tolist())