**1. Lambda Functions:**

`lambda arguments: expression`

- Create an anonymous inline function.

`square = lambda x: x ** 2`

result = square(5) # Result is 25

**2. Filter Function:**

`filter(function, iterable)`

- Filter elements from an iterable based on a function.

`numbers = [1, 2, 3, 4, 5, 6]`

even_numbers = list(filter(lambda x: x % 2 == 0, numbers)) # [2, 4, 6]

**3. Map Function:**

`map(function, iterable)`

- Apply a function to each element in an iterable and return a map object.

`numbers = [1, 2, 3, 4, 5]`

squared = list(map(lambda x: x ** 2, numbers)) # [1, 4, 9, 16, 25]

**4. Reduce Function (functools):**

`functools.reduce(function, iterable, initializer=None)`

- Applies a binary function cumulatively to the items of an iterable, reducing them to a single value.

`from functools import reduce`

numbers = [1, 2, 3, 4, 5]

product = reduce(lambda x, y: x * y, numbers)

# Result is 120 (1*2*3*4*5)

**5. Apply Function (Pandas):**

In pandas, the `apply()`

function is used to apply a function along the axis of a DataFrame or Series.

`df.apply(func, axis=0)`

- Apply a function to each column (axis=0) or row (axis=1) of a DataFrame.`series.apply(func)`

- Apply a function to each element in a Series.

Example (DataFrame):

`import pandas as pd`

data = {'A': [1, 2, 3], 'B': [4, 5, 6]}

df = pd.DataFrame(data)

# Apply a custom function to each column

result = df.apply(lambda x: x * 2, axis=0)

# Resulting DataFrame:

# A B

# 0 2 8

# 1 4 10

# 2 6 12

Example (Series):

`import pandas as pd`

data = {'A': [1, 2, 3, 4, 5]}

series = pd.Series(data['A'])

# Apply a lambda function to each element

result = series.apply(lambda x: x ** 2)

# Resulting Series: [1, 4, 9, 16, 25]