The **`map`** method in pandas maps values from one series to another based on a common column. Mapping is based on values within a series. 

> Since mapping is one-to-one, the series must have only unique values. An error will occur otherwise. 

The illustration below shows how the `map` function works:

# Syntax

The `map` method has the following syntax:

```python
Series.map(arg, na_action=None)
```

# Parameters

The `map` method takes two parameters:

* `arg`: An object such as a dictionary, series, or function that determines the mapping.
* `na_action`: Determines how to deal with `NaN` values. It can take two forms; by default, it is `None`. If it is `ignore`, mapping is not applied to `NaN` values.

> `map` accepts values as dictionaries in the first parameter. If a value is not found in the dictionary, it is mapped to `NaN`.

Only the first parameter is compulsory.

# Return value

The `map` function returns a series with the mappings incorporated.

# Example

The code snippet below shows how we can use the `map` function in pandas:

import pandas as pd
import numpy as np

s = pd.Series(['cat', 'dog', np.nan, 'rabbit'])
print("Original Series")
print(s)
print('\n')

print("Mapping onto a dictiorary")
print(s.map({'cat': 'kitten', 'dog': 'puppy'}))
print('\n')

print("We can also map to a function")
print(s.map('I am a {}'.format))
print('\n')

print("Use the na_action parameter to avoid mapping NaN")
print(s.map('I am a {}'.format, na_action='ignore'))

What is the map method in pandas?

Maps values from one series to another using a common column. Requires unique values. Syntax: Series.map(arg, na_action=None). Returns a series with mappings incorporated.