In this post, you will learn about one of the important **Pandas fundamental data structure** namely **Series** and how it can be used as a **dictionary**. It will be useful for beginner data scientist to understand the concept of Pandas Series object.

A dictionary is a structure that maps arbitrary keys to a set of arbitrary values.

Pandas Series is a one-dimensional array of indexed data. It can be created using a list or an array. Pandas Series can be thought of as a special case of Python dictionary. It is a structure which maps typed keys to a set of typed values.

Here are the **three different ways** in which a dictionary can be created using Series object:

Table of Contents

## Series like one-dimensional Numpy Array

```
data = pd.Series(data=[85, 65, 92, 44]
```

In the above Series object, the indices default from 0 to 3. One can access values using syntax such as data[0] is 85, data[3] is 44. The values and index can be printed using commands such as **data.values **and **data.index. **

It may look like the ** Series **object is basically interchangeable with a one-dimensional NumPy array. The essential difference is the presence of the index: while the Numpy Array has an

*implicitly defined*integer index used to access the values, the

**Pandas**has an

`Series`

*explicitly defined*index associated with the values.

## Series with explicitly defined Index with values of any type

The following is another manner in which a dictionary from Series can be created:

```
data = pd.Series(data=[85, 65, 92, 44], index=['Mathematics', 'English', 'Science', 'Hindi'])
```

In the above **Series **object, you would see an explicitly defined index. This explicit index definition gives the **Series** bject additional capabilities. *The index need not be an integer. It can consist of values of any desired type. *The index must be a hashable type and need not be unique. The object supports both integer- and label-based indexing

## Series with Explicitly defined Index with values of any type – II

Here is another manner in which Pandas Series object can be created as a dictionary:

```
data = pd.Series(data={'Mathematics': 82, 'Science': 92, 'English': 64})
```

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