# Python Pandas - Series

Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index.

## pandas.Series

A pandas Series can be created using the following constructor −

pandas.Series( data, index, dtype, copy)

The parameters of the constructor are as follows −

Sr.No | Parameter & Description |
---|---|

1 |
data takes various forms like ndarray, list, constants |

2 |
Index values must be unique and hashable, same length as data. Default |

3 |
dtype is for data type. If None, data type will be inferred |

4 |
Copy data. Default False |

A series can be created using various inputs like −

- Array
- Dict
- Scalar value or constant

## Create an Empty Series

A basic series, which can be created is an Empty Series.

### Example

Live Demo#import the pandas library and aliasing as pd import pandas as pd s = pd.Series() print s

Its **output** is as follows −

Series([], dtype: float64)

## Create a Series from ndarray

If data is an ndarray, then index passed must be of the same length. If no index is passed, then by default index will be **range(n)** where **n** is array length, i.e., [0,1,2,3…. **range(len(array))-1].**

### Example 1

Live Demo#import the pandas library and aliasing as pd import pandas as pd import numpy as np data = np.array(['a','b','c','d']) s = pd.Series(data) print s

Its **output** is as follows −

0 a 1 b 2 c 3 d dtype: object

We did not pass any index, so by default, it assigned the indexes ranging from 0 to **len(data)-1**, i.e., 0 to 3.

### Example 2

Live Demo#import the pandas library and aliasing as pd import pandas as pd import numpy as np data = np.array(['a','b','c','d']) s = pd.Series(data,index=[100,101,102,103]) print s

Its **output** is as follows −

100 a 101 b 102 c 103 d dtype: object

We passed the index values here. Now we can see the customized indexed values in the output.

## Create a Series from dict

A **dict** can be passed as input and if no index is specified, then the dictionary keys are taken in a sorted order to construct index. If **index** is passed, the values in data corresponding to the labels in the index will be pulled out.

### Example 1

Live Demo#import the pandas library and aliasing as pd import pandas as pd import numpy as np data = {'a' : 0., 'b' : 1., 'c' : 2.} s = pd.Series(data) print s

Its **output** is as follows −

a 0.0 b 1.0 c 2.0 dtype: float64

**Observe** − Dictionary keys are used to construct index.

### Example 2

Live Demo#import the pandas library and aliasing as pd import pandas as pd import numpy as np data = {'a' : 0., 'b' : 1., 'c' : 2.} s = pd.Series(data,index=['b','c','d','a']) print s

Its **output** is as follows −

b 1.0 c 2.0 d NaN a 0.0 dtype: float64

**Observe** − Index order is persisted and the missing element is filled with NaN (Not a
Number).

## Create a Series from Scalar

If data is a scalar value, an index must be provided. The value will be repeated to match
the length of **index**

#import the pandas library and aliasing as pd import pandas as pd import numpy as np s = pd.Series(5, index=[0, 1, 2, 3]) print s

Its **output** is as follows −

0 5 1 5 2 5 3 5 dtype: int64

## Accessing Data from Series with Position

Data in the series can be accessed similar to that in an **ndarray.**

### Example 1

Retrieve the first element. As we already know, the counting starts from zero for the array,
which means the first element is stored at zero^{th} position and so on.

import pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve the first element print s[0]

Its **output** is as follows −

1

### Example 2

Retrieve the first three elements in the Series. If a : is inserted in front of it, all items from that index onwards will be extracted. If two parameters (with : between them) is used, items between the two indexes (not including the stop index)

Live Demoimport pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve the first three element print s[:3]

Its **output** is as follows −

a 1 b 2 c 3 dtype: int64

### Example 3

Retrieve the last three elements.

Live Demoimport pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve the last three element print s[-3:]

Its **output** is as follows −

c 3 d 4 e 5 dtype: int64

## Retrieve Data Using Label (Index)

A Series is like a fixed-size **dict** in that you can get and set values by index label.

### Example 1

Retrieve a single element using index label value.

Live Demoimport pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve a single element print s['a']

Its **output** is as follows −

1

### Example 2

Retrieve multiple elements using a list of index label values.

Live Demoimport pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve multiple elements print s[['a','c','d']]

Its **output** is as follows −

a 1 c 3 d 4 dtype: int64

### Example 3

If a label is not contained, an exception is raised.

import pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve multiple elements print s['f']

Its **output** is as follows −

… KeyError: 'f'