Table of Contents

Introduction

A pandas Series is a one-dimensional array of indexed data. It is capable of holding data of any type (integer, string, float, python objects, etc.).

Syntax

A pandas Series can be created using the following constructor.

Syntax

The pandas.Series function.

1pandas.Series(data=None, index=None, dtype=None, copy=False)
Parameter Required? Default Value Description
data ✔️ Yes NA Array-like, Iterable, dict, or scalar value.
index ❌ No np.arrange(n) Array-like or Index (1d) where n is the length of data. See Index Object.
dtype ❌ No Inferred from data Data type: str, numpy.dtype, or ExtensionDtype.
copy ❌ No False bool. Copy input data. Only affects Series or 1d ndarray input.

Constructing Series Objects

We first import the required libraries and modules.

1import numpy as np
2import pandas as pd
3import random as rd

A pandas Series can be created from a list as follows:

Example

Creating a pandas Series from a Python list.

1x = pd.Series([0.5, 1.0, 1.5, 2.0])
2x

0    0.5
1    1.0
2    1.5
3    2.0
dtype: float64

The pandas Series includes both a sequence of values and a sequence of indices, which we can access with the values and index attributes, respectively.

The values are simply a NumPy array.

Example

Accessing the values of a pandas Series .

1x.values

array([0.5, 1. , 1.5, 2. ])

The index is an array-like object of type pd.Index.

Example

Accessing the index of a pandas Series

1x.index

RangeIndex(start=0, stop=4, step=1)

Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation.

Example

Accessing values of a pandas Series by index.

1x[0]

0.5

Example

Slicing a pandas Series.

1x[1:3]

1    1.0
2    1.5
dtype: float64

The data can be just a single scalar, which is repeated to fill the (longer) specified index.

Example

A pandas Series where scalar data input is replicated.

1pd.Series(88, range(5))

0    88
1    88
2    88
3    88
4    88
dtype: int64

pandas Series vs NumPy Array

While the NumPy array has an implicitly defined integer index used to access the values, the Pandas Series has an explicitly defined index associated with the values.

As a consequence, the index needs not be an integer, but can consist of values of any desired type. For example, we can use strings as an index:

Example

Explicitly defined index of a pandas Series.

1x = pd.Series([0.5, 1.0, 1.5, 2.0], index=['a', 'b', 'c', 'd'])
2x

a    0.5
b    1.0
c    1.5
d    2.0
dtype: float64

We can access any value using its index, just like a NumPy array.

Example

Accessing a value of a pandas Series using its index.

1x['c']

1.5

Example

The index of a pandas Series can be unordered.

1y = pd.Series([0.5, 1.0, 1.5, 2.0], index=rd.sample(range(1,5),4))
2y

3    0.5
2    1.0
1    1.5
4    2.0
dtype: float64

1y[2]

1.0

Example

Duplicated indices are allowed.

1y1 = pd.Series([0.5, 1.0, 1.5, 2.0], index=rd.choices(range(1,4),k=4))
2y1

2    0.5
1    1.0
2    1.5
3    2.0
dtype: float64

1y1[2]

2    0.5
2    1.5
dtype: float64

The index can also be heterogeneous. In the following case, the index includes both characters and integers.

Example

The index of a pandas Series can be heterogeneous.

1z = pd.Series([0.5, 1.0, 1.5, 2.0], index=['a', 10, 'c', 'd'])
2z

a     0.5
10    1.0
c     1.5
d     2.0
dtype: float64

1z[10]

1.0

The values of a pandas Series can also be heterogeneous.

Example

The values of a pandas Series can be heterogeneous.

1z1 = pd.Series([0.5, 'apple', 1.5, 2.0], index=['a', 10, 'c', 'd'])
2z1

a       0.5
10    apple
c       1.5
d       2.0
dtype: object

1z1[10]

'apple'
Caution

In the above example, the Series now has type object instead of float64. However, do note that the main benefit of pandas, i.e. vectorized operations, is lost as soon as we start mixing data types in a series. Therefore, using mixed data types in a Series should be avoided whenever possible.

pandas Series vs Python Dictionary

You may recall that a Python dictionary is a data structure that maps arbitrary keys to a set of arbitrary values. On the other hand, a pandas Series is a structure that maps typed keys to a set of typed values.

Just as the type-specific compiled code behind a NumPy array makes it more efficient than a Python list for certain operations, the type information of a pandas Series makes it much more efficient than Python dictionaries for certain operations.

In the following example, we construct a Series object directly from a Python dictionary.

Example

Constructing a pandas Series from a Python dictionary.

1age_dict = {'Tom': 32,
2            'Gary': 26,
3            'Lois': 22,
4            'Wendy': 31,
5            'Betty': 35}
6age_dict

{'Tom': 32, 'Gary': 26, 'Lois': 22, 'Wendy': 31, 'Betty': 35}

The associated pandas Series can then be constructed directly from the above dictionary using pd.Series.

1age = pd.Series(age_dict)
2age

Tom      32
Gary     26
Lois     22
Wendy    31
Betty    35
dtype: int64

We can also create an index which is sorted from the dictionary keys using the sort_index() method.

Example

Constructing a Series from a dictionary with sorted index.

1age = pd.Series(age_dict).sort_index()
2age

Betty    35
Gary     26
Lois     22
Tom      32
Wendy    31
dtype: int64

We can also sort the indices in descending order by using the argument ascending=False.

Example

Constructing a Series from a dictionary with sorted (reversed) index.

1age = pd.Series(age_dict).sort_index(ascending=False)
2age

Wendy    31
Tom      32
Lois     22
Gary     26
Betty    35
dtype: int64

We can now easily access the elements using indexing and slicing.

Example

Access elements of a pandas Series using indexing.

1age['Wendy']

31

Example

Access elements of a pandas Series using slicing.

1age['Tom':'Gary']

Tom     32
Lois    22
Gary    26
dtype: int64
Note

In contrast to a NumPy array, the slicing of a pandas Series includes the element at the end-index.

Also, we can create a Series from a dictionary by explicitly specifying the index which may include only specfic keys in any desired order.

Example

Creating a Series from a dictionary with specific keys only.

1 pd.Series({1:'a', 2:'b', 3:'c', 4:'d'}, index=[3, 1, 2])

3    c
1    a
2    b
dtype: object