A pandas Series is a one-dimensional array-like object containing a sequence of values of the same type and an associated array of data labels, called its index. Again, all the elements are of the same type. You can think of a series as a column of data. It’s like a Python list and a Python dictionary combined. A list stores elements in order. A dictionary has association, meaning there are key value pairs. A series stores your elements in order and gives each one an identifier, namely an index. If we don’t specify an index, pandas will assign it a number, starting with zero and incrementing by one.

What does a small series look like in Jupyter Notebook? Here is some code from my Jupyter notebook that I named PandasSeries.

import numpy as np
import pandas as pd
ser = pd.Series([3, 7, 2, 9, 5])
ser

my_list = [2, 8, 1, 6]
ser_2 = pd.Series(my_list)

Both the Series object itself and the index have a name attribute.

ser.name = "my first series"
ser.index.name = "idx"
ser

You can specify your own index if you prefer. That index may be strings. This is quite similar to a dictionary and can even be used like a dictionary. A Series is like a fixed-length ordered dictionary.

ser1 = pd.Series([2,17,5], index=["d","a", "f"])
ser1.name = "Series1"
ser1.index.name = 'idx'
ser1

ser1["a"]

This returns 17.

ser1[["f", "a"]]    # ["f", "a"] is interpreted as a list of indicies

Convert a Python dictionary to a pandas Series