Panda Series -

Firstly the import for the panda library is as following :

import pandas as pd

Series through the panda library will allow us to do additional things. Panda series might look like numpy or normal arrays , however, they are similar to python Dict.

g7_pop = pd.series([35.467, 63.951, 80.940, 60.665, 127.061, 64.511, 318.523])
#Population in millions

g7_pop.name = 'G7 population in millions'

Similarly to numpy arrays, we can also treat series the same by checking the type, the values, and the type

g7_pop.dtype #gets the data type of the series

g7_pop.values #Showcases the values 

type(g7_pop.values) #Shows whether its an array etc

A series has an index, which is similar to the automatic index assigned to python list. WE can think of this as a dictionary as each value has an index we can address.

g7_pop[0] etc etc

However, in contrast to lists, we can explicitly define the indexes ourself. Because of this, we can say that the series looks like “Ordered Dictionaries”

g7_pop.index = [
    'Canada',
    'France',
    'Germany',
    'Italy',
    'Japan',
    'United Kingdom',
    'United States',
]

We can also create series out of dictionaries

pd.Series({
    'Canada': 35.467,
    'France': 63.951,
    'Germany': 80.94,
    'Italy': 60.665,
    'Japan': 127.061,
    'United Kingdom': 64.511,
    'United States': 318.523
}, name='G7 Population in millions')

Indexing with Series -

Indexing works very similarly to how lists and dictionaries apply and use them. You can use the index of the element you're looking for

g7_pop['Canada']

#numeric positions can also be used through iloc
g7_pop.iloc[0]

#You can also select multiple items at once
g7_pop[['Canada', 'France']]

#Slicing in pandas
g7_pop['Canada' : 'Italy']