To start using pandas, you need to import it into your Python script or Jupyter Notebook. The standard way to import Pandas is as follows:

import pandas as pd

The two primary data structures provided by Pandas are:

1. Series: A Series in Pandas is a one-dimensional labeled array that can hold any data type. It is similar to a column in a spreadsheet or a single column of a SQL table. Each element in a Series is associated with a unique label called an index.

2. DataFrame: A two-dimensional labeled data structure with columns of potentially different types. It is similar to a spreadsheet or a SQL table.

2.2. Series

Here is an example where we create a list of data containing some numbers and an index list with corresponding labels. We then use these lists to create a Series using pd.Series() function.

data = [10, 20, 30, 40, 50]
index = ['A', 'B', 'C', 'D', 'E']

s = pd.Series(data, index=index)
print("\nOriginal series:")
print (s)

Original series:
A    10
B    20
C    30
D    40
E    50
dtype: int64

We also change the created labels without having any effect on the data as follows:

s.index = ['X', 'Y', 'Zebra', 'W', 'V']
print("\nUpdated series:")
print(s)

Updated series:
X        10
Y        20
Zebra    30
W        40
V        50
dtype: int64

Two helpful functions when working with pandas are the iloc[ ] and loc[ ] functions. For more information see the table below:

Function	Description	Example
iloc[ ]	Integer-based indexing and selection	s.iloc[0] accesses the first row of a DataFrame
		s.iloc[2:4] accesses a slice of rows in a DataFrame
loc[ ]	Label-based indexing and selection	s.loc[‘X’] accesses a row labeled ‘A’ in a DataFrame
		s.loc[[‘X’, ‘W’]] accesses multiple rows in a DataFrame

s.iloc[2:4] 

Zebra    30
W        40
dtype: int64