## Default Indexing
We can access elements via their **numerical (default) indices** using the usual syntax `series_name[index]`:

import pandas as pd

temperatures = pd.Series(
  [22, 18, 25, 19]
)

# Access the 3rd element
print(temperatures[2])

If the Series has **Custom Indices**, then it's recommended to use the `iloc` property for indexing:

import pandas as pd

temperatures = pd.Series(
    [22, 18, 25, 19],
    index=["Monday", "Tuesday", "Wednesday", "Thursday"]
)

# Accessing the 3rd element
print(temperatures.iloc[2])

## Custom Indexing
The syntax of **Custom Indexing** is very similar to accessing elements of a dictionary:

import pandas as pd

temperatures = pd.Series(
    [22, 18, 25, 19],
    index=["Monday", "Tuesday", "Wednesday", "Thursday"]
)

# Accessing the temperature on "Wednesday"
print(temperatures["Wednesday"])

## Slicing with Default Indices
We can slice a **Series** using the syntax:
```
series_name[start:end]
```
It's important to note that the in case of **default indices**, the `end` is exclusive, which can be better understood with the following example:

import pandas as pd

temperatures = pd.Series(
    [22, 18, 25, 19, 21, 23, 20],
    index=["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
)

# The index "6" is excluded
weekdays = temperatures.iloc[0:6]

print(weekdays)

## Slicing with Custom Indices
We can slice a series using it's custom indices too. In this case the syntax is the same as before, except the `end` element is also included in the slice:

import pandas as pd

temperatures = pd.Series(
    [22, 18, 25, 19, 21, 23, 20],
    index=["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
)

# "Friday" is included in the slice
weekdays = temperatures['Monday':'Friday']

print(weekdays)

What will be the output of the following code?

Which method is recommended to access elements by position when using custom indices?

In this course, we will explore the basics of Pandas – a powerful Python library for organizing, manipulating and analyzing data. Each concept will be explained through short and clear video lessons, followed by fun and practical microtasks, which are designed to help you understand the syntax and remember how to use different features and functions in Pandas with confidence.

In this section, we will explore some introductory concepts related to Pandas – which will help you get a basic understanding of the library and become more comfortable in working with it.

In this section, we will learn what are DataFrames, how to create them, and how to additional rows or columns to existing DataFrames. This section will prepare us for 
 the upcoming sections where we will explore more complex features of DataFrames.

In this section we will explore some methods of fetching data from DataFrames. Each topic will be accompanied with some tasks to help you with remembering the syntax and methods.

Accessing Elements from a Series

Default Indexing

Custom Indexing

Slicing with Default Indices

Slicing with Custom Indices

1. What will be the output of the following code?

2. Which method is recommended to access elements by position when using custom indices?

3. What is the output of this code?