Pandas in Python| Series in Pandas | Pandas Series to Dataframe | Pandas Series to List |

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Introduction to Pandas...✨

What is Pandas?

-- Pandas is a Python library used for working with data sets.
It has functions for analyzing, cleaning, exploring, and manipulating data.
-- The name "Pandas" has a reference to both "Panel Data" and "Python Data Analysis".
-- It is backbone of any data science project.

Why Use Pandas?

-- It allows us to analyze big data and make decision which are backed by data & statistical theories.
It also helps to clean dirty data sets, and make them readable and relevant.

What Can Pandas Do?

-- Pandas can help you analysis and summarization of data with the help of operations like:
🔹 Correlation between two or more columns
🔸 Average value of a Column.
🔹 Max value of Column
🔸 Min value of Column

It can also clean data by deleting irrelevant columns like columns that contain wrong values, NULL values etc.

In this section of the course we will learn how to use pandas for data analysis. You can think of pandas as an extremely powerful version of Excel, with a lot more features.
In this section of the course, you should go through the notebooks in this order:
🔹Series
🔸DataFrames
🔹 Missing Data
🔸 GroupBy
🔹 Merging, Joining and Concatenating
🔸 Operations
🔹 Data Input and Output

What is Series in Pandas?

-- A Pandas Series is like a column in a table. It is a one-dimensional array holding data of any type.
-- A Series is very similar to a NumPy array (in fact it is built on top of the NumPy array object).
In pandas, a Series is a one-dimensional labeled array that can hold any data type, such as integers, strings, or floating-point numbers. A Series is similar to a NumPy array, but it has a few key differences.

What differentiates the NumPy array from a Series ?
One of the main differences between a Series and a NumPy array is that a Series has an index, which labels each element in the array. The index can be either a string, integer, or other data type. This makes it easy to refer to specific elements in the Series using their index labels.
-- A Series can have axis labels, meaning it can be indexed by a label, instead of just a number location.
-- It also doesn't need to hold numeric data, it can hold any arbitrary Python Object.