Pandas Tutorial

What is Pandas?

Pandas is an acronym for "PythoN DAta AnalysiS". It can be viewed as an instrument to help us fully understand our data in considerable detail which serves as an all-purpose library, reading the dataset, manipulating missing values, and information retrieval.

When should you start using Pandas?

You should start using Pandas when you need to work with structured data, such as tables or time series, in your Python projects. Pandas is a powerful library for data manipulation and analysis, offering data structures like DataFrame and Series for handling data efficiently. It is especially useful when you need to clean, filter, transform, aggregate, or visualize data. Learning Pandas early in your data science journey will help you tackle real-world data challenges and streamline your data preprocessing and analysis workflows.

Audience

This topic can be utilized by anyone who wants to diversify their understanding of data analysis and manipulation. Both professionals who want to broaden their skill set and university students looking to build projects using various datasets are welcome.

We will cover topics from basics like creating a data frame to learning pandas in detail for retrieving and querying information.

Prerequisite

The following are some prerequisites to learn Pandas library in Python:

• Basic understanding of Computer Science
• Knowledge of Python as a programming language
• Acquaintance of either online or offline programming IDE's

Why learn Pandas?

There are a bunch of advantages to learning pandas -

• Open Source - The code is widely available and accessible, thus making it easier to learn and revise as per requirements.
• Pandas in Python is specifically tailored to work with massive datasets required in Data Analysis.
• Functionalities like tidying, subsetting, grouping, querying, and summarising the data make Pandas unique and extensive.
• Statistical functions like average, summation, minimum, maximum, deviation, and mean can be easily computed.
• Time Series Forecasting categorises the data based on dates and events. This is easily feasible using Pandas, making it popular for Time Series problems.
• The execution is swift and effortless.

And most importantly, data science and analysis are incomplete without learning Pandas as it is a very effective tool in projects and python development.

How difficult is it to learn Pandas?

It is not very challenging to understand Pandas. Since Pandas in Python is open source, several datasets include similar codes that can be used as references. Additionally, it has excellent documentation.

In Scaler Topics, we intend to cover Pandas as much as possible with clear and concise examples.

How long does it take to learn Pandas?

Pandas is just a Python library. Learning pandas is made much easier by having some familiarity with Python. Usually, it depends on the amount of time spent and the number of topics covered. Therefore, even for a complete novice,** a month or a month and a half** is sufficient to master pandas.

How to learn Pandas: Step-by-Step

1. Brush up on Python: Since Pandas is a Python library, a working knowledge of Python is required to proceed with ease.
2. Setting up an environment: As previously mentioned, learning Python requires an IDE or environment. It can also be a virtual environment or an offline application.
3. Practicing from the beginning: The next step in learning Pandas is to fully understand, and then use methods and functions. Once the foundations are grasped, more complex and difficult concepts will follow. But even so, consistency and implementation are the secrets to perfecting Pandas in Python.
4. Projects: A good way to evaluate your comprehension of a subject is through projects. There are many different datasets to work with, and Pandas has several applications you can try out.

Install and import

To install Pandas, you can use pip or conda depending on your Python environment. Here's how:

Using pip:

For Python 2.x:

pip install pandas

For Python 3.x:

pip3 install pandas

Using conda (if you're using the Anaconda distribution):

conda install pandas

After you have installed Pandas, you can import it in your Python script or Jupyter Notebook using the following line of code:

import pandas as pd

The as pd part is an alias that allows you to use the shorthand pd instead of typing pandas every time you want to access Pandas functions or methods. This is a common convention in the Python community.

Core components of Pandas: Series and DataFrames

Pandas has two core components: Series and DataFrames. These are the primary data structures used for data manipulation and analysis in Pandas.

Series:

A Series is a one-dimensional labeled array capable of holding any data type, such as integers, floats, strings, or even Python objects. It has an index, which provides labels for each data point in the Series. You can think of a Series as a single column of data.

Example of creating a Series:

import pandas as pd

data = [1, 2, 3, 4]
ser = pd.Series(data)
print(ser)

Output:

0    1
1    2
2    3
3    4
dtype: int64

DataFrame:

A DataFrame is a two-dimensional labeled data structure with columns of potentially different data types. You can think of it like a spreadsheet or a SQL table, where each column is a Series. DataFrames are one of the most powerful and commonly used data structures in Pandas, providing extensive functionality for data manipulation, filtering, aggregation, and more.

Example of creating a DataFrame:

import pandas as pd

data = {
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
}

df = pd.DataFrame(data)
print(df)

Output:

   A  B  C
0  1  4  7
1  2  5  8
2  3  6  9

Series and DataFrames form the foundation for data analysis with Pandas, allowing you to work efficiently with structured data, clean and transform it, and derive insights using various statistical methods and visualizations.

About this Pandas Tutorial

In this Pandas tutorial, we will cover some of the most exciting and interesting topics in Pandas in Python.

First, we will begin with the basics of Pandas right from the introduction and installation to more complex topics like series manipulation. Additionally, there are numerous FAQs about various techniques for a deeper understanding.

Take-Away Skills from This Pandas Tutorial

Some of the key concepts acquired from this Pandas tutorial are:

• Series Manipulation - Timeseries Analysis, Timedelta, etc
• Dataframe Manipulation - Merging, Slicing, Selecting, Extracting, Truncating, Deleting, Indexing, Loading, Reading, Writing, etc
• Methods - explode(), replace(), agg(), transform(), ewm(), map(), sample(), melt(), etc