Basic Syntax and First Program in Pandas

Introduction

Pandas is the python library that is built on the top NumPy package of python. The basic syntax of Pandas needs to be followed for writing any pandas code error-free. Before It was impossible to start with pandas without installing them, after installation, they needed to be imported and pandas data structures are used to create the pandas program.

Pandas Basic Syntax

To run code without errors, one must follow the syntax, which is a set of pre-defined instructions. Pandas should be installed before running any pandas program.

Steps to install pandas are as follows:

• Download python from www.python.org

• Open the terminal and type the command pip install pandas to install the pandas library.

After installing Pandas now, let's see what the basic syntax for writing the pandas program is given as follows:

• Pandas need to be imported before running the code by typing the following command import pandas as pd pd is an attribute that is used in place of pandas, we can also use something else like import pandas as db etc.

• For creating the Pandas code, DataFrame and series are used. Series (1-Dimensional columns) and DataFrame (multidimensional, made up of a collection of series) are the few components of pandas.

eg. data=pd.series() or data=pd.DataFrame()

Pandas First Program

Now let's understand all the above points with the help of examples.

Code:

Output:

Explanation:

In the above code example pandas are imported as pd. A list of names is created and stored in the data variable. Then this list is converted into pandas series using pd.series() function.

Code:

Output:

Explanation:

Here, pandas are first imported as pd and the data list is stored in df as pandas DataFrame.

Code:

Output:

Explanation:

In this above code example, a list of index values is assigned to the series data.

Code:

Output:

Explanation:

Here, a list of index values is added to the data. As it is DataFrame multidimensional, 0 is for the columns and the list of values that we passed is for rows.

Code:

Output:

Explanation:

In this example, we store the dictionary data and then store it as pandas Series in the df variable. After printing it we get the one-dimensional linear.

Code:

Output:

Explanation:

Here, data is created in the form of a dictionary and stored as DataFrame in the df variable. After printing, we get 2-dimensional data as output in the form of rows and columns.

Code:

Output:

Explanation:

In this example, a desired list of the index is added to the data.

Where is the Pandas Codebase?

The collection of source code of an application or software program is called a codebase. Pandas source code can be found in this GitHub repository.

Applications of Pandas

Pandas are used in various fields as explained below:

• Statistical Analysis As statistics work with a lot of data so, a library like pandas, which primarily deals with various forms of data handling, might be useful in a variety of ways. To perform statistical computations, the functions of mean, median, and mode are just the most fundamental ones.
  In addition to these, pandas and statistics have a lot of other complex functions which are very useful to get excellent results.

• Recommendation Systems We have seen lots of recommendations while streaming on online media platforms like Hotstar, youtube, Netflix, etc. All this is done by deep learning. Different models provide recommendations that consider different criteria and provide recommendations. These systems are one of the most popular applications for pandas. Pandas is the best library to use when working with such data in these models, as these models are typically written in Python.
  Pandas can handle large amounts of data, which helps them learn the huge amounts of data needed to create an effective recommendation system with the help of functions like mapping and GroupBy.

• Economics For economists, it is important to analyze data to identify patterns and to understand trends in the growth of the economy in various areas. To analyze large datasets, many economists have begun to use Python and Pandas as Pandas is an effective package because of its large number of built-in tools and features. Dataframes and file handling are just some of the many tools provided by Pandas that are used for accessing and modifying data very easily to get the desired outcomes. Economists from all across the world have made great achievements because of these uses of Pandas.

• Stock Prediction The stock market is highly unpredictable. We can quickly create models that can forecast the outcomes of the financial markets with the help of Pandas and a few additional libraries, like NumPy and Matplotlib. This is possible because there are a lot of previous stock data stocks that provide information about how they behave. Additionally, a model can predict the next move to be made with some accuracy by learning these data of stocks. Along with this, people can use such prediction models to automate the purchasing and selling of stocks.

• Neuroscience Understanding the nervous system has always been in the minds of humankind because there are a lot of potential mysteries about our bodies that we haven’t solved as of yet. Machine learning has helped this field immensely with the help of various applications of Pandas. With the help of different Pandas applications, machine learning has extremely helped this field. Pandas play an important role in compiling a large amount of data as it has high data manipulation capabilities, which helped neuroscientists a lot to understand the trends within our body and the impact of different things on the entire nervous system.

• Natural Language Processing (NLP) Natural Language Processing, or NLP, is a term that is frequently used these days and has made an impressive entrance into the world. This idea's major goal is to make computers capable of comprehending ordinary human speech and all of its subtleties. Even though this is a challenging assignment, you can build a simple NLP model with the help of Scikit-Learn and Pandas, which you can then improve. To assist you, employ a variety of functions and additional libraries.

• Advertising Marketing has advanced more than any other industry since the turn of the twenty-first century. A lot more personalization is being used in advertising. Deep Learning and Machine Learning have once more been the driving forces behind this development.

• Analytics Analytics are used frequently. Pandas assist you in doing everything, whether you want to analyze a website or any other platform. Its effective data processing and dynamic data manipulation capabilities, as well as its incredible visualization capabilities, all play a significant part in how well it performs in this area.

• Data science It is a broad field that encompasses all disciplines involving the handling, analysis, and manipulation of data. As a result, it applies to practically all pandas applications.

• Big Data Spark and Hadoop work very well together with Python. Pandas can therefore operate with big data as well.

Large datasets up to petabytes in size can be processed and stored effectively using the open-source Apache Hadoop framework. Big data workloads are processed using the open-source distributed processing technology Apache Spark.