Aspiring data analysts and data scientists know that data wrangling is a vital step in any data analysis algorithm or machine learning project. Pandas, a powerful and widely used Python package, is used in data analysis and to perform data operations. It is built on top of NumPy.

This article will underline data analysis using Pandas. But before that, let’s understand Pandas and why it should be used in the first place.

- 1. Pandas overview
- 2. Why Pandas?
- 2.1. Pandas download
- 2.1.1. Downloading Anaconda
- 2.1.2. Starting with JupyterLab
- 2.1.3. Creating a new Python notebook
- 2.1.4. Importing Pandas
- 2.2. Pandas installation
- 3. Data analysis with Pandas
- 3.1. Series
- 3.1.1. Case 1
- 3.1.2. Case 2
- 3.1.3. Case 3
- 3.2. Pandas DataFrame
- 3.2.1. Case 1
- 3.2.2. Case 2
- 3.2.3. Case 3
- 4. Pandas in Data Science and Machine Learning
- 4.1. How Pandas streamlines ML model-building
- 4.1.1. Importing the data
- 4.1.2. Finding missing data
- 4.1.3. Visualizing the data
- 4.1.4. Feature transformation

Pandas is a powerful package that is commonly used for data analysis. It streamlines data loading from external sources and assists with data analysis.

The features offered by Pandas help automate common operations like data analysis and manipulation. You can do all this without having to write any code in the Python language. If you have used the NumPy package or the R’s DataFrames before, you will find similarities in the Python Pandas package.

Pandas is an open-source Python library. According to the official website, it is a flexible and easy-to-use data analysis and manipulation tool built on Python.

As mentioned, Pandas is built on top of NumPy, which is a Python library used for scientific computing and data analysis. NumPy helps developers to extract valuable insights about different datasets. Apart from this, it is also ideal for data manipulation in Excel spreadsheets and SQL tables.

Pandas is a package that enjoys growing popularity. It is used across a wide range of business verticals and industries including data analytics, financial trading, automation, and more.

The image below depicts how Pandas has grown rapidly in the Python developers’ community. According to Stack Overflow, it shows strong growth compared to other Python libraries.

Image source: Stack Overflow

DataFrame, a 2D table, is the main structure in Pandas. It supports various data formats including JSON, CSV, SQL, XLSX, and more. With just a few lines of code, Python developers can edit, delete, and manipulate data in the 2D table.

The Pandas download process is easy and does not take much time. Here are the steps:

Download Anaconda on your operating system, along with the latest Python version, and run the installer. Downloading Anaconda is easy; just follow the steps and prompts.

Before initiating the Pandas download, keep in mind a few things:

- Anacondas is not compulsory to install and it is strictly discouraged to install it as an administrator.
- You need to select yes and initialize Anaconda3 when prompted.
- You need to restart the terminal after successful installation.

Here is an image to help you understand how to start with JupyterLab in the Anaconda terminal.

Create a new Python Notebook in JupyterLab.

Image source: Pandas.pydata

You can now use Pandas and write your code in the cells.

Now that we have seen how to download Anaconda and Pandas, let’s look at how to install this library.

Write the following command to install Pandas.

Or

After installing it, you need to import Pandas and use it on the Jupyter Notebook.

Image source: Towardsdatascience

To avoid writing the full word (pandas) every time, you can import it as ‘as pd’ to call a Pandas function.

The Pandas library offers back-end source code that is written in Python or C.

Data analysis can be performed by implementing two approaches:

- Series
- DataFrames

Series is an array defined in Pandas that is used to store any data. It is a 1D array or a single column of a matrix. With specific index values attached to each row, a series is a set of data values that are attached to a particular label. These unique index values are automatically defined when creating a series.

**Code for creating a series:**

Let’s examine different cases.

*When data contains scalar values.*

**Code:**

**Output:**

*When data contains a dictionary.*

**Code:**

**Output:**

*When data contains ndarray.*

**Code:**

**Output:**

Image source: GeeksForGeeks

A Pandas DataFrame is a 2D data structure defined in Pandas that consists of rows and columns. The next important structure in Pandas, it is a multi-dimensional table in an Excel sheet and is made up of a group of series. It streamlines tabular data where every row depicts observations and every column represents variables.

You can read and create a Pandas DataFrame after installing and importing Pandas. Here’s an example to understand how the DataFrame works. The code fragment below depicts the same.

**Code:**

**Output**

Img Src: w3schools

Let’s examine a few cases.

*When data contains scalar values.*

**Code:**

**Output:**

*When data contains series.*

**Code:**

**Output:**

*When data is a 2D NumPy ndarray.*

You need to keep the dimensions of a 2D array the same when creating a DataFrame.

**Code:**

**Output:**

Image source: GeeksforGeeks

Once the data is collected, it is stored in different databases where it is retrieved for use in various data science projects and operations. There are two phases in a data science project:

- Data cleaning phase
- Exploratory data analysis

These phases provide a high-grade dataset to work with. This filtered dataset serves as a starting point for building a machine learning model. The Pandas library offers a large set of features that enable you to perform tasks from the first intake of raw data to produce high-quality data for further testing.

The insights gained from the data analysis serve as a starting point that helps developers find the right direction for in-depth analysis and machine learning models. The statistical analysis can entail the comparison of the different subsets obtained by performing different operations and processes using Pandas.

We have seen how Pandas is used in data analysis and how it manipulates the data. Let's go behind the scenes and understand how data is manipulated for machine learning.

A significant amount of time is required in any machine learning project. This is because it includes different procedures like analyzing the basic patterns and trends before building an ML model. The Python Pandas library offers different tools for data analysis and manipulation.

Pandas plays a vital role in ML model-building. Here are a few operations.

The Pandas library offers a wide range of tools to read data from different sources. You can use the CSV file as a dataset function which has a large number of options for parsing the data. Here’s the code fragment to import the data.

Pandas offers a function to find the number of functions to deal with missing data. To start with, you can use the ISNA() function to analyze and detect the missing values in the data. This function looks at every value of the rows and columns. If the value is missing, it returns True, otherwise it returns False.

Plotting in Pandas can be an efficient way to visualize the data. You can call the plt.plot() in a DataFrame. Plotting requires you to first import the matplotlib. This function supports multiple data visualization types including histograms, boxplots, lines, bars, and scatter plots. The plotting function becomes very useful when combined with the data aggregation function.

Pandas offers multiple functions for feature transformation. The commonly used machine libraries accept only numerical data and, thus, it is necessary to transform the non-numeric feature. Pandas has a method to implement feature transformation - the function get_dummies converts each unique value into a binary column when applied to a data column.

Image source: Towardsdatascience

Many data scientists and professionals use Pandas for data analysis and data science projects. Pandas DataFrame enables them to manipulate the data and build machine learning models. Although the learning curve is a bit steep, it significantly increases the efficiency of data manipulation.

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