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- Python's prowess in data analysis owes much to its rich ecosystem of libraries explicitly tailored for this purpose. Let's unveil Python's data analysis arsenal! Among these, pandas reigns supreme for data manipulation and analysis; its powerful tools handle datasets, Excel files, and other data sources with ease.
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Jan 17, 2024 · As you might expect, Python lends itself readily to data analysis. Once Python has analyzed your data, you can then use your findings to make good business decisions, improve procedures, and even make informed predictions based on what you’ve discovered. In this tutorial, you’ll: Understand the need for a sound data analysis workflow
Apr 22, 2024 · Let's unveil Python's data analysis arsenal! Among these, pandas reigns supreme for data manipulation and analysis; its powerful tools handle datasets, Excel files, and other data sources with ease.
Jul 6, 2024 · When we talk about using Python for data analysis, we're referring to the process of leveraging Python's capabilities to examine, clean, transform, and model data in order to extract valuable insights, make decisions, and communicate findings. In this tutorial, we will indulge into all steps for Data Analysis with Python.
Apr 8, 2024 · In this step-by-step guide, we’ll show you a Python data analysis example and demonstrate how to analyze a dataset. A great way to get practical experience in Python and accelerate your learning is by doing data analysis challenges.
- Table of Contents
- Real-Life Data Analysis Example
- What Software to Use For Data Analysis?
- How to Prepare For Data Analysis in Python
- How to Perform Univariate Analysis
- How to Analyze The Relationship Between Variables
- How to Visualize The Relationship Between Variables
- What Are The Data Analysis Outcomes?
- Data Analysis in Python: Next Steps
Let’s take a simple example to understand the workflow of a real-life data analysis project. Suppose that Store A has a databaseof all the customers who have made purchases from them in the past year. They plan to use it to come up with personalized promotions and products to target different customer groups. For this reason, they hire a data analy...
With the computing power available today, it is possible to perform data analysis on millions of data points in just a couple of minutes. In general, data scientists use statistical software like R or programming languages like Python. In this guide, we will show you how to analyze data using 2 popular Python libraries — pandas and Seaborn.
Python Installation Pre-Requisites
To follow along with this tutorial, you will need to have a Python IDE running on your device. We suggest using a Jupyter Notebooksince its interface makes it easier for you to create and view visualizations. Then, install the pandas and Seabornlibrary on your device.
The Titanic Dataset
We will be using the Kaggle Titanic datasetfor this tutorial, so before we begin, download it on your device. Note: Make sure to only download the file named ‘train.csv’ as we won’t need the rest. The file contains information about passengers who were on board the Titanic when the collision took place. We will use this data to perform exploratory data analysis in Pythonand better understand the factors that contributed to a passenger’s survival of the incident.
Loading the Dataset
Open your Jupyter Notebookand navigate to the directory where you’ve saved the dataset. Then, create a new Python file and run the following lines of code: It will generate output that looks like this: Notice that the data frame has 12 columns. Here’s a description of each of these variables: 1. PassengerId: a unique ID assigned to each passenger. 2. Survived: whether a passenger survived the collision. A label of 1 indicates that the passenger survived, while a label of 0 – that they didn’t....
Univariate analysis is the process of performing a statistical review on a single variable. We will start by creating a simple visualization to understand the distribution of the ‘Survived’variable in the Titanic dataset. Our aim is to answer simple questions with the help of available data, such as: 1. How many passengers survived the Titanic coll...
Now, we can move on to analyzing the relationships between different variables in our dataset. Before starting any analysis, however, it is important to frame data questions. These will tell us exactly what we want to know from the information we have at hand — and it is useless to start exploring data with no end goal in mind. In this case, we wil...
First, let’s create a boxplot to visualize the relationship between a passenger’s age and the class they were traveling in: You will see a plot like this appear on your screen: If you haven’t seen a boxplot before, here’s how to read one: 1. Quartiles: the edges of the boxplot represent the 1st and 3rd quartile of the variable. Meanwhile, the line ...
Performing the analysis has helped us come up with answers for the questions we outlined earlier: 1. Did a passenger’s age have any impact on what class they traveled in? Yes, older passengers were more likely to travel first class. 2. Did the class that passengers traveled in have any correlation with their ticket fares? Yes, first-class passenger...
In most real-world projects, data scientists are often presented with a business use case. They then transform this use case into a set of questions like we did above and validate their assumptions with the help of data. Then, they present their findings in a format that is easy for stakeholders to understand. Moreover, pandas and Seabornare Python...
Learn core Python and data science skills in a hands-on class. Clean and analyze data, use Python's major libraries, create visualizations, and get familiar with the logic of...
Mar 20, 2024 · In this article, we will discuss how to do data analysis with Python. We will discuss all sorts of data analysis i.e. analyzing numerical data with NumPy, Tabular data with Pandas, data visualization Matplotlib, and Exploratory data analysis.
