Master Data Analysis using Trustworthy Databases

Python is an excellent programming language for beginners to handle and analyze data. We’ll also talk about how to find relevant data sources and a bit about statistical testing. Let’s break this down into a few steps: <h1>Step 1: Finding Relevant and Trustworthy Databases</h1> Depending on your field, there are many places where you can find datasets. For instance, some common data repositories are <a href="https://archive.ics.uci.edu/" rel="noopener ugc nofollow" target="_blank">UCI Machine Learning Repository</a>, <a href="https://www.kaggle.com/datasets" rel="noopener ugc nofollow" target="_blank">Kaggle</a>, <a href="https://datasetsearch.research.google.com/" rel="noopener ugc nofollow" target="_blank">Google Dataset Search</a>, and Government databases (e.g., <a href="https://data.gov/" rel="noopener ugc nofollow" target="_blank">data.gov</a>, <a href="https://ec.europa.eu/eurostat/data/database" rel="noopener ugc nofollow" target="_blank">Eurostat</a>). Make sure to verify the credibility of the data source and its relevance to your field. I will tell you how to do that in the last section of this article. Also, note the data format (CSV, JSON, SQL, etc.). CSV is the simplest and most common format. <h1>Step 2: Collecting the Data in a Table</h1> Let’s use Python’s <code>pandas</code> library to handle our data. First, let’s import <a href="https://pandas.pydata.org/" rel="noopener ugc nofollow" target="_blank">pandas</a>. If you don’t have it, install it via pip: <pre> pip install pandas</pre> Then, in your Python script, do the following: <pre> import pandas as pd</pre> Assuming we have a CSV file named <code>my_data.csv</code>, we can load this into a pandas DataFrame (which is essentially a table) like so: <pre> df = pd.read_csv('my_data.csv')</pre> You can visualize the first 5 lines of your dataframe using <a href="https://levelup.gitconnected.com/master-data-analysis-using-trustworthy-databases-15d83c7d084e">Read More</a>