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A Guide to Your First Python Data Analysis Project


Analyzing Spotify Song Attributes with Pandas, Matplotlib, and Seaborn

This guide walks you through a full, real-world data analysis workflow using Python. Our goal is to explore a large dataset of Spotify songs and uncover patterns in characteristics like energy, danceability, popularity, and genre. Along the way, we will learn essential analysis skills including loading data, examining its structure, cleaning it, filtering it, summarizing it, visualizing relationships, and interpreting a simple regression.
In this guide, you will learn how to:
  • Import and load data
  • Explore structure and content
  • Identify and clean missing values
  • Filter subsets of data
  • Compute descriptive statistics
  • Compare categories
  • Create visualizations
  • Interpret regression results

1.Understanding the Fundamentals of Data Analysis


Before writing any code, it is useful to understand a few foundational concepts. These concepts shape how we analyze, filter, and visualize data.


Types of Data in a Dataset


Every dataset is made up of different kinds of variables, and each type determines what you can do analytically.

Numerical Variables

These include values that can be counted or measured, such as tempo, energy, loudness, danceability, and popularity.

Numerical variables allow you to compute:
  • averages
  • correlations
  • minimum and maximum values
  • distributions
  • regression models

They also work well in visualizations like histograms, line charts, bar charts, heatmaps, and scatterplots.

Categorical Variables

These represent groups, categories, or labels (such as genre, artist_name, key, or mode). They are essential for:
  • filtering subsets of the data
  • grouping values by category
  • comparing differences between groups
  • computing category-level statistics (e.g., average danceability by genre)

Together, numerical and categorical variables form the backbone of most data analysis tasks. Recognizing them helps you choose appropriate methods and avoid errors. For example, you would not calculate the “mean genre,” nor would you plot text as a scatterplot axis.


What Pandas Does and Why We Use It


Pandas is a Python library designed for working with tabular data. If Excel could be programmed, automated, expanded, and connected to other analytical tools, that would be Pandas. It allows us to:
  • load data from many sources (CSV, Excel, SQL, APIs)
  • inspect and summarize tables
  • clean and fix messy datasets
  • filter rows using conditions
  • compute aggregated statistics
  • reshape and merge datasets


2.Setting Up Your Python Analysis Environment


To complete this project, you will use Python along with three key libraries: Pandas (for data handling), Matplotlib (for basic visualizations), and Seaborn (for statistical graphics built on top of Matplotlib).

If you are working in Google Colab or Jupyter Notebook, these libraries are typically pre-installed. If working locally, they can be installed using:
Python                                                                                                
pip install pandas matplotlib seaborn
Once installed, you can import them:
Python                                                                                                
import pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns
The imports establish the tools you will rely on throughout the rest of the guide.

3.Loading and Exploring the Spotify Dataset


The dataset for this project is a CSV file that includes more than one hundred thousand tracks from Spotify across multiple genres. Each song includes attributes like acousticness, tempo, loudness, energy, popularity, and danceability.

You can load it directly from a GitHub link:
Python                                                                                                
url = “https://raw.githubusercontent.com/sushmaakoju/spotify-tracks-data-analysis/main/SpotifyFeatures.csv”df = pd.read_csv(url)
df
You now have a DataFrame (Pandas’ core data table object) ready for analysis.


Examining the Structure of Your Dataset


Exploration is a critical step because it helps you understand what you are working with before making any assumptions or conducting deeper analysis.

Previewing the First Few Rows

Python                                                                                                
df.head(5)
This gives you a sense of:
  • how clean the data is
  • whether values make sense
  • what types of variables exist
  • the range of genres and artists

Previewing also helps you spot obvious issues like strange text values, misplaced columns, or duplicated rows.

Listing All Column Names

Python                                                                                                
df.columns
This helps you understand the set of variables in the dataset that you can use to ask questions.

Understanding Data Types and Missing Values

Python                                                                                                
df.info()
This command reveals:
  • which columns are integers, floats, or text
  • whether any columns contain missing values
  • the overall size of the dataset

Recognizing data types matters because certain analyses only work with numeric variables, and handling missing value incorrectly can distort results.

Summary Statistics for Numerical Variables

Python                                                                                                
df.describe()
This provides key descriptive measures including:
  • mean
  • standard deviation
  • quartiles
  • min and max values

Beginning our analysis with these summaries will help us have an idea of the data’s distribution and scale.

4.Identifying and Handling Missing Data


Almost all real-world datasets contain missing information. Missing values can interrupt mathematical calculations, skew charts, and cause functions to fail.

Checking for Missing Values

Python                                                                                                
df.isna().sum()
This produces a count of missing entries in each column. If there are many, you might need to consider imputation, removal, or deeper cleaning. If there are few, you can drop rows without significant loss.

Removing Missing Data

df = df.dropna()

This removes all rows containing missing values. Be cautious – doing so without inspection may unintentionally remove important or rare data points.