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https://github.com/mathpfreitas/top-hits-spotify-2000-to-2019-

# 🎧 Top Hits Spotify (2000 - 2019)Explore music trends from 2000 to 2019 with this dataset of songs, artists, and genres. Use the insights to understand what makes a hit in today's music landscape. 🐙💻
https://github.com/mathpfreitas/top-hits-spotify-2000-to-2019-

analysis analytics chart data-analysis data-visualization exploratory-data-analysis hits interactivedashboards jupyter-notebook matplotlib music musical numpy pandas plotly python timeseries track-hits

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# 🎧 Top Hits Spotify (2000 - 2019)Explore music trends from 2000 to 2019 with this dataset of songs, artists, and genres. Use the insights to understand what makes a hit in today's music landscape. 🐙💻

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README 

          
# 🎶 Top Hits from Spotify (2000-2019) 🎶



![Spotify Hits](https://img.shields.io/badge/Download%20Dataset-brightgreen?style=for-the-badge&logo=spotify&logoColor=white)



Welcome to the **Top Hits from Spotify (2000-2019)** repository! This dataset offers a rich collection of Spotify music information featuring hit songs spanning from 2000 to 2019. The aim is to explore this data by genre, year, artist, and other musical characteristics.



## 📦 Getting Started



To get started, you can download the dataset from the [Releases section](https://github.com/mathpfreitas/Top-Hits-Spotify-2000-to-2019-/releases). This section contains all the necessary files you need to begin your exploration. 



### 🔍 Purpose of the Dataset



The dataset is designed for music lovers, data analysts, and anyone interested in understanding trends in popular music over nearly two decades. You can analyze:



- **Genres**: Discover which genres dominated the charts.

- **Years**: Track how music trends evolved from 2000 to 2019.

- **Artists**: Identify which artists had the most hits.

- **Musical Characteristics**: Explore various attributes like tempo, danceability, and more.



## 📊 Topics Covered



This repository touches on various topics related to data analysis and visualization, including:



- **Analytics**: Understand the numbers behind the hits.

- **Data Analysis**: Dive deep into the dataset to extract meaningful insights.

- **Data Visualization**: Create charts and graphs to represent your findings visually.

- **Jupyter Notebook**: Utilize Jupyter for interactive data analysis.

- **Matplotlib**: Leverage Matplotlib for creating stunning visualizations.

- **Pandas**: Use Pandas for data manipulation and analysis.

- **Music Trends**: Analyze trends in popular music over the years.



## 📈 Dataset Structure



The dataset consists of several key columns that provide valuable insights:



- **Track Name**: The name of the song.

- **Artist**: The artist or band performing the song.

- **Genre**: The genre of the music.

- **Year**: The year the song was released.

- **Danceability**: A measure of how suitable a track is for dancing.

- **Energy**: A measure of intensity and activity.

- **Loudness**: The overall loudness of a track in decibels (dB).

- **Tempo**: The speed of the music, measured in beats per minute (BPM).



### 📊 Sample Data



Here’s a glimpse of what the dataset looks like:



| Track Name           | Artist           | Genre         | Year | Danceability | Energy | Loudness | Tempo |

|----------------------|------------------|---------------|------|--------------|--------|----------|-------|

| Shape of You         | Ed Sheeran       | Pop           | 2017 | 0.82         | 0.73   | -4.02    | 95.0  |

| Rolling in the Deep   | Adele            | Soul          | 2011 | 0.68         | 0.60   | -5.20    | 105.0 |

| Blinding Lights      | The Weeknd       | Synth-pop     | 2019 | 0.85         | 0.80   | -3.50    | 171.0 |

| ...                  | ...              | ...           | ...  | ...          | ...    | ...      | ...   |



## 🛠️ Tools and Libraries



For your analysis, you can use the following tools and libraries:



- **Python**: The main programming language for data analysis.

- **Pandas**: For data manipulation and analysis.

- **Matplotlib**: For data visualization.

- **Jupyter Notebook**: For an interactive coding environment.



### 📚 Installation



Make sure you have Python installed. You can download it from the [official Python website](https://www.python.org/downloads/).



After installing Python, you can set up your environment:



```bash

pip install pandas matplotlib

```



## 📊 Visualization Examples



Visualizing data can provide deeper insights. Here are some examples of what you can create with this dataset:



### 1. Genre Distribution



You can create a pie chart to visualize the distribution of genres in the dataset.



```python

import pandas as pd

import matplotlib.pyplot as plt



# Load the dataset

data = pd.read_csv('spotify_hits.csv')



# Count genres

genre_counts = data['Genre'].value_counts()



# Plot

plt.figure(figsize=(10, 6))

genre_counts.plot(kind='pie', autopct='%1.1f%%')

plt.title('Genre Distribution of Top Hits (2000-2019)')

plt.ylabel('')

plt.show()

```



### 2. Yearly Trends



A line graph can help illustrate how the number of hits per year has changed.



```python

# Count hits by year

yearly_hits = data['Year'].value_counts().sort_index()



# Plot

plt.figure(figsize=(10, 6))

yearly_hits.plot(kind='line', marker='o')

plt.title('Number of Hits by Year (2000-2019)')

plt.xlabel('Year')

plt.ylabel('Number of Hits')

plt.grid()

plt.show()

```



### 3. Energy vs. Danceability



You can create a scatter plot to explore the relationship between energy and danceability.



```python

# Scatter plot

plt.figure(figsize=(10, 6))

plt.scatter(data['Danceability'], data['Energy'], alpha=0.5)

plt.title('Energy vs. Danceability of Top Hits')

plt.xlabel('Danceability')

plt.ylabel('Energy')

plt.grid()

plt.show()

```



## 🔄 Updating the Dataset



We plan to keep this dataset updated. To get the latest version, check the [Releases section](https://github.com/mathpfreitas/Top-Hits-Spotify-2000-to-2019-/releases) regularly.



## 🤝 Contributing



Contributions are welcome! If you have suggestions or improvements, feel free to create a pull request or open an issue. 



### How to Contribute



1. Fork the repository.

2. Create a new branch for your feature or fix.

3. Make your changes.

4. Commit your changes.

5. Push to the branch.

6. Create a pull request.



## 📝 License



This dataset is shared under the MIT License. Feel free to use it for personal or educational purposes.



## 🌟 Acknowledgments



Special thanks to Spotify for providing a rich dataset that allows us to explore the world of music. 



## 📬 Contact



For any questions or suggestions, feel free to reach out. You can contact me through GitHub.



## 🚀 Conclusion



Dive into the world of music from 2000 to 2019. Use this dataset to explore, analyze, and visualize the trends that shaped the music industry. Download the dataset from the [Releases section](https://github.com/mathpfreitas/Top-Hits-Spotify-2000-to-2019-/releases) and start your journey today!