Albums released in the late 1960s and early 1970s were also found abundantly.
Maximum number of albums in the list were released in 1970. To get this, I used groupby() on the year column and took a count following which I sorted the data by year and plotted the resulting data frame using a line chart from bokeh.charts. Year-wise count of the number of albums in the list Top 10 artistsĬlearly, the Beatles, Bob Dylan, and the Rolling Stones topped the list with 10 albums apiece. To visualize the results, I used the a figure object from the otting library and drew black circles using the circle() method. To get the top 10 artists, I used groupby() on the artists column, took a count, and sorted the resulting data frame to get the top 10 artists having the most number of albums.
The top-10 artists who have the most albums on the list Here are the questions I posed and their resulting visualizations. I fed the resulting data frames into bokeh charts and figures. I adopted the split-apply-combine strategy using pandas inbuilt groupby() function in most cases and the reshaping strategy using pandas inbuilt pivot_table() function for a single case. My master data frame Exploring the Data and Gaining Insights I used a Python 3.5.2 kernel (Anaconda 4.2.0 distribution) on a Jupyter notebook. The master data frame became 500 x 8 after the Genres_Refined and Subgenres_Refined columns were added. The Genre and Subgenre columns had multiple comma separated values in a string, so I had to split the string at the first comma and keep just the first value in new columns as the most relevant categorization of the album’s Genre and Subgenre. The data frame had 500 rows, one for each album listing the Chart Number, Year, Album, Artist, Genre, and Subgenre. All I had to do was read it into a pandas data frame directly by using the read_excel() function.
Getting the data was simple, since it was in a 500 x 6 excel spreadsheet. This post will describe the approaches I took, complete with my visualizations and the insight I gained from building them.
PYTHON LIST OF DICTIONARIES ARTIST AND ALBUM CODE
The complete code I used for reading, refining, exploring, and visualizing the data can be found on my GitHub page, and also in this notebook submitted on Kaggle. It features a powerful interface that supports high-level charting, intermediate-level plotting, and lower-level modeling. I decided to get my hands dirty by using pandas to explore the data and bokeh to visualize the results.īokeh is a Python library for interactive visualization. One day I while browsing through Kaggle to pick up a simple data set and test my newly acquired data visualization skills, I stumbled upon the list uploaded as a CSV dataset. The Rolling Stones list served as an introduction to rock music for me back in the day. The list - which they revised in 2012 - mainly features American and British music from the 1960s and the 1970s.Īs an ardent music fan and an aspiring music producer, I listen to a wide variety of genres. The result was a special issue titled “The 500 Greatest Albums of All Time.” In 2003, Rolling Stones Magazine polled musicians, producers, and industry executives about their favorite albums. By Gautham Koorma Rolling Stone’s 500 Greatest Albums Visualized Using Pandas and Bokeh Source: 500 Greatest Albums of All Time
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