python list of dictionaries artist and album

#Python list of dictionaries artist and album generator#
#Python list of dictionaries artist and album code#

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.

#Python list of dictionaries artist and album generator#

of 7 runs, 1 loop each)Īs you can see, generators are a better solution in comparison to the others, map is also slower compared to generator for reason I will leave up to OP to figure out.By Gautham Koorma Rolling Stone’s 500 Greatest Albums Visualized Using Pandas and Bokeh Source: 500 Greatest Albums of All Time

of 7 runs, 1 loop each)Ģ.29 s ± 4.58 ms per loop (mean ± std. : m = map(lambda i:i.get('value', None), l)ġ72 ns ± 0.393 ns per loop (mean ± std. Here's an example with the library named dpath, showing how it can simplify something just a bit more complicated: > dd = ] * 9000000 And there are nice libraries on PyPI for them.

There are a few different (quasi-)standard ways to specify XPath-style searches on nested dict-and-list structures, such as JSONPath, DPath, and KVC. For a very simple case like this, a comprehension, as in Ismail Badawi's answer is definitely the way to go.īut when things get more complicated, and you need to start writing multi-clause or nested comprehensions with complex expressions in them, it's worth looking into other alternatives.