P O P D A T A ### A Hundred Years of Dance Fads

•   Audrey Taylor-Akwenye
•   21-April

Here's are some quick visulatizations for plotting data counts. It shows the count of Dance Fads over the last 100 years. I saw a slight increase in dance fads that coincide with changes in music consumption mediums from radio, to TV, to streaming. Below is a histogram that may better show the spikes. ### The Code

1. #### Load the Data

Import pandas and read the csv.

``````
import pandas as pd

dances = pd.read_csv('dance.csv')
dances["Year"].astype(int); #ensure year is interger
dances.head()
```
```
2. #### Line Chart

Import Seaborn and Matplotlib. The x is the index as we are doing a line plot with cumulative counts. The y is the year. ``````
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt

x = dances.index
y = dances.Year
fig = plt.figure(figsize=(8, 6))

#adding plot title
plt.title('Dance Fad over the last 100 Years')

#adding horizontal lines
plt.axhline(y=1926, color='y', linestyle='-')
plt.axhline(y=1962, color='r', linestyle='-')
plt.axhline(y=1990, color='b', linestyle='-')
plt.axhline(y=2006, color='g', linestyle='-')

#adding test to the chart
plt.text(10, 1940, 'Radio Age')
plt.text(40, 1970, 'TV Age')
plt.text(60, 2000, 'MTV')
plt.text(80, 2010, 'YouTube')

ax = sns.lineplot(x=x, y=y, data=dances)

plt.show()
```
```
3. #### Bar Chart

Here we create a bar chart of the top ten years for Dance Fads. ``````
top_years = dances['Year'].value_counts().head(10)
top_years.plot.barh(color='purple')

plt.xlabel('Dance Fad Count')
plt.ylabel('Year')

plt.title("Top Years for Dance Fads", fontweight='bold');
```
``` ##### Audrey Taylor-Akwenye

Data Scientist, Educator, Entrepreneur