Plotting 5 Most Popular Languages on AO3

tags: Python Seaborn barplot

• Data Cleaning
• Simple Bar Plot

Data Cleaning

Let's quickly go over how we load file, select column, and find the 5 most popular languages on AO3. For detailed explanations of what's going on, check out previous posts on this subject.

# Load Python library
import pandas as pd

# Load file
path="/home/pi/Downloads/works-20210226.csv"
chunker = pd.read_csv(path, chunksize=10000)
works = pd.concat(chunker, ignore_index=True)

# Select language col, drop na values, count frequencies of each language
top = works.language.dropna().value_counts().reset_index()
top.columns = ['language', 'work_count']

# Choose top 5 most popular languages
top5 = top[:5].copy()
top5

	language	work_count
0	en	6587693
1	zh	335179
2	ru	136724
3	es	70645
4	fr	32145

Simple Bar Plot

We use Seaborn library to plot the data into a simple bar plot.

# Import libraries
# Top line is Jupyter Notebook specific

%matplotlib inline

import matplotlib.pyplot as plt
import seaborn as sns

# Plot using Seaborn library
ax = sns.barplot(x="language", y="work_count", data=top5)

# Add title
ax.set_title("5 Most Popular Languages on AO3 \n 2008-2021")

# Prevent scientific notation with ticklabel_format()
ax.ticklabel_format(style='plain', axis='y')

# Add number annotation
for i in range(0,5):
    ax.annotate(str(top5['work_count'][i]), xy=(i,top5['work_count'][i]), horizontalalignment="center")