Data from FantLab Library (https://fantlab.ru/)
import pandas as pd
df = pd.read_csv('FantLab-data-Kaggle.csv', sep=';')
df.head()
df.count()
df[0:50]
df['class-label'].value_counts().plot(kind='bar')
df['year'].value_counts().plot(kind='bar', figsize=(25,8))
df['year'].min()
df['year'].max()
df['year'].value_counts()[df['year'].value_counts() > 40].plot(kind='bar', figsize=(20,8))
df['place of action'].value_counts().plot(kind='bar', figsize=(15,8))
df['language'].value_counts().plot(kind='bar', figsize=(15,8))
df['author'].value_counts()[df['author'].value_counts() > 8].plot(kind='bar', figsize=(15,8))
Sherratt, Tim. (2019, November 17). GLAM-Workbench/csv-explorer (Version v0.1.0). Zenodo. http://doi.org/10.5281/zenodo.3544712
Data from FantLab Library (https://fantlab.ru/)