text_plain_output_1.png
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]

df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.shape

journals = df1[['title', 'abstract', 'publish_time', 'url', 'authors']]
journals['words'] = journals.abstract.str.strip().str.split('[\\W_]+')

abstracts = journals[journals.words.str.len() > 0]
abstracts.to_csv('COVID19_Journal_Abrstracts.csv')

abstracts.shape

Q1A = abstracts[abstracts['abstract'].str.contains('communicating')]
Q1A.shape

Q1B = abstracts[abstracts['abstract'].str.contains('reaching out')]
Q1B

Q1C = abstracts[abstracts['abstract'].str.contains('contacting')]
Q1C.shape

Q1D = abstracts[abstracts['abstract'].str.contains('elderly')]
Q1D.shape

Q1D = abstracts[abstracts['abstract'].str.contains('health care workers')]
Q1D.shape

Q1E = abstracts[abstracts['abstract'].str.contains('risk population')]
Q1E.shape

Question1 = pd.concat([Q1A, Q1B, Q1C, Q1D, Q1E])
Question1.dropna(inplace=True)
Question1.shape