<jupyter_start><jupyter_text>Exploratory Data Analysis for International ***Author: <NAME>*** ***Last Updated: 4/1/2020******Description: Initial investigations on COVID-19 International data so as to discover patterns, spot anomalies, test hypothesis and check assumptions with the help of summary statistics and graphical representations. (Data from NextStrain and Coronavirus COVID19 API)*** **Importing Libraries**<jupyter_code>import os
import json
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
from matplotlib import style
style.use('ggplot')<jupyter_output><empty_output><jupyter_text>**Reading Data**<jupyter_code>os.chdir(r".\..\..\util\data")
df = pd.read_csv('international.csv')
api_url = 'https://api.covid19api.com/all'
response = requests.get(api_url)
data = ""
if response.status_code == 200:
    data = json.loads(response.content.decode('utf-8'))
else:
    data = "Error"
print(data[1])
df.head()
df.shape
nextstrain_total = df.groupby('country')['country'].count()
nextstrain_total.to_frame().style.background_gradient(cmap='Pastel1')<jupyter_output><empty_output><jupyter_text>Out of the 2887 cases in this dataset USA has the most with 634 following by UK with 426.<jupyter_code>gender_total = df.groupby('sex')['sex'].count().drop(index=['?', 'Unknown', 'unknwon'])
gender_total.to_frame().style.background_gradient(cmap='Pastel1')<jupyter_output><empty_output><jupyter_text>According to the data, there are more cases with males than females.<jupyter_code>age_total = df.groupby('age')['age'].count()
age_total = age_total.drop(index=['?'])

age_total.to_frame().style.background_gradient(cmap='Pastel1')
line = age_total.plot.line()
line.set_ylabel('Number of cases')
line.set_xlabel('Ages')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14042786615469008, 'fraction_numerical': 0.016456390565002744, 'mean_word_length': 7.5233644859813085, 'pattern_counts': {'":': 0, '<': 18, '<?xml version=': 0, '>': 18, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '390031', 'license': 'MIT', 'n_tokens_mistral': 622, 'n_tokens_neox': 584, 'n_words': 137, 'path': 'src/exploratory-data-analysis/EDA-International.ipynb', 'repo_name': 'UNCG-CSC-410/covid-19-data-analysis', 'size': '1828'}