bchan01

import glob
import zipfile
# Assume there's a directory 'test-dir' with few .txt files in it
files = glob.glob("test-dir/*.txt")
zip_file = zipfile.ZipFile('test.zip', 'w')
with zip_file:
    # writing each file one by one
    for file in files:
      zip_file.write(file)

bchan01

import glob
import zipfile
# Assume there's a directory 'test-dir' with few .txt files in it
files = glob.glob("test-dir/*.txt")
zip_file = zipfile.ZipFile('test.zip', 'w')
with zip_file:
    # writing each file one by one
    for file in files:
      zip_file.write(file)

bchan01

import glob
import zipfile
# Assume there's a directory 'test-dir' with few .txt files in it
files = glob.glob("test-dir/*.txt")
zip_file = zipfile.ZipFile('test.zip', 'w')
with zip_file:
    # writing each file one by one
    for file in files:
      zip_file.write(file)

bchan01

exports.handler = async (event, context) => {
  let count = 0;
  event.Records.forEach(record => {
    console.log('RECEIVE RECORD: ' + JSON.stringify(record, null, 4));
    count++;
  });
  return {'records' : count};
}

bchan01

from presidio_analyzer import AnalyzerEngine
import json

engine = AnalyzerEngine()

inputText = "Yo! Brandon is your SSN 100-11-1111? I want to charge $10.0 to your card 5555555555554444. Call me at 215-327-8888. I will pay you back at 36036545555"

# https://microsoft.github.io/presidio/field_types.html
response = engine.analyze(correlation_id=0,
                          text=inputText,

bchan01

import folium as folium

# Function to color code categories for number of positive cases
def get_icon_color(cases):
    if cases <= 10:
        return 'green'
    elif cases > 10 and cases < 100:
        return 'orange'
    else:
        return 'red'

bchan01

# Plot lines
ax = df_diff.plot(colormap='jet', marker='.', markersize=10, 
            title='Daily Changes in Cases',  figsize=(20,10))
# set labels for X and Y axes
ax.set_xlabel("Count")
ax.set_ylabel("Date")

bchan01

# Reload DataFrame and sort by Date
df_summary = pd.read_csv("pa_summary.csv")

# convert column to Integer for calculation
df_summary = df_summary.astype({"Positive": int, "Negative": int, "Deaths" : int})
df_summary['Tested'] = df_summary['Positive'] + df_summary['Negative']

# Set Index and sort by Date
df_summary.set_index('Date', inplace=True)
df_summary = df_summary.sort_values('Date', ascending = True)

bchan01

# Setup DataFrame for plotting
df_percent = pd.DataFrame(df, columns=['Date', 'Positive_Percent','Negative_Percent'])
df_percent.set_index("Date", inplace = True)

# Plot bar charts
style.use('ggplot')
bar_chart = df_percent.plot.bar(stacked=False, width=0.8, figsize=(20,15), title="Percentage of Positive and Negative Cases")
plt.xlabel('Date')
plt.ylabel('Percentage')
bar_chart.legend(loc=2)

bchan01

# Plot PA COVID-19 cases to date
style.use('ggplot')
ax = plt.gca()
df.plot(kind='line',x='Date',y='Positive', color='green', ax=ax, figsize=(20,10))
df.plot(kind='line',x='Date',y='Negative', color='blue', ax=ax)
df.plot(kind='line',x='Date',y='Deaths', color='red', ax=ax)
df.plot(kind='line',x='Date',y='Tested', color='black', ax=ax)
plt.title('Number of Cases and Deaths by Date')
plt.xlabel('Date')
plt.ylabel('Count')