Ebola Data Loader

In this notebook, we’ll format data from The World Health Organization for future analysis

%pylab inline
import pandas as pd
import re

Populating the interactive namespace from numpy and matplotlib

#read in the raw data
rawdata = pd.read_csv('Ebola_in_SL_Raw_WHO_Data.csv')
rawdata.iloc[1]

COUNTRY (CODE)                                     SLE
COUNTRY (DISPLAY)                         Sierra Leone
COUNTRY (URL)                                      NaN
EBOLA_MEASURE (CODE)                             CASES
EBOLA_MEASURE (DISPLAY)                Number of cases
EBOLA_MEASURE (URL)                                NaN
CASE_DEFINITION (CODE)                       CONFIRMED
CASE_DEFINITION (DISPLAY)                    Confirmed
CASE_DEFINITION (URL)                              NaN
EBOLA_DATA_SOURCE (CODE)                     PATIENTDB
EBOLA_DATA_SOURCE (DISPLAY)           Patient database
EBOLA_DATA_SOURCE (URL)                            NaN
EPI_WEEK (CODE)                               2015-W07
EPI_WEEK (DISPLAY)              09 to 15 February 2015
EPI_WEEK (URL)                                     NaN
INDICATOR_TYPE (CODE)                       SITREP_NEW
INDICATOR_TYPE (DISPLAY)                           New
INDICATOR_TYPE (URL)                               NaN
DATAPACKAGEID (CODE)                        2015-04-22
DATAPACKAGEID (DISPLAY)        Data package 2015-04-22
DATAPACKAGEID (URL)                                NaN
Display Value                                     92.0
Numeric                                           92.0
Low                                                NaN
High                                               NaN
Comments                                           NaN
Name: 1, dtype: object

#parse the dates column
import dateutil

def parsedate(week_string):
    end_date_str = re.split(' to ', week_string)[1]
    return(dateutil.parser.parse(end_date_str))

rawdata['End Date'] = rawdata['EPI_WEEK (DISPLAY)'].apply(parsedate)
rawdata.head()
COUNTRY (CODE) COUNTRY (DISPLAY) COUNTRY (URL) EBOLA_MEASURE (CODE) EBOLA_MEASURE (DISPLAY) EBOLA_MEASURE (URL) CASE_DEFINITION (CODE) CASE_DEFINITION (DISPLAY) CASE_DEFINITION (URL) EBOLA_DATA_SOURCE (CODE) ... INDICATOR_TYPE (URL) DATAPACKAGEID (CODE) DATAPACKAGEID (DISPLAY) DATAPACKAGEID (URL) Display Value Numeric Low High Comments End Date
0 SLE Sierra Leone NaN CASES Number of cases NaN CONFIRMED Confirmed NaN SITREP ... NaN 2015-04-22 Data package 2015-04-22 NaN NaN NaN NaN NaN NaN 2014-02-23
1 SLE Sierra Leone NaN CASES Number of cases NaN CONFIRMED Confirmed NaN PATIENTDB ... NaN 2015-04-22 Data package 2015-04-22 NaN 92.0 92.0 NaN NaN NaN 2015-02-15
2 SLE Sierra Leone NaN CASES Number of cases NaN CONFIRMED Confirmed NaN PATIENTDB ... NaN 2015-04-22 Data package 2015-04-22 NaN 455.0 455.0 NaN NaN NaN 2014-10-19
3 SLE Sierra Leone NaN CASES Number of cases NaN CONFIRMED Confirmed NaN SITREP ... NaN 2015-04-22 Data package 2015-04-22 NaN 63.0 63.0 NaN NaN NaN 2015-02-22
4 SLE Sierra Leone NaN CASES Number of cases NaN CONFIRMED Confirmed NaN SITREP ... NaN 2015-04-22 Data package 2015-04-22 NaN 80.0 80.0 NaN NaN NaN 2015-02-01

5 rows × 27 columns

data = rawdata[rawdata['EBOLA_DATA_SOURCE (CODE)']=='PATIENTDB']
data = data[['End Date','Numeric']]
data.sort_values('End Date', inplace=True)
data.dropna(inplace=True)
data['Timedelta'] = data['End Date']-data['End Date'].iloc[0]
data['Weeks'] = data['Timedelta'].apply(lambda a: pd.Timedelta(a).days/7)
data.set_index('Weeks', inplace=True)
data = data[['Numeric']]
data.columns=['New Reported Cases']
data['Cumulative Cases'] = data['New Reported Cases'].cumsum()

data.plot()

<AxesSubplot:xlabel='Weeks'>
../../_images/Ebola_Data_Loader_5_1.png 
data.to_csv('Ebola_in_SL_Data.csv')