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main.py
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import os
import pandas as pd
from helpers import csv_to_df, round_float, dict_to_csv
dirname = os.path.dirname(__file__)
GOV_SOURCE_URL = 'https://raw.githubusercontent.com/cisagov/dotgov-data/main/current-federal.csv'
PULSE_SOURCE_URL = 'https://raw.githubusercontent.com/GSA/federal-website-index/main/data/dataset/pulse.csv'
DAP_SOURCE_URL = 'https://analytics.usa.gov/data/live/sites-extended.csv'
OMB_IDEA_SOURCE_URL = 'https://raw.githubusercontent.com/GSA/federal-website-index/main/data/dataset/omb_idea.csv'
EOTW_2020_SOURCE_URL = 'https://raw.githubusercontent.com/GSA/federal-website-index/main/data/dataset/2020_eot.csv'
USAGOV_DIRECTORY_SOURCE_URL = 'https://raw.githubusercontent.com/GSA/federal-website-index/main/data/dataset/usagov_directory.csv'
GOV_MAN_22_SOURCE_URL = 'https://raw.githubusercontent.com/GSA/federal-website-index/main/data/dataset/gov_man-22.csv'
USCOURTS_SOURCE_URL = 'https://raw.githubusercontent.com/GSA/federal-website-index/main/data/dataset/uscourts.csv'
OIRA_SOURCE_URL = 'https://raw.githubusercontent.com/GSA/federal-website-index/main/data/dataset/oira.csv'
MIL_SOURCE_URL_1 = 'https://raw.githubusercontent.com/GSA/federal-website-index/main/data/dataset/dotmil_websites.csv'
MIL_SOURCE_URL_2 = 'https://raw.githubusercontent.com/GSA/federal-website-index/main/data/dataset/dotmil_websites-2.csv'
MIL_DOMAINS_URL = 'https://raw.githubusercontent.com/GSA/federal-website-index/main/data/dataset/dotmil_domains.csv'
BRANCH_SOURCE_LIST_URL = 'https://raw.githubusercontent.com/cisagov/dotgov-data/main/current-federal.csv'
OTHER_WEBSITES_PATH = os.path.join(dirname, '../data/dataset/other-websites.csv')
INGORE_LIST_BEGINS_PATH= os.path.join(dirname, '../criteria/ignore-list-begins.csv')
IGNORE_LIST_CONTAINS_PATH= os.path.join(dirname, '../criteria/ignore-list-contains.csv')
IGNORE_LIST_EXCEPT_PATH = os.path.join(dirname, '../criteria/ignore-except.csv')
TARGET_URL_LIST_PATH = os.path.join(dirname, '../data/site-scanning-target-url-list.csv')
GOV_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/gov.csv')
PULSE_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/pulse.csv')
DAP_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/dap.csv')
OMB_IDEA_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/omb_idea.csv')
EOTW_2020_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/2020_eot.csv')
USAGOV_DIRECTORY_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/usagov_directory.csv')
GOV_MAN_22_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/gov_man_22.csv')
USCOURTS_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/uscourts.csv')
OIRA_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/oira.csv')
OTHER_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/other.csv')
COMBINED_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/combined.csv')
REMOVE_IGNORE_BEGINS_PATH = os.path.join(dirname, '../data/snapshots/remove-ignore-begins.csv')
REMOVE_IGNORE_CONTAINS_PATH = os.path.join(dirname, '../data/snapshots/remove-ignore-contains.csv')
DEDUPED_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/combined-dedup.csv')
DEDUP_REMOVED_SNAPSHOT_PATH = os.path.join(dirname, '../data/snapshots/dedup-removed.csv')
INGORED_REMOVED_BEGINS_PATH = os.path.join(dirname, '../data/snapshots/ignored-removed-begins.csv')
IGNORED_REMOVED_CONTAINS_PATH = os.path.join(dirname, '../data/snapshots/ignored-removed-contains.csv')
NONFEDERAL_REMOVED_PATH = os.path.join(dirname, '../data/snapshots/nonfederal-removed.csv')
ANALYSIS_CSV_PATH = os.path.join(dirname, '../data/site-scanning-target-url-list-analysis.csv')
URL_DF_PRE_BASE_DOMAINS_MERGED = os.path.join(dirname, '../data/test/url_df_pre_base_domains_merged.csv')
URL_DF_POST_BASE_DOMAINS_MERGED = os.path.join(dirname, '../data/test/url_df_post_base_domains_merged.csv')
def fetch_data(analysis):
# import data
gov_df = csv_to_df(GOV_SOURCE_URL)
pulse_df = csv_to_df(PULSE_SOURCE_URL)
dap_df = csv_to_df(DAP_SOURCE_URL)
# datasets added in February, 2024
omb_idea_df = csv_to_df(OMB_IDEA_SOURCE_URL)
eotw_df = csv_to_df(EOTW_2020_SOURCE_URL)
usagov_df = csv_to_df(USAGOV_DIRECTORY_SOURCE_URL, has_headers=False)
gov_man_df = csv_to_df(GOV_MAN_22_SOURCE_URL, has_headers=False)
uscourts_df = csv_to_df(USCOURTS_SOURCE_URL, has_headers=False)
oira_df = csv_to_df(OIRA_SOURCE_URL)
# other websites
other_df = pd.read_csv(OTHER_WEBSITES_PATH)
# military
first_mil_df= csv_to_df(MIL_SOURCE_URL_1)
second_mil_df = csv_to_df(MIL_SOURCE_URL_2, has_headers=False)
# track length of source datasets
analysis['gov url list length'] = len(gov_df.index)
analysis['pulse url list length'] = len(pulse_df.index)
analysis['dap url list length'] = len(dap_df.index)
analysis['omb idea url list length'] = len(omb_idea_df.index)
analysis['eotw url list length'] = len(eotw_df.index)
analysis['usagov url list length'] = len(usagov_df.index)
analysis['gov_man url list length'] = len(gov_man_df.index)
analysis['uscourts url list length'] = len(uscourts_df.index)
analysis['oira url list length'] = len(oira_df.index)
analysis['.mil first url list length'] = len(first_mil_df.index)
analysis['.mil second url list length'] = len(second_mil_df.index)
analysis['other website url list length'] = len(other_df.index)
# create new snapshots of source files
gov_df.to_csv(GOV_SNAPSHOT_PATH, index=False)
pulse_df.to_csv(PULSE_SNAPSHOT_PATH, index=False)
dap_df.to_csv(DAP_SNAPSHOT_PATH, index=False)
omb_idea_df.to_csv(OMB_IDEA_SNAPSHOT_PATH, index=False)
eotw_df.to_csv(EOTW_2020_SNAPSHOT_PATH, index=False)
usagov_df.to_csv(USAGOV_DIRECTORY_SNAPSHOT_PATH, index=False)
gov_man_df.to_csv(GOV_MAN_22_SNAPSHOT_PATH, index=False)
uscourts_df.to_csv(USCOURTS_SNAPSHOT_PATH, index=False)
oira_df.to_csv(OIRA_SNAPSHOT_PATH, index=False)
other_df.to_csv(OTHER_SNAPSHOT_PATH, index=False)
return gov_df, pulse_df, dap_df, omb_idea_df, eotw_df, usagov_df, gov_man_df, \
uscourts_df, oira_df, other_df, first_mil_df, second_mil_df, analysis
def format_gov_df(df):
# drop unnecessary columns
df = df.drop(columns=['City', 'State', 'Security contact email'])
# rename columns
df = df.rename(columns={'Domain name': 'target_url', 'Domain type': 'branch', 'Agency': 'agency', 'Organization name': 'bureau'})
# convert to lowercase
df['target_url'] = df['target_url'].str.lower()
# remove duplicates
df = df.drop_duplicates(subset='target_url')
# set base domain
df['base_domain_gov'] = df['target_url']
# set source column
df['source_list_federal_domains'] = 'TRUE'
# strip out 'Federal - ' leading string from domain type column for .gov data
df['branch'] = df['branch'].map(lambda x: x.lstrip('Federal - '))
# add www. to .gov URLs
www_gov_df = df.copy()
www_gov_df['target_url'] = 'www.' + www_gov_df['target_url'].astype(str)
df = pd.concat([df, www_gov_df])
return df
def format_pulse_df(df):
# drop unnecessary columns
df = df.drop(columns=['URL', 'Agency', 'Sources', 'Compliant with M-15-13 and BOD 18-01', 'Enforces HTTPS',
'Strict Transport Security (HSTS)', 'Free of RC4/3DES and SSLv2/SSLv3', '3DES', 'RC4', 'SSLv2', 'SSLv3',
'Preloaded'])
# rename columns
df = df.rename(columns={'Domain': 'target_url', 'Base Domain': 'base_domain_pulse'})
# get subset
df = df[['target_url', 'base_domain_pulse']]
# convert to lowercase
df['target_url'] = df['target_url'].str.lower()
# remove duplicates
df = df.drop_duplicates(subset='target_url')
# set source column
df['source_list_pulse'] = 'TRUE'
return df
def format_dap_df(df):
df = df.drop(columns=['visits'])
df = df.rename(columns={'domain': 'target_url'})
df['target_url'] = df['target_url'].str.lower()
df = df.drop_duplicates(subset='target_url')
df['source_list_dap'] = 'TRUE'
return df
def format_omb_idea_df(df):
df = df.rename(columns={'Website': 'target_url', 'Public-Facing': 'omb_idea_public'})
df['target_url'] = df['target_url'].str.lower()
df = df.drop_duplicates(subset='target_url')
df['source_list_omb_idea'] = 'TRUE'
df['omb_idea_public'] = df['omb_idea_public'].map({'Yes': 'TRUE', 'No': 'FALSE'})
df = df.drop_duplicates()
return df
def format_eotw_df(df):
df = df.rename(columns={'URL': 'target_url'})
df['target_url'] = df['target_url'].str.lower()
df = df.drop_duplicates(subset='target_url')
df['source_list_eotw'] = 'TRUE'
return df
def format_usagov_df(df):
df = df.rename(columns={0: 'target_url'})
df['target_url'] = df['target_url'].str.lower()
df = df.drop_duplicates(subset='target_url')
df['source_list_usagov'] = 'TRUE'
return df
def format_gov_man_df(df):
df = df.rename(columns={0: 'target_url'})
df['target_url'] = df['target_url'].str.lower()
df = df.drop_duplicates(subset='target_url')
df['source_list_gov_man'] = 'TRUE'
return df
def format_uscourts_df(df):
df = df.rename(columns={0: 'target_url'})
df['target_url'] = df['target_url'].str.lower()
df = df.drop_duplicates(subset='target_url')
df['source_list_uscourts'] = 'TRUE'
return df
def format_oira_df(df):
df = df.rename(columns={'URL': 'target_url'})
df['target_url'] = df['target_url'].str.lower()
df = df.drop_duplicates(subset='target_url')
df['source_list_oira'] = 'TRUE'
return df
def format_other_df(df):
df['source_list_other'] = 'TRUE'
return df
def format_first_mil_df(df):
df = df.rename(columns={'Website': 'target_url'})
df = df.apply(lambda col: col.map(lambda x: x.lower() if isinstance(x, str) else x))
df = df.drop_duplicates(subset='target_url')
df['source_list_mil_1'] = 'TRUE'
return df
def format_second_mil_df(df):
df = df.rename(columns={0: 'target_url'})
df = df.apply(lambda col: col.map(lambda x: x.lower() if isinstance(x, str) else x))
df = df.drop_duplicates(subset='target_url')
df['source_list_mil_2'] = 'TRUE'
return df
def merge_agencies(df, agency_df):
df = df.merge(agency_df, on='base_domain', how='left')
df = df.fillna('')
df['agency'] = ''
for idx, row in df.iterrows():
if row['agency'] == '':
if row['agency_x'] != '':
df.at[idx, 'agency'] = row['agency_x']
else:
df.at[idx, 'agency'] = row['agency_y']
# drop temp agency columns
df = df.drop(columns=['agency_x', 'agency_y'])
return df
def merge_bureaus(df, bureau_df):
df = df.merge(bureau_df, on='base_domain', how='left')
df = df.fillna('')
df['bureau'] = ''
for idx, row in df.iterrows():
if row['bureau'] == '':
if row['bureau_x'] != '':
df.at[idx, 'bureau'] = row['bureau_x']
else:
df.at[idx, 'bureau'] = row['bureau_y']
# drop temp bureau columns
df = df.drop(columns=['bureau_x', 'bureau_y'])
return df
def build_target_url_list():
# initialize analysis dict
analysis = {}
# import data
gov_df_raw, pulse_df_raw, dap_df_raw, omb_idea_df_raw, eotw_df_raw, usagov_df_raw, gov_man_df_raw, \
uscourts_df_raw, oira_df_raw, other_df_raw, first_mil_df_raw, second_mil_df_raw, analysis = fetch_data(analysis)
# format and clean data
gov_df = format_gov_df(gov_df_raw)
pulse_df = format_pulse_df(pulse_df_raw)
dap_df = format_dap_df(dap_df_raw)
other_df = format_other_df(other_df_raw)
omb_idea_df = format_omb_idea_df(omb_idea_df_raw)
eotw_df = format_eotw_df(eotw_df_raw)
usagov_df = format_usagov_df(usagov_df_raw)
gov_man_df = format_gov_man_df(gov_man_df_raw)
uscourts_df = format_uscourts_df(uscourts_df_raw)
oira_df = format_oira_df(oira_df_raw)
first_mil_df = format_first_mil_df(first_mil_df_raw)
second_mil_df = format_second_mil_df(second_mil_df_raw)
# combine all URLs into one column
print("Combining all URLs into one column")
url_series = pd.concat([gov_df['target_url'], pulse_df['target_url'],
dap_df['target_url'], other_df['target_url'],
omb_idea_df['target_url'], eotw_df['target_url'],
usagov_df['target_url'], gov_man_df['target_url'],
uscourts_df['target_url'], oira_df['target_url'],
first_mil_df['target_url'], second_mil_df['target_url']])
url_df = pd.DataFrame(url_series)
analysis['combined url list length'] = len(url_df.index)
url_df.to_csv(COMBINED_SNAPSHOT_PATH, index=False)
# remove duplicates
url_series = url_df['target_url']
duplicated_df = url_df[url_series.isin(url_series[url_series.duplicated()])].sort_values("target_url")
duplicated_df = duplicated_df.drop_duplicates()
duplicated_df.to_csv(DEDUP_REMOVED_SNAPSHOT_PATH, index=False)
url_df = url_df.drop_duplicates('target_url')
url_df = url_df.dropna()
analysis['deduped url list length'] = len(url_df.index)
url_df.to_csv(DEDUPED_SNAPSHOT_PATH, index=False)
# remove URLs with ignore-listed strings at the beginning of urls
ignore_df = pd.read_csv(INGORE_LIST_BEGINS_PATH)
ignore_series = ignore_df['URL begins with:']
ignored_df = url_df[url_df['target_url'].str.startswith(tuple(ignore_series))]
ignored_df.to_csv(INGORED_REMOVED_BEGINS_PATH, index=False)
url_df = url_df[~url_df['target_url'].str.startswith(tuple(ignore_series))]
analysis['url list length after ignore list checking beginnning of urls processed'] = len(url_df.index)
url_df.to_csv(REMOVE_IGNORE_BEGINS_PATH, index=False)
# remove URLs with ignore-listed strings contained anywhere in urls
ignore_df = pd.read_csv(IGNORE_LIST_CONTAINS_PATH)
ignore_series = ignore_df['URL contains between non-word characters:']
pattern = r'[^a-zA-Z0-9](?:{})[^a-zA-Z0-9]'.format('|'.join(ignore_series.array))
ignored_df = url_df[url_df['target_url'].str.contains(pattern)]
ignored_df.to_csv(IGNORED_REMOVED_CONTAINS_PATH, index=False)
url_df = url_df[~url_df['target_url'].str.contains(pattern)]
analysis['url list length after ignore list checking entire url'] = len(url_df.index)
url_df.to_csv(REMOVE_IGNORE_CONTAINS_PATH, index=False)
# ...and then reinstate URLs that we should not ignore
ignore_except_df = pd.read_csv(IGNORE_LIST_EXCEPT_PATH)
ignore_except_df = ignore_except_df.rename(columns={'URL': 'target_url'})
url_df = pd.concat([url_df, ignore_except_df])
# merge data back in
url_df = url_df.merge(gov_df, on='target_url', how='left')
url_df = url_df.merge(pulse_df, on='target_url', how='left')
url_df = url_df.merge(dap_df, on='target_url', how='left')
url_df = url_df.merge(omb_idea_df, on='target_url', how='left')
url_df = url_df.merge(eotw_df, on='target_url', how='left',)
url_df = url_df.merge(usagov_df, on='target_url', how='left')
url_df = url_df.merge(gov_man_df, on='target_url', how='left')
url_df = url_df.merge(uscourts_df, on='target_url', how='left')
url_df = url_df.merge(oira_df, on='target_url', how='left')
url_df = url_df.merge(other_df, on='target_url', how='left')
url_df = url_df.merge(first_mil_df, on='target_url', how='left')
url_df = url_df.merge(second_mil_df, on='target_url', how='left')
url_df = url_df.fillna('')
url_df.to_csv(URL_DF_PRE_BASE_DOMAINS_MERGED, index=False)
# populate base domain column
url_df['base_domain'] = ''
for idx, row in url_df.iterrows():
if row['base_domain_gov'] != '':
url_df.at[idx, 'base_domain'] = row['base_domain_gov']
elif row['base_domain_pulse'] != '':
url_df.at[idx, 'base_domain'] = row['base_domain_pulse']
else:
url_df.at[idx, 'base_domain'] = '.'.join(row['target_url'].split('.')[-2:])
url_df.to_csv(URL_DF_POST_BASE_DOMAINS_MERGED, index=False)
# get relevant subset
url_df = url_df[['target_url', 'base_domain', 'branch', 'agency', 'bureau',
'source_list_federal_domains', 'source_list_pulse',
'source_list_dap', 'source_list_omb_idea', 'source_list_eotw',
'source_list_usagov', 'source_list_gov_man', 'source_list_uscourts',
'source_list_oira','source_list_other', 'source_list_mil_1',
'source_list_mil_2', 'omb_idea_public']]
# format source columns
url_df['source_list_federal_domains'] = url_df['source_list_federal_domains'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_pulse'] = url_df['source_list_pulse'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_dap'] = url_df['source_list_dap'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_omb_idea'] = url_df['source_list_omb_idea'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_eotw'] = url_df['source_list_eotw'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_usagov'] = url_df['source_list_usagov'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_gov_man'] = url_df['source_list_gov_man'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_uscourts'] = url_df['source_list_uscourts'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_oira'] = url_df['source_list_oira'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_other'] = url_df['source_list_other'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_mil_1'] = url_df['source_list_mil_1'].map(lambda x: 'FALSE' if x == '' else x)
url_df['source_list_mil_2'] = url_df['source_list_mil_2'].map(lambda x: 'FALSE' if x == '' else x)
# populate branch field
branch_df = csv_to_df(BRANCH_SOURCE_LIST_URL)
merged_df = pd.merge(url_df, branch_df, left_on='base_domain', right_on='Domain name', how='left')
url_df['branch'] = url_df['branch'].combine_first(merged_df['Domain type'])
url_df.loc[url_df['branch'] == '', 'branch'] = merged_df['Domain type']
url_df['branch'] = url_df['branch'].str.replace('^Federal - ', '', regex=True)
# get lookup table of agencies mapped to base domain for .gov urls
agency_df = gov_df[['base_domain_gov', 'agency']]
agency_df = agency_df.rename(columns={'base_domain_gov': 'base_domain'})
agency_df = agency_df.drop_duplicates()
# get lookup table of bureaus mapped to base domain for .gov urls
bureau_df = gov_df[['base_domain_gov', 'bureau']]
bureau_df = bureau_df.rename(columns={'base_domain_gov': 'base_domain'})
bureau_df = bureau_df.drop_duplicates()
# merge in agencies for .gov urls
url_df = merge_agencies(url_df, agency_df)
# merge in bureaus for .gov urls
url_df = merge_bureaus(url_df, bureau_df)
# populate agencies and bureaus for .mil
mil_domains_df = csv_to_df(MIL_DOMAINS_URL)
mil_domains_df = mil_domains_df.rename(columns={'Domain name': 'base_domain', 'Agency': 'agency', 'Organization name': 'bureau', 'Domain type': 'branch'})
mil_domains_df['branch'] = mil_domains_df['branch'].map(lambda x: x.lstrip('Federal - '))
url_df = url_df.merge(mil_domains_df, on='base_domain', how='left')
# merge in agencies for .mil urls
url_df = merge_agencies(url_df, agency_df)
# merge in bureaus for .mil urls
url_df = merge_bureaus(url_df, bureau_df)
# merge branch column
url_df['branch'] = ''
for idx, row in url_df.iterrows():
if row['branch_x'] != '':
url_df.at[idx, 'branch'] = row['branch_x']
elif row['branch_y'] != '':
url_df.at[idx, 'branch'] = row['branch_y']
url_df = url_df.drop(columns=['branch_x'])
url_df = url_df.drop(columns=['branch_y'])
# reorder columns, sort
url_df = url_df[['target_url', 'base_domain', 'branch', 'agency', 'bureau',
'source_list_federal_domains', 'source_list_dap', 'source_list_pulse',
'source_list_omb_idea', 'source_list_eotw', 'source_list_usagov',
'source_list_gov_man', 'source_list_uscourts', 'source_list_oira',
'source_list_other', 'source_list_mil_1', 'source_list_mil_2',
'omb_idea_public']]
url_df = url_df.sort_values(by=['base_domain', 'target_url'])
# remove all non-.gov and non-.mil urls
gov_base_domains = set(gov_df.base_domain_gov)
analysis['number of .gov base domains'] = len(gov_base_domains)
mil_domains_df = csv_to_df(MIL_DOMAINS_URL)
mil_domains_set = set(mil_domains_df['Domain name'])
analysis['number of .mil base domains'] = len(mil_domains_set)
url_df['is_gov'] = url_df['base_domain'].apply(lambda x: x in gov_base_domains)
url_df['is_mil'] = url_df['base_domain'].apply(lambda x: x in mil_domains_set)
# populate top_level_domain column
url_df['top_level_domain'] = url_df.apply(lambda row: '.gov' if row['is_gov'] else ('.mil' if row['is_mil'] else None), axis=1)
non_gov_df = url_df[(url_df['is_gov'] == False) & (url_df['is_mil'] == False)]
non_gov_df = non_gov_df.drop(columns=['is_gov'])
non_gov_df = non_gov_df.drop(columns=['is_mil'])
non_gov_df.to_csv(NONFEDERAL_REMOVED_PATH, index=False)
analysis['number of urls with non-.gov or non-.mil base domains removed'] = len(non_gov_df.index)
url_df = url_df[(url_df['is_gov'] == True) | (url_df['is_mil'] == True)]
url_df = url_df.drop(columns=['is_gov'])
url_df = url_df.drop(columns=['is_mil'])
analysis['url list length after non-federal urls removed'] = len(url_df.index)
# log omb_idea_public counts
true_count = url_df['omb_idea_public'].value_counts().get('TRUE', 0)
false_count = url_df['omb_idea_public'].value_counts().get('FALSE', 0)
analysis['Number of omb_idea_public fields = TRUE'] = true_count
analysis['Number of omb_idea_public fields = FALSE'] = false_count
blank_count = url_df['omb_idea_public'].isnull().sum() + (url_df['omb_idea_public'] == '').sum()
analysis['Number of omb_idea_public fields = blank'] = blank_count
other_count = len(url_df) - (true_count + false_count + blank_count)
analysis['Number of omb_idea_public fields that != TRUE FALSE or blank'] = other_count
# reorder columns
url_df = url_df[['target_url', 'base_domain', 'top_level_domain', 'branch', 'agency',
'bureau', 'source_list_federal_domains', 'source_list_dap',
'source_list_pulse', 'source_list_omb_idea', 'source_list_eotw',
'source_list_usagov', 'source_list_gov_man', 'source_list_uscourts',
'source_list_oira', 'source_list_other', 'source_list_mil_1',
'source_list_mil_2', 'omb_idea_public']]
# final deduplication
final_df = url_df.drop_duplicates(subset='target_url')
# write list to csv
final_df.to_csv(TARGET_URL_LIST_PATH, index=False)
# write analysis to csv
dict_to_csv(ANALYSIS_CSV_PATH, analysis)
if __name__ == '__main__':
build_target_url_list()