diff --git a/Data Packages/us-elections.html b/Data Packages/us-elections.html index 1ed5733..3ca5002 100644 --- a/Data Packages/us-elections.html +++ b/Data Packages/us-elections.html @@ -401,7 +401,8 @@

Contents

LoC Data Package Tutorial: United States Elections, Web Archives Data Package#

-

version 2.0

+

Version of this notebook: 2.1

+

Last run: 2025-01-17

This notebook will demonstrate basic usage of using Python for interacting with data packages from the Library of Congress via the United States Elections, Web Archives Data Package which is derived from the Library’s United States Elections Web Archive. We will:

  1. Output data package sumary

    2. @@ -418,6 +419,7 @@

    Output data package summary 
    import ast  # For reading structured data from metadata.csv
+import io # For reading remote CSV files with the pandas module
 import pandas as pd  # For reading, manipulating, and displaying data
 import requests  # For retrieving online files
 import sys # For general system tasks
@@ -487,11 +489,11 @@ 
    Query the metadata in the data packagemetadata.csv file can be used to target content just from political candidate website domains.
    
 
    
 
    
-
    metadata_url = f'{DATA_URL}metadata.json'
+
    metadata_url = f'{DATA_URL}metadata.csv'
 is_blocked, response = make_request(metadata_url, headers=HEADERS)
-data = response.json()
+data = io.StringIO(response.text)
 
-metadata_df = pd.DataFrame(data)
+metadata_df = pd.read_csv(data)
 
 print(f'Loaded metadata file with {len(metadata_df):,} entries.')
 
    
@@ -506,8 +508,7 @@ 
    Query the metadata in the data package
 
    
-
    # metadata_df = pd.read_csv(r"C:\Users\rtrent\git\lcwa-election-datasets\metadata\full_metadata_2000-2016.csv", dtype=str) # just for testing
-print(', '.join(metadata_df.columns.to_list()))
+
    print(', '.join(metadata_df.columns.to_list()))
 
    
 
    
 
    
@@ -539,74 +540,15 @@ 
    Interpreting the metadata fields3460 (which we happen to know represents the only candidate in metadata.csv to have campaigned in two races in the same year under different parties):
    
 
    
 
    
-
    # First, let's make sure that our dataframe columns with lists are interpretted correctly. 
-metadata_df['website_elections'] = metadata_df['website_elections'].apply(ast.literal_eval)
-metadata_df['website_parties'] = metadata_df['website_parties'].apply(ast.literal_eval)
-metadata_df['website_places'] = metadata_df['website_places'].apply(ast.literal_eval)
-metadata_df['website_districts'] = metadata_df['website_districts'].apply(ast.literal_eval)
-metadata_df['item_all_years'] = metadata_df['item_all_years'].apply(ast.literal_eval)
-metadata_df['website_all_years'] = metadata_df['website_all_years'].apply(ast.literal_eval)
-metadata_df['website_scopes'] = metadata_df['website_scopes'].apply(ast.literal_eval)
-
    
-
    
-
    
-
    
-
    
-
    
-
    row = 3460 # You can change this row number
+
    row_num = 3460 # You can change this row number
 
-# We'll grab all the info we need from our row. 
-item_title = metadata_df.iloc[row]['item_title']
-website_url = metadata_df.iloc[row]['website_url']
-collection = metadata_df.iloc[row]['collection']
-candidate_name = item_title.split('-')[1].strip()
-year = collection.split(',')[1].strip()
-campaign_count = len(metadata_df.iloc[row]['website_elections'])
-website_elections = metadata_df.iloc[row]['website_elections']
-website_parties = metadata_df.iloc[row]['website_parties']
-website_places = metadata_df.iloc[row]['website_places']
-website_districts = metadata_df.iloc[row]['website_districts']
-website_all_years = metadata_df.iloc[row]['website_all_years']
-website_all_years.sort()
-item_all_years = metadata_df.iloc[row]['item_all_years']
-item_all_years.sort()
-item_id = metadata_df.iloc[row]['item_id']
-mods_url = metadata_df.iloc[row]['mods_url']
-
-# Now we'll plug those variables into our sentences.
-print(f'Record #{row} in the metadata.csv is: {website_url}, from the collection "{collection}".')
-print(f'This row represents the website in {year}, used for campaign(s) of the candidate: {candidate_name}.') 
-print(f'In {year}, this candidate used this website in {campaign_count} campaign(s):')
-i=0
-while i < campaign_count:
-    if website_districts[i] is None:
-        house_district = ''  
-    else:
-        house_district = website_districts[i]
-    print(f'  {i}. {website_elections[i]} | {website_parties[i]} | {website_places[i]} | {house_district}')
-    i += 1
-if len(website_all_years)>1:   
-    print(f'This website ({website_url}) was also used for these other campaign year(s) for {candidate_name}: {list(set(website_all_years)-set(year))}')
-print(f'In total, this and possibly other websites were collected for this candidate in the following year(s): {list(set(item_all_years))}')
-print(f'The loc.gov item record for {candidate_name} campaign sites can be viewed at {item_id}, and its MODS record can be viewed at {mods_url}.')
-
-# The next line displays our dataframe as a table. Let's set it to show up to 300 characters in each cell
-pd.options.display.max_colwidth = 300
-
-print('Here is how this row appears in `metadata.csv`:')                       
-metadata_df[row:row+1]
+print(f'Here is how row #{row_num} appears in `metadata.csv`:')                       
+metadata_df[row_num:row_num+1]
 
    
 
    
 
    
 
    
-
    Record #3460 in the metadata.csv is: http://www.usmjp.com/, from the collection "United States Elections, 2012".
-This row represents the website in 2012, used for campaign(s) of the candidate: Cris Ericson.
-In 2012, this candidate used this website in 2 campaign(s):
-  0. United States. Congress. Senate | U.S. Marijuana Party | Vermont | 
-  1. Vermont. Governor | Independent candidates | Vermont | 
-In total, this and possibly other websites were collected for this candidate in the following year(s): [2018, 2002, 2004, 2006, 2008, 2010, 2012]
-The loc.gov item record for Cris Ericson campaign sites can be viewed at http://www.loc.gov/item/lcwaN0002501/, and its MODS record can be viewed at https://tile.loc.gov/storage-services/service/webcapture/project_1/mods/united-states-elections-web-archive/lcwaN0002501.xml.
-Here is how this row appears in `metadata.csv`:
+
    Here is how row #3460 appears in `metadata.csv`:
 
    
 
    
 
    
@@ -653,31 +595,101 @@ 
    Interpreting the metadata fields
 
    
-
    # We'll create a function to generate summary information about a given type of election
+
    metadata_df['website_elections'] = metadata_df['website_elections'].apply(ast.literal_eval)
+metadata_df['website_parties'] = metadata_df['website_parties'].apply(ast.literal_eval)
+metadata_df['website_places'] = metadata_df['website_places'].apply(ast.literal_eval)
+metadata_df['website_districts'] = metadata_df['website_districts'].apply(ast.literal_eval)
+metadata_df['item_all_years'] = metadata_df['item_all_years'].apply(ast.literal_eval)
+metadata_df['website_all_years'] = metadata_df['website_all_years'].apply(ast.literal_eval)
+metadata_df['website_scopes'] = metadata_df['website_scopes'].apply(ast.literal_eval)
+
    
+
    
+
    
+
    
+
    In the next cell, we’ll grab all the info we need, then plug that info into a statement that describes our CSV row of data.
    
+
    
+
    
+
    row = metadata_df.iloc[row_num]
+item_title = row['item_title']
+website_url = row['website_url']
+collection = row['collection']
+candidate_name = item_title.split('-')[1].strip()
+year = collection.split(',')[1].strip()
+campaign_count = len(row['website_elections'])
+website_elections = row['website_elections']
+website_parties = row['website_parties']
+website_places = row['website_places']
+website_districts = row['website_districts']
+website_all_years = row['website_all_years']
+website_all_years.sort()
+item_all_years = row['item_all_years']
+item_all_years.sort()
+item_id = row['item_id']
+mods_url = row['mods_url']
+
+# Now we'll plug those variables into our sentences.
+print(f'Record #{row_num} in the metadata.csv is: {website_url}, from the collection "{collection}".')
+print(f'This row represents the website in {year}, used for campaign(s) of the candidate: {candidate_name}.') 
+print(f'In {year}, this candidate used this website in {campaign_count} campaign(s):')
+i=0
+while i < campaign_count:
+    if website_districts[i] is None:
+        house_district = ''  
+    else:
+        house_district = website_districts[i]
+    print(f'  {i}. {website_elections[i]} | {website_parties[i]} | {website_places[i]} | {house_district}')
+    i += 1
+if len(website_all_years)>1:   
+    print(f'This website ({website_url}) was also used for these other campaign year(s) for {candidate_name}: {list(set(website_all_years)-set(year))}')
+print(f'In total, this and possibly other websites were collected for this candidate in the following year(s): {list(set(item_all_years))}')
+print(f'The loc.gov item record for {candidate_name} campaign sites can be viewed at {item_id}, and its MODS record can be viewed at {mods_url}.')
 
-def election_summary(election_type):
-    websites_by_year = metadata_df[metadata_df['website_elections'].apply(lambda elections: any(election_type is election for election in elections ))]
+# The next line displays our dataframe as a table. Let's set it to show up to 300 characters in each cell
+pd.options.display.max_colwidth = 300
+
    
+
    
+
    
+
    
+
    Record #3460 in the metadata.csv is: http://www.usmjp.com/, from the collection "United States Elections, 2012".
+This row represents the website in 2012, used for campaign(s) of the candidate: Cris Ericson.
+In 2012, this candidate used this website in 2 campaign(s):
+  0. United States. Congress. Senate | U.S. Marijuana Party | Vermont | 
+  1. Vermont. Governor | Independent candidates | Vermont | 
+In total, this and possibly other websites were collected for this candidate in the following year(s): [2018, 2002, 2004, 2006, 2008, 2010, 2012]
+The loc.gov item record for Cris Ericson campaign sites can be viewed at http://www.loc.gov/item/lcwaN0002501/, and its MODS record can be viewed at https://tile.loc.gov/storage-services/service/webcapture/project_1/mods/united-states-elections-web-archive/lcwaN0002501.xml.
+
    
+
    
+
    
+
    
+
    Now let’s create a function that allows us to get summary information about any election, like the election for Vermont Governor.
    
+
    
+
    
+
    def election_summary(election_type):
+    '''
+    Input an election title. Output is a summary of CSV rows from that election. 
+    '''
+    websites_by_year = metadata_df[metadata_df['website_elections'].apply(lambda elections: any([election == election_type for election in elections]))]
     candidates = websites_by_year['item_title'].unique()
     websites = websites_by_year['website_url'].unique()
     years = [collection.split(',')[1].strip() for collection in websites_by_year['collection'].unique()]
@@ -686,14 +698,20 @@ 
    Interpreting the metadata fieldsmulti_year_websites = websites_by_year[websites_by_year['website_all_years'].str.len()>1]['website_url'].unique()
     print(f'Found in metadata.csv: {len(websites)} unique campaign websites for {len(candidates)} "{election_type}" candidates, ranging from years {min_year} - {max_year}.')
     print(f'{len(multi_year_websites)} of these websites were used multiple years.')
-
-election_summary('Vermont. Governor')
+
    
+
    
+
    
+
    
+
    Let’s try our function to get get as summary of Vermont gubernatorial candidates represented in this data package.
    
+
    
+
    
+
    election_summary('Vermont. Governor')
 
    
 
    
 
    
 
    
-
    Found in metadata.csv: 0 unique campaign websites for 0 "Vermont. Governor" candidates, ranging from years n/a - n/a.
-0 of these websites were used multiple years.
+
    Found in metadata.csv: 31 unique campaign websites for 22 "Vermont. Governor" candidates, ranging from years 2002 - 2016.
+13 of these websites were used multiple years.
 
    
 
    
 
    
@@ -1968,13 +1986,13 @@ 
    Filtering by domain
    Domains from the 2000 US Elections collection:
 
    
 
    
-
    ['algore2000.com',
- 'harrybrowne2000.org',
- 'gopatgo2000.org',
- 'algore.com',
+
    ['gopatgo2000.org',
  'keyes2000.org',
+ 'forum.hagelin.org',
  'hagelin.org',
- 'forum.hagelin.org']
+ 'algore2000.com',
+ 'algore.com',
+ 'harrybrowne2000.org']
 
    
 
    
 
    
diff --git a/_sources/Data Packages/us-elections.ipynb b/_sources/Data Packages/us-elections.ipynb
index 55a17f6..ba553a2 100644
--- a/_sources/Data Packages/us-elections.ipynb	
+++ b/_sources/Data Packages/us-elections.ipynb	
@@ -6,7 +6,9 @@
    "source": [
     "# LoC Data Package Tutorial: United States Elections, Web Archives Data Package\n",
     "\n",
-    "version 2.0\n",
+    "**Version of this notebook**: 2.1\n",
+    "\n",
+    "**Last run**: 2025-01-17\n",
     "\n",
     "This notebook will demonstrate basic usage of using Python for interacting with [data packages from the Library of Congress](https://data.labs.loc.gov/packages/) via the [United States Elections, Web Archives Data Package](https://data.labs.loc.gov/us-elections/) which is derived from the Library's [United States Elections Web Archive](https://www.loc.gov/collections/united-states-elections-web-archive/). We will:\n",
     "\n",
@@ -30,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -82,6 +84,7 @@
    ],
    "source": [
     "import ast  # For reading structured data from metadata.csv\n",
+    "import io # For reading remote CSV files with the pandas module\n",
     "import pandas as pd  # For reading, manipulating, and displaying data\n",
     "import requests  # For retrieving online files\n",
     "import sys # For general system tasks\n",
@@ -134,11 +137,11 @@
     }
    ],
    "source": [
-    "metadata_url = f'{DATA_URL}metadata.json'\n",
+    "metadata_url = f'{DATA_URL}metadata.csv'\n",
     "is_blocked, response = make_request(metadata_url, headers=HEADERS)\n",
-    "data = response.json()\n",
+    "data = io.StringIO(response.text)\n",
     "\n",
-    "metadata_df = pd.DataFrame(data)\n",
+    "metadata_df = pd.read_csv(data)\n",
     "\n",
     "print(f'Loaded metadata file with {len(metadata_df):,} entries.')"
    ]
@@ -164,7 +167,6 @@
     }
    ],
    "source": [
-    "# metadata_df = pd.read_csv(r\"C:\\Users\\rtrent\\git\\lcwa-election-datasets\\metadata\\full_metadata_2000-2016.csv\", dtype=str) # just for testing\n",
     "print(', '.join(metadata_df.columns.to_list()))"
    ]
   },
@@ -213,35 +215,12 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First, let's make sure that our dataframe columns with lists are interpretted correctly. \n",
-    "metadata_df['website_elections'] = metadata_df['website_elections'].apply(ast.literal_eval)\n",
-    "metadata_df['website_parties'] = metadata_df['website_parties'].apply(ast.literal_eval)\n",
-    "metadata_df['website_places'] = metadata_df['website_places'].apply(ast.literal_eval)\n",
-    "metadata_df['website_districts'] = metadata_df['website_districts'].apply(ast.literal_eval)\n",
-    "metadata_df['item_all_years'] = metadata_df['item_all_years'].apply(ast.literal_eval)\n",
-    "metadata_df['website_all_years'] = metadata_df['website_all_years'].apply(ast.literal_eval)\n",
-    "metadata_df['website_scopes'] = metadata_df['website_scopes'].apply(ast.literal_eval)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Record #3460 in the metadata.csv is: http://www.usmjp.com/, from the collection \"United States Elections, 2012\".\n",
-      "This row represents the website in 2012, used for campaign(s) of the candidate: Cris Ericson.\n",
-      "In 2012, this candidate used this website in 2 campaign(s):\n",
-      "  0. United States. Congress. Senate | U.S. Marijuana Party | Vermont | \n",
-      "  1. Vermont. Governor | Independent candidates | Vermont | \n",
-      "In total, this and possibly other websites were collected for this candidate in the following year(s): [2018, 2002, 2004, 2006, 2008, 2010, 2012]\n",
-      "The loc.gov item record for Cris Ericson campaign sites can be viewed at http://www.loc.gov/item/lcwaN0002501/, and its MODS record can be viewed at https://tile.loc.gov/storage-services/service/webcapture/project_1/mods/united-states-elections-web-archive/lcwaN0002501.xml.\n",
-      "Here is how this row appears in `metadata.csv`:\n"
+      "Here is how row #3460 appears in `metadata.csv`:\n"
      ]
     },
     {
@@ -291,19 +270,19 @@
        "      Official Campaign Web Site - Cris Ericson\n",
        "      http://www.usmjp.com/\n",
        "      3415\n",
-       "      [http://crisericson.com, http://vermontnews.livejournal.com, http://www.myspace.com/usmjp2010, http://crisericson2010.blogspot.com]\n",
+       "      ['http://crisericson.com', 'http://vermontnews...\n",
        "      United States Elections, 2012\n",
-       "      [United States. Congress. Senate, Vermont. Governor]\n",
-       "      [U.S. Marijuana Party, Independent candidates]\n",
-       "      [Vermont, Vermont]\n",
+       "      ['United States. Congress. Senate', 'Vermont. ...\n",
+       "      ['U.S. Marijuana Party', 'Independent candidat...\n",
+       "      ['Vermont', 'Vermont']\n",
        "      [None, None]\n",
-       "      http://cdn.loc.gov/service/webcapture/project_1/thumbnails/lcwaS0003415.jpg\n",
+       "      http://cdn.loc.gov/service/webcapture/project_...\n",
        "      20121003\n",
        "      20121019\n",
-       "      [2002, 2004, 2004, 2006, 2008, 2010, 2012, 2012, 2018, 2018]\n",
+       "      [2002, 2004, 2004, 2006, 2008, 2010, 2012, 201...\n",
        "      [2012]\n",
-       "      https://tile.loc.gov/storage-services/service/webcapture/project_1/mods/united-states-elections-web-archive/lcwaN0002501.xml\n",
-       "      None\n",
+       "      https://tile.loc.gov/storage-services/service/...\n",
+       "      NaN\n",
        "    \n",
        "  \n",
        "\n",
@@ -316,71 +295,116 @@
        "                                     item_title            website_url  \\\n",
        "3460  Official Campaign Web Site - Cris Ericson  http://www.usmjp.com/   \n",
        "\n",
-       "     website_id  \\\n",
-       "3460       3415   \n",
-       "\n",
-       "                                                                                                                           website_scopes  \\\n",
-       "3460  [http://crisericson.com, http://vermontnews.livejournal.com, http://www.myspace.com/usmjp2010, http://crisericson2010.blogspot.com]   \n",
+       "      website_id                                     website_scopes  \\\n",
+       "3460        3415  ['http://crisericson.com', 'http://vermontnews...   \n",
        "\n",
        "                         collection  \\\n",
        "3460  United States Elections, 2012   \n",
        "\n",
-       "                                         website_elections  \\\n",
-       "3460  [United States. Congress. Senate, Vermont. Governor]   \n",
-       "\n",
-       "                                     website_parties      website_places  \\\n",
-       "3460  [U.S. Marijuana Party, Independent candidates]  [Vermont, Vermont]   \n",
+       "                                      website_elections  \\\n",
+       "3460  ['United States. Congress. Senate', 'Vermont. ...   \n",
        "\n",
-       "     website_districts  \\\n",
-       "3460      [None, None]   \n",
+       "                                        website_parties  \\\n",
+       "3460  ['U.S. Marijuana Party', 'Independent candidat...   \n",
        "\n",
-       "                                                                website_thumbnail  \\\n",
-       "3460  http://cdn.loc.gov/service/webcapture/project_1/thumbnails/lcwaS0003415.jpg   \n",
+       "              website_places website_districts  \\\n",
+       "3460  ['Vermont', 'Vermont']      [None, None]   \n",
        "\n",
-       "     website_start_date website_end_date  \\\n",
-       "3460           20121003         20121019   \n",
+       "                                      website_thumbnail  website_start_date  \\\n",
+       "3460  http://cdn.loc.gov/service/webcapture/project_...            20121003   \n",
        "\n",
-       "                                                    item_all_years  \\\n",
-       "3460  [2002, 2004, 2004, 2006, 2008, 2010, 2012, 2012, 2018, 2018]   \n",
+       "      website_end_date                                     item_all_years  \\\n",
+       "3460          20121019  [2002, 2004, 2004, 2006, 2008, 2010, 2012, 201...   \n",
        "\n",
-       "     website_all_years  \\\n",
-       "3460            [2012]   \n",
-       "\n",
-       "                                                                                                                          mods_url  \\\n",
-       "3460  https://tile.loc.gov/storage-services/service/webcapture/project_1/mods/united-states-elections-web-archive/lcwaN0002501.xml   \n",
+       "     website_all_years                                           mods_url  \\\n",
+       "3460            [2012]  https://tile.loc.gov/storage-services/service/...   \n",
        "\n",
        "     access_condition  \n",
-       "3460             None  "
+       "3460              NaN  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "row = 3460 # You can change this row number\n",
+    "row_num = 3460 # You can change this row number\n",
     "\n",
-    "# We'll grab all the info we need from our row. \n",
-    "item_title = metadata_df.iloc[row]['item_title']\n",
-    "website_url = metadata_df.iloc[row]['website_url']\n",
-    "collection = metadata_df.iloc[row]['collection']\n",
+    "print(f'Here is how row #{row_num} appears in `metadata.csv`:')                       \n",
+    "metadata_df[row_num:row_num+1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, let's use the next cell to make sure that our dataframe columns with lists are interpretted correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "metadata_df['website_elections'] = metadata_df['website_elections'].apply(ast.literal_eval)\n",
+    "metadata_df['website_parties'] = metadata_df['website_parties'].apply(ast.literal_eval)\n",
+    "metadata_df['website_places'] = metadata_df['website_places'].apply(ast.literal_eval)\n",
+    "metadata_df['website_districts'] = metadata_df['website_districts'].apply(ast.literal_eval)\n",
+    "metadata_df['item_all_years'] = metadata_df['item_all_years'].apply(ast.literal_eval)\n",
+    "metadata_df['website_all_years'] = metadata_df['website_all_years'].apply(ast.literal_eval)\n",
+    "metadata_df['website_scopes'] = metadata_df['website_scopes'].apply(ast.literal_eval)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next cell, we'll grab all the info we need, then plug that info into a statement that describes our CSV row of data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Record #3460 in the metadata.csv is: http://www.usmjp.com/, from the collection \"United States Elections, 2012\".\n",
+      "This row represents the website in 2012, used for campaign(s) of the candidate: Cris Ericson.\n",
+      "In 2012, this candidate used this website in 2 campaign(s):\n",
+      "  0. United States. Congress. Senate | U.S. Marijuana Party | Vermont | \n",
+      "  1. Vermont. Governor | Independent candidates | Vermont | \n",
+      "In total, this and possibly other websites were collected for this candidate in the following year(s): [2018, 2002, 2004, 2006, 2008, 2010, 2012]\n",
+      "The loc.gov item record for Cris Ericson campaign sites can be viewed at http://www.loc.gov/item/lcwaN0002501/, and its MODS record can be viewed at https://tile.loc.gov/storage-services/service/webcapture/project_1/mods/united-states-elections-web-archive/lcwaN0002501.xml.\n"
+     ]
+    }
+   ],
+   "source": [
+    "row = metadata_df.iloc[row_num]\n",
+    "item_title = row['item_title']\n",
+    "website_url = row['website_url']\n",
+    "collection = row['collection']\n",
     "candidate_name = item_title.split('-')[1].strip()\n",
     "year = collection.split(',')[1].strip()\n",
-    "campaign_count = len(metadata_df.iloc[row]['website_elections'])\n",
-    "website_elections = metadata_df.iloc[row]['website_elections']\n",
-    "website_parties = metadata_df.iloc[row]['website_parties']\n",
-    "website_places = metadata_df.iloc[row]['website_places']\n",
-    "website_districts = metadata_df.iloc[row]['website_districts']\n",
-    "website_all_years = metadata_df.iloc[row]['website_all_years']\n",
+    "campaign_count = len(row['website_elections'])\n",
+    "website_elections = row['website_elections']\n",
+    "website_parties = row['website_parties']\n",
+    "website_places = row['website_places']\n",
+    "website_districts = row['website_districts']\n",
+    "website_all_years = row['website_all_years']\n",
     "website_all_years.sort()\n",
-    "item_all_years = metadata_df.iloc[row]['item_all_years']\n",
+    "item_all_years = row['item_all_years']\n",
     "item_all_years.sort()\n",
-    "item_id = metadata_df.iloc[row]['item_id']\n",
-    "mods_url = metadata_df.iloc[row]['mods_url']\n",
+    "item_id = row['item_id']\n",
+    "mods_url = row['mods_url']\n",
     "\n",
     "# Now we'll plug those variables into our sentences.\n",
-    "print(f'Record #{row} in the metadata.csv is: {website_url}, from the collection \"{collection}\".')\n",
+    "print(f'Record #{row_num} in the metadata.csv is: {website_url}, from the collection \"{collection}\".')\n",
     "print(f'This row represents the website in {year}, used for campaign(s) of the candidate: {candidate_name}.') \n",
     "print(f'In {year}, this candidate used this website in {campaign_count} campaign(s):')\n",
     "i=0\n",
@@ -397,38 +421,27 @@
     "print(f'The loc.gov item record for {candidate_name} campaign sites can be viewed at {item_id}, and its MODS record can be viewed at {mods_url}.')\n",
     "\n",
     "# The next line displays our dataframe as a table. Let's set it to show up to 300 characters in each cell\n",
-    "pd.options.display.max_colwidth = 300\n",
-    "\n",
-    "print('Here is how this row appears in `metadata.csv`:')                       \n",
-    "metadata_df[row:row+1]"
+    "pd.options.display.max_colwidth = 300\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's see about all the Vermont gubernatorial candidates represented in this data package."
+    "Now let's create a function that allows us to get summary information about any election, like the election for Vermont Governor. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found in metadata.csv: 0 unique campaign websites for 0 \"Vermont. Governor\" candidates, ranging from years n/a - n/a.\n",
-      "0 of these websites were used multiple years.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# We'll create a function to generate summary information about a given type of election\n",
-    "\n",
     "def election_summary(election_type):\n",
-    "    websites_by_year = metadata_df[metadata_df['website_elections'].apply(lambda elections: any(election_type is election for election in elections ))]\n",
+    "    '''\n",
+    "    Input an election title. Output is a summary of CSV rows from that election. \n",
+    "    '''\n",
+    "    websites_by_year = metadata_df[metadata_df['website_elections'].apply(lambda elections: any([election == election_type for election in elections]))]\n",
     "    candidates = websites_by_year['item_title'].unique()\n",
     "    websites = websites_by_year['website_url'].unique()\n",
     "    years = [collection.split(',')[1].strip() for collection in websites_by_year['collection'].unique()]\n",
@@ -436,8 +449,31 @@
     "    max_year = max(years) if years else 'n/a'\n",
     "    multi_year_websites = websites_by_year[websites_by_year['website_all_years'].str.len()>1]['website_url'].unique()\n",
     "    print(f'Found in metadata.csv: {len(websites)} unique campaign websites for {len(candidates)} \"{election_type}\" candidates, ranging from years {min_year} - {max_year}.')\n",
-    "    print(f'{len(multi_year_websites)} of these websites were used multiple years.')\n",
-    "\n",
+    "    print(f'{len(multi_year_websites)} of these websites were used multiple years.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try our function to get get as summary of Vermont gubernatorial candidates represented in this data package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found in metadata.csv: 31 unique campaign websites for 22 \"Vermont. Governor\" candidates, ranging from years 2002 - 2016.\n",
+      "13 of these websites were used multiple years.\n"
+     ]
+    }
+   ],
+   "source": [
     "election_summary('Vermont. Governor')"
    ]
   },
@@ -454,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -483,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -724,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -740,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -753,7 +789,7 @@
        " 'https://data.labs.loc.gov/us-elections/by-year/2000/cdx/unique.20010415095459.surt.cdx.gz']"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -773,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -782,7 +818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -795,7 +831,7 @@
        " 'com,voter)/home/candidates/info/0,1214,2-18886-,00.html 20001002185816 http://www.voter.com:80/home/candidates/info/0,1214,2-18886-,00.html text/html 200 QAM7JW7S4CNYMP6HLA6DASOXTO2SIGWO - - 4823 20105 unique.20010415093936.arc.gz']"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -813,7 +849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1079,7 +1115,7 @@
        "[1096880 rows x 11 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1102,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1111,7 +1147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1377,7 +1413,7 @@
        "[1541579 rows x 11 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1397,7 +1433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1406,7 +1442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1443,7 +1479,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1463,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1955,7 +1991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1968,16 +2004,16 @@
     {
      "data": {
       "text/plain": [
-       "['algore2000.com',\n",
-       " 'harrybrowne2000.org',\n",
-       " 'gopatgo2000.org',\n",
-       " 'algore.com',\n",
+       "['gopatgo2000.org',\n",
        " 'keyes2000.org',\n",
+       " 'forum.hagelin.org',\n",
        " 'hagelin.org',\n",
-       " 'forum.hagelin.org']"
+       " 'algore2000.com',\n",
+       " 'algore.com',\n",
+       " 'harrybrowne2000.org']"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2010,7 +2046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -2301,7 +2337,7 @@
        "[6448 rows x 12 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2328,7 +2364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2346,7 +2382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2380,7 +2416,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2392,7 +2428,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "public-demos",
    "language": "python",
    "name": "python3"
   },
diff --git a/searchindex.js b/searchindex.js
index ecf007c..b72221c 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"1) Get the Data Set": [[18, "get-the-data-set"]], "1. Initial exploration & verification of the data set": [[19, "initial-exploration-verification-of-the-data-set"]], "1. Required Prep: Import all of the Python modules we\u2019ll need": [[44, "required-prep-import-all-of-the-python-modules-we-ll-need"]], "1. Required Prep: Install and import all of the Python modules we\u2019ll need": [[43, "required-prep-install-and-import-all-of-the-python-modules-we-ll-need"]], "19th century Sanborn maps of Springfield (any state)": [[44, "th-century-sanborn-maps-of-springfield-any-state"]], "2) Load the Data Set": [[18, "load-the-data-set"]], "2. Count and display information about the memes represented in the data set": [[19, "count-and-display-information-about-the-memes-represented-in-the-data-set"]], "2. Practice: Get familiar with the loc.gov API": [[44, "practice-get-familiar-with-the-loc-gov-api"]], "2. Query for list of records": [[43, "query-for-list-of-records"]], "3) Chart the Import Dates": [[18, "chart-the-import-dates"]], "3. Count & visualize the types of memes in the dataset": [[19, "count-visualize-the-types-of-memes-in-the-dataset"]], "3. Harvest the metadata": [[43, "harvest-the-metadata"]], "3. Practice: Understand how pagination works": [[44, "practice-understand-how-pagination-works"]], "4) Smallest & Largest": [[18, "smallest-largest"]], "4. Basic metadata analysis": [[43, "basic-metadata-analysis"]], "4. Download files: retrieve files associated with any given query": [[44, "download-files-retrieve-files-associated-with-any-given-query"]], "4. Retrieve and display meme images": [[19, "retrieve-and-display-meme-images"]], "5) Search the Titles": [[18, "search-the-titles"]], "5. Examples: Advanced Querying": [[44, "examples-advanced-querying"]], "5. Metadata charts": [[43, "metadata-charts"]], "6) Hydrate the Dataset": [[18, "hydrate-the-dataset"]], "6. Metadata maps and Wikidata": [[43, "metadata-maps-and-wikidata"]], "A look at the trending content": [[41, "a-look-at-the-trending-content"]], "About the Library of Congress\u2019s cartographic materials": [[44, "about-the-library-of-congress-s-cartographic-materials"]], "About this Repository": [[34, "about-this-repository"]], "About this repository": [[24, "about-this-repository"]], "Access": [[34, "access"]], "Accessing images": [[27, "accessing-images"]], "Accessing images from the loc.gov JSON API for image analysis": [[27, null]], "Additional Resources": [[34, "additional-resources"]], "Advanced: Post-query filtering": [[44, "advanced-post-query-filtering"]], "Analyzing and visualizing cartographic metadata from loc.gov": [[43, null]], "Analyzing the thumbnail images for color clusters": [[38, "analyzing-the-thumbnail-images-for-color-clusters"]], "Apply Visualizations to your Data Table": [[30, "apply-visualizations-to-your-data-table"]], "Asking another question": [[41, "asking-another-question"]], "Binder": [[35, "id1"]], "Bite-size ideas for the \u201cDoing DH @ LC\u201d Create-a-thon": [[21, null]], "Bulk downloading": [[4, "bulk-downloading"]], "Calling the API and retrieving image URLs": [[27, "calling-the-api-and-retrieving-image-urls"]], "Chrome": [[47, "chrome"]], "ChronAm API Guide": [[0, null]], "Chronicling America API": [[1, null]], "Chronicling America Title Essay Datasets": [[35, null]], "Chronicling America: Digital Collection of Historic American Newspapers": [[34, "chronicling-america-digital-collection-of-historic-american-newspapers"]], "Clean up the data and map it!": [[43, "clean-up-the-data-and-map-it"]], "Color clusters in images in Library of Congress collections": [[38, null]], "Colors in other collections": [[38, "colors-in-other-collections"]], "Conclusion": [[18, "conclusion"], [27, "conclusion"]], "Connecting the image file to the metadata": [[27, "connecting-the-image-file-to-the-metadata"], [39, null]], "Context": [[40, "context"]], "Create JSON files from the data": [[38, "create-json-files-from-the-data"]], "Credits": [[34, "credits"]], "Data Package notebooks": [[4, "data-package-notebooks"]], "Define and perform a query": [[33, "define-and-perform-a-query"]], "Define your API Search Query and Generating Download List": [[28, "define-your-api-search-query-and-generating-download-list"]], "Define your API Search Query and Save Location [Required]": [[29, "define-your-api-search-query-and-save-location-required"]], "Defining the Functions": [[18, "defining-the-functions"]], "Definitions: API Query Paramenters for Newspapers": [[34, "definitions-api-query-paramenters-for-newspapers"]], "Demo Data": [[40, "demo-data"]], "Digging into the results": [[41, "digging-into-the-results"]], "Digital Humanities Preconference, August 5, 2024": [[21, "digital-humanities-preconference-august-5-2024"]], "Discover stuff!": [[0, "discover-stuff"]], "Download Files": [[29, "download-files"]], "Download an audio file and visualize it": [[11, "download-an-audio-file-and-visualize-it"]], "Download and display images": [[9, "download-and-display-images"], [12, "download-and-display-images"], [13, "download-and-display-images"]], "Download and visualize GeoTIFF": [[5, "download-and-visualize-geotiff"]], "Download full text files and visualize it": [[14, "download-full-text-files-and-visualize-it"]], "Downloading Newspaper Titles and Batches from Chronicling America": [[28, null]], "Downloading Search Results from Chronicling America": [[29, null]], "Downloading an item with multiple pages to PDF": [[45, null]], "Drawing color swatches": [[38, "drawing-color-swatches"]], "Election 2000 DataFrame": [[15, "election-2000-dataframe"]], "Events": [[25, "events"]], "Example Notebook 1: Basic API Tasks": [[34, "example-notebook-1-basic-api-tasks"]], "Example Notebook 2: Analyzing Specific Newspapers for Content": [[34, "example-notebook-2-analyzing-specific-newspapers-for-content"]], "Example Notebook 3: Analyzing Word Frequency on Newspaper Front Pages": [[34, "example-notebook-3-analyzing-word-frequency-on-newspaper-front-pages"]], "Example Notebook 4: Analyzing Word Frequency and Location": [[34, "example-notebook-4-analyzing-word-frequency-and-location"]], "Example Notebook 5: Downloading Search Results": [[34, "example-notebook-5-downloading-search-results"]], "Example Notebook 6: Part 1: Downloading a Newspaper Title": [[34, "example-notebook-6-part-1-downloading-a-newspaper-title"]], "Example Notebook 6: Part 2: Download an NDNP Batch": [[34, "example-notebook-6-part-2-download-an-ndnp-batch"]], "Example Notebooks (Performing Basic Tasks, Analysis, and Downloads)": [[34, "example-notebooks-performing-basic-tasks-analysis-and-downloads"]], "Exploring Results: Arizona": [[0, "exploring-results-arizona"]], "Exploring the GIPHY.com Metadata": [[18, null]], "Exploring the Memegenerator Metadata": [[19, null]], "Exploring the United States Election Datasets": [[17, null]], "Export Metadata of Downloads to a CSV File.": [[29, "export-metadata-of-downloads-to-a-csv-file"]], "Export Metadata of Search Results to a CSV File": [[30, "export-metadata-of-search-results-to-a-csv-file"], [31, "export-metadata-of-search-results-to-a-csv-file"], [32, "export-metadata-of-search-results-to-a-csv-file"]], "Extracting location data from the loc.gov API for geovisualization": [[40, null]], "Fetching the Text": [[15, "fetching-the-text"]], "Filter and Download CDX index files, analyze text": [[15, "filter-and-download-cdx-index-files-analyze-text"]], "Filtering by domain": [[15, "filtering-by-domain"]], "Firefox": [[47, "firefox"]], "Further Research": [[0, "further-research"], [41, "further-research"]], "Gathering the list of CDX Files": [[15, "gathering-the-list-of-cdx-files"]], "General Search and Query": [[25, "general-search-and-query"]], "Geographic Data and Maps": [[25, "geographic-data-and-maps"]], "Geovisualization": [[40, "geovisualization"]], "Get Basic Metadata/Information for your Downloaded Results": [[29, "get-basic-metadata-information-for-your-downloaded-results"]], "Get Basic Metadata/Information for your Query and Store It in a List": [[30, "get-basic-metadata-information-for-your-query-and-store-it-in-a-list"], [31, "get-basic-metadata-information-for-your-query-and-store-it-in-a-list"], [32, "get-basic-metadata-information-for-your-query-and-store-it-in-a-list"]], "Get image URLs and thumbnails for each image in a specific collection": [[38, "get-image-urls-and-thumbnails-for-each-image-in-a-specific-collection"]], "Gotchas": [[26, "gotchas"]], "Historic Newspapers": [[25, "historic-newspapers"]], "How do I Mirror an image?": [[26, "how-do-i-mirror-an-image"]], "How do I get different sizes?": [[26, "how-do-i-get-different-sizes"]], "How do I get the image?": [[26, "how-do-i-get-the-image"]], "How do I rotate a image?": [[26, "how-do-i-rotate-a-image"]], "How do I select a region?": [[26, "how-do-i-select-a-region"]], "How to read the JSON above": [[44, "how-to-read-the-json-above"]], "I. Imports": [[45, "i-imports"]], "II. Create a request URL": [[45, "ii-create-a-request-url"]], "III. Request Data": [[45, "iii-request-data"]], "IIIF API Guide": [[26, null]], "IV. Resource Data and Extracting Resource URLS": [[45, "iv-resource-data-and-extracting-resource-urls"]], "Identifying items": [[27, "identifying-items"]], "Image sizes and information about collections": [[27, "image-sizes-and-information-about-collections"]], "Images": [[25, "images"]], "Importing Modules": [[33, "importing-modules"]], "Importing Modules [Required]": [[28, "importing-modules-required"], [29, "importing-modules-required"], [30, "importing-modules-required"], [31, "importing-modules-required"], [32, "importing-modules-required"]], "In conclusion..": [[40, "in-conclusion"]], "Inspect a sample CDX File": [[15, "inspect-a-sample-cdx-file"]], "Instructions for publishing notebooks": [[48, null]], "Interpreting the metadata fields": [[15, "interpreting-the-metadata-fields"]], "LC Maps for Robots": [[42, null]], "LOC Goes Lofi": [[20, null]], "LOC.GOV API": [[34, "loc-gov-api"]], "LOC.gov JSON API Guide": [[41, null]], "Learning and Applying Basic API Tasks in Chronicling America": [[33, null]], "Let\u2019s Discover and Make": [[0, "let-s-discover-and-make"]], "Let\u2019s try it!": [[47, "lets-try-it"]], "Library of Congress Data Exploration": [[24, null]], "LoC Data Package Tutorial: 1000 Dot Gov PDF Dataset": [[8, null]], "LoC Data Package Tutorial: Austro-Hungarian maps set": [[5, null]], "LoC Data Package Tutorial: City and Telephone Directories": [[7, null]], "LoC Data Package Tutorial: Digitized Telephone Directories Data Package": [[14, null]], "LoC Data Package Tutorial: Free to Use collection": [[9, null]], "LoC Data Package Tutorial: General Collections Assessment": [[10, null]], "LoC Data Package Tutorial: National Jukebox collection": [[11, null]], "LoC Data Package Tutorial: Sanborn Maps collection": [[12, null]], "LoC Data Package Tutorial: Selected Digitized Books Data Package": [[6, null]], "LoC Data Package Tutorial: Stereograph Card Collection": [[13, null]], "LoC Data Package Tutorial: United States Elections, Web Archives Data 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\ No newline at end of file
+Search.setIndex({"alltitles": {"1) Get the Data Set": [[18, "get-the-data-set"]], "1. Initial exploration & verification of the data set": [[19, "initial-exploration-verification-of-the-data-set"]], "1. Required Prep: Import all of the Python modules we\u2019ll need": [[44, "required-prep-import-all-of-the-python-modules-we-ll-need"]], "1. Required Prep: Install and import all of the Python modules we\u2019ll need": [[43, "required-prep-install-and-import-all-of-the-python-modules-we-ll-need"]], "19th century Sanborn maps of Springfield (any state)": [[44, "th-century-sanborn-maps-of-springfield-any-state"]], "2) Load the Data Set": [[18, "load-the-data-set"]], "2. Count and display information about the memes represented in the data set": [[19, "count-and-display-information-about-the-memes-represented-in-the-data-set"]], "2. Practice: Get familiar with the loc.gov API": [[44, "practice-get-familiar-with-the-loc-gov-api"]], "2. Query for list of records": [[43, "query-for-list-of-records"]], "3) Chart the Import Dates": [[18, "chart-the-import-dates"]], "3. Count & visualize the types of memes in the dataset": [[19, "count-visualize-the-types-of-memes-in-the-dataset"]], "3. Harvest the metadata": [[43, "harvest-the-metadata"]], "3. Practice: Understand how pagination works": [[44, "practice-understand-how-pagination-works"]], "4) Smallest & Largest": [[18, "smallest-largest"]], "4. Basic metadata analysis": [[43, "basic-metadata-analysis"]], "4. Download files: retrieve files associated with any given query": [[44, "download-files-retrieve-files-associated-with-any-given-query"]], "4. Retrieve and display meme images": [[19, "retrieve-and-display-meme-images"]], "5) Search the Titles": [[18, "search-the-titles"]], "5. Examples: Advanced Querying": [[44, "examples-advanced-querying"]], "5. Metadata charts": [[43, "metadata-charts"]], "6) Hydrate the Dataset": [[18, "hydrate-the-dataset"]], "6. Metadata maps and Wikidata": [[43, "metadata-maps-and-wikidata"]], "A look at the trending content": [[41, "a-look-at-the-trending-content"]], "About the Library of Congress\u2019s cartographic materials": [[44, "about-the-library-of-congress-s-cartographic-materials"]], "About this Repository": [[34, "about-this-repository"]], "About this repository": [[24, "about-this-repository"]], "Access": [[34, "access"]], "Accessing images": [[27, "accessing-images"]], "Accessing images from the loc.gov JSON API for image analysis": [[27, null]], "Additional Resources": [[34, "additional-resources"]], "Advanced: Post-query filtering": [[44, "advanced-post-query-filtering"]], "Analyzing and visualizing cartographic metadata from loc.gov": [[43, null]], "Analyzing the thumbnail images for color clusters": [[38, "analyzing-the-thumbnail-images-for-color-clusters"]], "Apply Visualizations to your Data Table": [[30, "apply-visualizations-to-your-data-table"]], "Asking another question": [[41, "asking-another-question"]], "Binder": [[35, "id1"]], "Bite-size ideas for the \u201cDoing DH @ LC\u201d Create-a-thon": [[21, null]], "Bulk downloading": [[4, "bulk-downloading"]], "Calling the API and retrieving image URLs": [[27, "calling-the-api-and-retrieving-image-urls"]], "Chrome": [[47, "chrome"]], "ChronAm API Guide": [[0, null]], "Chronicling America API": [[1, null]], "Chronicling America Title Essay Datasets": [[35, null]], "Chronicling America: Digital Collection of Historic American Newspapers": [[34, "chronicling-america-digital-collection-of-historic-american-newspapers"]], "Clean up the data and map it!": [[43, "clean-up-the-data-and-map-it"]], "Color clusters in images in Library of Congress collections": [[38, null]], "Colors in other collections": [[38, "colors-in-other-collections"]], "Conclusion": [[18, "conclusion"], [27, "conclusion"]], "Connecting the image file to the metadata": [[27, "connecting-the-image-file-to-the-metadata"], [39, null]], "Context": [[40, "context"]], "Create JSON files from the data": [[38, "create-json-files-from-the-data"]], "Credits": [[34, "credits"]], "Data Package notebooks": [[4, "data-package-notebooks"]], "Define and perform a query": [[33, "define-and-perform-a-query"]], "Define your API Search Query and Generating Download List": [[28, "define-your-api-search-query-and-generating-download-list"]], "Define your API Search Query and Save Location [Required]": [[29, "define-your-api-search-query-and-save-location-required"]], "Defining the Functions": [[18, "defining-the-functions"]], "Definitions: API Query Paramenters for Newspapers": [[34, "definitions-api-query-paramenters-for-newspapers"]], "Demo Data": [[40, "demo-data"]], "Digging into the results": [[41, "digging-into-the-results"]], "Digital Humanities Preconference, August 5, 2024": [[21, "digital-humanities-preconference-august-5-2024"]], "Discover stuff!": [[0, "discover-stuff"]], "Download Files": [[29, "download-files"]], "Download an audio file and visualize it": [[11, "download-an-audio-file-and-visualize-it"]], "Download and display images": [[9, "download-and-display-images"], [12, "download-and-display-images"], [13, "download-and-display-images"]], "Download and visualize GeoTIFF": [[5, "download-and-visualize-geotiff"]], "Download full text files and visualize it": [[14, "download-full-text-files-and-visualize-it"]], "Downloading Newspaper Titles and Batches from Chronicling America": [[28, null]], "Downloading Search Results from Chronicling America": [[29, null]], "Downloading an item with multiple pages to PDF": [[45, null]], "Drawing color swatches": [[38, "drawing-color-swatches"]], "Election 2000 DataFrame": [[15, "election-2000-dataframe"]], "Events": [[25, "events"]], "Example Notebook 1: Basic API Tasks": [[34, "example-notebook-1-basic-api-tasks"]], "Example Notebook 2: Analyzing Specific Newspapers for Content": [[34, "example-notebook-2-analyzing-specific-newspapers-for-content"]], "Example Notebook 3: 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