The state has the highest number of counties is Texas within its borders, which has 64516. Using idxmax() function to get the Name of State and the max() to get the quantity.
For comprehensive information and detailed instructions, please refer to the official documentation.
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.idxmax.html
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.max.html

                    
                        greatest_counties = df.groupby(['STNAME']).sum()['COUNTY'].idxmax()

                        quantity_greatest_county = df.groupby(['STNAME']).sum()['COUNTY'].max()

Find the top ten populous states in descendent order, using the column CENSUS2010POP. To make more complex was filtered with code 50 to group by Counties. The groupby, sort_values, and aggregation functions are used to accomplish this task.

	STNAME	CENSUS2010POP
0	California	37253956
1	Texas	25145561
2	New York	19378102
3	Florida	18801310
4	Illinois	12830632
5	Pennsylvania	12702379
6	Ohio	11536504
7	Michigan	9883640
8	Georgia	9687653
9	North Carolina	9535483

                    
                        condition_state = df['SUMLEV'] == 50
                        new_condition_state = df[condition_state]

                        most_population_state = new_condition_state.sort_values(['STNAME', 'CENSUS2010POP'], ascending=[True, False])

                        population = most_population_state.groupby('STNAME').agg('sum').sort_values('CENSUS2010POP', ascending=False)

                        highest_state = population.head(10).reset_index()

                        context = { 'most_population_state': highest_state[['STNAME', 'CENSUS2010POP']].to_html(classes='table table-bordered table-striped', index=True)}

Identify the City with the largest absolute change in population between the years 2010 and 2015.
The code calculates the population change for each city and selects the top ten cities with the highest change.

County Name	Population Change
Harris County	429841
Los Angeles County	344283
Maricopa County	342350
San Diego County	195135
Miami-Dade County	184946
Dallas County	179921
King County	179426
Bexar County	174764
Tarrant County	165970
Clark County	161423

                    
                        def find_min_max(row):
                            columns_of_interest = ['POPESTIMATE2010', 'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013', 'POPESTIMATE2014', 'POPESTIMATE2015']

                            min_value = row[columns_of_interest].min()

                            max_value = row[columns_of_interest].max()

                            return pd.Series({'MIN_POP': min_value, 'MAX_POP': max_value, 'DIF_POP': max_value-min_value})

                        min_max_values = df_pop.apply(find_min_max, axis=1)

                        df = pd.concat([df_pop, min_max_values], axis=1)

Query the dataset to retrieve parameters passed through GET methods in the URL for columns related to regions and the starting city name.
Validate if there has been an increase in population from 2014 to 2015 for the specified counties using conditional filtering and string operations. The code then extracts and returns these counties as a DataFrame.

Search Form

REGION	County Name	CTYNAME	POPESTIMATE2014	POPESTIMATE2015
1	Rhode Island	Washington County	126430	126517
1	Pennsylvania	Washington County	208175	208261
2	Iowa	Washington County	22087	22247
2	Minnesota	Washington County	249320	251597
2	Wisconsin	Washington County	133301	133674
3	Louisiana	Washington Parish	46287	46371
3	Georgia	Washington County	20608	20816
3	Arkansas	Washington County	220682	225477
3	Texas	Washington County	34413	34765
3	Maryland	Washington County	149423	149585
3	Kentucky	Washington County	11955	12063
3	Oklahoma	Washington County	51967	52021
3	Tennessee	Washington County	125862	126302
3	Florida	Washington County	24435	24687
4	Oregon	Washington County	563273	574326
4	Colorado	Washington County	4786	4864
4	Utah	Washington County	151876	155602

                    
        if request.GET.get("region", None) is not None:
            region = request.GET.get("region")
            # filter region condition
            condition_region = (region_df['SUMLEV'] == 50) & (region_df['REGION'] == int(region))
            region_df = region_df[condition_region]
        else:
            # If 'region' parameter is not provided, retrieve all values
            condition_region = (region_df['SUMLEV'] == 50) & (region_df['REGION'].isin([1, 2, 3, 4]))
            region_df = region_df[condition_region]

        # Filter county name that start with some text.
        if request.GET.get("county", None) is not None:
            county = request.GET.get("county")
            condition_starts_name = region_df['CTYNAME'].str.startswith(county)
            start_city = region_df[condition_starts_name]
        else:
            # If 'region' parameter is not provided, retrieve Washington
            condition_starts_name = region_df['CTYNAME'].str.startswith('Washington')
            start_city = region_df[condition_starts_name]

        # Validate increase Population 2015/2016
        condition_comparation_population = start_city['POPESTIMATE2015'] > start_city['POPESTIMATE2014']
        fiend_counties = start_city[condition_comparation_population]

Data Cleaning with Pandas Library

Show Code

views.py Code

# start_census
def find_min_max(row):
    # Select the columns of interest
    columns_of_interest = ['POPESTIMATE2010', 'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013',
                           'POPESTIMATE2014', 'POPESTIMATE2015']
    # Find the minimum and maximum values for the selected columns in the row
    min_value = row[columns_of_interest].min()
    max_value = row[columns_of_interest].max()

    return pd.Series({'MIN_POP': min_value, 'MAX_POP': max_value, 'Population Change': max_value - min_value})


def data_cleaning_census(request):
    search_line = "# start_census"
    pattern = "# finish_census"
    content_json = "/static/json/data-science/content.json"
    project_key = "census/pandas/data-cleaning"
    template_path = "portfolio/templates/data-science/pandas/data-cleaning-census.html"
    answer = []  # Initialize list to store results

    try:
        # Call the get_views_code to retrieve code in the views file
        views_code = get_views_code(search_line, pattern, module_dir)
        content_json_data = get_content_json_data(content_json, project_key)
        template_code = get_template_code(template_path)

    except FileNotFoundError:
        result = "The file could not be found."

    try:
        # The census file never changes, so the aggregations and HTML tables are cached
        # for 15 minutes (same as the KNN demo): two heavy demos requested at once were
        # enough to make the dyno drop connections (H13/H18).
        cached = cache.get("census_demo:results")
        if cached is not None:
            context = {'views': views_code, 'content': content_json_data, 'template': template_code, **cached}
            return render(request, 'data-science/pandas/data-cleaning-census.html', context)

        file_path = os.path.join(static_dir, 'datasets/census.csv')
        df = pd.read_csv(file_path)
        # Identify the state that encompasses the greatest number of counties. The {{ result.0 }} is the State with more Counties in the United State they are {{ result.1 }}
        greatest_counties = df.groupby(['STNAME']).sum()['COUNTY'].idxmax()
        quantity_greatest_county = df.groupby(['STNAME']).sum()['COUNTY'].max()
        answer.append(greatest_counties)
        answer.append(quantity_greatest_county)
        # Find the top ten populous states in descendent order, using the column CENSUS2010POP. To make more complex was filtered with code 50 to group by Counties
        # The key for SUMLEV is as follows:
        # 040 = State and/or Statistical Equivalent
        # 050 = County and /or Statistical Equivalent
        condition_state = df['SUMLEV'] == 50  # Select only STATE, Drop rows that contain state data
        new_condition_state = df[condition_state]
        # Sort the county-level data by state ('STNAME') and population ('CENSUS2010POP') in descending order
        most_population_state = new_condition_state.sort_values(['STNAME', 'CENSUS2010POP'], ascending=[True, False])
        # Calculate the total population for each state and sort them in descending order
        population = most_population_state.groupby('STNAME').agg('sum').sort_values('CENSUS2010POP', ascending=False)
        highest_state = population.head(10).reset_index()

        # The next task is to identify the City Name with the largest absolute change in population between the years 2010 and 2015.
        # The code calculates the population change for each county and selects the top ten city with the highest change.

        df_pop = df[condition_state]
        # Apply the function to each row to find the minimum and maximum values
        min_max_values = df_pop.apply(find_min_max, axis=1)

        # Concatenate the resulting DataFrame with the original DataFrame
        df = pd.concat([df_pop, min_max_values], axis=1)

        pop_diff = df[['CTYNAME', 'Population Change']].sort_values('Population Change', ascending=False)
        pop_change = pop_diff.head(10).rename(columns={'CTYNAME': 'County Name'})
        pop_change_to_html = th_scope(pop_change.to_html(classes='table table-bordered table-striped text-center', index=False))

        # Query the dataset to retrieve parameters passed through GET methods in the URL for columns related to regions and the starting city name.
        # Validate if there has been an increase in population from 2014 to 2015 for the specified counties using conditional filtering and string operations. The code then extracts and returns these counties as a DataFrame."
        region_df = df.copy()

        if request.GET.get("region", None) is not None:
            region = request.GET.get("region")
            # filter region condition
            condition_region = (region_df['SUMLEV'] == 50) & (region_df['REGION'] == int(region))
            region_df = region_df[condition_region]
        else:
            # If 'region' parameter is not provided, retrieve all values
            condition_region = (region_df['SUMLEV'] == 50) & (region_df['REGION'].isin([1, 2, 3, 4]))
            region_df = region_df[condition_region]

        # Filter county name that start with some text.
        if request.GET.get("county", None) is not None:
            county = request.GET.get("county")
            condition_starts_name = region_df['CTYNAME'].str.startswith(county)
            start_city = region_df[condition_starts_name]
        else:
            # If 'region' parameter is not provided, retrieve Washington
            condition_starts_name = region_df['CTYNAME'].str.startswith('Washington')
            start_city = region_df[condition_starts_name]

        # Validate increase Population 2015/2016
        condition_comparation_population = start_city['POPESTIMATE2015'] > start_city['POPESTIMATE2014']
        fiend_counties = start_city[condition_comparation_population]
        found_counties = fiend_counties[
            ['REGION', 'STNAME', 'CTYNAME', 'POPESTIMATE2014', 'POPESTIMATE2015']].sort_values('REGION',
                                                                                               ascending=True).rename(
            columns={'STNAME': 'County Name'})
        found_counties_to_html = th_scope(found_counties.to_html(classes='table table-bordered table-striped to_html',
                                                        index=False))

        computed = {'answer': answer,
                    'most_population_state': th_scope(highest_state[['STNAME', 'CENSUS2010POP']].to_html(
                        classes='table table-bordered table-striped', index=True)),
                    'pop_change': pop_change_to_html, 'counties': found_counties_to_html}
        cache.set("census_demo:results", computed, 60 * 15)
        context = {'views': views_code, 'content': content_json_data, 'template': template_code, **computed}
        return render(request, 'data-science/pandas/data-cleaning-census.html', context)
    except FileNotFoundError:
        context = {'description': 'File not found. Please check the file path or create the file if it does not',
                   'reference': "Books Store connection from XML File"}
        return render(request, 'data-science/retrieve_error.html', context)

    except TemplateDoesNotExist:
        context = {'description': 'Template not found. Please check the file path or create the file if it does not',
                   'reference': "Books Store connection from XML File"}
        return render(request, 'data-science/retrieve_error.html', context)

Template

{% extends 'base.html' %}

{% load i18n %}
{% load static %}
{% load cached_pygments %}
{% block title %}{% trans "Census Data Cleaning (pandas)" %} | Marco A. Parra F.{% endblock %}
{% block meta_description %}{% trans "Clean and aggregate census data with pandas, with the code and the resulting tables." %}{% endblock %}
{% block content %}
<br>
<br>

<section class="container-section mb-4">
    <div class="container-fluid">
        <div class="row g-0">
            <div class="portfolio-box-caption">
                <div class="card">
                {% for key, values in content.items %}
                {% for items in content.values %}
                {% for value in items %}
                    <h2 class="cart-title text-center">{{ value.title }}</h2>
                    <hr>
                    <div class="card-body mb-3">
                        <h5 class="card-subtitle mb-3">{{ value.description }}</h5>
                        <p class="card-text">{{ value.key_components }}</p>
                    </div>
                {% endfor %}
                {% endfor %}
                {% endfor %}
                </div>
            </div>
        </div>
        <hr>
        <div class="container border" style="background-color: aliceblue;">
            <div class="row g-0">
                <p>
                    The state has the highest number of counties is {{ answer.0 }} within its borders, which has {{ answer.1 }}. Using idxmax() function to get the Name of State and the max() to get the quantity.<br>
                    <q>For comprehensive information and detailed instructions, please refer to the official documentation. </q><br>
                    <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.idxmax.html" target="_blank">https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.idxmax.html</a><br>
                    <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.max.html" target="_blank">https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.max.html</a>
                </p>
                <pre>
                    <code class="python">
                        greatest_counties = df.groupby(['STNAME']).sum()['COUNTY'].idxmax()<br>
                        quantity_greatest_county = df.groupby(['STNAME']).sum()['COUNTY'].max()
                    </code>
                </pre>
            </div>
            <hr>
            <div class="row g-0">
                <p>Find the top ten populous states in descendent order, using the column CENSUS2010POP. To make more complex was filtered with code 50 to group by Counties. The groupby, sort_values, and aggregation functions are used to accomplish this task.</p>
                <br>
                <div class="row" style="width: 85%; max-height: 25rem; /* Set the desired height here */
        overflow-y: scroll; /* Enable vertical scrolling */">
                    <div class="table-responsive">
                        {{ most_population_state|safe }}
                    </div>
                </div>
                <pre>
                    <code class="python">
                        condition_state = df['SUMLEV'] == 50
                        new_condition_state = df[condition_state]<br>
                        most_population_state = new_condition_state.sort_values(['STNAME', 'CENSUS2010POP'], ascending=[True, False])<br>
                        population = most_population_state.groupby('STNAME').agg('sum').sort_values('CENSUS2010POP', ascending=False)<br>
                        highest_state = population.head(10).reset_index()<br>
                        context = { 'most_population_state': highest_state[['STNAME', 'CENSUS2010POP']].to_html(classes='table table-bordered table-striped', index=True)}
                    </code>
                </pre>
            </div>
            <hr>
            <div class="row g-0">
                <p>Identify the City with the largest absolute change in population between the years 2010 and 2015.
                    <br>The code calculates the population change for each city and selects the top ten cities with the highest change.</p>
                <br>
                    <div class="row" style="width: 85%; max-height: 25rem; /* Set the desired height here */
            overflow-y: scroll; /* Enable vertical scrolling */">
                        <div class="table-responsive">
                            {{ pop_change|safe }}
                        </div>
                    </div>
                <pre>
                    <code class="python">
                        def find_min_max(row):
                            columns_of_interest = ['POPESTIMATE2010', 'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013', 'POPESTIMATE2014', 'POPESTIMATE2015']<br>
                            min_value = row[columns_of_interest].min()<br>
                            max_value = row[columns_of_interest].max()<br>
                            return pd.Series({'MIN_POP': min_value, 'MAX_POP': max_value, 'DIF_POP': max_value-min_value})<br>
                        min_max_values = df_pop.apply(find_min_max, axis=1)<br>
                        df = pd.concat([df_pop, min_max_values], axis=1)
                    </code>
                </pre>
            </div>
            <hr>
            <div class="row g-0">
                <p>Query the dataset to retrieve parameters passed through GET methods in the URL for columns related to regions and the starting city name.
                    <br>Validate if there has been an increase in population from 2014 to 2015 for the specified counties using conditional filtering and string operations. The code then extracts and returns these counties as a DataFrame.</p>
                <br>
                <h2>{% trans "Search Form" %}</h2>
                <hr>
                <form method="get" action="{% url 'pandas-census' %}">
                    {% csrf_token %}
                    <div class="row">
                        <div class="col-12 col-sm-6">
                            <div class="form-group mb-3">
                                <label for="region">Region ID:</label>
                                <input type="text" class="form-control" id="region" name="region" placeholder="Enter a value [1, 2, 3,4]">
                            </div>
                        </div>
                        <div class="col-12 col-sm-6">
                            <div class="form-group mb-3">
                                <label for="county">County Name:</label>
                                <input type="text" class="form-control" id="county" name="county" placeholder="Enter a value">
                            </div>
                        </div>
                    </div>
                    <button type="submit" class="btn btn-primary">{% trans "Search" %}</button>
                </form>

                <div class="row" style="width: 85%; max-height: 25rem; /* Set the desired height here */
        overflow-y: scroll; /* Enable vertical scrolling */">
                    <div class="table-responsive">
                        {{ counties|safe }}
                    </div>
                </div>
                <pre>
                    <code class="python">
        if request.GET.get("region", None) is not None:
            region = request.GET.get("region")
            # filter region condition
            condition_region = (region_df['SUMLEV'] == 50) & (region_df['REGION'] == int(region))
            region_df = region_df[condition_region]
        else:
            # If 'region' parameter is not provided, retrieve all values
            condition_region = (region_df['SUMLEV'] == 50) & (region_df['REGION'].isin([1, 2, 3, 4]))
            region_df = region_df[condition_region]

        # Filter county name that start with some text.
        if request.GET.get("county", None) is not None:
            county = request.GET.get("county")
            condition_starts_name = region_df['CTYNAME'].str.startswith(county)
            start_city = region_df[condition_starts_name]
        else:
            # If 'region' parameter is not provided, retrieve Washington
            condition_starts_name = region_df['CTYNAME'].str.startswith('Washington')
            start_city = region_df[condition_starts_name]

        # Validate increase Population 2015/2016
        condition_comparation_population = start_city['POPESTIMATE2015'] > start_city['POPESTIMATE2014']
        fiend_counties = start_city[condition_comparation_population]
                    </code>
                </pre>
            </div>
            <hr>
        </div>
    </div>
</section>

<section class="container-section mb-4">
    <div class="container-fluid">
        <div class="row g-0">
            <div class="portfolio-box-caption">
                <h1>Data Cleaning with Pandas Library</h1>
                <hr>
                <div class="container">

                </div>
                <hr>
                <div>
                    <div class="row" style="width: 100%; max-height: 50rem; /* Set the desired height here */
            overflow-y: scroll; /* Enable vertical scrolling */">
                        <div class="table-responsive">
                            {{ data|safe }}
                        </div>
                    </div>
                </div>
            </div>
        </div>
    </div>
</section>

<section class="container-section mb-4">
    <div class="container-fluid">
        <div class="row g-0">
            <div class="portfolio-box-caption">
                <div class="row">
                    <div class="col-12 col-sm-10">
                        <h3 class="portfolio-box-headers" style="text-align:center">
                            {% trans "Show Code" %}</h3>
                    </div>
                    <div class="col">
                        <button type="button" class="btn btn-light" onclick="$('#section-w').toggle();">
                            Show/Hide
                        </button>
                    </div>
                </div>
            </div>
            <hr>
            <div class="container" style="display:none;" id="section-w">
                <div class="row mb-4">
                    <div class="col">
                        <h4>views.py Code</h4>
                        <div class="sourcecode">
                            {% for item in views %}
                            {{ item|pygmentize:"python3" }}
                            {% endfor %}
                        </div>
                    </div>
                    <div class="col">
                        <h4>Template</h4>
                        <div class="sourcecode">
                            {{ template|pygmentize:"html" }}
                        </div>
                    </div>
                </div>
            </div>
        </div>
    </div>
</section>

{% endblock %}