Life Expectancy by Age, Gender and Counties in Kenya

2019 Kenya Population and Housing Census - KPHC Census Analytical Report on Population Dynamics Volume VIII

data-science
Author

Kabui, Charles

Published

18 March 2025

Keywords

kenya, population, 2019-census, year-2021, life-expectancy

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We plot and analyze the life expectancy in Kenya by age, gender and counties. The data is from the 2019 Kenya Population and Housing Census. The data is available in the Kenya National Bureau of Statistics website 1. We will extract the data from Table 5.7, page 70 of the KPHC Census Analytical Report on Population Dynamics Volume VIII 2

The following is an image of the table we will be using:

Table 5.7, page 70 of the 2019 Kenya Population and Housing Census Analytical Report on Population Dynamics Vol VIII.

Table 5.7, page 70 of the 2019 Kenya Population and Housing Census Analytical Report on Population Dynamics Vol VIII.
Code 
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from adjustText import adjust_text
from IPython.display import display, Markdown
Code 
data_dict = {
    ('', 'COUNTY/RESIDENCE'): [
        "Kenya",
        "Rural",
        "Urban",
        "Mombasa",
        "Kwale",
        "Kilifi",
        "Tana River",
        "Lamu",
        "Taita Taveta",
        "Garissa",
        "Wajir",
        "Mandera",
        "Marsabit",
        "Isiolo",
        "Meru",
        "Tharaka-Nithi",
        "Embu",
        "Kitui",
        "Machakos",
        "Makueni",
        "Nyandarua",
        "Nyeri",
        "Kirinyaga",
        "Murang'a",
        "Kiambu",
        "Turkana",
        "West Pokot",
        "Samburu",
        "Trans Nzoia",
        "Uasin Gishu",
        "Elgeyo Marakwet",
        "Nandi",
        "Baringo",
        "Laikipia",
        "Nakuru",
        "Narok",
        "Kajiado",
        "Kericho",
        "Bomet",
        "Kakamega",
        "Vihiga",
        "Bungoma",
        "Busia",
        "Siaya",
        "Kisumu",
        "Homa Bay",
        "Migori",
        "Kisii",
        "Nyamira",
        "Nairobi"
    ],
    ("MALE", "birth (age 0)"): [
        60.6,
        60.3,
        63.0,
        65.7,
        58.2,
        57.8,
        56.2,
        64.1,
        61.2,
        57.4,
        56.7,
        57.3,
        57.9,
        60.1,
        62.2,
        63.8,
        58.7,
        55.7,
        57.9,
        58.1,
        58.0,
        66.4,
        59.9,
        58.1,
        60.8,
        55.9,
        57.6,
        58.5,
        64.4,
        63.6,
        57.9,
        60.0,
        65.4,
        59.2,
        58.3,
        57.8,
        57.8,
        58.1,
        58.1,
        63.4,
        59.8,
        65.5,
        62.3,
        53.4,
        54.0,
        50.5,
        50.5,
        56.1,
        55.3,
        62.6
    ],
    ("MALE", "age 20"): [
        44.9,
        44.6,
        47.2,
        51.0,
        42.9,
        43.0,
        42.5,
        50.9,
        45.9,
        43.1,
        42.8,
        43.0,
        42.6,
        45.9,
        47.8,
        47.3,
        43.3,
        40.3,
        41.9,
        42.2,
        42.2,
        49.9,
        44.0,
        42.6,
        44.4,
        42.1,
        42.9,
        42.6,
        49.6,
        48.0,
        42.4,
        44.6,
        50.3,
        42.6,
        43.6,
        43.0,
        41.9,
        42.4,
        42.9,
        49.5,
        46.2,
        50.6,
        48.8,
        39.9,
        40.1,
        38.7,
        40.1,
        40.7,
        40.0,
        48.4
    ],
    ("MALE", "age 60"): [
        14.2,
        14.5,
        15.3,
        17.7,
        14.0,
        14.1,
        14.0,
        17.7,
        15.7,
        14.1,
        14.1,
        14.1,
        14.7,
        15.7,
        16.4,
        16.2,
        14.9,
        14.0,
        13.7,
        13.8,
        13.8,
        16.3,
        15.1,
        13.9,
        15.2,
        14.5,
        14.1,
        14.6,
        17.1,
        16.5,
        13.9,
        15.3,
        17.4,
        14.7,
        15.0,
        14.1,
        13.7,
        13.9,
        14.0,
        17.1,
        15.8,
        17.6,
        16.8,
        13.9,
        14.0,
        13.7,
        14.0,
        14.2,
        14.0,
        16.6
    ],
    ("MALE", "age 80"): [
        5.7,
        5.4,
        5.6,
        5.9,
        5.3,
        5.3,
        5.2,
        5.9,
        5.6,
        5.2,
        5.2,
        5.2,
        5.5,
        5.6,
        5.7,
        5.7,
        5.5,
        5.4,
        5.3,
        5.3,
        5.3,
        6.0,
        5.5,
        5.3,
        5.5,
        5.4,
        5.2,
        5.5,
        5.8,
        5.7,
        5.3,
        5.5,
        5.9,
        5.5,
        5.5,
        5.3,
        5.3,
        5.3,
        5.3,
        5.8,
        5.6,
        5.9,
        5.7,
        5.4,
        5.4,
        5.3,
        5.4,
        5.4,
        5.4,
        5.7
    ],
    ("FEMALE", "birth (age 0)"): [
        66.5,
        66.2,
        68.6,
        71.1,
        60.7,
        59.2,
        58.6,
        68.9,
        69.2,
        60.5,
        62.0,
        60.5,
        62.6,
        59.6,
        68.2,
        70.3,
        70.1,
        68.8,
        63.9,
        62.4,
        63.2,
        75.8,
        71.1,
        64.7,
        72.8,
        60.0,
        61.1,
        67.4,
        67.9,
        71.7,
        62.5,
        70.4,
        68.9,
        71.9,
        69.1,
        61.9,
        63.5,
        62.9,
        63.6,
        66.1,
        62.1,
        68.2,
        66.2,
        61.6,
        63.1,
        60.2,
        60.6,
        66.9,
        66.2,
        65.2
    ],
    ("FEMALE", "age 20"): [
        50.8,
        50.9,
        52.9,
        55.3,
        45.5,
        44.6,
        44.4,
        53.3,
        52.9,
        45.6,
        46.4,
        45.5,
        46.4,
        44.4,
        52.8,
        53.1,
        53.9,
        52.3,
        47.7,
        46.6,
        47.2,
        59.6,
        54.8,
        48.3,
        55.8,
        45.6,
        45.8,
        51.3,
        52.5,
        55.2,
        46.7,
        54.3,
        52.4,
        54.5,
        53.8,
        46.3,
        47.4,
        47.0,
        47.5,
        51.3,
        49.0,
        52.6,
        51.8,
        47.2,
        48.6,
        47.6,
        48.9,
        50.6,
        50.2,
        50.2
    ],
    ("FEMALE", "age 60"): [
        17.2,
        17.7,
        18.4,
        20.0,
        16.2,
        15.9,
        15.9,
        18.9,
        18.7,
        16.2,
        16.4,
        16.2,
        16.0,
        15.4,
        18.7,
        18.8,
        19.2,
        18.4,
        16.8,
        16.5,
        16.7,
        22.2,
        19.7,
        17.0,
        20.2,
        15.7,
        16.3,
        18.0,
        18.5,
        19.9,
        16.5,
        19.4,
        18.5,
        19.5,
        19.2,
        16.4,
        16.7,
        16.6,
        16.8,
        18.0,
        17.0,
        18.6,
        18.2,
        16.3,
        16.8,
        16.5,
        17.0,
        17.7,
        17.5,
        17.5
    ],
    ("FEMALE", "age 80"): [
        6.2,
        6.3,
        6.4,
        6.5,
        6.1,
        6.0,
        6.0,
        6.3,
        6.2,
        6.1,
        6.2,
        6.1,
        5.7,
        5.7,
        6.2,
        6.3,
        6.4,
        6.2,
        6.3,
        6.2,
        6.2,
        7.4,
        6.5,
        6.3,
        6.6,
        5.7,
        6.1,
        6.1,
        6.2,
        6.5,
        6.2,
        6.4,
        6.2,
        6.4,
        6.3,
        6.2,
        6.3,
        6.2,
        6.3,
        6.1,
        5.9,
        6.2,
        6.1,
        5.8,
        5.9,
        5.8,
        5.9,
        6.0,
        6.0,
        6.0
    ]
}
df = pd.DataFrame(data_dict)
df.set_index(df.columns[0], inplace=True)
df
Life Expectancy by Age, Gender and Region at Age 0, Age 20, Age 60 and Age 80 (in Years)
MALE FEMALE
birth (age 0) age 20 age 60 age 80 birth (age 0) age 20 age 60 age 80
(, COUNTY/RESIDENCE)
Kenya 60.6 44.9 14.2 5.7 66.5 50.8 17.2 6.2
Rural 60.3 44.6 14.5 5.4 66.2 50.9 17.7 6.3
Urban 63.0 47.2 15.3 5.6 68.6 52.9 18.4 6.4
Mombasa 65.7 51.0 17.7 5.9 71.1 55.3 20.0 6.5
Kwale 58.2 42.9 14.0 5.3 60.7 45.5 16.2 6.1
Kilifi 57.8 43.0 14.1 5.3 59.2 44.6 15.9 6.0
Tana River 56.2 42.5 14.0 5.2 58.6 44.4 15.9 6.0
Lamu 64.1 50.9 17.7 5.9 68.9 53.3 18.9 6.3
Taita Taveta 61.2 45.9 15.7 5.6 69.2 52.9 18.7 6.2
Garissa 57.4 43.1 14.1 5.2 60.5 45.6 16.2 6.1
Wajir 56.7 42.8 14.1 5.2 62.0 46.4 16.4 6.2
Mandera 57.3 43.0 14.1 5.2 60.5 45.5 16.2 6.1
Marsabit 57.9 42.6 14.7 5.5 62.6 46.4 16.0 5.7
Isiolo 60.1 45.9 15.7 5.6 59.6 44.4 15.4 5.7
Meru 62.2 47.8 16.4 5.7 68.2 52.8 18.7 6.2
Tharaka-Nithi 63.8 47.3 16.2 5.7 70.3 53.1 18.8 6.3
Embu 58.7 43.3 14.9 5.5 70.1 53.9 19.2 6.4
Kitui 55.7 40.3 14.0 5.4 68.8 52.3 18.4 6.2
Machakos 57.9 41.9 13.7 5.3 63.9 47.7 16.8 6.3
Makueni 58.1 42.2 13.8 5.3 62.4 46.6 16.5 6.2
Nyandarua 58.0 42.2 13.8 5.3 63.2 47.2 16.7 6.2
Nyeri 66.4 49.9 16.3 6.0 75.8 59.6 22.2 7.4
Kirinyaga 59.9 44.0 15.1 5.5 71.1 54.8 19.7 6.5
Murang'a 58.1 42.6 13.9 5.3 64.7 48.3 17.0 6.3
Kiambu 60.8 44.4 15.2 5.5 72.8 55.8 20.2 6.6
Turkana 55.9 42.1 14.5 5.4 60.0 45.6 15.7 5.7
West Pokot 57.6 42.9 14.1 5.2 61.1 45.8 16.3 6.1
Samburu 58.5 42.6 14.6 5.5 67.4 51.3 18.0 6.1
Trans Nzoia 64.4 49.6 17.1 5.8 67.9 52.5 18.5 6.2
Uasin Gishu 63.6 48.0 16.5 5.7 71.7 55.2 19.9 6.5
Elgeyo Marakwet 57.9 42.4 13.9 5.3 62.5 46.7 16.5 6.2
Nandi 60.0 44.6 15.3 5.5 70.4 54.3 19.4 6.4
Baringo 65.4 50.3 17.4 5.9 68.9 52.4 18.5 6.2
Laikipia 59.2 42.6 14.7 5.5 71.9 54.5 19.5 6.4
Nakuru 58.3 43.6 15.0 5.5 69.1 53.8 19.2 6.3
Narok 57.8 43.0 14.1 5.3 61.9 46.3 16.4 6.2
Kajiado 57.8 41.9 13.7 5.3 63.5 47.4 16.7 6.3
Kericho 58.1 42.4 13.9 5.3 62.9 47.0 16.6 6.2
Bomet 58.1 42.9 14.0 5.3 63.6 47.5 16.8 6.3
Kakamega 63.4 49.5 17.1 5.8 66.1 51.3 18.0 6.1
Vihiga 59.8 46.2 15.8 5.6 62.1 49.0 17.0 5.9
Bungoma 65.5 50.6 17.6 5.9 68.2 52.6 18.6 6.2
Busia 62.3 48.8 16.8 5.7 66.2 51.8 18.2 6.1
Siaya 53.4 39.9 13.9 5.4 61.6 47.2 16.3 5.8
Kisumu 54.0 40.1 14.0 5.4 63.1 48.6 16.8 5.9
Homa Bay 50.5 38.7 13.7 5.3 60.2 47.6 16.5 5.8
Migori 50.5 40.1 14.0 5.4 60.6 48.9 17.0 5.9
Kisii 56.1 40.7 14.2 5.4 66.9 50.6 17.7 6.0
Nyamira 55.3 40.0 14.0 5.4 66.2 50.2 17.5 6.0
Nairobi 62.6 48.4 16.6 5.7 65.2 50.2 17.5 6.0

Plots and Insights

Code 
def life_expectancy_analysis(age_column: str, axis_margin: float = 1.2):
    ### Life Expectancy
    display(Markdown(
        "\n"
        f"### Life Expectancy at {age_column}"
        "\n"))
    kenya = df.loc["Kenya"]
    rural = df.loc["Rural"]
    urban = df.loc["Urban"]
    drop_columns = [kenya.name, rural.name, urban.name]
    males = df[('MALE', age_column)].drop(drop_columns).tolist()
    females = df[('FEMALE', age_column)].drop(drop_columns).tolist()
    counties = df.index.drop(drop_columns).tolist()
    #### Plotting
    display(Markdown(
        "\n"
        f"#### Plot - {age_column}"
        "\n"))
    plt.figure(figsize=(12, 10))
    # Plot the counties
    sns.scatterplot(
        x=males, y=females, s=60, alpha=0.9, label='Counties Life Expectancy')
    # Plot reference lines for Kenya, Rural, Urban
    plt.axvline(
        x=kenya[('MALE', age_column)], color='r', linestyle='dashdot',  alpha=0.3)
    plt.axhline(
        y=kenya[('FEMALE', age_column)], color='r', linestyle='dashdot', alpha=0.3, 
        label='National Life Expectancy')
    plt.axvline(
        x=rural[('MALE', age_column)], color='g', linestyle='dashdot', alpha=0.3)
    plt.axhline(
        y=rural[('FEMALE', age_column)], color='g', linestyle='dashdot', alpha=0.3, 
        label='Rural Life Expectancy')
    plt.axvline(
        x=urban[('MALE', age_column)], color='b', linestyle='dashdot', alpha=0.3)
    plt.axhline(
        y=urban[('FEMALE', age_column)], color='b', linestyle='dashdot', alpha=0.3, 
        label='Urban Life Expectancy')
    # Add diagonal line representing equal life expectancy
    min_val = min(min(males), min(females))
    max_val = max(max(males), max(females))
    plt.plot(
        [min_val, max_val], [min_val, max_val], color='gray', linestyle='solid', 
        alpha=0.3, label='Equal Life Expectancy')
    # Add labels for reference points
    texts = [
        plt.text(males[i], females[i], counties[i], fontsize=10) 
        for i 
        in range(len(counties))]
    adjust_text(texts, arrowprops=dict(arrowstyle='-', color='black', lw=0.5))
    # Adjust limits based on scatter data
    plt.xlim(min(males) - axis_margin, max(males) + axis_margin) 
    plt.ylim(min(females) - axis_margin, max(females) + axis_margin)
    plt.grid(True, alpha=0.2)
    plt.title(
        f'Life Expectancy at {age_column} by County in Kenya: Male vs Female', 
        fontsize=16)
    plt.xlabel('Male Life Expectancy (years)', fontsize=14)
    plt.ylabel('Female Life Expectancy (years)', fontsize=14)
    plt.legend(loc='upper left')
    plt.tight_layout()
    plt.show()
    #### Analysis of Life Expectancy
    display(Markdown(
        "\n"
        f"#### Analysis of Life Expectancy at {age_column}"
        "\n"))
    kenya_males = kenya[('MALE', age_column)]
    kenya_females = kenya[('FEMALE', age_column)]
    rural_males = rural[('MALE', age_column)]
    rural_females = rural[('FEMALE', age_column)]
    urban_males = urban[('MALE', age_column)]
    urban_females = urban[('FEMALE', age_column)]
    county_analysis_df = pd.DataFrame({ 
        'COUNTY': counties, 
        'MALE': males, 
        'FEMALE': females 
    })
    ##### Top 5 counties greatest and least life expectancy
    display(Markdown(
        "\n"
        f"##### Top 5 counties with greatest and least life expectancy - {age_column}"
        "\n"))
    men_by_county_asc = county_analysis_df\
        .sort_values("MALE", ascending=True)\
        .head(5)\
        .apply(lambda x: f'{x["COUNTY"] } - {x["MALE"]}', axis=1)\
        .tolist()
    men_by_county_desc = county_analysis_df\
        .sort_values("MALE", ascending=False)\
        .head(5)\
        .apply(lambda x: f'{x["COUNTY"] } - {x["MALE"]}', axis=1)\
        .tolist()
    women_by_county_asc = county_analysis_df\
        .sort_values("FEMALE", ascending=True)\
        .head(5)\
        .apply(lambda x: f'{x["COUNTY"] } - {x["FEMALE"]}', axis=1)\
        .tolist()
    women_by_county_desc = county_analysis_df\
        .sort_values("FEMALE", ascending=False)\
        .head(5)\
        .apply(lambda x: f'{x["COUNTY"] } - {x["FEMALE"]}', axis=1)\
        .tolist()
    display(pd.DataFrame({ 
        '[men] by_county_desc': men_by_county_desc,
        '[men] by_county_asc': men_by_county_asc, 
        '[women] by_county_desc': women_by_county_desc,
        '[women] by_county_asc': women_by_county_asc,
    }, index=range(1, 6)))
    # Analysis
    def above_or_below(reference: str, males: float, females: float):
        men_by_county_below = county_analysis_df\
            [county_analysis_df["MALE"] <= males]\
            .sort_values("MALE", ascending=True)\
            .apply(lambda x: f'{x["COUNTY"] } - {x["MALE"]}', axis=1)\
            .tolist()
        men_by_county_above = county_analysis_df\
            [county_analysis_df["MALE"] > males]\
            .sort_values("MALE", ascending=False)\
            .apply(lambda x: f'{x["COUNTY"] } - {x["MALE"]}', axis=1)\
            .tolist()
        women_by_county_below = county_analysis_df\
            [county_analysis_df["FEMALE"] <= females]\
            .sort_values("FEMALE", ascending=True)\
            .apply(lambda x: f'{x["COUNTY"] } - {x["FEMALE"]}', axis=1)\
            .tolist()
        women_by_county_above = county_analysis_df\
            [county_analysis_df["FEMALE"] > females]\
            .sort_values("FEMALE", ascending=False)\
            .apply(lambda x: f'{x["COUNTY"] } - {x["FEMALE"]}', axis=1)\
            .tolist()
        return pd.DataFrame.from_dict({ 
            f'({len(men_by_county_above)}) [men] above {reference}': men_by_county_above[:6],
            f'({len(men_by_county_below)}) [men] below {reference}': men_by_county_below[:6], 
            f'({len(women_by_county_above)}) [women] above {reference}': women_by_county_above[:6],
            f'({len(women_by_county_below)}) [women] below {reference}': women_by_county_below[:6],
        }, orient='index').T.reindex(index=range(1, 6))
    ##### Top 5 counties above and below national life expectancy"
    display(Markdown(
        "\n"
        f"##### Top 5 counties above and below National life expectancy - {age_column}"
        "\n"))
    display(above_or_below('national', kenya_males, kenya_females))
    ##### Top 5 counties above and below urban life expectancy
    display(Markdown(
        "\n"
        f"##### Top 5 counties above and below Rural life expectancy - {age_column}"
        "\n"))
    display(above_or_below('rural', rural_males, rural_females))
    ##### Top 5 counties above and below urban life expectancy
    display(Markdown(
        "\n"
        f"##### Top 5 counties above and below Urban life expectancy - {age_column}"
        "\n"))
    display(above_or_below('urban', urban_males, urban_females))
    ##### Top 5 counties where men have higher life expectancy than women
    men_greater_than_women = county_analysis_df\
        [county_analysis_df["FEMALE"] < county_analysis_df["MALE"]]
    if men_greater_than_women is not None and not men_greater_than_women.empty:
        display(Markdown(
            "\n"
            f"##### Top 5 counties where men have higher life expectancy than women - {age_column}"
            "\n"))
        display(men_greater_than_women.sort_values("FEMALE", ascending=True))
    display(Markdown(
        "\n"
        f"##### Other Statistics - {age_column}"
        "\n"))
    display(county_analysis_df.describe())
Code 
life_expectancy_analysis('birth (age 0)')

Life Expectancy at birth (age 0)

Plot - birth (age 0)

Analysis of Life Expectancy at birth (age 0)

Top 5 counties with greatest and least life expectancy - birth (age 0)
[men] by_county_desc [men] by_county_asc [women] by_county_desc [women] by_county_asc
1 Nyeri - 66.4 Homa Bay - 50.5 Nyeri - 75.8 Tana River - 58.6
2 Mombasa - 65.7 Migori - 50.5 Kiambu - 72.8 Kilifi - 59.2
3 Bungoma - 65.5 Siaya - 53.4 Laikipia - 71.9 Isiolo - 59.6
4 Baringo - 65.4 Kisumu - 54.0 Uasin Gishu - 71.7 Turkana - 60.0
5 Trans Nzoia - 64.4 Nyamira - 55.3 Kirinyaga - 71.1 Homa Bay - 60.2
Top 5 counties above and below National life expectancy - birth (age 0)
(14) [men] above national (33) [men] below national (19) [women] above national (28) [women] below national
1 Mombasa - 65.7 Homa Bay - 50.5 Kiambu - 72.8 Kilifi - 59.2
2 Bungoma - 65.5 Siaya - 53.4 Laikipia - 71.9 Isiolo - 59.6
3 Baringo - 65.4 Kisumu - 54.0 Uasin Gishu - 71.7 Turkana - 60.0
4 Trans Nzoia - 64.4 Nyamira - 55.3 Kirinyaga - 71.1 Homa Bay - 60.2
5 Lamu - 64.1 Kitui - 55.7 Mombasa - 71.1 Garissa - 60.5
Top 5 counties above and below Rural life expectancy - birth (age 0)
(14) [men] above rural (33) [men] below rural (19) [women] above rural (28) [women] below rural
1 Mombasa - 65.7 Homa Bay - 50.5 Kiambu - 72.8 Kilifi - 59.2
2 Bungoma - 65.5 Siaya - 53.4 Laikipia - 71.9 Isiolo - 59.6
3 Baringo - 65.4 Kisumu - 54.0 Uasin Gishu - 71.7 Turkana - 60.0
4 Trans Nzoia - 64.4 Nyamira - 55.3 Kirinyaga - 71.1 Homa Bay - 60.2
5 Lamu - 64.1 Kitui - 55.7 Mombasa - 71.1 Garissa - 60.5
Top 5 counties above and below Urban life expectancy - birth (age 0)
(9) [men] above urban (38) [men] below urban (14) [women] above urban (33) [women] below urban
1 Mombasa - 65.7 Migori - 50.5 Kiambu - 72.8 Kilifi - 59.2
2 Bungoma - 65.5 Siaya - 53.4 Laikipia - 71.9 Isiolo - 59.6
3 Baringo - 65.4 Kisumu - 54.0 Uasin Gishu - 71.7 Turkana - 60.0
4 Trans Nzoia - 64.4 Nyamira - 55.3 Mombasa - 71.1 Homa Bay - 60.2
5 Lamu - 64.1 Kitui - 55.7 Kirinyaga - 71.1 Garissa - 60.5
Top 5 counties where men have higher life expectancy than women - birth (age 0)
COUNTY MALE FEMALE
10 Isiolo 60.1 59.6
Other Statistics - birth (age 0)
MALE FEMALE
count 47.000000 47.000000
mean 59.110638 65.391489
std 3.684706 4.327959
min 50.500000 58.600000
25% 57.500000 61.950000
50% 58.100000 64.700000
75% 61.700000 68.900000
max 66.400000 75.800000
Code 
life_expectancy_analysis('age 20')

Life Expectancy at age 20

Plot - age 20

Analysis of Life Expectancy at age 20

Top 5 counties with greatest and least life expectancy - age 20
[men] by_county_desc [men] by_county_asc [women] by_county_desc [women] by_county_asc
1 Mombasa - 51.0 Homa Bay - 38.7 Nyeri - 59.6 Tana River - 44.4
2 Lamu - 50.9 Siaya - 39.9 Kiambu - 55.8 Isiolo - 44.4
3 Bungoma - 50.6 Nyamira - 40.0 Mombasa - 55.3 Kilifi - 44.6
4 Baringo - 50.3 Migori - 40.1 Uasin Gishu - 55.2 Kwale - 45.5
5 Nyeri - 49.9 Kisumu - 40.1 Kirinyaga - 54.8 Mandera - 45.5
Top 5 counties above and below National life expectancy - age 20
(15) [men] above national (32) [men] below national (20) [women] above national (27) [women] below national
1 Lamu - 50.9 Siaya - 39.9 Kiambu - 55.8 Isiolo - 44.4
2 Bungoma - 50.6 Nyamira - 40.0 Mombasa - 55.3 Kilifi - 44.6
3 Baringo - 50.3 Migori - 40.1 Uasin Gishu - 55.2 Kwale - 45.5
4 Nyeri - 49.9 Kisumu - 40.1 Kirinyaga - 54.8 Mandera - 45.5
5 Trans Nzoia - 49.6 Kitui - 40.3 Laikipia - 54.5 Garissa - 45.6
Top 5 counties above and below Rural life expectancy - age 20
(15) [men] above rural (32) [men] below rural (20) [women] above rural (27) [women] below rural
1 Lamu - 50.9 Siaya - 39.9 Kiambu - 55.8 Isiolo - 44.4
2 Bungoma - 50.6 Nyamira - 40.0 Mombasa - 55.3 Kilifi - 44.6
3 Baringo - 50.3 Migori - 40.1 Uasin Gishu - 55.2 Kwale - 45.5
4 Nyeri - 49.9 Kisumu - 40.1 Kirinyaga - 54.8 Mandera - 45.5
5 Trans Nzoia - 49.6 Kitui - 40.3 Laikipia - 54.5 Garissa - 45.6
Top 5 counties above and below Urban life expectancy - age 20
(12) [men] above urban (35) [men] below urban (11) [women] above urban (36) [women] below urban
1 Lamu - 50.9 Siaya - 39.9 Kiambu - 55.8 Isiolo - 44.4
2 Bungoma - 50.6 Nyamira - 40.0 Mombasa - 55.3 Kilifi - 44.6
3 Baringo - 50.3 Migori - 40.1 Uasin Gishu - 55.2 Kwale - 45.5
4 Nyeri - 49.9 Kisumu - 40.1 Kirinyaga - 54.8 Mandera - 45.5
5 Trans Nzoia - 49.6 Kitui - 40.3 Laikipia - 54.5 Garissa - 45.6
Top 5 counties where men have higher life expectancy than women - age 20
COUNTY MALE FEMALE
10 Isiolo 45.9 44.4
Other Statistics - age 20
MALE FEMALE
count 47.000000 47.000000
mean 44.285106 49.887234
std 3.388983 3.748137
min 38.700000 44.400000
25% 42.300000 46.650000
50% 43.000000 49.000000
75% 46.750000 52.850000
max 51.000000 59.600000
Code 
life_expectancy_analysis('age 60')

Life Expectancy at age 60

Plot - age 60

Analysis of Life Expectancy at age 60

Top 5 counties with greatest and least life expectancy - age 60
[men] by_county_desc [men] by_county_asc [women] by_county_desc [women] by_county_asc
1 Mombasa - 17.7 Homa Bay - 13.7 Nyeri - 22.2 Isiolo - 15.4
2 Lamu - 17.7 Machakos - 13.7 Kiambu - 20.2 Turkana - 15.7
3 Bungoma - 17.6 Kajiado - 13.7 Mombasa - 20.0 Kilifi - 15.9
4 Baringo - 17.4 Nyandarua - 13.8 Uasin Gishu - 19.9 Tana River - 15.9
5 Trans Nzoia - 17.1 Makueni - 13.8 Kirinyaga - 19.7 Marsabit - 16.0
Top 5 counties above and below National life expectancy - age 60
(24) [men] above national (23) [men] below national (23) [women] above national (24) [women] below national
1 Lamu - 17.7 Machakos - 13.7 Kiambu - 20.2 Turkana - 15.7
2 Bungoma - 17.6 Kajiado - 13.7 Mombasa - 20.0 Kilifi - 15.9
3 Baringo - 17.4 Makueni - 13.8 Uasin Gishu - 19.9 Tana River - 15.9
4 Kakamega - 17.1 Nyandarua - 13.8 Kirinyaga - 19.7 Marsabit - 16.0
5 Trans Nzoia - 17.1 Siaya - 13.9 Laikipia - 19.5 Kwale - 16.2
Top 5 counties above and below Rural life expectancy - age 60
(23) [men] above rural (24) [men] below rural (20) [women] above rural (27) [women] below rural
1 Lamu - 17.7 Machakos - 13.7 Kiambu - 20.2 Turkana - 15.7
2 Bungoma - 17.6 Kajiado - 13.7 Mombasa - 20.0 Kilifi - 15.9
3 Baringo - 17.4 Makueni - 13.8 Uasin Gishu - 19.9 Tana River - 15.9
4 Kakamega - 17.1 Nyandarua - 13.8 Kirinyaga - 19.7 Marsabit - 16.0
5 Trans Nzoia - 17.1 Siaya - 13.9 Laikipia - 19.5 Kwale - 16.2
Top 5 counties above and below Urban life expectancy - age 60
(15) [men] above urban (32) [men] below urban (16) [women] above urban (31) [women] below urban
1 Lamu - 17.7 Kajiado - 13.7 Kiambu - 20.2 Turkana - 15.7
2 Bungoma - 17.6 Machakos - 13.7 Mombasa - 20.0 Kilifi - 15.9
3 Baringo - 17.4 Makueni - 13.8 Uasin Gishu - 19.9 Tana River - 15.9
4 Trans Nzoia - 17.1 Nyandarua - 13.8 Kirinyaga - 19.7 Marsabit - 16.0
5 Kakamega - 17.1 Siaya - 13.9 Laikipia - 19.5 Kwale - 16.2
Top 5 counties where men have higher life expectancy than women - age 60
COUNTY MALE FEMALE
10 Isiolo 15.7 15.4
Other Statistics - age 60
MALE FEMALE
count 47.000000 47.000000
mean 15.014894 17.640426
std 1.291271 1.496178
min 13.700000 15.400000
25% 14.000000 16.450000
50% 14.500000 17.000000
75% 16.000000 18.700000
max 17.700000 22.200000
Code 
life_expectancy_analysis('age 80', axis_margin = 0.1)

Life Expectancy at age 80

Plot - age 80

20 [ 0.85928444 -0.45533174] 35 [-0.08290397 -0.92990256]

Analysis of Life Expectancy at age 80

Top 5 counties with greatest and least life expectancy - age 80
[men] by_county_desc [men] by_county_asc [women] by_county_desc [women] by_county_asc
1 Nyeri - 6.0 West Pokot - 5.2 Nyeri - 7.4 Marsabit - 5.7
2 Mombasa - 5.9 Tana River - 5.2 Kiambu - 6.6 Isiolo - 5.7
3 Bungoma - 5.9 Garissa - 5.2 Mombasa - 6.5 Turkana - 5.7
4 Lamu - 5.9 Wajir - 5.2 Uasin Gishu - 6.5 Homa Bay - 5.8
5 Baringo - 5.9 Mandera - 5.2 Kirinyaga - 6.5 Siaya - 5.8
Top 5 counties above and below National life expectancy - age 80
(7) [men] above national (40) [men] below national (15) [women] above national (32) [women] below national
1 Mombasa - 5.9 Tana River - 5.2 Kiambu - 6.6 Isiolo - 5.7
2 Lamu - 5.9 Garissa - 5.2 Mombasa - 6.5 Turkana - 5.7
3 Baringo - 5.9 Wajir - 5.2 Kirinyaga - 6.5 Homa Bay - 5.8
4 Bungoma - 5.9 Mandera - 5.2 Uasin Gishu - 6.5 Siaya - 5.8
5 Trans Nzoia - 5.8 Narok - 5.3 Embu - 6.4 Migori - 5.9
Top 5 counties above and below Rural life expectancy - age 80
(23) [men] above rural (24) [men] below rural (8) [women] above rural (39) [women] below rural
1 Mombasa - 5.9 Garissa - 5.2 Kiambu - 6.6 Isiolo - 5.7
2 Lamu - 5.9 Wajir - 5.2 Mombasa - 6.5 Turkana - 5.7
3 Bungoma - 5.9 Mandera - 5.2 Kirinyaga - 6.5 Homa Bay - 5.8
4 Baringo - 5.9 West Pokot - 5.2 Uasin Gishu - 6.5 Siaya - 5.8
5 Kakamega - 5.8 Kwale - 5.3 Embu - 6.4 Migori - 5.9
Top 5 counties above and below Urban life expectancy - age 80
(12) [men] above urban (35) [men] below urban (5) [women] above urban (42) [women] below urban
1 Mombasa - 5.9 Garissa - 5.2 Kiambu - 6.6 Isiolo - 5.7
2 Lamu - 5.9 Wajir - 5.2 Mombasa - 6.5 Turkana - 5.7
3 Baringo - 5.9 Mandera - 5.2 Kirinyaga - 6.5 Homa Bay - 5.8
4 Bungoma - 5.9 West Pokot - 5.2 Uasin Gishu - 6.5 Siaya - 5.8
5 Trans Nzoia - 5.8 Kwale - 5.3 None Migori - 5.9
Other Statistics - age 80
MALE FEMALE
count 47.000000 47.000000
mean 5.487234 6.176596
std 0.223234 0.281466
min 5.200000 5.700000
25% 5.300000 6.000000
50% 5.400000 6.200000
75% 5.650000 6.300000
max 6.000000 7.400000

Insights

  • The rural and national life expectancy is are close, which confirms that majority of Kenyans lives in rural areas.
  • Men and Women of Nyeri county Have the greatest overall life expectancy, in all ranges.
  • Isiolo county is the only county men below 80 years live longer than women.

Disclaimer: For information only. Accuracy or completeness not guaranteed. Illegal use prohibited. Not professional advice or solicitation. Read more: /terms-of-service

Footnotes / Citations / References

  1. Kenya National Bureau of Statistics website↩︎

  2. KPHC Census Analytical Report on Population Dynamics Volume VIII↩︎

Reuse

GNU GENERAL PUBLIC LICENSE v3.0(View License)

Citation

BibTeX citation:
@misc{kabui2025,
  author = {{Kabui, Charles}},
  title = {Life {Expectancy} by {Age,} {Gender} and {Counties} in
    {Kenya}},
  date = {2025-03-18},
  url = {https://toknow.ai/posts/life-expectancy-age-gender-and-counties-in-kenya/index.html},
  langid = {en-GB}
}
For attribution, please cite this work as:
Kabui, Charles. 2025. “Life Expectancy by Age, Gender and Counties in Kenya.” https://toknow.ai/posts/life-expectancy-age-gender-and-counties-in-kenya/index.html.