Client Report - What’s in a Name? (Project 1)

Course DS 250

Author

Brayden McAllister

Elevator pitch

paste your elevator pitch here A SHORT (4-5 SENTENCES) PARAGRAPH THAT DESCRIBES KEY INSIGHTS TAKEN FROM METRICS IN THE PROJECT RESULTS THINK TOP OR MOST IMPORTANT RESULTS.

Read and format project data 
# Include and execute your code here
df = pd.read_csv("https://github.com/byuidatascience/data4names/raw/master/data-raw/names_year/names_year.csv")

QUESTION|TASK 1

How does your name at your birth year compare to its use historically?

type your results and analysis here

Read and format data 
# Include and execute your code here
my_name = (df
    .query('name == "Brayden"')
    .filter(['name', 'year', 'Total'])
    .reset_index()
)

my_name_chart = px.bar(my_name,
    x='year',
    y='Total',
    color = 'year',
    title='The use of the name "Brayden"'
)
my_name_chart.show()

include figures in chunks and discuss your findings in the figure.

::: {#cell-Q1 chart .cell execution_count=4}

plot example 
# Include and execute your code here
chart = px.bar(df.head(200),
    x="name", 
    y="AK"
)
chart.show()

My useless chart

:::

::: {#cell-Q1 table .cell .tbl-cap-location-top tbl-cap=‘Not much of a table’ execution_count=5}

table example 
# Include and execute your code here
mydat = df.head(1000)\
    .groupby('year')\
    .sum()\
    .reset_index()\
    .tail(10)\
    .filter(["year", "AK","AR"])

display(mydat)
year AK AR
96 2006 21.0 183.0
97 2007 28.0 153.0
98 2008 36.0 212.0
99 2009 34.0 179.0
100 2010 22.0 196.0
101 2011 41.0 148.0
102 2012 28.0 140.0
103 2013 26.0 134.0
104 2014 20.0 114.0
105 2015 28.0 121.0

:::

QUESTION|TASK 2

If you talked to someone named Brittany on the phone, what is your guess of his or her age? What ages would you not guess?

type your results and analysis here

Read and format data 
# Include and execute your code here

include figures in chunks and discuss your findings in the figure.

::: {#cell-Q2 chart .cell execution_count=7}

plot example 
# Include and execute your code here
chart = px.bar(df.head(200),
    x="name", 
    y="AK"
)
chart.show()

My useless chart

:::

::: {#cell-Q2 table .cell .tbl-cap-location-top tbl-cap=‘Not much of a table’ execution_count=8}

table example 
# Include and execute your code here
mydat = df.head(1000)\
    .groupby('year')\
    .sum()\
    .reset_index()\
    .tail(10)\
    .filter(["year", "AK","AR"])

display(mydat)
year AK AR
96 2006 21.0 183.0
97 2007 28.0 153.0
98 2008 36.0 212.0
99 2009 34.0 179.0
100 2010 22.0 196.0
101 2011 41.0 148.0
102 2012 28.0 140.0
103 2013 26.0 134.0
104 2014 20.0 114.0
105 2015 28.0 121.0

:::

QUESTION|TASK 3

Mary, Martha, Peter, and Paul are all Christian names. From 1920 - 2000, compare the name usage of each of the four names. What trends do you notice?

type your results and analysis here

Read and format data 
# Include and execute your code here

include figures in chunks and discuss your findings in the figure.

::: {#cell-Q3 chart .cell execution_count=10}

plot example 
# Include and execute your code here
chart = px.bar(df.head(200),
    x="name", 
    y="AK"
)
chart.show()

My useless chart

:::

::: {#cell-Q3 table .cell .tbl-cap-location-top tbl-cap=‘Not much of a table’ execution_count=11}

table example 
# Include and execute your code here

list_of_names = (df
    # .query('first_letter == "B"')
    .query('name == "Brayden"')
    .groupby('name')
    .Total.sum()
    .reset_index()
    # .filter(["year", "AK","AR"])
)

list_of_names
name Total
0 Brayden 100013.0

:::

QUESTION|TASK 4

Think of a unique name from a famous movie. Plot the usage of that name and see how changes line up with the movie release. Does it look like the movie had an effect on usage?

type your results and analysis here

#| label: Names from Movie Graph
# Include and execute your code here
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