![\"\"](\"https:\/\/wollen.org\/blog\/wp-content\/uploads\/2025\/02\/echo_info.jpg\")
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1. Prepare the data.<\/h4>\n
Begin by visiting ssa.gov<\/a> and clicking the “National data” link. Download the archive and unzip it within the same folder as your Python script. The names<\/em> folder will contain a comma-delimited text file for every year dating back to 1880. For example, the most recent file is named yob2023.txt<\/em>. If you open the file, you’ll find the first few lines look like this:<\/p>\n There’s no header row but we can see there are three columns: name, sex, and count. In 2023, there were 15,270 female babies named Olivia.<\/p>\n To create our time series we’ll want to concatenate all the files into a single DataFrame, then filter it down to Alexa rows. Using a Create a view of the DataFrame that’s limited to Alexa rows. Let’s begin with 1975 to make the plot more readable.<\/p>\n Now we have a 49-row DataFrame with everything we need to create a plot. I’ll use a custom mplstyle<\/em> inspired by the Amazon color scheme below. It will be linked at the bottom of this post.<\/p>\n Set the style and create an Axes instance on which we can plot.<\/p>\n We’re working with a time series so a continuous line plot makes sense. But with annual data points I think it’s a good idea to add markers at each point. It isn’t strictly necessary but it helps to communicate how the data is reported.<\/p>\n It would also be okay to represent the data as a bar plot, especially with data reported annually. But when highlighting a trend I prefer to plot a line.<\/p>\n We can combine ticks and window limits into a single Since we’re analyzing how the Echo affected baby name trends, let’s highlight its launch with an annotation. I like wedges but Matplotlib has several other built-in arrow style options<\/a>.<\/p>\n Use Finally, set a title and save the figure.<\/p>\n The downward curve almost looks like an Amazon logo!<\/p>\n The Echo was rolled out in late 2014 and 2015 so it’s fair to say the name Alexa was already past its peak. It’s normal for names to rise and fall, although this fall appears steeper than it may have been otherwise. Alexa’s trend looks less like Allison’s 35-year decline from similar levels of popularity and more like the O.J. baby name crash of 1994.<\/p>\n If nothing else, it’s safe to attribute the short-lived 40% spike in 2015 to the Echo launch. Society was much more optimistic about AI-powered assistants a decade ago and “Alexa” captured that positive spirit. Of course, the practical problem of sharing the name quickly made itself apparent and the resurgence fell off a cliff. We’re now down to a few hundred Alexa babies per year.<\/p>\nOlivia,F,15270\r\nEmma,F,13527\r\nCharlotte,F,12596\r\nAmelia,F,12311\r\nSophia,F,11944<\/pre>\n
for<\/code> loop, read each file into a DataFrame and append<\/code> it to a list. Then use the concat<\/code> method to stack<\/em> all the individual DataFrames into a single large one. Note that we have to manually define column labels because they aren’t present in the data.<\/p>\nimport pandas as pd\r\n\r\ndf_list = []\r\n\r\nfor year in range(1880, 2024):\r\n df = pd.read_csv(f\"names\/yob{year}.txt\", names=[\"name\", \"sex\", \"count\"])\r\n\r\n df.loc[:, 'year'] = year\r\n\r\n df_list.append(df)\r\n\r\ndf = pd.concat(df_list)<\/pre>\ndf = df[df['name'] == \"Alexa\"]\r\n\r\ndf = df[df['sex'] == \"F\"]\r\n\r\ndf = df[df['year'] >= 1975]<\/pre>\n
df.head()<\/code> looks like this:<\/p>\n name sex count year\r\n701 Alexa F 211 1975\r\n722 Alexa F 202 1976\r\n791 Alexa F 189 1977\r\n845 Alexa F 175 1978\r\n874 Alexa F 176 1979<\/pre>\n
\n2. Plot the data.<\/h4>\n
<\/a><\/p>\nplt.style.use(\"wollen_amazon.mplstyle\")\r\n\r\nfig, ax = plt.subplots()<\/pre>\n
ax.plot(df['year'], df['count'], marker=\"o\", markersize=5)<\/pre>\n
set<\/code> method.<\/p>\nax.set(xticks=range(1975, 2030, 5),\r\n xlim=(1974, 2026),\r\n yticks=range(0, 8000, 1000),\r\n ylim=(-100, 7100))<\/pre>\n
ax.annotate(\"Amazon Echo\\nLaunched\",\r\n xy=(2014, 4100), xytext=(2014, 3200),\r\n size=10, ha=\"center\", va=\"center\",\r\n arrowprops={\"arrowstyle\": \"wedge\", \"color\": '#DDD'})<\/pre>\ntext<\/code> to cite the Social Security Administration. Let’s place this in the upper-left corner. A left horizontal alignment (ha<\/code>) and top vertical alignment (va<\/code>) help snug up the text to grid lines.<\/p>\nax.text(1975.2, 6950,\r\n \"Data: U.S. Social Security Administration\",\r\n size=10, ha=\"left\", va=\"top\")<\/pre>\n
ax.set_title('Number of Babies Named \"Alexa\" | 1975\u20132023')\r\n\r\nplt.savefig(\"alexa_1975-2023.png\", dpi=150)<\/pre>\n
\n3. The output.<\/h4>\n
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