Unraveling Name Trends: Analyzing and Comparing Name Popularity Over Time
Introduction:
Exploring the evolution of name popularity provides a fascinating insight into societal trends and cultural shifts across different eras. In this blog post, we delve into the methodology of analyzing and comparing name popularity using Python and Pandas. By leveraging multi-indexing and visualization techniques, we aim to uncover the nuances of name trends over the years. Join us as we navigate through the realm of data analysis to unravel the stories behind popular names.
Analyzing Name Popularity with Pandas:
After consolidating our dataset and creating a comprehensive data frame, the next step is to delve into the analysis of name popularity. By utilizing Pandas’ powerful functionalities, we can reframe the data to facilitate easy comparison and visualization. Multi-indexing plays a pivotal role in structuring the data based on sex, name, and year, enabling us to effectively analyze the changes in name popularity over time.
Example Code Snippet:
import pandas as pd
import matplotlib.pyplot as plt
# Function to plot name popularity
def plot_name(name, data):
series = data.loc[('F', name)]
plt.plot(series.index.get_level_values('Year'), series.values, label=name)
# Function to compare multiple names
def compare_names(names, data):
for name in names:
plot_name(name, data)
plt.legend()
plt.show()
# Comparing name popularity for Michael, John, David, and Martin
compare_names(['Michael', 'John', 'David', 'Martin'], merged_data)
# Comparing name popularity for Emily, Anna, Claire, and Elizabeth
compare_names(['Emily', 'Anna', 'Claire', 'Elizabeth'], merged_data)
Exploring Name Variants and Stacked Plots:
Diving deeper into the analysis, we examine the variance of name spellings and explore stacked plots to visualize the cumulative counts of different name variants over time. By comparing variants such as Claire, Clara, and Ciara, we uncover intriguing trends in name popularity and resurgence across different periods. The utilization of stacked plots provides a comprehensive view of how the popularity of various name variants has evolved over the years.
Conclusion:
In conclusion, the analysis of name popularity offers a valuable lens through which we can understand the ever-changing landscape of societal preferences and cultural influences. By leveraging Python and Pandas, we have embarked on a journey of exploration, delving into the intricacies of name trends over the years. Through the use of multi-indexing, visualization techniques, and data manipulation, we have uncovered insights into the popularity of names and their variations across different periods. Join us in this captivating journey of data analysis as we continue to unravel the stories embedded in the world of names and trends.