Q&A 35 How do you visualize trends across an ordered variable using a line plot?

35.1 Explanation

A line plot connects data points to show trends over a continuous variable — commonly time, index, or ordered categories.

• Useful when data is sequential or ordered
• Highlights rising/falling trends and fluctuations
• Requires x-axis to represent a logical order

In the case of the iris dataset, we can simulate a sorted index to demonstrate this.

35.2 Python Code

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_csv("data/iris.csv")
df_sorted = df.sort_values("sepal_length").reset_index()

# Line plot showing sepal width trend over sorted sepal length
plt.figure(figsize=(6, 4))
sns.lineplot(data=df_sorted, x="sepal_length", y="sepal_width", hue="species", palette="Set1")
plt.title("Line Plot: Sepal Width by Sepal Length (Sorted)")
plt.tight_layout()
plt.show()

35.3 R Code

library(ggplot2)
library(readr)
df <- read_csv("data/iris.csv")
df_sorted <- df[order(df$sepal_length), ]

ggplot(df_sorted, aes(x = sepal_length, y = sepal_width, color = species)) +
  geom_line() +
  labs(title = "Line Plot: Sepal Width by Sepal Length (Sorted)") +
  theme_minimal()