Basic Connected Scatterplot for Evolution of Variables

logo of a chart:ScatterPlot

A connected scatterplot is useful to display the evolution of 2 variables while displaying other information.

In this, post, we will explain how to use Matplotlib to create a connected scatterplot in Python and how to add annotations to it.

Libraries

First, we need to load a few libraries:

import pandas as pd
import matplotlib.pyplot as plt

Dataset

Our dataset is about the number of people having a certain name, and the evolution of this number over the years. Let's load it with pandas:

# import dataset
df = pd.read_csv("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/5_OneCatSevNumOrdered.csv")
df.head()
year sex name n prop
0 1880 F Helen 636 0.006516
1 1880 F Amanda 241 0.002469
2 1880 F Betty 117 0.001199
3 1880 F Dorothy 112 0.001147
4 1880 F Linda 27 0.000277

Our goal here is to explore the evolution of the number of people named "Ashley" and "Amanda" over the years in a single scatter plot.

For this, we need to change a bit the dataset:

  • filter on those 2 names
  • filter on only date after 1970
  • pivot the table to have the names as columns and the years as rows, using the pivot_table() function
# filter data
df = df.loc[(df.name=="Ashley") | (df.name=="Amanda")]
df = df.loc[(df.sex=="F") & (df.year>1970)]
df = pd.pivot_table(df, values='n', index=['year'], columns=['name'])

df.head()
name Amanda Ashley
year
1971 4133.0 1164.0
1972 4181.0 1176.0
1973 5627.0 1253.0
1974 7476.0 1626.0
1975 12653.0 1988.0

Connected scatterplot for evolution

In practice, we just have to call the plot() function with our columns as arguments. We specify that markers have to be used with marker='o' and that the lines have to be connected with linestyle='-'.

fig, ax = plt.subplots(figsize=(8, 6))

# plot the connected scatterplot
ax.plot(df.Amanda, df.Ashley, linestyle='-', marker='o')

# x axis label
plt.xlabel('Amanda')

# y axis label
plt.ylabel('Ashley')

# show the graph
plt.show()

Annotations

Our last chart has a major issue: we don't know which year is represented by each point.

To fix this, we can use the annotate() function to add a text next to each point. However, it's not necessary to add all the years, and decide to only plot 1 out of 3 years.

In practice, we loop over the number of rows with a step of 3 (thanks to range(0, len(df), 3)) and add an annotation with the annotate() function.

fig, ax = plt.subplots(figsize=(8, 6))

# plot the connected scatterplot
ax.plot(df.Amanda, df.Ashley, linestyle='-', marker='o')

# add annotations in every 3 data points with a loop
for line in range(0, df.shape[0], 3):
     ax.annotate(
          df.index[line], 
          (df.Amanda.iloc[line], df.Ashley.iloc[line]+1000) ,
          va='bottom',
          ha='center'
     )

# labels and display
plt.xlabel('Amanda')
plt.ylabel('Ashley')
plt.show()

What a nice way to visualize the evolution of the number of people named Ashley and Amanda over the years!

Going further

This post explains how to create a connected scatterplot with matplotlib.

You might be interested in how to reproduce this beautiful connected scatter plot and how to create multiple connected scatter plots on the same chart.

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