3 4 Scatter Chart Explained

Key Concepts

• Scatter Chart: A type of chart that displays individual data points as dots on a two-dimensional plane.
• Data Points: Individual observations or measurements represented by dots on the chart.
• Axes: The horizontal (x-axis) and vertical (y-axis) lines that define the coordinate system of the chart.
• Customization: Options to customize the appearance of the scatter chart, such as colors, sizes, and labels.

Explanation

1. Scatter Chart

A scatter chart is a graphical representation of data points plotted on a Cartesian plane. Each data point is represented by a dot, and the position of the dot corresponds to the values of the variables being plotted on the x-axis and y-axis.

2. Data Points

Data points are the individual observations or measurements that are plotted on the scatter chart. Each data point has an x-value and a y-value, which determine its position on the chart. Data points can be used to identify patterns, correlations, or outliers in the data.

3. Axes

The axes of a scatter chart define the coordinate system in which the data points are plotted. The x-axis represents the independent variable, while the y-axis represents the dependent variable. The range of values on each axis can be adjusted to fit the data being plotted.

4. Customization

Scatter charts can be customized to enhance their visual appeal and clarity. Customization options include changing the color and size of the data points, adding labels, and adjusting the scales of the axes. These customizations can help to highlight important features of the data and make the chart more informative.

Examples

Example 1: Basic Scatter Chart

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

data = {
    'x': [1, 2, 3, 4, 5],
    'y': [2, 3, 5, 7, 11]
}
df = pd.DataFrame(data)

fig, ax = plt.subplots()
ax.scatter(df['x'], df['y'])
st.pyplot(fig)
    

Example 2: Customized Scatter Chart

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

data = {
    'x': [1, 2, 3, 4, 5],
    'y': [2, 3, 5, 7, 11]
}
df = pd.DataFrame(data)

fig, ax = plt.subplots()
ax.scatter(df['x'], df['y'], color='red', s=100)
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_title('Customized Scatter Chart')
st.pyplot(fig)
    

Analogies

Think of a scatter chart as a map where each dot represents a location. The x-axis is like the longitude, and the y-axis is like the latitude. By plotting these locations, you can see patterns or clusters of locations, just as you can see patterns or clusters of data points in a scatter chart.

By mastering scatter charts in Streamlit, you can effectively visualize and analyze relationships between variables, making your data more accessible and understandable.