3 3 2 Bar Chart Explained

3 2 Bar Chart Explained

Key Concepts

Bar Chart: A graphical representation of data using rectangular bars.
Data Series: The set of data points being plotted.
Categories: The different groups or labels on the x-axis.
Bar Height: The length of the bar, representing the value of the data point.
Axes Labels: Descriptive text for the x-axis and y-axis.

Bar Chart

A bar chart is a type of chart that displays data using rectangular bars. The length of each bar corresponds to the value of the data point it represents. Bar charts are useful for comparing different categories or groups of data.

Data Series

The data series is the set of data points that are plotted on the bar chart. Each data point corresponds to a bar in the chart. The data series can be a list, array, or any iterable data structure.

Bar Height

The height of each bar in the bar chart represents the value of the corresponding data point. The taller the bar, the higher the value. This visual representation makes it easy to compare values across different categories.

Axes Labels

Axes labels are descriptive text for the x-axis and y-axis. The x-axis label typically describes the categories, while the y-axis label describes the values being measured. Proper labeling helps in understanding the chart.

Examples

Example 1: Basic Bar Chart

import streamlit as st
import matplotlib.pyplot as plt

data = {'Category A': 10, 'Category B': 20, 'Category C': 30}
categories = list(data.keys())
values = list(data.values())

fig, ax = plt.subplots()
ax.bar(categories, values)
ax.set_xlabel('Categories')
ax.set_ylabel('Values')
ax.set_title('Basic Bar Chart')

st.pyplot(fig)

Example 2: Bar Chart with Multiple Data Series

import streamlit as st
import matplotlib.pyplot as plt

data1 = {'Category A': 10, 'Category B': 20, 'Category C': 30}
data2 = {'Category A': 15, 'Category B': 25, 'Category C': 35}
categories = list(data1.keys())
values1 = list(data1.values())
values2 = list(data2.values())

fig, ax = plt.subplots()
ax.bar(categories, values1, label='Series 1')
ax.bar(categories, values2, bottom=values1, label='Series 2')
ax.set_xlabel('Categories')
ax.set_ylabel('Values')
ax.set_title('Bar Chart with Multiple Data Series')
ax.legend()

st.pyplot(fig)

Analogies

Think of a bar chart as a set of building blocks where each block represents a category and its height represents the value. The x-axis is like a street with different buildings (categories), and the y-axis is like the height of each building (value). The labels on the axes are like signs that tell you what each street and building represents.

By mastering bar charts in Streamlit, you can create powerful visualizations that help in comparing and understanding data across different categories.