Practical Steps
- Identify the data type and intended audience. Bar plots and histograms are suitable for categorical and quantitative data, respectively. Scatter plots reveal relationships between variables, while pie charts show proportions. Box plots summarize distributions.
- Choose appropriate plot type: See table below for guidance.
| Data Type |
Plot Type |
| Categorical |
Bar plot |
| Quantitative |
Histogram, Scatter plot |
| Categorical and quantitative |
Box plot |
| Proportional |
Pie chart |
- Prepare data. Clean and transform data as needed to match the plot type.
- Create the plot. Use a plotting library (e.g., Matplotlib, Seaborn) to generate the plot.
- Customize the plot. Adjust colors, labels, titles, and other settings for clarity and readability.
Key Concepts
- Bar plots: Rectangular bars representing categories or values.
- Histograms: Bars representing the frequency of values within bins.
- Scatter plots: Points representing the relationship between two variables.
- Pie charts: Circular wedges representing proportions of categories.
- Box plots: Boxes showing the median, quartiles, and outliers of a distribution.
Python Example
import matplotlib.pyplot as plt
# Bar plot
plt.bar([1, 2, 3], [5, 10, 15])
plt.xlabel("Categories")
plt.ylabel("Values")
plt.title("Bar Plot")
plt.show()
# Histogram
plt.hist([1, 2, 3, 4, 5])
plt.xlabel("Values")
plt.ylabel("Frequency")
plt.title("Histogram")
plt.show()
# Scatter plot
plt.scatter([1, 2, 3], [4, 5, 6])
plt.xlabel("Variable 1")
plt.ylabel("Variable 2")
plt.title("Scatter Plot")
plt.show()
# Pie chart
plt.pie([10, 20, 30])
plt.title("Pie Chart")
plt.show()
# Box plot
plt.boxplot([1, 2, 3, 4, 5, 6, 7, 8])
plt.title("Box Plot")
plt.show()