Data Visualization with Python

Visualization Libraries in Python

• Seaborn and Matplotlib
  • Install or import Seaborn (import seaborn as sns).
  • Import Matplotlib (import matplotlib.pyplot as plt).
  • Retrieve sample datasets from the Seaborn library.
  • Load the tips dataset (df = sns.load_dataset('tips')).
  • Perform data exploration:
    • Check variable types.
    • Preview the top 5 rows.
    • Return a summary of the DataFrame.

Key Visualization Tasks

• Relationship Between Total Bill and Tip Amount:
  • Use a scatter plot to visualize and analyze the relationship.
  • Determine the type of correlation (positive, negative, or none).

• Strip Plot:
  • Visualize average tip amounts by day of the week and time of day:
    • tip vs. day
    • tip vs. time

• Bar Plot:
  • Display average tip amounts:
    • By day of the week.
    • By party size.
    • By smoker status.
    • By gender.

• Pair Plot:
  • Plot pairwise relationships in the tips dataset.
  • Use the hue parameter (e.g., by sex).

• Distribution Plot:
  • Use displot() to visualize a univariate variable distribution:
    • Plot a histogram with a kernel density estimate (KDE).
    • Calculate and annotate the mean, median, and mode.

• Count Plot:
  • Visualize counts of observations in each category:
    • Create a count plot by day, with time as the hue.

• Heatmap:
  • Display correlations as a two-dimensional heatmap:
    • Each square represents the correlation between two variables.

• Scatter Plot:
  • Customize scatter plots for total_bill vs. tip:
    • Experiment with colors, opacity, and shapes of data points.

• Bar Plot:
  • Create vertical bar plots to display categorical data:
    • Plot smoker and non-smoker counts using Matplotlib.

• Pie Plot:
  • Visualize univariate data distribution:
    • Plot the occurrence of different days.

• Exploded Pie Plot:
  • Separate one or more sectors from the pie:
    • Plot the occurrence of days with an exploded view.

• Histogram:
  • Analyze the distribution and spread of continuous variables:
    • Plot a histogram for the tip variable.

• Box Plot:
  • Visualize the five-number summary:
    • Plot the boxplot of total_bill to check for outliers.

• Subplots:
  • Create multiple plots within a single canvas:
    • Use plt.subplot(numrows, numcols, plot_number) to position plots.
    • Add a strip plot to visualize tip vs. day.