seaborn_pandas_visualization_python libraries.pptx

seaborn_pandas_visualization_python libraries.pptx

• 1.
  Seaborn & PandasData Visualization Introduction, Common Plots, and Customization
• 2.
  Introduction to Seaborn •Seaborn: Python library for data visualization • Built on top of Matplotlib • Integrates with Pandas DataFrames • Advantages: Easy to use, better aesthetics, statistical plots
• 3.
  Common Seaborn Visualizations •Univariate: histplot, kdeplot, countplot • Bivariate: scatterplot, lineplot, barplot • Categorical: boxplot, violinplot, stripplot • Relationships: heatmap, pairplot, jointplot
• 4.
  Data Visualization withPandas • Pandas has built-in plotting via .plot() • Quick exploratory visualizations • Uses Matplotlib under the hood • Syntax: df.plot(kind='line'), df['col'].plot(kind='hist')
• 5.
  Plot Types inPandas • Line plot: df.plot.line() • Bar plot: df.plot.bar(), df.plot.barh() • Histogram: df.plot.hist() • Pie chart: df.plot.pie() • Area plot: df.plot.area() • Scatter: df.plot.scatter(x, y) • Box plot: df.plot.box()
• 6.
  Customization in PandasPlotting • Titles, labels, legends with plt.title, plt.xlabel, plt.ylabel • Colors, styles, markers: df.plot(style='--o', color='red') • Subplots with subplots=True • Grid control: plt.grid(True) • Tick customization: plt.xticks(rotation=45) • Styling with plt.style.use('ggplot')
• 7.
  Pandas vs Seaborn •Pandas: Quick, simple, built-in, great for data exploration • Seaborn: Advanced, statistical plots, better aesthetics • Use Pandas for quick checks • Use Seaborn for polished, presentation-ready visuals