KEMBAR78
seaborn_pandas_visualization_python libraries.pptx
Seaborn & Pandas Data
Visualization
Introduction, Common Plots, and
Customization
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
Common Seaborn Visualizations
• Univariate: histplot, kdeplot, countplot
• Bivariate: scatterplot, lineplot, barplot
• Categorical: boxplot, violinplot, stripplot
• Relationships: heatmap, pairplot, jointplot
Data Visualization with Pandas
• 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')
Plot Types in Pandas
• 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()
Customization in Pandas Plotting
• 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')
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

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