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Deep Dive into the seaborn Visualization
course content

Course Content

Deep Dive into the seaborn Visualization

Deep Dive into the seaborn Visualization

1. Light Start
Reading the FileColors
2. Distributions of Data
HistplotKdeplotRugplotEcdfplotChallengeDisplot
3. Categorical Plot Types
StripplotSwarmplotBoxplotViolinplotBarplotPointplotChallengeCatplot
4. Matrix Plots
HeatmapClustermap
5. Multi-Plot Grids
FacetGridPairGridJointGrid
6. Regression Models
RegplotLmplot

bookEcdfplot

An ecdfplot represents the proportion or count of observations falling below each unique value in a dataset. Compared to a histogram or density plot, it has the advantage that each observation is visualized directly, meaning that no binning or smoothing parameters need to be adjusted.

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Task

  1. Create the ecdfplot using the seaborn library:
  • Set the x parameter equals the bill_length_mm;
  • Set the hue parameter equals the 'island';
  • Add the complementary parameter;
  • Set the stat parameter equals the 'count';
  • Set the palette equals the 'mako';
  • Set the data.

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Section 2. Chapter 4
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bookEcdfplot

An ecdfplot represents the proportion or count of observations falling below each unique value in a dataset. Compared to a histogram or density plot, it has the advantage that each observation is visualized directly, meaning that no binning or smoothing parameters need to be adjusted.

carousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-img

Task

  1. Create the ecdfplot using the seaborn library:
  • Set the x parameter equals the bill_length_mm;
  • Set the hue parameter equals the 'island';
  • Add the complementary parameter;
  • Set the stat parameter equals the 'count';
  • Set the palette equals the 'mako';
  • Set the data.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 4
toggle bottom row

bookEcdfplot

An ecdfplot represents the proportion or count of observations falling below each unique value in a dataset. Compared to a histogram or density plot, it has the advantage that each observation is visualized directly, meaning that no binning or smoothing parameters need to be adjusted.

carousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-img

Task

  1. Create the ecdfplot using the seaborn library:
  • Set the x parameter equals the bill_length_mm;
  • Set the hue parameter equals the 'island';
  • Add the complementary parameter;
  • Set the stat parameter equals the 'count';
  • Set the palette equals the 'mako';
  • Set the data.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

An ecdfplot represents the proportion or count of observations falling below each unique value in a dataset. Compared to a histogram or density plot, it has the advantage that each observation is visualized directly, meaning that no binning or smoothing parameters need to be adjusted.

carousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-img

Task

  1. Create the ecdfplot using the seaborn library:
  • Set the x parameter equals the bill_length_mm;
  • Set the hue parameter equals the 'island';
  • Add the complementary parameter;
  • Set the stat parameter equals the 'count';
  • Set the palette equals the 'mako';
  • Set the data.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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