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Ecdfplot | Distributions of Data
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
2. Distributions of Data
3. Categorical Plot Types
4. Matrix Plots
5. Multi-Plot Grids
6. Regression Models

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
test

Swipe to show code editor

  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
test

Swipe to show code editor

  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
test

Swipe to show code editor

  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
test

Swipe to show code editor

  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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