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Deep Dive into the seaborn Visualization
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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

Ecdfplot

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

Tarefa

  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.

Tarefa

  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.

Tudo estava claro?

Seção 2. Capítulo 4
toggle bottom row

Ecdfplot

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

Tarefa

  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.

Tarefa

  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.

Tudo estava claro?

Seção 2. Capítulo 4
toggle bottom row

Ecdfplot

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

Tarefa

  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.

Tarefa

  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.

Tudo estava claro?

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

Tarefa

  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.

Seção 2. Capítulo 4
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