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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Plot a univariate distribution along the x axis
Step1:  Flip the plot by assigning the data variable to the y axis
Step2:  Plot distributions for each column of a wide-form dataset
Step3:  Use less smoothing
Step4:  Use more smoothing, but don't smooth past the extreme data points
Step5:  Plot conditional distributions with hue mapping of a second variable
Step6:  "Stack" the conditional distributions
Step7:  Normalize the stacked distribution at each value in the grid
Step8:  Estimate the cumulative distribution function(s), normalizing each subset
Step9:  Estimate distribution from aggregated data, using weights
Step10:  Map the data variable with log scaling
Step11:  Use numeric hue mapping
Step12:  Modify the appearance of the plot
Step13:  Plot a bivariate distribution
Step14:  Map a third variable with a hue semantic to show conditional distributions
Step15:  Show filled contours
Step16:  Show fewer contour levels, covering less of the distribution
Step17:  Fill the axes extent with a smooth distribution, using a different colormap
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Python Code:
tips = sns.load_dataset("tips")
sns.kdeplot(data=tips, x="total_bill")
Explanation: Plot a univariate distribution along the x axis:
End of explanation
sns.kdeplot(data=tips, y="total_bill")
Explanation: Flip the plot by assigning the data variable to the y axis:
End of explanation
iris = sns.load_dataset("iris")
sns.kdeplot(data=iris)
Explanation: Plot distributions for each column of a wide-form dataset:
End of explanation
sns.kdeplot(data=tips, x="total_bill", bw_adjust=.2)
Explanation: Use less smoothing:
End of explanation
ax= sns.kdeplot(data=tips, x="total_bill", bw_adjust=5, cut=0)
Explanation: Use more smoothing, but don't smooth past the extreme data points:
End of explanation
sns.kdeplot(data=tips, x="total_bill", hue="time")
Explanation: Plot conditional distributions with hue mapping of a second variable:
End of explanation
sns.kdeplot(data=tips, x="total_bill", hue="time", multiple="stack")
Explanation: "Stack" the conditional distributions:
End of explanation
sns.kdeplot(data=tips, x="total_bill", hue="time", multiple="fill")
Explanation: Normalize the stacked distribution at each value in the grid:
End of explanation
sns.kdeplot(
    data=tips, x="total_bill", hue="time",
    cumulative=True, common_norm=False, common_grid=True,
)
Explanation: Estimate the cumulative distribution function(s), normalizing each subset:
End of explanation
tips_agg = (tips
    .groupby("size")
    .agg(total_bill=("total_bill", "mean"), n=("total_bill", "count"))
)
sns.kdeplot(data=tips_agg, x="total_bill", weights="n")
Explanation: Estimate distribution from aggregated data, using weights:
End of explanation
diamonds = sns.load_dataset("diamonds")
sns.kdeplot(data=diamonds, x="price", log_scale=True)
Explanation: Map the data variable with log scaling:
End of explanation
sns.kdeplot(data=tips, x="total_bill", hue="size")
Explanation: Use numeric hue mapping:
End of explanation
sns.kdeplot(
   data=tips, x="total_bill", hue="size",
   fill=True, common_norm=False, palette="crest",
   alpha=.5, linewidth=0,
)
Explanation: Modify the appearance of the plot:
End of explanation
geyser = sns.load_dataset("geyser")
sns.kdeplot(data=geyser, x="waiting", y="duration")
Explanation: Plot a bivariate distribution:
End of explanation
sns.kdeplot(data=geyser, x="waiting", y="duration", hue="kind")
Explanation: Map a third variable with a hue semantic to show conditional distributions:
End of explanation
sns.kdeplot(
    data=geyser, x="waiting", y="duration", hue="kind", fill=True,
)
Explanation: Show filled contours:
End of explanation
sns.kdeplot(
    data=geyser, x="waiting", y="duration", hue="kind",
    levels=5, thresh=.2,
)
Explanation: Show fewer contour levels, covering less of the distribution:
End of explanation
sns.kdeplot(
    data=geyser, x="waiting", y="duration",
    fill=True, thresh=0, levels=100, cmap="mako",
)
Explanation: Fill the axes extent with a smooth distribution, using a different colormap:
End of explanation
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