Hướng dẫn area plot python seaborn
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You can use the following basic syntax to create an area chart in seaborn: Show import matplotlib.pyplot as plt import seaborn as sns #set seaborn style sns.set_theme() #create seaborn area chart plt.stackplot(df.x, df.y1, df.y2, df.y3) The following examples show how to use this syntax in practice. Example 1: Create Basic Area Chart in SeabornThe following code shows how to create a basic area chart in seaborn: import pandas as pd import matplotlib.pyplot as plt import seaborn as sns #set seaborn style sns.set_theme() #define DataFrame df = pd.DataFrame({'period': [1, 2, 3, 4, 5, 6, 7, 8], 'team_A': [20, 12, 15, 14, 19, 23, 25, 29], 'team_B': [5, 7, 7, 9, 12, 9, 9, 4], 'team_C': [11, 8, 10, 6, 6, 5, 9, 12]}) #create area chart plt.stackplot(df.period, df.team_A, df.team_B, df.team_C)  The x-axis displays the period variable and the y-axis displays the values for each of the three teams over time. Example 2: Create Custom Area Chart in SeabornThe following code shows how to modify the colors of the area chart and add a legend with specific labels: import pandas as pd import matplotlib.pyplot as plt import seaborn as sns #set seaborn style sns.set_theme() #define DataFrame df = pd.DataFrame({'period': [1, 2, 3, 4, 5, 6, 7, 8], 'team_A': [20, 12, 15, 14, 19, 23, 25, 29], 'team_B': [5, 7, 7, 9, 12, 9, 9, 4], 'team_C': [11, 8, 10, 6, 6, 5, 9, 12]}) #define colors to use in chart color_map = ['red', 'steelblue', 'pink'] #create area chart plt.stackplot(df.period, df.team_A, df.team_B, df.team_C, labels=['Team A', 'Team B', 'Team C'], colors=color_map) #add legend plt.legend(loc='upper left') #add axis labels plt.xlabel('Period') plt.ylabel('Points Scored') #display area chart plt.show() Note that the colors argument accepts color names along with hex color codes. Additional ResourcesThe following tutorials explain how to create other common plots in seaborn: How to Create a Time Series Plot in Seaborn Plot univariate or bivariate distributions using kernel density estimation. A kernel density estimate (KDE) plot is a method for visualizing the distribution of observations in a dataset, analogous to a histogram. KDE represents the data using a continuous probability density curve in one or more dimensions. The approach is explained further in the
user guide. Relative to a histogram, KDE can produce a plot that is less cluttered and more interpretable, especially when drawing multiple distributions. But it has the potential to introduce distortions if the underlying distribution is bounded or not smooth. Like a histogram, the quality of the representation also depends on the selection of good smoothing parameters. Input data structure. Either a long-form collection of vectors that can be
assigned to named variables or a wide-form dataset that will be internally reshaped. Variables that specify positions on the x and y axes. Semantic variable that is mapped to determine the color of plot elements. If provided, weight the kernel
density estimation using these values. Method for choosing the colors to use when mapping the Specify the order of processing and plotting for categorical levels of the Either a pair of values that set the normalization range in data units or an object that will map from data units into a [0, 1] interval. Usage implies numeric mapping. Single color specification for when hue mapping is not used. Otherwise, the plot will try to hook into the matplotlib property cycle. If True, fill in the area under univariate density curves or between bivariate contours. If None, the default depends on Method for drawing multiple elements when semantic mapping creates subsets. Only relevant with univariate data. common_normboolIf True, scale each conditional density by the number of observations such that the total area under all densities sums to 1. Otherwise, normalize each density independently. common_gridboolIf True, use the same evaluation grid for each kernel density estimate. Only relevant with univariate data. cumulativebool, optionalIf True, estimate a cumulative distribution function. Requires scipy. bw_methodstring, scalar, or callable, optionalMethod for determining the smoothing bandwidth to use; passed to
Factor that multiplicatively scales the value chosen using If True, issue a warning when trying to estimate the density of data with zero variance. log_scalebool or number, or pair of bools or numbersSet axis scale(s) to log. A single value sets the data axis for univariate distributions and both axes for bivariate distributions. A pair of values sets each axis independently. Numeric values are interpreted as the desired base (default 10). If Number of contour levels or values to draw contours at. A vector argument must have increasing values in [0, 1]. Levels correspond to iso-proportions of the density: e.g., 20% of the probability mass will lie below the contour drawn for 0.2. Only relevant with bivariate data. threshnumber in [0, 1]Lowest iso-proportion level at which to draw a contour line. Ignored when Number of points on each dimension of the evaluation grid. cutnumber, optionalFactor, multiplied by the smoothing bandwidth, that determines how far the evaluation grid extends past the extreme datapoints. When set to 0, truncate the curve at the data limits. clippair of numbers or None, or a pair of such pairsDo not evaluate the density outside of these limits. legendboolIf False, suppress the legend for semantic variables. cbarboolIf True, add a colorbar to annotate the color mapping in a bivariate plot. Note: Does not currently support plots with a matplotlib.axes.AxesPre-existing axes for the colorbar. cbar_kwsdictAdditional parameters passed to matplotlib.axes.AxesPre-existing axes for the plot. Otherwise, call Other keyword arguments are passed to one of the following matplotlib functions:
matplotlib.axes.AxesThe matplotlib axes containing the plot. See also displotFigure-level interface to distribution plot functions. histplotPlot a histogram of binned counts with optional normalization or smoothing. ecdfplotPlot empirical cumulative distribution functions. jointplotDraw a bivariate plot with univariate marginal distributions. violinplotDraw an enhanced boxplot using kernel density estimation. Notes The bandwidth, or standard deviation of the smoothing kernel, is an important parameter. Misspecification of the bandwidth can produce a distorted representation of the data. Much like the choice of bin width in a histogram, an over-smoothed
curve can erase true features of a distribution, while an under-smoothed curve can create false features out of random variability. The rule-of-thumb that sets the default bandwidth works best when the true distribution is smooth, unimodal, and roughly bell-shaped. It is always a good idea to check the default behavior by using Because the smoothing algorithm uses a Gaussian kernel, the estimated density curve can extend to values
that do not make sense for a particular dataset. For example, the curve may be drawn over negative values when smoothing data that are naturally positive. The Similar considerations apply when a dataset is naturally discrete or “spiky” (containing many repeated observations of the same value). Kernel density estimation will always produce a smooth curve, which would be misleading in these situations. The units on the density axis are a common source of confusion. While kernel density estimation produces a probability distribution, the height of the curve at each point gives a density, not a probability. A probability can be obtained only by integrating the density across a range. The curve is normalized so that the integral over all possible values is 1, meaning that the scale of the density axis depends on the data values. Examples Plot a univariate distribution along the x axis: tips = sns.load_dataset("tips") sns.kdeplot(data=tips, x="total_bill") Flip the plot by assigning the data variable to the y axis: sns.kdeplot(data=tips, y="total_bill") Plot distributions for each column of a wide-form dataset: iris = sns.load_dataset("iris") sns.kdeplot(data=iris) Use less smoothing: sns.kdeplot(data=tips, x="total_bill", bw_adjust=.2) Use more smoothing, but don’t smooth past the extreme data points: ax= sns.kdeplot(data=tips, x="total_bill", bw_adjust=5, cut=0) Plot conditional distributions with hue mapping of a second variable: sns.kdeplot(data=tips, x="total_bill", hue="time") “Stack” the conditional distributions: sns.kdeplot(data=tips, x="total_bill", hue="time", multiple="stack") Normalize the stacked distribution at each value in the grid: sns.kdeplot(data=tips, x="total_bill", hue="time", multiple="fill") Estimate the cumulative distribution function(s), normalizing each subset: sns.kdeplot( data=tips, x="total_bill", hue="time", cumulative=True, common_norm=False, common_grid=True, ) Estimate distribution from aggregated data, using weights: 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") Map the data variable with log scaling: diamonds = sns.load_dataset("diamonds") sns.kdeplot(data=diamonds, x="price", log_scale=True) Use numeric hue mapping: sns.kdeplot(data=tips, x="total_bill", hue="size") Modify the appearance of the plot: sns.kdeplot( data=tips, x="total_bill", hue="size", fill=True, common_norm=False, palette="crest", alpha=.5, linewidth=0, ) Plot a bivariate distribution: geyser = sns.load_dataset("geyser") sns.kdeplot(data=geyser, x="waiting", y="duration") Map a third variable with a hue semantic to show conditional distributions: sns.kdeplot(data=geyser, x="waiting", y="duration", hue="kind") Show filled contours: sns.kdeplot( data=geyser, x="waiting", y="duration", hue="kind", fill=True, ) Show fewer contour levels, covering less of the distribution: sns.kdeplot( data=geyser, x="waiting", y="duration", hue="kind", levels=5, thresh=.2, ) Fill the axes extent with a smooth distribution, using a different colormap: sns.kdeplot( data=geyser, x="waiting", y="duration", fill=True, thresh=0, levels=100, cmap="mako", ) |
