直方圖\密度圖
直方圖和密度圖一般用於分布數據的可視化。
distplot
用於繪制單變量的分布圖，包括直方圖和密度圖。
sns.distplot(
a,
bins=None,
hist=True,
kde=True,
rug=False,
fit=None,
hist_kws=None,
kde_kws=None,
rug_kws=None,
fit_kws=None,
color=None,
vertical=False,
norm_hist=False,
axlabel=None,
label=None,
ax=None,
)
Docstring:
Flexibly plot a univariate distribution of observations.
This function combines the matplotlib ``hist`` function (with automatic
calculation of a good default bin size) with the seaborn :func:`kdeplot`
and :func:`rugplot` functions. It can also fit ``scipy.stats``
distributions and plot the estimated PDF over the data.
Parameters
----------
a : Series, 1d-array, or list.
Observed data. If this is a Series object with a ``name`` attribute,
the name will be used to label the data axis.
bins : argument for matplotlib hist(), or None, optional
Specification of hist bins, or None to use Freedman-Diaconis rule.
hist : bool, optional
Whether to plot a (normed) histogram.
kde : bool, optional
Whether to plot a gaussian kernel density estimate.
rug : bool, optional
Whether to draw a rugplot on the support axis.
fit : random variable object, optional
An object with `fit` method, returning a tuple that can be passed to a
`pdf` method a positional arguments following an grid of values to
evaluate the pdf on.
{hist, kde, rug, fit}_kws : dictionaries, optional
Keyword arguments for underlying plotting functions.
color : matplotlib color, optional
Color to plot everything but the fitted curve in.
vertical : bool, optional
If True, observed values are on y-axis.
norm_hist : bool, optional
If True, the histogram height shows a density rather than a count.
This is implied if a KDE or fitted density is plotted.
axlabel : string, False, or None, optional
Name for the support axis label. If None, will try to get it
from a.namel if False, do not set a label.
label : string, optional
Legend label for the relevent component of the plot
ax : matplotlib axis, optional
if provided, plot on this axis
Returns
-------
ax : matplotlib Axes
Returns the Axes object with the plot for further tweaking.
See Also
--------
kdeplot : Show a univariate or bivariate distribution with a kernel
density estimate.
rugplot : Draw small vertical lines to show each observation in a
distribution.
kdeplot
用於繪制單變量或雙變量的核密度圖。
sns.kdeplot(
data,
data2=None,
shade=False,
vertical=False,
kernel='gau',
bw='scott',
gridsize=100,
cut=3,
clip=None,
legend=True,
cumulative=False,
shade_lowest=True,
cbar=False,
cbar_ax=None,
cbar_kws=None,
ax=None,
**kwargs,
)
Docstring:
Fit and plot a univariate or bivariate kernel density estimate.
Parameters
----------
data : 1d array-like
Input data.
data2: 1d array-like, optional
Second input data. If present, a bivariate KDE will be estimated.
shade : bool, optional
If True, shade in the area under the KDE curve (or draw with filled
contours when data is bivariate).
vertical : bool, optional
If True, density is on x-axis.
kernel : {'gau' | 'cos' | 'biw' | 'epa' | 'tri' | 'triw' }, optional
Code for shape of kernel to fit with. Bivariate KDE can only use
gaussian kernel.
bw : {'scott' | 'silverman' | scalar | pair of scalars }, optional
Name of reference method to determine kernel size, scalar factor,
or scalar for each dimension of the bivariate plot. Note that the
underlying computational libraries have different interperetations
for this parameter: ``statsmodels`` uses it directly, but ``scipy``
treats it as a scaling factor for the standard deviation of the
data.
gridsize : int, optional
Number of discrete points in the evaluation grid.
cut : scalar, optional
Draw the estimate to cut * bw from the extreme data points.
clip : pair of scalars, or pair of pair of scalars, optional
Lower and upper bounds for datapoints used to fit KDE. Can provide
a pair of (low, high) bounds for bivariate plots.
legend : bool, optional
If True, add a legend or label the axes when possible.
cumulative : bool, optional
If True, draw the cumulative distribution estimated by the kde.
shade_lowest : bool, optional
If True, shade the lowest contour of a bivariate KDE plot. Not
relevant when drawing a univariate plot or when ``shade=False``.
Setting this to ``False`` can be useful when you want multiple
densities on the same Axes.
cbar : bool, optional
If True and drawing a bivariate KDE plot, add a colorbar.
cbar_ax : matplotlib axes, optional
Existing axes to draw the colorbar onto, otherwise space is taken
from the main axes.
cbar_kws : dict, optional
Keyword arguments for ``fig.colorbar()``.
ax : matplotlib axes, optional
Axes to plot on, otherwise uses current axes.
kwargs : key, value pairings
Other keyword arguments are passed to ``plt.plot()`` or
``plt.contour{f}`` depending on whether a univariate or bivariate
plot is being drawn.
Returns
-------
ax : matplotlib Axes
Axes with plot.
See Also
--------
distplot: Flexibly plot a univariate distribution of observations.
jointplot: Plot a joint dataset with bivariate and marginal distributions.
rugplot
用於在坐標軸上繪制數據點，顯示數據分布情況，一般結合distplot和kdeplot一起使用。
sns.rugplot(a, height=0.05, axis='x', ax=None, **kwargs)
Docstring:
Plot datapoints in an array as sticks on an axis.
Parameters
----------
a : vector
1D array of observations.
height : scalar, optional
Height of ticks as proportion of the axis.
axis : {'x' | 'y'}, optional
Axis to draw rugplot on.
ax : matplotlib axes, optional
Axes to draw plot into; otherwise grabs current axes.
kwargs : key, value pairings
Other keyword arguments are passed to ``LineCollection``.
Returns
-------
ax : matplotlib axes
The Axes object with the plot on it.
一維數據可視化
distplot()
#直方圖distplot()
#參數：bins->箱數， hist->是否顯示箱曲線， kde->是否顯示密度曲線， norm_hist->直方圖是否按照密度來表示
#rug->是否顯示數據分布情況， vertical->是否水平顯示，label->設置圖例， axlabel->設置x軸標注
rs = np.random.RandomState(123) #設定隨機種子
datas = pd.Series(rs.randn(100)) #創建包含100個隨機數據的Series
sns.distplot(a=datas, bins=10, hist=True, kde=False, norm_hist=False,
rug=True, vertical=False, color='r', label='distplot', axlabel='x')
plt.legend()
#kde=True設置密度曲線
sns.distplot(a=datas, bins=10, hist=True, kde=True, norm_hist=False,
rug=True, vertical=False, color='r', label='distplot', axlabel='x')
plt.legend()
#norm_hist設置直方圖按照密度曲線顯示，實現hist=True 加 kde=True 共同的效果
sns.distplot(a=datas, bins=10, norm_hist=True,
rug=True, vertical=False, color='r', label='distplot', axlabel='x')
plt.legend()
#rug=False不顯示頻率分布，vertical=False橫向放置圖形
sns.distplot(a=datas, bins=10, norm_hist=True,
rug=False, vertical=False, color='r', label='distplot', axlabel='x')
plt.legend()
#總體參數設置
sns.distplot(datas, rug=True,
#rug_kws設置數據頻率分布顏色
rug_kws={'color':'y'},
#kde_kws設置密度曲線顏色、線寬、標注、線型
kde_kws={'color':'r', 'lw':1, 'label':'KDE', 'linestyle':'--'},
#hist_kws設置箱子的風格、線寬、透明度、顏色
#histtype包括’bar'、‘barstacked’,'step','stepfilled'
hist_kws={'histtype':'step', 'linewidth':1, 'alpha':1, 'color':'k'})
kdeplot()
#密度圖 -- kdeplot()
#shade--> 填充設置
sns.kdeplot(datas, shade=True, color='r', vertical=False)
#bw --> 擬合參數
sns.kdeplot(datas, bw=5, label='bw:0.2',
linestyle='-', linewidth=1.2, alpha=0.5)
sns.kdeplot(datas, bw=20, label='bw:2',
linestyle='-', linewidth=1.2, alpha=0.5)
#rugplot()設置頻率分布圖
sns.rugplot(datas, height=0.1, color='k', alpha=0.5)
二維數據可視化
kdeplot()
#二維數據密度圖
rs = np.random.RandomState(12345)
df = pd.DataFrame(rs.randn(100,2),
columns=['A','B'])
sns.kdeplot(df['A'],df['B'],
cbar = True, #設置顯示顏色圖例條
shade = True, #是否填充
cmap = 'Reds', #設置調色盤
shade_lowest = 'False', #設置最外圍顏色是否顯示
n_levels = 10) #設置曲線個數（越多越平滑）
#分別設置x,y軸的頻率分布圖
sns.rugplot(df['A'], color='y', axis='x', alpha=0.5)
sns.rugplot(df['B'], color='k', axis='y', alpha=0.5)
sns.kdeplot(df['A'],df['B'],
cbar = True,
shade = False, #不填充
cmap = 'Reds',
shade_lowest = 'False',
n_levels = 10)
#分別設置x,y軸的頻率分布圖
sns.rugplot(df['A'], color='y', axis='x', alpha=0.5)
sns.rugplot(df['B'], color='k', axis='y', alpha=0.5)
sns.kdeplot(df['A'],df['B'],
cbar = True,
shade = True,
cmap = 'Reds',
# shade_lowest = 'False', #設置最外圍顏色是否顯示,與shade配合使用
n_levels = 10) #設置曲線個數（越多越平滑）
#分別設置x,y軸的頻率分布圖
sns.rugplot(df['A'], color='y', axis='x', alpha=0.5)
sns.rugplot(df['B'], color='k', axis='y', alpha=0.5)
sns.kdeplot(df['A'],df['B'],
cbar = True,
shade = True,
cmap = 'Reds',
# shade_lowest = 'False', #設置最外圍顏色是否顯示,與shade配合使用
n_levels = 100) #設置曲線個數（越多則邊界漸變越平滑）
#分別設置x,y軸的頻率分布圖
sns.rugplot(df['A'], color='y', axis='x', alpha=0.5)
sns.rugplot(df['B'], color='k', axis='y', alpha=0.5)
#多個密度圖
#創建兩個DataFrame數組
rs1 = np.random.RandomState(12)
rs2 = np.random.RandomState(21)
df1 = pd.DataFrame(rs1.randn(100,2)+2, columns=['A','B'])
df2 = pd.DataFrame(rs2.randn(100,2)-2, columns=['A','B'])
#創建密度圖
sns.kdeplot(df1['A'], df1['B'], cmap='Greens',
shade=True, shade_lowest=False)
sns.kdeplot(df2['A'], df2['B'], cmap='Blues',
shade=True, shade_lowest=False)
