matplotlib中plt.hist()參數(shù)解釋及應(yīng)用實(shí)例
一、plt.hist()參數(shù)詳解
簡介:
plt.hist():直方圖,一種特殊的柱狀圖。
將統(tǒng)計(jì)值的范圍分段,即將整個(gè)值的范圍分成一系列間隔,然后計(jì)算每個(gè)間隔中有多少值。
直方圖也可以被歸一化以顯示“相對(duì)”頻率。 然后,它顯示了屬于幾個(gè)類別中的每個(gè)類別的占比,其高度總和等于1。
import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib.pyplot import MultipleLocator from matplotlib import ticker %matplotlib inline plt.hist(x, bins=None, range=None, density=None, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, normed=None, *, data=None, **kwargs)
常用參數(shù)解釋:
x: 作直方圖所要用的數(shù)據(jù),必須是一維數(shù)組;多維數(shù)組可以先進(jìn)行扁平化再作圖;必選參數(shù);
bins: 直方圖的柱數(shù),即要分的組數(shù),默認(rèn)為10;
range:元組(tuple)或None;剔除較大和較小的離群值,給出全局范圍;如果為None,則默認(rèn)為(x.min(), x.max());即x軸的范圍;
density:布爾值。如果為true,則返回的元組的第一個(gè)參數(shù)n將為頻率而非默認(rèn)的頻數(shù);
weights:與x形狀相同的權(quán)重?cái)?shù)組;將x中的每個(gè)元素乘以對(duì)應(yīng)權(quán)重值再計(jì)數(shù);如果normed或density取值為True,則會(huì)對(duì)權(quán)重進(jìn)行歸一化處理。這個(gè)參數(shù)可用于繪制已合并的數(shù)據(jù)的直方圖;
cumulative:布爾值;如果為True,則計(jì)算累計(jì)頻數(shù);如果normed或density取值為True,則計(jì)算累計(jì)頻率;
bottom:數(shù)組,標(biāo)量值或None;每個(gè)柱子底部相對(duì)于y=0的位置。如果是標(biāo)量值,則每個(gè)柱子相對(duì)于y=0向上/向下的偏移量相同。如果是數(shù)組,則根據(jù)數(shù)組元素取值移動(dòng)對(duì)應(yīng)的柱子;即直方圖上下便宜距離;
histtype:{‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’};'bar’是傳統(tǒng)的條形直方圖;'barstacked’是堆疊的條形直方圖;'step’是未填充的條形直方圖,只有外邊框;‘stepfilled’是有填充的直方圖;當(dāng)histtype取值為’step’或’stepfilled’,rwidth設(shè)置失效,即不能指定柱子之間的間隔,默認(rèn)連接在一起;
align:{‘left’, ‘mid’, ‘right’};‘left’:柱子的中心位于bins的左邊緣;‘mid’:柱子位于bins左右邊緣之間;‘right’:柱子的中心位于bins的右邊緣;
orientation:{‘horizontal’, ‘vertical’}:如果取值為horizontal,則條形圖將以y軸為基線,水平排列;簡單理解為類似bar()轉(zhuǎn)換成barh(),旋轉(zhuǎn)90°;
rwidth:標(biāo)量值或None。柱子的寬度占bins寬的比例;
log:布爾值。如果取值為True,則坐標(biāo)軸的刻度為對(duì)數(shù)刻度;如果log為True且x是一維數(shù)組,則計(jì)數(shù)為0的取值將被剔除,僅返回非空的(frequency, bins, patches);
color:具體顏色,數(shù)組(元素為顏色)或None。
label:字符串(序列)或None;有多個(gè)數(shù)據(jù)集時(shí),用label參數(shù)做標(biāo)注區(qū)分;
stacked:布爾值。如果取值為True,則輸出的圖為多個(gè)數(shù)據(jù)集堆疊累計(jì)的結(jié)果;如果取值為False且histtype=‘bar’或’step’,則多個(gè)數(shù)據(jù)集的柱子并排排列;
normed: 是否將得到的直方圖向量歸一化,即顯示占比,默認(rèn)為0,不歸一化;不推薦使用,建議改用density參數(shù);
edgecolor: 直方圖邊框顏色;
alpha: 透明度;
返回值(用參數(shù)接收返回值,便于設(shè)置數(shù)據(jù)標(biāo)簽):
n:直方圖向量,即每個(gè)分組下的統(tǒng)計(jì)值,是否歸一化由參數(shù)normed設(shè)定。當(dāng)normed取默認(rèn)值時(shí),n即為直方圖各組內(nèi)元素的數(shù)量(各組頻數(shù));
bins: 返回各個(gè)bin的區(qū)間范圍;
patches:返回每個(gè)bin里面包含的數(shù)據(jù),是一個(gè)list。
其他參數(shù)與plt.bar()類似。
二、plt.hist()簡單應(yīng)用
import matplotlib.pyplot as plt %matplotlib inline # 最簡單,只傳遞x,組數(shù),寬度,范圍 plt.hist(data13['carrier_no'], bins=11, rwidth=0.8, range=(1,12), align='left') plt.show()
三、plt.bar()綜合應(yīng)用
import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib.pyplot import MultipleLocator from matplotlib import ticker %matplotlib inline plt.figure(figsize=(8,5), dpi=80) # 拿參數(shù)接收hist返回值,主要用于記錄分組返回的值,標(biāo)記數(shù)據(jù)標(biāo)簽 n, bins, patches = plt.hist(data13['carrier_no'], bins=11, rwidth=0.8, range=(1,12), align='left', label='xx直方圖') for i in range(len(n)): plt.text(bins[i], n[i]*1.02, int(n[i]), fontsize=12, horizontalalignment="center") #打標(biāo)簽,在合適的位置標(biāo)注每個(gè)直方圖上面樣本數(shù) plt.ylim(0,16000) plt.title('直方圖') plt.legend() # plt.savefig('直方圖'+'.png') plt.show()
附官方參數(shù)解釋
Parameters ---------- x : (n,) array or sequence of (n,) arrays Input values, this takes either a single array or a sequence of arrays which are not required to be of the same length. bins : int or sequence or str, optional If an integer is given, ``bins + 1`` bin edges are calculated and returned, consistent with `numpy.histogram`. If `bins` is a sequence, gives bin edges, including left edge of first bin and right edge of last bin. In this case, `bins` is returned unmodified. All but the last (righthand-most) bin is half-open. In other words, if `bins` is:: [1, 2, 3, 4] then the first bin is ``[1, 2)`` (including 1, but excluding 2) and the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which *includes* 4. Unequally spaced bins are supported if *bins* is a sequence. With Numpy 1.11 or newer, you can alternatively provide a string describing a binning strategy, such as 'auto', 'sturges', 'fd', 'doane', 'scott', 'rice' or 'sqrt', see `numpy.histogram`. The default is taken from :rc:`hist.bins`. range : tuple or None, optional The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, *range* is ``(x.min(), x.max())``. Range has no effect if *bins* is a sequence. If *bins* is a sequence or *range* is specified, autoscaling is based on the specified bin range instead of the range of x. Default is ``None`` density : bool, optional If ``True``, the first element of the return tuple will be the counts normalized to form a probability density, i.e., the area (or integral) under the histogram will sum to 1. This is achieved by dividing the count by the number of observations times the bin width and not dividing by the total number of observations. If *stacked* is also ``True``, the sum of the histograms is normalized to 1. Default is ``None`` for both *normed* and *density*. If either is set, then that value will be used. If neither are set, then the args will be treated as ``False``. If both *density* and *normed* are set an error is raised. weights : (n, ) array_like or None, optional An array of weights, of the same shape as *x*. Each value in *x* only contributes its associated weight towards the bin count (instead of 1). If *normed* or *density* is ``True``, the weights are normalized, so that the integral of the density over the range remains 1. Default is ``None``. This parameter can be used to draw a histogram of data that has already been binned, e.g. using `np.histogram` (by treating each bin as a single point with a weight equal to its count) :: counts, bins = np.histogram(data) plt.hist(bins[:-1], bins, weights=counts) (or you may alternatively use `~.bar()`). cumulative : bool, optional If ``True``, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. The last bin gives the total number of datapoints. If *normed* or *density* is also ``True`` then the histogram is normalized such that the last bin equals 1. If *cumulative* evaluates to less than 0 (e.g., -1), the direction of accumulation is reversed. In this case, if *normed* and/or *density* is also ``True``, then the histogram is normalized such that the first bin equals 1. Default is ``False`` bottom : array_like, scalar, or None Location of the bottom baseline of each bin. If a scalar, the base line for each bin is shifted by the same amount. If an array, each bin is shifted independently and the length of bottom must match the number of bins. If None, defaults to 0. Default is ``None`` histtype : {'bar', 'barstacked', 'step', 'stepfilled'}, optional The type of histogram to draw. - 'bar' is a traditional bar-type histogram. If multiple data are given the bars are arranged side by side. - 'barstacked' is a bar-type histogram where multiple data are stacked on top of each other. - 'step' generates a lineplot that is by default unfilled. - 'stepfilled' generates a lineplot that is by default filled. Default is 'bar' align : {'left', 'mid', 'right'}, optional Controls how the histogram is plotted. - 'left': bars are centered on the left bin edges. - 'mid': bars are centered between the bin edges. - 'right': bars are centered on the right bin edges. Default is 'mid' orientation : {'horizontal', 'vertical'}, optional If 'horizontal', `~matplotlib.pyplot.barh` will be used for bar-type histograms and the *bottom* kwarg will be the left edges. rwidth : scalar or None, optional The relative width of the bars as a fraction of the bin width. If ``None``, automatically compute the width. Ignored if *histtype* is 'step' or 'stepfilled'. Default is ``None`` log : bool, optional If ``True``, the histogram axis will be set to a log scale. If *log* is ``True`` and *x* is a 1D array, empty bins will be filtered out and only the non-empty ``(n, bins, patches)`` will be returned. Default is ``False`` color : color or array_like of colors or None, optional Color spec or sequence of color specs, one per dataset. Default (``None``) uses the standard line color sequence. Default is ``None`` label : str or None, optional String, or sequence of strings to match multiple datasets. Bar charts yield multiple patches per dataset, but only the first gets the label, so that the legend command will work as expected. default is ``None`` stacked : bool, optional If ``True``, multiple data are stacked on top of each other If ``False`` multiple data are arranged side by side if histtype is 'bar' or on top of each other if histtype is 'step' Default is ``False`` normed : bool, optional Deprecated; use the density keyword argument instead. Returns ------- n : array or list of arrays The values of the histogram bins. See *density* and *weights* for a description of the possible semantics. If input *x* is an array, then this is an array of length *nbins*. If input is a sequence of arrays ``[data1, data2,..]``, then this is a list of arrays with the values of the histograms for each of the arrays in the same order. The dtype of the array *n* (or of its element arrays) will always be float even if no weighting or normalization is used. bins : array The edges of the bins. Length nbins + 1 (nbins left edges and right edge of last bin). Always a single array even when multiple data sets are passed in. patches : list or list of lists Silent list of individual patches used to create the histogram or list of such list if multiple input datasets. Other Parameters ---------------- **kwargs : `~matplotlib.patches.Patch` properties See also -------- hist2d : 2D histograms Notes ----- .. note:: In addition to the above described arguments, this function can take a **data** keyword argument. If such a **data** argument is given, the following arguments are replaced by **data[<arg>]**: * All arguments with the following names: 'weights', 'x'. Objects passed as **data** must support item access (``data[<arg>]``) and membership test (``<arg> in data``).
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