使用matplotlib中scatter方法畫(huà)散點(diǎn)圖
本文實(shí)例為大家分享了用matplotlib中scatter方法畫(huà)散點(diǎn)圖的具體代碼,供大家參考,具體內(nèi)容如下
1、最簡(jiǎn)單的繪制方式
繪制散點(diǎn)圖是數(shù)據(jù)分析過(guò)程中的常見(jiàn)需求。python中最有名的畫(huà)圖工具是matplotlib,matplotlib中的scatter方法可以方便實(shí)現(xiàn)畫(huà)散點(diǎn)圖的需求。下面我們來(lái)繪制一個(gè)最簡(jiǎn)單的散點(diǎn)圖。
數(shù)據(jù)格式如下:
0 746403
1 1263043
2 982360
3 1202602
...
其中第一列為X坐標(biāo),第二列為Y坐標(biāo)。下面我們來(lái)畫(huà)圖。
#!/usr/bin/env python #coding:utf-8 import matplotlib.pyplot as plt def pltpicture(): file = "xxx" xlist = [] ylist = [] with open(file, "r") as f: for line in f.readlines(): lines = line.strip().split() if len(lines) != 2 or int(lines[1]) < 100000: continue x, y = int(lines[0]), int(lines[1]) xlist.append(x) ylist.append(y) plt.xlabel('X') plt.ylabel('Y') plt.scatter(xlist, ylist) plt.show()
2、更漂亮一些的畫(huà)圖方式
上面的圖片比較粗糙,是最簡(jiǎn)單的方式,沒(méi)有任何相關(guān)的配置項(xiàng)。下面我們?cè)儆昧硗庖环輸?shù)據(jù)集畫(huà)出更漂亮一點(diǎn)的圖。
數(shù)據(jù)集來(lái)自網(wǎng)絡(luò)的公開(kāi)數(shù)據(jù)集,數(shù)據(jù)格式如下:
40920 8.326976 0.953952 3
14488 7.153469 1.673904 2
26052 1.441871 0.805124 1
75136 13.147394 0.428964 1
...
第一列每年獲得的飛行??屠锍虜?shù);
第二列玩視頻游戲所耗時(shí)間百分比;
第三列每周消費(fèi)的冰淇淋公升數(shù);
第四列為label:
1表示不喜歡的人
2表示魅力一般的人
3表示極具魅力的人
現(xiàn)在將每年獲取的飛行里程數(shù)作為X坐標(biāo),玩視頻游戲所消耗的事件百分比作為Y坐標(biāo),畫(huà)出圖。
from matplotlib import pyplot as plt file = "/home/mi/wanglei/data/datingTestSet2.txt" label1X, label1Y, label2X, label2Y, label3X, label3Y = [], [], [], [], [], [] with open(file, "r") as f: for line in f: lines = line.strip().split() if len(lines) != 4: continue distance, rate, label = lines[0], lines[1], lines[3] if label == "1": label1X.append(distance) label1Y.append(rate) elif label == "2": label2X.append(distance) label2Y.append(rate) elif label == "3": label3X.append(distance) label3Y.append(rate) plt.figure(figsize=(8, 5), dpi=80) axes = plt.subplot(111) label1 = axes.scatter(label1X, label1Y, s=20, c="red") label2 = axes.scatter(label2X, label2Y, s=40, c="green") label3 = axes.scatter(label3X, label3Y, s=50, c="blue") plt.xlabel("every year fly distance") plt.ylabel("play video game rate") axes.legend((label1, label2, label3), ("don't like", "attraction common", "attraction perfect"), loc=2) plt.show()
最后效果圖:
3、scatter函數(shù)詳解
我們來(lái)看看scatter函數(shù)的簽名:
def scatter(self, x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, **kwargs): """ Make a scatter plot of `x` vs `y` Marker size is scaled by `s` and marker color is mapped to `c` Parameters ---------- x, y : array_like, shape (n, ) Input data s : scalar or array_like, shape (n, ), optional size in points^2. Default is `rcParams['lines.markersize'] ** 2`. c : color, sequence, or sequence of color, optional, default: 'b' `c` can be a single color format string, or a sequence of color specifications of length `N`, or a sequence of `N` numbers to be mapped to colors using the `cmap` and `norm` specified via kwargs (see below). Note that `c` should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. `c` can be a 2-D array in which the rows are RGB or RGBA, however, including the case of a single row to specify the same color for all points. marker : `~matplotlib.markers.MarkerStyle`, optional, default: 'o' See `~matplotlib.markers` for more information on the different styles of markers scatter supports. `marker` can be either an instance of the class or the text shorthand for a particular marker. cmap : `~matplotlib.colors.Colormap`, optional, default: None A `~matplotlib.colors.Colormap` instance or registered name. `cmap` is only used if `c` is an array of floats. If None, defaults to rc `image.cmap`. norm : `~matplotlib.colors.Normalize`, optional, default: None A `~matplotlib.colors.Normalize` instance is used to scale luminance data to 0, 1. `norm` is only used if `c` is an array of floats. If `None`, use the default :func:`normalize`. vmin, vmax : scalar, optional, default: None `vmin` and `vmax` are used in conjunction with `norm` to normalize luminance data. If either are `None`, the min and max of the color array is used. Note if you pass a `norm` instance, your settings for `vmin` and `vmax` will be ignored. alpha : scalar, optional, default: None The alpha blending value, between 0 (transparent) and 1 (opaque) linewidths : scalar or array_like, optional, default: None If None, defaults to (lines.linewidth,). verts : sequence of (x, y), optional If `marker` is None, these vertices will be used to construct the marker. The center of the marker is located at (0,0) in normalized units. The overall marker is rescaled by ``s``. edgecolors : color or sequence of color, optional, default: None If None, defaults to 'face' If 'face', the edge color will always be the same as the face color. If it is 'none', the patch boundary will not be drawn. For non-filled markers, the `edgecolors` kwarg is ignored and forced to 'face' internally. Returns ------- paths : `~matplotlib.collections.PathCollection` Other parameters ---------------- kwargs : `~matplotlib.collections.Collection` properties See Also -------- plot : to plot scatter plots when markers are identical in size and color Notes ----- * The `plot` function will be faster for scatterplots where markers don't vary in size or color. * Any or all of `x`, `y`, `s`, and `c` may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted. Fundamentally, scatter works with 1-D arrays; `x`, `y`, `s`, and `c` may be input as 2-D arrays, but within scatter they will be flattened. The exception is `c`, which will be flattened only if its size matches the size of `x` and `y`. Examples -------- .. plot:: mpl_examples/shapes_and_collections/scatter_demo.py """
其中具體的參數(shù)含義如下:
x,y是相同長(zhǎng)度的數(shù)組。
s可以是標(biāo)量,或者與x,y長(zhǎng)度相同的數(shù)組,表明散點(diǎn)的大小。默認(rèn)為20。
c即color,表示點(diǎn)的顏色。
marker 是散點(diǎn)的形狀。
以上就是本文的全部?jī)?nèi)容,希望對(duì)大家的學(xué)習(xí)有所幫助,也希望大家多多支持腳本之家。
- python學(xué)習(xí)之matplotlib繪制散點(diǎn)圖實(shí)例
- Python+matplotlib繪制不同大小和顏色散點(diǎn)圖實(shí)例
- Python?Matplotlib實(shí)現(xiàn)三維數(shù)據(jù)的散點(diǎn)圖繪制
- Python利用matplotlib繪制散點(diǎn)圖的新手教程
- Python使用Matplotlib繪制三維散點(diǎn)圖詳解流程
- python3使用matplotlib繪制散點(diǎn)圖
- Python matplotlib繪制散點(diǎn)圖的實(shí)例代碼
- 使用matplotlib畫(huà)散點(diǎn)圖的方法
- matplotlib一維散點(diǎn)分布圖的實(shí)現(xiàn)
相關(guān)文章
基于Python實(shí)現(xiàn)一個(gè)簡(jiǎn)單的學(xué)生管理系統(tǒng)
這篇文章主要為大家詳細(xì)介紹了如何利用python實(shí)現(xiàn)簡(jiǎn)單的學(xué)生信息管理系統(tǒng),文中示例代碼介紹的非常詳細(xì),具有一定的參考價(jià)值,感興趣的小伙伴們可以參考一下2022-12-12簡(jiǎn)單談?wù)凱ython中的json與pickle
下面小編就為大家?guī)?lái)一篇簡(jiǎn)單談?wù)凱ython中的json與pickle。小編覺(jué)得挺不錯(cuò)的,現(xiàn)在就分享給大家,也給大家做個(gè)參考。一起跟隨小編過(guò)來(lái)看看吧2017-07-07TensorFlow基本的常量、變量和運(yùn)算操作詳解
今天小編就為大家分享一篇TensorFlow基本的常量、變量和運(yùn)算操作詳解,具有很好的參考價(jià)值,希望對(duì)大家有所幫助。一起跟隨小編過(guò)來(lái)看看吧2020-02-02PyG搭建GCN模型實(shí)現(xiàn)節(jié)點(diǎn)分類GCNConv參數(shù)詳解
這篇文章主要為大家介紹了PyG搭建GCN模型實(shí)現(xiàn)節(jié)點(diǎn)分類GCNConv參數(shù)詳解,有需要的朋友可以借鑒參考下,希望能夠有所幫助,祝大家多多進(jìn)步,早日升職加薪2022-05-05Pycharm debug調(diào)試時(shí)帶參數(shù)過(guò)程解析
這篇文章主要介紹了Pycharm debug調(diào)試時(shí)帶參數(shù)過(guò)程解析,文中通過(guò)示例代碼介紹的非常詳細(xì),對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值,需要的朋友可以參考下2020-02-02