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使用matplotlib中scatter方法畫散點圖

 更新時間:2019年03月19日 09:49:00   作者:bitcarmanlee  
這篇文章主要為大家詳細介紹了使用matplotlib中scatter方法畫散點圖,具有一定的參考價值,感興趣的小伙伴們可以參考一下

本文實例為大家分享了用matplotlib中scatter方法畫散點圖的具體代碼,供大家參考,具體內(nèi)容如下

1、最簡單的繪制方式

繪制散點圖是數(shù)據(jù)分析過程中的常見需求。python中最有名的畫圖工具是matplotlib,matplotlib中的scatter方法可以方便實現(xiàn)畫散點圖的需求。下面我們來繪制一個最簡單的散點圖。

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,第二列為Y坐標。下面我們來畫圖。

#!/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、更漂亮一些的畫圖方式

上面的圖片比較粗糙,是最簡單的方式,沒有任何相關(guān)的配置項。下面我們再用另外一份數(shù)據(jù)集畫出更漂亮一點的圖。
數(shù)據(jù)集來自網(wǎng)絡(luò)的公開數(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間百分比;
第三列每周消費的冰淇淋公升數(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,玩視頻游戲所消耗的事件百分比作為Y坐標,畫出圖。

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是相同長度的數(shù)組。
s可以是標量,或者與x,y長度相同的數(shù)組,表明散點的大小。默認為20。
c即color,表示點的顏色。
marker 是散點的形狀。

以上就是本文的全部內(nèi)容,希望對大家的學(xué)習(xí)有所幫助,也希望大家多多支持腳本之家。

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