1.最簡單的繪製方式

繪製散點圖是資料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方法可以方便實現畫散點圖的需求。下面我們來繪製一個最簡單的散點圖。
資料格式如下：

0   746403                                                                                                                                                                
1   1263043
 

2   982360
3   1202602
...

其中第一列為X座標，第二列為Y座標。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，沒有任何相關的配置項。下面我們再用另外一份資料集畫出更漂亮一點的圖。
資料集來自網路的公開資料集，資料格式如下：

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常客里程數；
第二列玩視訊遊戲所耗時間百分比；
第三列每週消費的冰淇淋公升數；
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

現在將每年獲取的飛行里程數作為X座標，玩視訊遊戲所消耗的事件百分比作為Y座標，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()

最後效果圖：

3.scatter函式詳解

我們來看看scatter函式的簽名：

    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

        """

其中具體的引數含義如下：

x,y是相同長度的陣列。
s可以是標量，或者與x,y長度相同的陣列，表明散點的大小。預設為20。
c即color，表示點的顏色。
marker 是散點的形狀。