pandas的基本用法(八)——数据的绘制

文章作者：Tyan
博客：noahsnail.com  |  CSDN  |  简书

本文主要是关于pandas的一些数据的绘制。

Demo 1

• Code

import pandas as pdimport numpy as npimport matplotlib.pyplot as plt# 定义Seriesdata = pd.Series(np.random.randn(1000), index = np.arange(1000))print data# 累加数据data = data.cumsum()# 绘制数据data.plot()# 显示数据plt.show()

• Data

0      1.5483461      0.5727422      1.1047053      1.5487044      0.2873015     -0.6563146     -0.2028017     -0.1967918      0.0969119     -0.11394210    -0.88367311     1.50195512    -0.71112213     0.14291114    -0.94170115     1.52227216     0.61048317    -0.21963818     1.74561119     0.03191120     0.88991321     0.77115822    -0.58617423     0.87376924    -2.18616625     1.64383226     0.52221827    -0.30117128    -1.12854229     0.085811         ...   970   -0.823560971    0.828570972    0.344901973   -1.700792974   -0.458375975    0.846068976    1.054396977   -0.338136978    1.039985979    0.132224980   -0.152097981    1.034157982   -0.950993983    1.934781984    0.301666985   -0.910372986    0.606312987    1.562350988    0.979057989    0.262618990    0.105402991    0.352259992    0.462557993   -0.686371994    1.125795995   -1.202305996   -0.879454997    0.479948998   -0.058433999    1.150558dtype: float64

• Image

Demo 2

• Code

# 定义DataFramedata = pd.DataFrame(np.random.randn(1000, 4), index = np.arange(1000), columns = list('ABCD'))print data# 累加数据data = data.cumsum()# print data# 绘制数据data.plot()# 显示数据plt.show()

• Data

            A         B         C         D0    0.188169 -0.410177 -0.035167 -0.6325301    1.902968 -0.253942  0.116262 -0.4099002   -0.477557 -0.544720  0.475352 -0.7631753   -0.131545  0.276950 -0.309663 -1.7046754   -0.497051 -0.786458  0.142589 -1.6587235   -1.219892 -2.844160  0.923590  1.4637196   -0.729045 -1.040011 -0.453982 -0.5893237    1.235946 -0.616109 -0.160319 -1.1017108    0.064108 -0.880624  0.291627 -0.4815249    1.178941 -0.812158 -0.440956  0.01745610   0.246466  1.173672 -1.010398  0.49364411   0.228121 -0.715523  0.287755 -0.22771612   0.435218 -1.112818  1.938080 -0.34813313  -1.154960  0.090186 -0.365532 -0.51331814   1.061165 -0.040768 -0.994464  1.18317215   0.138335  0.690717  0.485866 -0.01497716   0.938048  0.251487  0.009421 -0.80959317  -1.480628 -0.270541  0.882366 -1.80801418  -1.122170  0.791330 -1.122514 -1.24846719   0.736545  1.094979 -0.926841 -0.22358020   0.439745  0.505928 -0.425728  0.30673821   0.117386 -3.699946  0.050963 -1.16693522  -1.433574  0.311665  2.226888 -1.13963023   1.641118 -0.198970 -0.240798  0.72033724   0.722513  1.714796 -0.542274  0.44397125   0.154177  0.701450  0.832888 -1.89857426  -0.713805 -1.184821 -0.531134  0.06821727   0.694963 -0.318380  1.437368  0.21308028   0.331043  1.892780 -0.256899 -1.18991229  -0.247650  1.601953 -1.695998 -1.001989..        ...       ...       ...       ...970  1.096683  0.796003  0.258615 -1.275517971 -1.302741  1.864113 -0.753244 -0.035786972 -0.259019  0.760312 -1.273606  0.896497973 -0.060886  1.100344  2.051858 -0.898953974  0.058918  0.123978 -0.534120  1.256028975 -0.813877 -0.344310 -1.149161  0.768660976 -0.234716 -1.039258  0.592899  0.662823977  0.353870 -0.536609 -1.078631  1.716869978 -2.455930 -0.022458  1.159104  1.597242979 -1.318595 -0.716460  1.254460 -2.477972980 -0.655070 -1.299694  0.442306  0.685829981 -0.242390  0.495463 -0.746983  1.224797982 -0.452496 -0.961725 -0.795946  1.296465983 -0.118532  0.136311 -0.311137 -0.205128984 -0.395279  0.646056  1.757899  0.089445985  1.459979  0.024268 -0.294394  1.992585986  0.915223 -0.313486  0.873132 -1.046711987 -1.483945  0.520361  0.728229  1.279807988  1.496952  0.793115 -0.717488 -0.367732989 -0.913652 -1.891394 -0.692108 -0.478300990 -1.384200  0.167642  0.077620  0.741487991 -0.895972 -0.393196 -0.694417 -1.110403992  1.045946 -0.618238  1.229456  0.467488993 -0.199291 -0.199487  1.714675  0.371975994  0.653998  0.548682  0.598073 -0.668729995 -0.522661  1.547213  0.684786  0.991293996 -0.682826  1.844690 -0.577090  0.440919997 -0.935643 -0.264333  1.067578  0.677179998  0.957670 -1.024795  0.607110 -0.475680999 -0.854264 -0.680246 -0.166721 -0.394088[1000 rows x 4 columns]

• Image

Demo 3

• Code

# 绘制散点图ax = data.plot.scatter(x = 'A', y = 'B', color = 'DarkBlue')data.plot.scatter(x = 'A', y = 'C', color = 'DarkGreen', ax = ax)plt.show()

• Image