数据可视化分析

数据科学第四章

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1.Python绘图基础

• Part 1

import matplotlib.pyplot as plt
from pandas import DataFrame, Series

（1）快速绘图

• Part 2

gdp = [41.3, 48.9, 54.0, 59.5, 64.4, 68.9, 74.4]
data = DataFrame({'GDP: Trillion': gdp}, index=['2010', '2011', '2012', '2013', '2014', '2015', '2016'])
data.plot()
plt.show()           # 显示图形

• Part 3

data.plot(title='2010-2016 GDP', LineWidth=2, marker='o', linestyle='dashed', color='r',
          grid=True, alpha=0.8, use_index=True, yticks=[35, 40, 45, 50, 55, 60, 65 ,70, 75])
plt.show()

参数名	说明
x	x轴数据， 默认值为None
y	y轴数据， 默认值为None
kind	绘图类型。'line'：折线图， 默认值；'bar'：垂直柱状图；'barth'：水平柱状图；'hist'：直方图；'box'：箱式图；'kde'/'density'：Kernel核密度估计图；'pie'：饼图；'scatter'：散点图
title	图形标题，字符串
color	画笔颜色。用颜色缩写，如'r', 'b'，或者RGB值，如'#CECECE'。主要颜色缩写：'b'：blue；'c'：cyan；'g'：green；'k'：black
grid	图形是否有网格，默认为None
fontsize	坐标轴（包括x轴和y轴）刻度的字体大小。整数，默认为None
alpha	透明度0~1
use_index	默认为True，用索引作为x轴刻度
linewidth	绘图线宽
linestyle	绘图线形
marker	标记风格，即点型
xlim、ylim	x轴,y轴的范围，二元组表示最小值和最大值
ax	axes对象

（2）精细画图

• Part 4

plt.figure()       # 创建绘图对象
GDPdata = [41.3, 48.9, 54.0, 59.5, 64.4, 68.9, 74.4]
plt.plot(GDPdata, color='r', linewidth=2, linestyle='dashed', marker='o', label='GDP')    # 绘图

# 精细设置图元
plt.title('2010~2016 GDP: Trillion')
plt.xlim(0, 6)     # x轴绘图范围
plt.ylim(35, 75)   # y轴绘图范围
plt.xticks(range(0, 7), ('2010', '2011', '2012', '2013', '2014', '2015', '2016'))    # 将x轴刻度映射为字符串
plt.legend(loc='upper left')      # 图例说明位置
plt.grid()         # 显示网格线
plt.show()         # 显示并关闭绘图

（3）多子图

• figure.add_subplot(numRows, numCols, plotNum)

• numRows: 绘图区域划分行数
• numCols: 绘图区域划分列数
• plotNum: 绘图区域所在区域

• Part 5

data = Series(GDPdata, index=['2010', '2011', '2012', '2013', '2014', '2015', '2016'])
fig = plt.figure(figsize=(6, 6))      # 定义图形大小

ax1 = fig.add_subplot(2, 1, 1)        # 创建子图1
ax1.plot(data)                        # 用AxesSubplot绘制折线图

ax2 = fig.add_subplot(2, 2, 3)        # 创建子图2
data.plot(kind='bar', use_index=True, fontsize='small', ax=ax2)

ax3 = fig.add_subplot(2, 2, 4)        # 创建子图3
data.plot(kind='box', xticks=[], fontsize='small', ax=ax3)

<matplotlib.axes._subplots.AxesSubplot at 0x27876b1d4c8>

（4）设置图元属性和说明，保存操作

• Part 6

data.plot(title='2010-2016 GDP', LineWidth=2, marker='o', linestyle='dashed', color='r', grid=True, alpha=0.9)

# 超出范围后不显示
plt.annotate('turning point', xy=(1, 48.5), xytext=(1.5, 43), arrowprops=dict(arrowstyle='->'))

plt.text(1.8, 70, 'GDP keeps booming!', fontsize='larger')

plt.xlabel('Year', fontsize=12)
plt.ylabel('GDP Increment Speed(%)', fontsize=12)

# 保存
plt.savefig('3th dataGDP.png', dpi=400, bbox_inches='tight')

2.其他数据可视化表示方法

（1）函数绘图

• Part 7

import numpy as np
import matplotlib.pyplot as plt
from pandas import DataFrame, Series
import pandas as pd

# 设置中文
import matplotlib
plt.rcParams['font.sans-serif'] = ['SimHei']
matplotlib.rcParams['axes.unicode_minus']=False

引入数据

• Part 8

GINI_COV = pd.read_csv('3th dataGINI_COV.csv').iloc[:, 1:]
GINI_COV

	中国	泰国	印度尼西亚	越南	柬埔寨	奥地利	比利时	瑞士	捷克	德国	...	厄瓜多尔	墨西哥	巴拿马	秘鲁	玻利维亚	危地马拉	洪都拉斯	巴拉圭	萨尔瓦多	南非
0	1.472413	0.574690	-1.138000	0.961833	0.990535	0.136190	0.030381	0.068476	0.042095	-0.323381	...	0.851048	-0.308119	0.322762	0.832905	0.049214	1.650081	0.367333	0.460762	0.765381	-0.248414
1	0.574690	3.887452	-6.833905	0.524238	3.745933	-1.119286	0.528905	1.387714	0.990286	-0.412095	...	6.916690	3.435619	3.936405	5.758643	9.400071	2.907842	3.463024	3.382095	6.296405	-1.531205
2	-1.138000	-6.833905	14.122667	-1.356048	-6.489586	1.886429	-0.741238	-2.683857	-1.907286	1.303905	...	-13.407095	-6.943881	-7.688381	-10.532714	-18.070571	-6.005233	-4.305619	-7.516762	-12.982667	4.659531
3	0.961833	0.524238	-1.356048	1.286024	0.095826	-0.047500	0.106048	-0.028000	0.267429	-0.462810	...	0.910262	0.341690	0.487833	0.707929	0.389357	1.017324	0.105167	0.893524	0.997833	-0.490467
4	0.990535	3.745933	-6.489586	0.095826	9.228565	-0.930857	1.087014	2.284157	0.442090	0.426067	...	8.361343	2.486307	3.289538	7.452195	11.003314	4.353009	4.024590	4.168433	5.390538	0.507772
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
62	1.650081	2.907842	-6.005233	1.017324	4.353009	-0.665786	-0.105945	1.220221	0.702843	-0.544824	...	6.064755	2.332612	3.492340	6.009764	7.909443	5.156259	2.263938	4.513252	6.001876	-1.842574
63	0.367333	3.463024	-4.305619	0.105167	4.024590	-1.337857	0.729190	1.060857	0.825429	0.308048	...	5.653190	2.753190	3.137762	5.501714	7.913857	2.263938	6.586381	2.893381	4.284190	0.413193
64	0.460762	3.382095	-7.516762	0.893524	4.168433	-1.205714	0.243190	1.571286	1.186000	-0.922810	...	7.842762	4.199548	4.723905	7.370429	11.137214	4.513252	2.893381	7.250667	7.554619	-2.830776
65	0.765381	6.296405	-12.982667	0.997833	5.390538	-1.812143	0.501952	2.529571	1.915143	-1.159619	...	12.506381	6.511738	7.451952	10.684857	17.325214	6.001876	4.284190	7.554619	13.171238	-4.599483
66	-0.248414	-1.531205	4.659531	-0.490467	0.507772	0.369179	0.430743	-0.412436	-0.717974	1.246205	...	-3.732493	-2.743832	-2.793590	-2.007002	-4.964467	-1.842574	0.413193	-2.830776	-4.599483	3.824582

67 rows × 67 columns

* Part 9

x = np.linspace(0, 6.28, 50)        # 在0~6.28设置50个采样点
y = np.sin(x)                       # 计算y=sin(x)数组
plt.plot(x, y, color='r')           # 绘制y=sin(x)
plt.plot(x, np.exp(-x), c='b')      # 绘制y=exp(-x)

[<matplotlib.lines.Line2D at 0x278769f6f48>]

（2）绘制散点图

• Part 10

data1 = GINI_COV['中国']
data2 = GINI_COV['俄罗斯']
data3 = GINI_COV['泰国']

# 绘制散点图
plt.figure()
plt.scatter(data1, data2, c='r', marker='s', label='China-Russia')
plt.scatter(data1, data3, c='b', marker='^', label='China-Thailand')
plt.title('GINI_COV')
plt.xlabel('China')
plt.ylabel('Other countries')
plt.grid()
plt.legend(loc='upper right')

<matplotlib.legend.Legend at 0x27876a50588>

（3）绘制散点图矩阵

• Part 11

# 绘制散点图矩阵
grr = pd.plotting.scatter_matrix(GINI_COV.iloc[:, :8], diagonal='kde', color='k', figsize=(15,15))

（4）柱状图

• Part 12

# 绘制复式柱状图
GINI_COV.plot(kind='bar', title='复式柱状图', figsize=(60,40), fontsize=50)

<matplotlib.axes._subplots.AxesSubplot at 0x2783b768508>

• Part 13

# 绘制堆叠柱状图
GINI_COV.plot(kind='bar', stacked=True, title='堆叠柱状图', figsize=(60,40), fontsize=50)

<matplotlib.axes._subplots.AxesSubplot at 0x2783fa77848>

• Part 14

# 绘制均值与标准差图
mean = np.mean(GINI_COV)         # 计算均值
std = np.std(GINI_COV)           # 计算标准差
fig = plt.figure(figsize = (60, 25))
plt.subplots_adjust(wspace=0.2)  # 设置纵向间隔

# 绘制均值的垂直和水平柱状图，标准差使用误差线表示
ax1 = fig.add_subplot(1, 2, 1)
mean.plot(kind='bar', yerr=std, color='cadetblue', title='MEAN', rot=45, ax=ax1)

ax2 = fig.add_subplot(1, 2, 2)
mean.plot(kind='barh', xerr=std, color='cadetblue', title='MEAN', rot=45, ax=ax2)

<matplotlib.axes._subplots.AxesSubplot at 0x21ddd87c488>

（5）折线图

• Part 15

GINI = pd.read_csv('3th dataGINI.csv')
GINI = GINI.iloc[:, 1:].T.interpolate(method='linear').fillna(method='bfill')
GINI

	0	1	2	3	4	5	6	7	8	9	...	148	149	150	151	152	153	154	155	156	157
2000	33.500	32.1	31.70	42.100000	38.100000	46.1	42.80	37.700	39.1	63.2600	...	44.400000	48.50	40.200	46.3	39.8000	42.20	37.300000	45.200000	42.2000	43.2
2001	33.500	32.1	31.70	42.100000	38.100000	46.1	42.35	37.700	39.1	59.0880	...	44.400000	49.20	40.200	46.3	39.8000	42.20	37.728571	45.200000	42.2000	43.2
2002	33.500	32.1	31.70	42.100000	38.100000	46.1	41.90	37.675	39.1	54.9160	...	44.400000	49.90	40.200	46.3	39.8000	42.20	38.157143	45.200000	42.2000	43.2
2003	33.500	32.1	31.70	41.733333	38.483333	46.1	42.20	37.650	39.1	50.7440	...	44.400000	50.60	40.200	46.3	39.8000	42.20	38.585714	44.433333	42.2000	43.2
2004	33.100	32.1	31.70	41.366667	38.866667	46.1	42.50	37.625	39.1	46.5720	...	44.400000	51.30	39.425	46.3	40.2375	42.20	39.014286	43.666667	42.2000	43.2
2005	33.675	32.1	31.70	41.000000	39.250000	46.1	42.15	37.600	39.1	42.4000	...	44.400000	52.00	38.650	46.3	40.6750	42.20	39.442857	42.900000	42.1875	43.2
2006	34.250	32.1	31.70	41.633333	39.633333	46.1	41.80	37.575	39.1	42.1125	...	40.850000	51.04	37.875	46.3	41.1125	42.20	39.871429	43.225000	42.1750	43.2
2007	34.825	32.1	32.00	42.266667	40.016667	46.1	39.80	37.550	39.1	41.8250	...	37.300000	50.08	37.100	46.3	41.5500	42.96	40.300000	43.550000	42.1625	43.2
2008	35.400	32.1	32.30	42.900000	40.400000	45.5	40.30	37.525	39.1	41.5375	...	35.850000	49.12	36.325	46.3	41.9875	43.72	39.675000	43.875000	42.1500	43.2
2009	35.050	32.1	32.15	43.300000	39.660000	46.3	39.60	37.500	39.1	41.2500	...	34.400000	48.16	35.550	46.3	42.4250	44.48	39.050000	44.200000	42.1375	43.2
2010	34.700	32.1	32.00	43.700000	38.920000	45.1	39.40	37.500	39.1	40.9625	...	32.950000	47.20	34.775	46.3	42.8625	45.24	38.425000	43.133333	42.1250	43.2
2011	34.975	32.1	31.80	42.400000	38.180000	43.9	37.50	37.500	39.1	40.6750	...	31.500000	46.50	34.000	46.3	43.3000	46.00	37.800000	42.066667	42.1125	43.2
2012	35.250	32.1	31.60	42.200000	37.440000	42.6	39.30	37.500	39.1	40.3875	...	32.433333	45.80	34.000	46.3	43.3000	46.00	37.800000	41.000000	42.1000	43.2
2013	35.525	32.1	31.60	39.700000	36.700000	41.3	37.80	37.500	39.1	40.1000	...	33.366667	45.10	34.000	46.3	43.3000	46.00	37.800000	41.000000	42.1000	43.2
2014	35.800	32.1	31.60	39.100000	36.700000	41.3	37.00	37.500	39.1	40.1000	...	34.300000	45.10	34.000	46.3	43.3000	46.00	37.800000	41.000000	42.1000	43.2

15 rows × 158 columns

* Part 16

GINI.plot(title='GINI', LineWidth=2, marker='o', linestyle='dashed', grid=True, use_index=True, figsize=(40, 40))

<matplotlib.axes._subplots.AxesSubplot at 0x21deb294848>

• Part 17

# 半对数折线图
GINI.plot(logy=True, title='GINI', LineWidth=2, marker='o', linestyle='dashed', grid=True, use_index=True, figsize=(40, 40))

<matplotlib.axes._subplots.AxesSubplot at 0x21deb7631c8>

（6）直方图和密度图

• Part 18

GINI_COV['中国'] .plot(kind='hist', density=True, bins=8, title='China')
GINI_COV['中国'] .plot(kind='kde', title='China', xlim=[-1.7, 2.2], style='k--')

<matplotlib.axes._subplots.AxesSubplot at 0x2461be0ed48>

（7）饼图

• Part 19

data = DataFrame([[230.1, 37.8, 69.2]], columns=['TV', 'Weibo', 'WeChat']).sum()
data

TV        230.1
Weibo      37.8
WeChat     69.2
dtype: float64

• Part 20

data.plot(kind='pie', figsize=(6, 6), title='Expenditure', fontsize=14, explode=[0, 0.2, 0], shadow=True, startangle=60, autopct='%1.1f%%')

<matplotlib.axes._subplots.AxesSubplot at 0x2462246ef08>

（8）箱型图

• Part 21

GINI_COV.plot(kind='box', figsize=(20, 10), title='Country', grid=True)

<matplotlib.axes._subplots.AxesSubplot at 0x24621b183c8>