Pandas绘图

上一篇：pandas数据分析给力教程【完整版】（六）

Series和DataFrame都有一个用于生成各类图表的plot方法。默认情况下，它们所生成的是线形图

线形图

简单的Series图表示例,plot()

import numpy as np
import pandas as pd
from pandas import Series,DataFrameimport matplotlib.pyplot as plt
%matplotlib inline

plot()

线形图反映的是趋势.

s = Series(data=np.random.randint(0,10, size=10))
s

0    4
1    6
2    5
3    8
4    7
5    7
6    5
7    6
8    3
9    0
dtype: int32

# 以index作为x轴数据, values作为y轴数据
s.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x110ee518>

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简单的DataFrame图表示例,plot()

图例的位置可能会随着数据的不同而不同

data = np.random.randint(0,150, size=(4,3))
index = ['张三', '李四', '王五', '赵六']
columns = ['语文', '数学', '英语']
df = DataFrame(index=index, data=data, columns=columns)
df

	语文	数学	英语
张三	32	72	142
李四	15	127	67
王五	66	9	99
赵六	29	93	95

df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x4a21208>d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 24352 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 19977 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 26446 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 22235 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 29579 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 20116 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 36213 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 20845 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 24352 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 19977 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 26446 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 22235 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 29579 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 20116 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 36213 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 20845 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 35821 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 25991 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 25968 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 23398 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 33521 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 35821 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 25991 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 25968 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 23398 missing from current font.font.set_text(s, 0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 33521 missing from current font.font.set_text(s, 0, flags=flags)

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-xMPs2UhB-1634125797109)(output_11_2.png)]

plt.rcParams['font.sans-serif'] = ['SimHei']

# 以dataframe的index作为x轴数据, 每一列是一条线
df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x161b4d30>

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df.index = [-1, -2, 0, 1]
df

	语文	数学	英语
-1	47	104	39
-2	46	10	130
0	78	86	27
1	125	90	140

df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x148a6438>d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:211: RuntimeWarning: Glyph 8722 missing from current font.font.set_text(s, 0.0, flags=flags)
d:\1903\.venv\lib\site-packages\matplotlib\backends\backend_agg.py:180: RuntimeWarning: Glyph 8722 missing from current font.font.set_text(s, 0, flags=flags)

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-viRbF1QR-1634125797112)(output_15_2.png)]

# 解决中文字体下负号无法正常显示的问题
plt.rcParams['axes.unicode_minus'] = False

df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x160f7c88>

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柱状图

Series柱状图示例,kind = ‘bar’/‘barh’

柱状图一般用来比较大小

s.plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1604f208>

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DataFrame柱状图示例

# 每一行数据放在一起比较大小
df.plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x13bd60f0>

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读取文件tips.csv，查看每天的聚会人数情况
每天各种聚会规模的比例

求和并df.sum()，注意灵活使用axis

tips = pd.read_csv('../data/tips.csv')
tips

	day	1	2	3	4	5	6
0	Fri	1	16	1	1	0	0
1	Stat	2	53	18	13	1	0
2	Sun	0	39	15	18	3	1
3	Thur	1	48	4	5	1	3

tips.set_index(keys='day', inplace=True)

tips.plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x168473c8>

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tips.sum(axis=1)

day
Fri     19
Stat    87
Sun     76
Thur    62
dtype: int64

# 先求每一行的和
result = tips.div(tips.sum(axis=1), axis=0)

16 / 19

0.8421052631578947

result.plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x16a575f8>

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直方图

直方图一般用来表示数据的分布情况

n = np.array([1,1,2,3,4,4,5,6,6,8])
s = Series(data=n)
# 直方图histogram
s.plot(kind='hist', bins=5,  density=True)
# 加入density=True之后,y轴的值变成了概率/组距的结果
# 如果bins设置不太合理,可以加上kde图来显示数据分布
s.plot(kind='kde')
# kde = kernel density estimate

<matplotlib.axes._subplots.AxesSubplot at 0x18c95400>

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!pip install scipy -i https://pypi.douban.com/simple

Looking in indexes: https://pypi.douban.com/simple
Collecting scipyUsing cached https://pypi.doubanio.com/packages/9e/fd/9a995b7fc18c6c17ce570b3cfdabffbd2718e4f1830e94777c4fd66e1179/scipy-1.3.0-cp36-cp36m-win_amd64.whl
Requirement already satisfied: numpy>=1.13.3 in d:\1903\.venv\lib\site-packages (from scipy) (1.17.0)
Installing collected packages: scipy
Successfully installed scipy-1.3.0

# 计算一组数据的直方数据
np.histogram(n,  bins=100, density=True)

(array([2.85714286, 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 1.42857143,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 1.42857143, 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 2.85714286, 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 1.42857143, 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 2.85714286, 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 0.        ,0.        , 0.        , 0.        , 0.        , 1.42857143]),array([1.  , 1.07, 1.14, 1.21, 1.28, 1.35, 1.42, 1.49, 1.56, 1.63, 1.7 ,1.77, 1.84, 1.91, 1.98, 2.05, 2.12, 2.19, 2.26, 2.33, 2.4 , 2.47,2.54, 2.61, 2.68, 2.75, 2.82, 2.89, 2.96, 3.03, 3.1 , 3.17, 3.24,3.31, 3.38, 3.45, 3.52, 3.59, 3.66, 3.73, 3.8 , 3.87, 3.94, 4.01,4.08, 4.15, 4.22, 4.29, 4.36, 4.43, 4.5 , 4.57, 4.64, 4.71, 4.78,4.85, 4.92, 4.99, 5.06, 5.13, 5.2 , 5.27, 5.34, 5.41, 5.48, 5.55,5.62, 5.69, 5.76, 5.83, 5.9 , 5.97, 6.04, 6.11, 6.18, 6.25, 6.32,6.39, 6.46, 6.53, 6.6 , 6.67, 6.74, 6.81, 6.88, 6.95, 7.02, 7.09,7.16, 7.23, 7.3 , 7.37, 7.44, 7.51, 7.58, 7.65, 7.72, 7.79, 7.86,7.93, 8.  ]))

0.2 / 0.07

2.857142857142857

rondom生成随机数百分比直方图，调用hist方法

柱高表示数据的频数，柱宽表示各组数据的组距
参数bins可以设置直方图方柱的个数上限，越大柱宽越小，数据分组越细致
设置density参数为True，可以把频数转换为概率

kde图：核密度估计，用于弥补直方图由于参数bins设置的不合理导致的精度缺失问题

练习

绘制一个由两个不同的正态分布组成的的双峰分布

n1 = np.random.normal(loc=5, scale=5, size=10000)
n2 = np.random.normal(loc=30, scale=8, size=10000)n = np.hstack((n1,n2))

s = Series(data=n)
s.plot(kind='hist', bins=500, density=True)
s.plot(kind='kde')

<matplotlib.axes._subplots.AxesSubplot at 0x1ada0f60>

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散布图(散点图)

散布图
散布图是观察两个一维数据数列之间的关系的有效方法,DataFrame对象可用

使用方法：
设置kind = ‘scatter’，给明标签columns

散点图研究两个一维数据之间的关系.

df = DataFrame({'A': np.random.randn(1000), 'B': np.random.randn(1000), 'C': np.random.randn(1000), 'D': np.random.randn(1000)})
df.head()

	A	B	C	D
0	1.350469	-0.757953	-0.130467	-0.558658
1	1.703911	-0.053533	-1.290569	0.619652
2	-0.132011	1.871678	-0.383147	-0.774807
3	-0.632711	1.487892	0.447683	-2.492515
4	0.937552	-0.200019	-0.449218	-0.776772

df.plot(x='A', y='B', kind='scatter')

<matplotlib.axes._subplots.AxesSubplot at 0x1ce364a8>

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x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)
df2 = DataFrame({'x': x, 'y':y})
df2.plot(x='x', y='y', kind='scatter')

<matplotlib.axes._subplots.AxesSubplot at 0x1d021908>

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散布图矩阵，当有多个点时，两两点的关系

使用函数：pd.plotting.scatter_matrix(),

参数diagnol：设置对角线的图像类型


_ = pd.plotting.scatter_matrix(df, figsize=(16,  16), alpha=0.6, diagonal='kde')

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pandas数据分析给力教程【完整版】（七）

Pandas绘图

线形图

柱状图

直方图

练习

散布图(散点图)

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