Python × 資料分析

Matplotlib & Seaborn

Kristen Chan

Agenda

---

• Basic
• Plots
• Supplement -- Seaborn

Review

Reference : https://www.wikiwand.com/en/Data_analysis

Note

• 探索型視覺化

  https://www.youtube.com/watch?v=DbJyPELmhJc&feature=youtu.be

• 解釋型視覺化

  https://www.youtube.com/watch?v=6F29XLEZCXc

Note

import matplotlib.pyplot as plt

%matplotlib inline

讓圖畫在 Jupyter Notebook 中

Note

import numpy as np
import pandas as pd

Matplotlib

---

Basic

Basic

---

• plt.xlabel( ) / plt.ylabel( )：X 軸標籤 / Y 軸標籤
• plt.xlim( ) / plt.ylim( )：X 軸範圍 / Y 軸範圍
• plt.title( )：圖標題
• plt.grid( )：格線
• plt.legend：圖例
• plt.subplot：子圖
• plt.show：將畫好的圖顯示出來
• plt.savefig：另存圖檔

Note

Create Demo Data -- Temperature

Year = ['2014','2014','2014','2014','2014','2014','2014','2014','2014','2014','2014','2014',
        '2015','2015','2015','2015','2015','2015','2015','2015','2015','2015','2015','2015',
        '2016','2016','2016','2016','2016','2016','2016','2016','2016','2016','2016','2016']
Month = ['JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC',
         'JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC',
         'JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC']
Season = ['Winter','Winter','Spring','Spring','Spring','Summer','Summer','Summer','Fall','Fall','Fall','Winter',
          'Winter','Winter','Spring','Spring','Spring','Summer','Summer','Summer','Fall','Fall','Fall','Winter',
          'Winter','Winter','Spring','Spring','Spring','Summer','Summer','Summer','Fall','Fall','Fall','Winter']
Taipei = [16.8,16.5,18.9,22.5,25.2,28.0,30.5,30.2,29.7,24.7,22.3,16.5,
          16.7,20.8,18.9,22.7,26.1,30.0,30.0,28.6,27.4,25.2,23.5,18.9,
          16.2,15.5,17.5,24.0,27.1,29.4,30.3,30.1,27.8,27.0,22.6,20.1]
Taichung = [16.4,17.0,19.8,24.1,25.8,28.6,30.1,28.9,29.2,25.6,23.1,17.1,
            17.1,18.1,20.7,24.4,26.9,29.7,29.2,28.0,27.8,26.2,24.3,19.6,
            16.8,16.4,18.3,24.9,27.6,28.8,29.4,28.9,27.9,27.5,23.4,20.4] 
Kaohsiung = [19.5,20.3,22.6,25.9,27.8,29.2,30.3,29.1,29.5,27.2,25.2,20.2,
             19.9,17.3,23.6,26.2,28.3,30.6,29.5,28.8,28.8,27.7,26.2,22.4,
             19.3,19.6,21.6,26.9,28.9,29.7,30.2,29.4,28.4,28.4,25.7,22.9]
Temperature_table = { "Year" : Year,
                      "Month" : Month,
                      "Season" : Season,
                      "Taipei" : Taipei,
                      "Taichung" : Taichung,
                      "Kaohsiung" : Kaohsiung }
Temperature = pd.DataFrame(Temperature_table)
print(Temperature.shape)
Temperature.head()

(36, 6)

	Kaohsiung	Month	Season	Taichung	Taipei	Year
0	19.5	JAN	Winter	16.4	16.8	2014
1	20.3	FEB	Winter	17.0	16.5	2014
2	22.6	MAR	Spring	19.8	18.9	2014
3	25.9	APR	Spring	24.1	22.5	2014
4	27.8	MAY	Spring	25.8	25.2	2014

Note

Create Demo Data -- Taipei_Season

Taipei_Season = Temperature['Taipei'].groupby(Temperature['Season']).mean()
Taipei_Season

Season
Fall      25.577778
Spring    22.544444
Summer    29.677778
Winter    17.555556
Name: Taipei, dtype: float64

Matplotlib

---

Plots

Plots

---

• scatter( )：Scatter plot 散佈圖
• plot( )：Line plot 線圖
• hist( )：Histogram 直方圖
• bar( ) / barh：Bar plot 長條圖

Plots

---

Scatter plot

想知道 Temperature 資料中 台北市 及 台中市 溫度的關係

Note

Temperature

	Kaohsiung	Month	Season	Taichung	Taipei	Year
0	19.5	JAN	Winter	16.4	16.8	2014
1	20.3	FEB	Winter	17.0	16.5	2014
2	22.6	MAR	Spring	19.8	18.9	2014
3	25.9	APR	Spring	24.1	22.5	2014
4	27.8	MAY	Spring	25.8	25.2	2014
5	29.2	JUN	Summer	28.6	28.0	2014
6	30.3	JUL	Summer	30.1	30.5	2014
7	29.1	AUG	Summer	28.9	30.2	2014
8	29.5	SEP	Fall	29.2	29.7	2014
9	27.2	OCT	Fall	25.6	24.7	2014
10	25.2	NOV	Fall	23.1	22.3	2014
11	20.2	DEC	Winter	17.1	16.5	2014
12	19.9	JAN	Winter	17.1	16.7	2015
13	17.3	FEB	Winter	18.1	20.8	2015
14	23.6	MAR	Spring	20.7	18.9	2015
15	26.2	APR	Spring	24.4	22.7	2015
16	28.3	MAY	Spring	26.9	26.1	2015
17	30.6	JUN	Summer	29.7	30.0	2015
18	29.5	JUL	Summer	29.2	30.0	2015
19	28.8	AUG	Summer	28.0	28.6	2015
20	28.8	SEP	Fall	27.8	27.4	2015
21	27.7	OCT	Fall	26.2	25.2	2015
22	26.2	NOV	Fall	24.3	23.5	2015
23	22.4	DEC	Winter	19.6	18.9	2015
24	19.3	JAN	Winter	16.8	16.2	2016
25	19.6	FEB	Winter	16.4	15.5	2016
26	21.6	MAR	Spring	18.3	17.5	2016
27	26.9	APR	Spring	24.9	24.0	2016
28	28.9	MAY	Spring	27.6	27.1	2016
29	29.7	JUN	Summer	28.8	29.4	2016
30	30.2	JUL	Summer	29.4	30.3	2016
31	29.4	AUG	Summer	28.9	30.1	2016
32	28.4	SEP	Fall	27.9	27.8	2016
33	28.4	OCT	Fall	27.5	27.0	2016
34	25.7	NOV	Fall	23.4	22.6	2016
35	22.9	DEC	Winter	20.4	20.1	2016

Plots

---

Scatter plot

想知道 Temperature 資料中 台北市 及 台中市 溫度的關係

plt.scatter(Temperature['Taipei'], Temperature['Taichung'])
plt.show()

Plots

---

Scatter plot

想知道 Temperature 資料中 台北市 及 台中市 溫度的關係，加入標籤及標題

x = Temperature['Taipei']
y = Temperature['Taichung']
plt.scatter(x, y)
plt.xlabel("Taipei")
plt.ylabel("Taichung")
plt.title("Taipei VS Taichung")
plt.show()

Plots

---

Scatter plot

想知道 Temperature 資料中 台北市 及 台中市 溫度的關係，加入標籤及標題，將標題改為中文

x = Temperature['Taipei']
y = Temperature['Taichung']
plt.scatter(x, y)
plt.xlabel("Taipei")
plt.ylabel("Taichung")
plt.title("台北市及台中市溫度的散佈圖")
plt.show()

Plots

---

Scatter plot

想知道 Temperature 資料中 台北市 及 台中市 溫度的關係，加入標籤及標題，將標題改為中文

Note

Mac 找一個中文字型，並複製他的路徑

Plots

---

Scatter plot

想知道 Temperature 資料中 台北市 及 台中市 溫度的關係，加入標籤及標題，將標題改為中文

Note

Windows 找一個中文字型，並複製他的路徑

Plots

---

Scatter plot

想知道 Temperature 資料中 台北市 及 台中市 溫度的關係，加入標籤及標題，將標題改為中文

Note

設定畫圖用字型

from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"/System/Library/Fonts/STHeiti Light.ttc",size=12)

Plots

---

Scatter plot

想知道 Temperature 資料中 台北市 及 台中市 溫度的關係，加入標籤及標題，將標題改為中文

x = Temperature['Taipei']
y = Temperature['Taichung']
plt.scatter(x, y)
plt.xlabel("Taipei")
plt.ylabel("Taichung")
plt.title(u"台北市及台中市溫度的散佈圖",fontproperties=font)
plt.show()

Plots

---

Line plot

Sine Function $$ f(x) = sin(x) ; -2 \le x \le 2 $$

x = np.arange(-2.0, 2.0, 0.01)
y = np.sin(2 * np.pi * x)

plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.title("f(x) = sin(x)")
plt.show()

Plots

---

Line plot

Sine Function，加入格線 $$ f(x) = sin(x) ; -2 \le x \le 2 $$

x = np.arange(-2.0, 2.0, 0.01)
y = np.sin(2 * np.pi * x)

plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.title("f(x) = sin(x)")
plt.grid(True)
plt.show()

Exercise

Q. Line Plot

$$ f(x) = x^2 ; -2 \le x \le 2 $$

Answers

x = np.arange(-2.0, 2.0, 0.01)
y = x**2

plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.title("$f(x) = x^2$")
plt.grid(True)
plt.show()

Exercise

Q. Line Plot

$$ f(x) = cos(x) ; -2 \le x \le 2 $$

Answers

x = np.arange(-2.0, 2.0, 0.01)
y = np.cos(2 * np.pi * x)

plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.title("f(x) = cos(x)")
plt.grid(True)
plt.show()

Plots

---

Line plot

疊圖
Sine Function & Cosine Function

x = np.arange(-2.0, 2.0, 0.01)
y_sin = np.sin(2 * np.pi * x)
y_cos = np.cos(2 * np.pi * x)

plt.plot(x, y_sin)
plt.plot(x, y_cos)
plt.xlabel("x")
plt.ylabel("y")
plt.title("f(x) = sin(x) & f(x) = cos(x)")
plt.grid(True)
plt.show()

Plots

---

Line plot

疊圖，改顏色
Sine Function(青色) & Cosine Function(紅色)

x = np.arange(-2.0, 2.0, 0.01)
y_sin = np.sin(2 * np.pi * x)
y_cos = np.cos(2 * np.pi * x)

plt.plot(x, y_sin, "cyan")
plt.plot(x, y_cos, "red")
plt.xlabel("x")
plt.ylabel("y")
plt.title("f(x) = sin(x) & f(x) = cos(x)")
plt.grid(True)
plt.show()

Plots

---

Line plot

疊圖，改顏色、線條型態
Sine Function(dashed) & Cosine Function(dotted)

x = np.arange(-2.0, 2.0, 0.01)
y_sin = np.sin(2 * np.pi * x)
y_cos = np.cos(2 * np.pi * x)

plt.plot(x, y_sin, "c--")
plt.plot(x, y_cos, "r:")
plt.xlabel("x")
plt.ylabel("y")
plt.title("f(x) = sin(x) & f(x) = cos(x)")
plt.grid(True)
plt.show()

Plots

---

Note

Color 顏色

• b : blue
• g : green
• r : red
• c : cyan
• m : magenta
• y : yellow
• k : black
• w : white

Plots

---

Note

Linestyle 線條樣式

• '-' or 'solid'：solid line
• '--' or 'dashed'：dashed line
• '-. ' or 'dashdot'：dash-dotted line
• ':' or 'dotted'：dotted line
• 'None' or ' ' or ''：draw nothing

Plots

---

Line plot

疊圖，改顏色、線條型態，加入圖例
Sine Function(dashed) & Cosine Function(dotted)

x = np.arange(-2.0, 2.0, 0.01)
y_sin = np.sin(2 * np.pi * x)
y_cos = np.cos(2 * np.pi * x)

plt.plot(x, y_sin, "c--", label="sin")
plt.plot(x, y_cos, "r:", label="cos")
plt.xlabel("x")
plt.ylabel("y")
plt.title("f(x) = sin(x) & f(x) = cos(x)")
plt.grid(True)
plt.legend()
plt.show()

Plots

---

Note

Legend 位置

plt.legend(loc='位置')

• best：最適合位置
• upper right：右上角
• upper left：左上角
• lower left：左下角
• lower right：右下角
• right：右邊
• center left：左邊中間
• center right：右邊中間
• lower center：下方中間
• upper center：上方中間
• center：正中間

Plots

---

Histogram plot

Normal distribution ( size = 10000 )

Review

隨機產生 n 個服從 Normal( $\mu$ , $\sigma$ )

np.random.normal( mu , sigma , n )

Plots

---

Histogram plot

Normal distribution ( size = 10000 )

normal_data = np.random.normal(size = 10000)
plt.hist(normal_data)
plt.title("Normal distribution")
plt.show()

Plots

---

Histogram plot

Uniform distribution ( size = 10000 )

Review

隨機產生 n 個介在 low~high 的值並服從 unifom

np.random.uniform( low , high , n  )

Plots

---

Histogram plot

Uniform distribution ( size = 10000 )

uniform_data = np.random.uniform(size = 10000)
plt.hist(uniform_data)
plt.title("Uniform distribution")
plt.show()

Plots

---

Histogram plot

左右圖
Normal distribution ( size = 10000) ← 左
Uniform distribution ( size = 10000 ) ← 右

normal_data = np.random.normal(size = 10000)
uniform_data = np.random.uniform(size = 10000)
plt.subplot(1, 2, 1)
plt.hist(normal_data)
plt.title("Normal distribution")
plt.subplot(1, 2, 2)
plt.hist(uniform_data)
plt.title("Uniform distribution")
plt.show()

Plots

---

Note

Subplot 位置擺放

plt.subplot(m, n, loc)

• m：產生 $m \times n$ 的 subplot
• n : 產生 $m \times n$ 的 subplot
• loc : 表示為第幾個 subplot ，計算順序依序為：從左到右，從上到下

Example：畫一個 $2 \times 3$

(2, 3, 1)	(2, 3, 2)	(2, 3, 3)
(2, 3, 4)	(2, 3, 5)	(2, 3, 6)

Exercise

Q. 使用 Temperature 這組資料，劃出一個 上下排列 台北、台中、高雄溫度的直方圖

Answers

plt.subplot(3, 1, 1)
plt.hist(Temperature['Taipei'])
plt.title("Taipei Temperature")
plt.subplot(3, 1, 2)
plt.hist(Temperature['Taichung'])
plt.title("Taichung Temperature")
plt.subplot(3, 1, 3)
plt.hist(Temperature['Kaohsiung'])
plt.title("Kaohsiung Temperature")
plt.tight_layout()
plt.show()

Plots

---

Bar plot

Note

台北市在各季節的溫度狀況

Taipei_Season

Season
Fall      25.577778
Spring    22.544444
Summer    29.677778
Winter    17.555556
Name: Taipei, dtype: float64

Plots

---

Bar plot

利用 Taipei_Season 畫一個，台北在不同季節下的溫度變化

season_cut = range(len(Taipei_Season))
plt.bar(season_cut , Taipei_Season, color="blue", align = "center")
plt.xticks(season_cut , Taipei_Season.index)
plt.show()

Plots

---

Bar plot

利用 Taipei_Season 畫一個，台北在不同季節下的溫度變化，在 Summer 的上方加入一個 Max 的標註

season_cut = range(len(Taipei_Season))
plt.bar(season_cut , Taipei_Season, color="blue", align = "center")
plt.xticks(season_cut , Taipei_Season.index)
plt.annotate( s='Max', xy=( 2 ,max(Taipei_Season) ), xytext=(2+1 ,max(Taipei_Season)+1),
              arrowprops=dict(facecolor='black'))
plt.show()

Plots

---

Note

Annotating 標註

plt.annotate( s='文字',
              xy=(標註圖_x座標, 標註圖_y座標), 
              xytext=(標註字_x座標, 標註字_y座標),
              arrowprops=dict(標註圖形態) )

Plots

---

Bar plot ( barh )，轉換圖的方向

利用 Taipei_Season 畫一個，台北在不同季節下的溫度變化

season_cut = range(len(Taipei_Season))
plt.barh(season_cut, Taipei_Season, color="blue", align = "center")
plt.yticks(season_cut,Taipei_Season.index )
plt.show()

Plots

---

Bar plot ( barh )，轉換圖的方向

利用 Taipei_Season 畫一個，台北在不同季節下的溫度變化，並存成 Taipei_Season.png

season_cut = range(len(Taipei_Season))
plt.barh(season_cut, Taipei_Season, color="blue", align = "center")
plt.yticks(season_cut,Taipei_Season.index )
plt.savefig(filename = "/Users/hsinyu/Desktop/Python資料分析/Taipei_Season.png", format = "png")

Plots

---

Pie plot

Note

台北市在各季節的溫度狀況

Taipei_Season

Season
Fall      25.577778
Spring    22.544444
Summer    29.677778
Winter    17.555556
Name: Taipei, dtype: float64

Plots

---

Pie plot

利用 Taipei_Season 畫一個，台北溫度在不同季節的分佈狀況

plt.pie( Taipei_Season, explode=(0, 0, 0, 0),
         labels=['Fall', 'Spring', 'Summer', 'Winter'],
         shadow=True, startangle=90 )
plt.axis('equal')
plt.legend(loc='best')
plt.show()

Plots

---

Pie plot

加入 百分比
利用 Taipei_Season 畫一個，台北溫度在不同季節的分佈狀況

plt.pie( Taipei_Season, explode=(0, 0, 0, 0),
         labels=['Fall', 'Spring', 'Summer', 'Winter'],
         autopct='%.1f%%', shadow=True, startangle=90 )
plt.axis('equal')
plt.legend(loc='best')
plt.show()

Plots

---

Pie plot

加入百分比，把 Summer 分離開表示
利用 Taipei_Season 畫一個，台北溫度在不同季節的分佈狀況

plt.pie( Taipei_Season, explode=(0, 0, 0.1, 0),
         labels=['Fall', 'Spring', 'Summer', 'Winter'],
         autopct='%.1f%%', shadow=True, startangle=90 )
plt.axis('equal')
plt.legend(loc='best')
plt.show()

Matplotlib

---

Supplement -- Seaborn

Seaborn

安裝 seaborn package

Anaconda Windows Open Anaconda Prompt

conda install seaborn

Seaborn

安裝 seaborn package

Anaconda Mac Open Terminal

conda install seaborn

Seaborn

import seaborn as sns

Seaborn

Scatter plot

想知道 Temperature 資料中 台北市 及 台中市 溫度的關係

x = Temperature['Taipei']
y = Temperature['Taichung']
sns.jointplot(x, y)

<seaborn.axisgrid.JointGrid at 0x109fd2550>

Seaborn

Histogram plot

Normal distribution ( size = 10000 )

normal_data = np.random.normal(size = 100000)
sns.distplot(normal_data)

<matplotlib.axes._subplots.AxesSubplot at 0x10a5f9f98>

Seaborn

Bar plot

利用 Taipei_Season 畫一個，台北在不同季節下的溫度變化

sns.barplot(Taipei_Season.index, Taipei_Season)

<matplotlib.axes._subplots.AxesSubplot at 0x10a214d68>