# **【Python 視覺化 : Matplotlib (Seaborn)、Plotly】**
:::info
- 前兩篇[【Python 資料處理_1 : NumPy】🔗](https://hackmd.io/@workcata/HJZIGYnJp)
- 前一篇[【Python 資料處理_2 : Pandas、實例應用】🔗](https://hackmd.io/@workcata/r1d6fSZeT)
- Matplotlib
- 基本介紹
- 繪製基礎圖表
- 創建圖表的三種方式
- 創建子圖的三種方式
- 創建圖中圖的一種方式
- 散佈圖 scatter
- 直方圖 hist
- 橫條圖 barh (直條圖 bar)
- 折線圖 plot
- 箱型圖 boxplot
- 補充: plt.style.use(' ')
- 面積圖 area
- 線圖 line
- 六邊形圖 hexbin
- 密度圖 density、kde
- Matplotlib (Seaborn)
- 基本介紹
- 創建圖中圖
- 散點圖 scatterplot
- 散點圖矩陣 pairplot
- 線圖 lineplot
- 直方圖 histplot (displot 更靈活)
- 條形圖 barplot
- 計數圖 countplot
- 聯合圖 jointplot
- 地毯圖 rugplot 、密度估計圖 kdeplot
- 箱型圖 boxplot
- 小提琴圖 viplinplot
- 帶狀圖 stirplot、swarmplot
- 繪製分類數據圖形 catplot
- 熱力圖 heatmap
- 補充: .set_style(' ')
- 數據透視表 pivot_table
- 關聯性網格圖 sns.FacetGrid
- 線性回歸模型 lmplot
- Plotly
- 初始化
- 散佈圖 scatter
- 直方圖 histplot
- 分布圖 distplot
- 直條圖 barplot
- 橫條圖 barhplot
- 圓餅圖 pie
- 箱型圖 boxplot
- 3D圖
- 氣泡圖 bubble
- 熱力圖 choropleth
- 熱力圖 heatmap
:::
PS 程式碼後面沒有print()、plt.show()，是因為用jupyter notebook
## Matplotlib
### 基本介紹
> Matplotlib 是Python的數據可視化庫，用於創建各種類型的圖形，折線圖、散點圖、直方圖、條形圖、餅圖...
```=
import matplotlib.pyplot as plt
%matplotlib inline # 如果用jupyter notebook 要加
```
- 假設我先創一個x,y
```=
import numpy as np
x = np.linspace(0,5,11)
y = x**2
```
x 為 array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])
y 為 array(\[ 0. , 0.25, 1. , 2.25, 4. , 6.25, 9. , 12.25, 16. , 20.25, 25. \])
### 繪製基礎圖表
```=
plt.plot(x,y)
plt.show()
```
<br/>
### 創建圖表的三種方式
方法一
```=
plt.figure(figsize=(8, 2), dpi=100) # 先設置畫布
plt.plot(x, y, 'r') # 繪製基礎圖、顏色
plt.plot(x+1, y+1, 'b')
plt.xlabel('X Axis Title Here') # 標註 label
plt.ylabel('Y Axis Title Here')
plt.title('String Title Here') # 標註 title
plt.legend(labels=['Line 1', 'Line 2'], loc=1) # 添加圖例，loc=1 在右上角
plt.savefig("filename.png") # 如果要存成檔案
plt.show()
```
<br/>
方法二
```=
fig, axes = plt.subplots(figsize=(8,2), dpi=100)
axes.plot(x, y, 'r')
axes.plot(x+1, y+1, 'b')
axes.set_xlabel('X Axis Title Here')
axes.set_ylabel('Y Axis Title Here')
axes.set_title('String Title Here')
axes.legend(labels=['Line 1', 'Line 2'], loc=1)
fig.savefig("filename.png")
plt.show()
```
創建了一個包含軸（axes）的圖表對象(figure)，後面都一樣
plt.figure() 用來創建一個圖，但plt.subplots() 之後可以產成多個子圖
<br/>
方法三
使用【對象導向】方法，明確地創建了圖形對象和軸對象，並通過添加軸來進行繪圖
[0.1, 0.1, 0.8, 0.8] 軸的左邊緣距離圖形左邊緣10％，底邊緣距離圖形底邊緣10％，寬度佔圖形寬度的80％，高度佔圖形高度的80％
```=
fig = plt.figure(figsize=(8, 2), dpi=100)
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes.plot(x, y, 'r')
axes.plot(x+1, y+1, 'b')
axes.set_xlabel('X Axis Title Here')
axes.set_ylabel('Y Axis Title Here')
axes.set_title('String Title Here')
axes.legend(labels=['Line 1', 'Line 2'], loc=1)
fig.savefig("filename.png")
plt.show()
```
<br/>
### 創建子圖的三種方式 (改欄/行的數量，就能增加子圖數量)
方法一
使用 plt.figure() 創建圖形，然後使用 plt.subplot() 來創建子圖
plt.subplot() 接受三個參數，(行數, 列數,子圖的索引)
```=
plt.figure(figsize=(8, 2), dpi=100)
plt.subplot(1,2,1) # (一欄,兩列,第一個圖)
plt.plot(x, y, 'r--')
plt.plot(x+1, y+1, 'b')
plt.xlabel('X1 Axis Title Here')
plt.ylabel('Y1 Axis Title Here')
plt.title('String Title Here')
plt.legend(labels=['Line 1', 'Line 2'], loc=1)
plt.subplot(1,2,2) # (一欄,兩列,第二個圖)
plt.plot(y, x, 'g*-')
plt.plot(y-1, x-1, 'g*-')
plt.xlabel('X2 Axis Title Here')
plt.ylabel('Y2 Axis Title Here')
plt.title('String Title Here')
plt.legend(labels=['Line 1', 'Line 2'], loc=1)
plt.savefig("filename.png")
plt.show()
```
<br/>
方法二
使用 plt.subplots() 創建一個包含兩個子圖的 fig 和 axes（軸）對象陣列
axes 是一個包含兩個軸對象的陣列，分別對應於兩個子圖
```=
fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(8,2), dpi=100)
axes[0].plot(x, y, 'r--' )
axes[0].plot(x+1, y-1, 'b--' )
axes[0].set_xlabel('X1 Axis Title Here')
axes[0].set_ylabel('Y1 Axis Title Here')
axes[0].set_title('String Title Here')
axes[0].legend(labels=['Line 1', 'Line 2'], loc=1)
axes[1].plot(y, x, 'g*-')
axes[1].plot(y-1, x+1, 'g*-')
axes[1].set_xlabel('X2 Axis Title Here')
axes[1].set_ylabel('Y2 Axis Title Here')
axes[1].set_title('String Title Here')
axes[1].legend(labels=['Line 1', 'Line 2'], loc=1)
fig.savefig("filename.png")
plt.show()
```
<br/>
方法三
使用【對象導向】方法，明確地創建了圖形對象和軸對象，並通過添加軸來進行繪圖
```=
fig = plt.figure(figsize=(8, 2), dpi=100)
axes1 = fig.add_axes([0.1, 0.1, 0.4, 0.8])
axes1.plot(x, y, 'r')
axes1.plot(x+1, y+1, 'b')
axes1.set_xlabel('X1 Axis Title Here')
axes1.set_ylabel('Y1 Axis Title Here')
axes1.set_title('String Title 1')
axes2 = fig.add_axes([0.6, 0.1, 0.4, 0.8])
axes2.plot(y, x, 'g*-')
axes2.plot(y+1, x+1, 'g*-')
axes2.set_xlabel('X2 Axis Title Here')
axes2.set_ylabel('Y2 Axis Title Here')
axes2.set_title('String Title 2')
plt.savefig("filename.png")
plt.show()
```
<br/>
> 補充
>
>import matplotlib.pyplot as plt
>
>fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 4), dpi=100)
>
>x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [1, 8, 27, 64, 125]
y3 = [1, 16, 81, 256, 625]
>
>for i, ax in enumerate(axes):
if i == 0:
new_x = [val + 1 for val in x]
new_y1 = [val + 3 for val in y1]
ax.plot(x, y1, 'r--')
ax.plot(new_x, new_y1, 'r--')
ax.set_title('Subplot 1')
ax.legend(labels=['Line 1', 'Line 2'], loc=1)
elif i == 1:
ax.plot(x, y2, 'g*-')
ax.set_title('Subplot 2')
else:
ax.plot(x, y3, 'b.-')
ax.set_title('Subplot 3')
ax.set_xlabel('X_label')
ax.set_ylabel('Y_label')
>
>
> #調整子圖距離
plt.tight_layout()
>
>plt.savefig("filename.png")
>
>plt.show()
>
<br/>
### 創建圖中圖的一種方式
先看比較
```=
fig = plt.figure()
axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3])
```
```=
fig = plt.figure()
axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes2 = fig.add_axes([0.8, 0.15, 0.4, 0.3])
```
圖中圖
```=
fig = plt.figure()
axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3])
axes1.plot(x, y, color="r", alpha=0.8, lw=1.5, ls='--', marker='s', markersize=10, markeredgewidth=1.5, markerfacecolor="yellow")
# 透明度 0.8, linewidth 1.5, linestyle --, marker 'square', markeredgewidth 標記外框線寬度
# 可以這樣標 ax.plot(x, x+1, color="#8B008B", alpha=0.5)
# linestyle 可以換成 ‘-‘, ‘–’, ‘-.’, ‘:’ ...
# marker 可以換成 '+', 'o', '*', 's', '^', ',', '.', '1', '2', '3' ...
axes1.set_xlabel('X1 Axis Title Here')
axes1.set_ylabel('Y1 Axis Title Here')
axes1.set_title('String Title Here')
axes2.plot(y, x, 'b')
axes2.set_xlabel('X2 Axis Title Here')
axes2.set_ylabel('Y2 Axis Title Here')
axes2.set_title('String Title Here')
plt.savefig("filename.png")
plt.show()
```
圖中圖練習
```=
fig = plt.figure(figsize=(7,2), dpi=100)
ax = fig.add_axes([0, 0, 0.8, 0.8])
ax.plot(x,y, color='purple', lw=4, ls='-.', marker='o', markersize='10', markerfacecolor='yellow',
alpha=0.3, label="X Squared")
ax.legend(loc=0)
```
```=
fig = plt.figure(figsize=(7,2), dpi=100)
ax = fig.add_axes([0, 0, 0.8, 0.8])
ax.plot(x,y, color='purple', lw=4, ls='-.', marker='o', markersize='10', markerfacecolor='yellow',
alpha=0.3, label="X Squared")
ax.set_xlim([0,1]) # 設置 x 和 y 軸的範圍
ax.set_ylim([0,2])
```
<br/>
### 散佈圖 scatter
```=
plt.scatter(x,y)
```
```=
plt.scatter(x=tips['total_bill'], y=tips['tip'])
```
加上c 分組, s 形狀大小
```=
titanic.plot.scatter(x='fare',y='age',c='survived',cmap='coolwarm')
```
<br/>
>https://matplotlib.org/stable/plot_types/index
'default'：默認風格
'classic'：經典風格，類似於 MATLAB 的風格
'ggplot'：仿照 ggplot2 的風格
'seaborn'：仿照 seaborn 的風格
'fivethirtyeight'：仿照 FiveThirtyEight 網站的風格
'bmh'：較簡單的風格，適合科學繪圖
```=
```=
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('_mpl-gallery') # 使用指定的風格
# 生成資料
np.random.seed(3)
x = 4 + np.random.normal(0, 2, 24)
y = 4 + np.random.normal(0, 2, len(x))
# 設定點的大小和顏色
sizes = np.random.uniform(15, 80, len(x))
colors = np.random.uniform(15, 80, len(x))
fig, ax = plt.subplots()
ax.scatter(x, y, s=sizes, c=colors, vmin=0, vmax=100)
ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8)) # vmin、vmax 顏色映射的範圍
plt.show()
```
帶入鳶尾花數據
```=
import seaborn as sns
iris = sns.load_dataset('iris')
iris.head()
import matplotlib.pyplot as plt
plt.scatter(iris['sepal_length'], iris['sepal_width'])
plt.xlabel('Sepal Length')
plt.ylabel('Sepal Width')
plt.show()
```
<br/>
### 直方圖 hist
```=
from random import sample
data = sample(range(1, 1000), 100)
plt.hist(data)
plt.xlabel('X Label')
plt.ylabel('Frequency')
plt.title('Histogram of y')
plt.show()
```
<br/>
加上bin，可以控制柱數
```=
from random import sample
data = sample(range(1, 1000), 100)
plt.hist(data, bins=6)
plt.xlabel('X Label')
plt.ylabel('Frequency')
plt.title('Histogram of y')
plt.show()
```
<br/>
加上框線
```=
from random import sample
data = sample(range(1, 1000), 100)
plt.hist(data, bins=6, edgecolor='black', lw= 0.3)
plt.xlabel('X Label')
plt.ylabel('Frequency')
plt.title('Histogram of y')
plt.show()
```
```=
tips['total_bill'].hist(alpha=0.5,bins=25)
plt.xlabel('total bill')
plt.ylabel('count')
```
帶入鳶尾花數據
```=
import seaborn as sns
iris = sns.load_dataset('iris')
iris.head()
import matplotlib.pyplot as plt
plt.hist(iris['sepal_length'])
plt.xlabel('Sepal Length')
plt.ylabel('Frequency')
plt.show()
```
<br/>
### 橫條圖 barh
假設已經有一個確診國家排名前20
```=
plt.figure(figsize=(15,10))
plots = plt.barh(top_20['Country'], top_20['Confirmed'],
height=0.5,
left=None,
align='center',
color=['lightsteelblue',
'cornflowerblue',
'royalblue',
'midnightblue',
'navy',
'darkblue',
'mediumblue'])
plt.xlabel('Confirmed')
plt.ylabel('Country')
plt.title('Covid 19 Statics Top 20 Countries')
plt.show()
```
```=
top_6_total_bill = tips['total_bill'].value_counts().head(6)
top_6_total_bill.plot(kind='barh')
```
PS 改成 plt.bar，就是直條圖
加下面這行，x 軸刻度旋轉 90 度，避免文字重疊
plt.xticks(rotation=90)
```=
plt.bar(x=tips['total_bill'], height=tips['tip'])
```
```=
top_6_total_bill = tips['total_bill'].value_counts().head(6)
top_6_total_bill.plot(kind='bar')
```
<br/>
### 折線圖 plot
```=
import matplotlib.pyplot as plt
plt.style.use('_mpl-gallery') # 使用指定的風格
# 月份
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
# 每個月的支出金額
expenses = [1500, 1800, 1200, 2000, 1600, 1900]
fig, ax = plt.subplots()
ax.plot(months, expenses, marker='o', linestyle='-', linewidth=2.0)
ax.set(xlabel='Month', ylabel='Expenses', title='Monthly Expenses')
plt.show()
```
```=
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('_mpl-gallery') # 使用指定的風格
# 生成資料
x = np.linspace(0, 10, 100)
y = 4 + 2 * np.sin(2 * x)
# 繪製圖表
fig, ax = plt.subplots()
ax.plot(x, y, linewidth=2.0)
ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
plt.show()
```
<br/>
也可以把表格某欄位設為索引
```=
import pandas as pd
# 建立要繪製的 DataFrame
data = top_20[['Country', 'Confirmed', 'Active', 'Recovered', 'Deaths']]
# 設定 Country 欄位為索引
data = data.set_index('Country')
data.plot(kind='line', marker='o')
plt.title('COVID-19 Statistics - Top 20 Countries')
plt.xlabel('Country')
plt.ylabel('Confirmed')
plt.show()
```
```=
tips['total_bill'].plot()
```
帶入鳶尾花數據
```=
import seaborn as sns
iris = sns.load_dataset('iris')
iris.head()
import matplotlib.pyplot as plt
plt.plot(iris['sepal_length'], label='Sepal Length')
plt.plot(iris['sepal_width'], label='Sepal Width')
plt.xlabel('Index')
plt.ylabel('Value')
plt.legend()
plt.show()
```
<br/>
### 箱型圖 boxplot
```=
data = [np.random.normal(0, std, 100) for std in range(1, 4)]
# rectangular box plot
boxprops = dict(facecolor='green', color='green')
plt.boxplot(data, vert=True, patch_artist=True, boxprops=boxprops) # vert 垂直, patch_artist 填充顏色
plt.xlabel('Data Set')
plt.ylabel('Value')
plt.title('Box Plot with Green Fill Color')
plt.show()
```
```=
plt.boxplot(tips['total_bill'])
```
<br/>
### 補充: plt.style.use(' ')
default'、ggplot'、'seaborn'、'fivethirtyeight'、'bmh'、'dark_background'
```=
import matplotlib.pyplot as plt
plt.style.use('bmh')
tips['total_bill'].hist()
plt.xlabel('total bill')
plt.ylabel('count')
```
<br/>
### 面積圖 area
```=
tips_2[['total_bill','tip']][0:30].plot.area(alpha=0.4)
```
<br/>
### 線圖 line
```=
tips.plot.line(x='total_bill',y='tip',figsize=(8,3),lw=1)
```
### 六邊形圖 hexbin
```=
tips.plot.hexbin(x='tip',y='total_bill',gridsize=25,cmap='Oranges')
```
<br/>
### 密度圖 density、kde
```=
tips.plot.density()
```
```=
tips.plot.kde()
```
```=
tips['total_bill'].plot.kde(lw=5,ls='--')
```
<br/>
<br/>
## Matplotlib (Seaborn)
### 基本介紹
> Seaborn 是建立在 Matplotlib 的高級數據可視化庫。簡化創建統計圖形
```=
import seaborn as sns
%matplotlib inline # 如果用jupyter notebook 要加
```
可以先預設上下文參數
poster 是相對較大的上下文
poster > talk > notebook > paper
```=
sns.set_context('poster',font_scale=0.6)
```
<br/>
seaborn 我最喜歡的部分!
有提供kaggle比較有熱門的開源資料: iris, tips, titanic, exercise, flights, anscombe, dots
直接抓取，不需要再下載csv :+1:
```=
tips = sns.load_dataset('tips')
titanic = sns.load_dataset('titanic')
flights = sns.load_dataset('flights')
```
<br/>
創建圖中圖
```=
fig = plt.figure(figsize=(6,4))
# 將 axe 新增到實例化後 2×2 的 figure 上，並指定第 1,2,3 個
ax_1 = fig.add_subplot(2, 2, 1)
ax_2 = fig.add_subplot(2, 2, 2)
ax_3 = fig.add_subplot(2, 2, 3)
```
<br/>
帶入鳶尾花數據
### 散點圖 scatterplot
```=
import seaborn as sns
iris = sns.load_dataset('iris')
iris.head()
sns.scatterplot(x='sepal_length', y='sepal_width', data=iris)
plt.show()
```
<br/>
### 散點圖矩陣 pairplot
```=
sns.pairplot(tips, hue='sex', palette='coolwarm', diag_kind='auto')
# 使用tips數據,'sex' 分組, 調色板 'coolwarm', 對角線上的圖形類型 'auto'
```
diag_kind='auto' ， 可以換成'hist'、'None'、diag_kind='kde'
<br/>
### 線圖 lineplot
帶入鳶尾花數據
```=
import seaborn as sns
iris = sns.load_dataset('iris')
iris.head()
sns.lineplot(data=iris[['sepal_length', 'sepal_width']])
plt.show()
```
<br/>
### 直方圖 histplot (displot 更靈活)
#### histplot
```=
sns.histplot(tips['total_bill'], kde=False)
```
```=
sns.histplot(tips['total_bill'], kde=True,bins=20)
```
帶入鳶尾花數據
```=
import seaborn as sns
iris = sns.load_dataset('iris')
iris.head()
sns.histplot(data=iris, x='sepal_length')
plt.show()
```
<br/>
#### displot
```=
sns.displot(tips['total_bill'], kde=True,bins=20)
```
PS displot 更靈活
加上不同的x軸
```=
sns.displot(data=data, x="total_bill", hue="sex", kde=True, bins=20)
```
多層次直方圖
```=
sns.displot(data=data, x="total_bill", hue="sex", multiple="stack")
```
加上時間序列，可以做直方圖
```=
time_data = sns.load_dataset("flights")
sns.displot(data=time_data, x="year", binwidth=5)
```
也可以做
```=
sns.displot(data=data, x="total_bill", y="tip", kind="kde")
```
```=
sns.displot(data=data, x="total_bill", y="tip", kind="hist")
```
<br/>
### 條形圖 barplot
```=
# estimator 默認: 平均值，可換 mean、max、min
sns.barplot(x='sex',y='total_bill',data=tips,estimator=np.std)
```
<br/>
### 計數圖 countplot
```=
sns.countplot(x='sex',data=tips)
```
<br/>
### 聯合圖 jointplot
直條 + 散點圖
```=
sns.jointplot(x='total_bill',y='tip',data=tips,kind='scatter')
```
直條 + 六邊形圖
```=
sns.jointplot(x='total_bill',y='tip',data=tips,kind='hex')
```
直條 + 密度圖
```=
sns.jointplot(x='total_bill',y='tip',data=tips,kind='kde', fill=True)
```
直條 + 線性迴歸散點圖
```=
sns.jointplot(x='total_bill',y='tip',data=tips,kind='reg')
```
<br/>
### 地毯圖 rugplot 、密度估計圖 kdeplot
```=
sns.rugplot(tips['total_bill'])
```
```=
sns.kdeplot(tips['total_bill'])
sns.rugplot(tips['total_bill'])
```
<br/>
### 箱型圖 boxplot
```=
sns.boxplot(x="day", y="total_bill", data=tips, palette='rainbow')
```
橫向
```=
sns.boxplot(x="total_bill", y="day", data=tips,palette='rainbow',orient='h')
```
hue 分組，類似 pandas groupby
```=
sns.boxplot(x="day", y="total_bill", hue="smoker",data=tips, palette="coolwarm")
```
<br/>
### 小提琴圖 viplinplot
```=
sns.violinplot(x="day", y="total_bill", data=tips,palette='rainbow')
```
橫向
```=
sns.violinplot(x="total_bill", y="day", data=tips,palette='rainbow',orient='h')
```
hue 分組
```=
sns.violinplot(x="day", y="total_bill", data=tips,hue='sex',palette='Set1')
```
split 切割
```=
sns.violinplot(x="day", y="total_bill", data=tips, hue='sex', split=True, palette='Set1')
```
<br/>
### 帶狀圖 stirplot、swarmplot
點有可能重疊
```=
sns.stripplot(x="day", y="total_bill", data=tips, dodge=True, hue='sex',palette='Set1')
```
點有可能重疊，hue 分組
```=
sns.stripplot(x="day", y="total_bill", data=tips, dodge=False, hue='sex', palette='Set1')
```
點不會重疊
```=
sns.swarmplot(x="day", y="total_bill", hue='sex', data=tips, palette="Set1", dodge=True)
```
點不會重疊，hue 分組
```=
sns.swarmplot(x="day", y="total_bill", hue='sex', data=tips, palette="Set1", dodge=False)
```
<br/>
PS 混和
```=
sns.violinplot(x="tip", y="day", data=tips,palette='rainbow')
sns.swarmplot(x="tip", y="day", data=tips,color='black',size=3)
```
<br/>
### 繪製分類數據圖形 catplot
可替換
bar - 長條圖
point - 散點圖
strip - 長條圖
box - 箱形圖
violin - 小提琴圖
swarm - 蜂群圖
boxen - 箱型圖與蜂群圖組合
pointplot - 點圖
```=
sns.catplot(x="sex", y="total_bill", data=tips, kind="box")
```
```=
sns.catplot(x="sex", y="total_bill", data=tips, kind="boxen")
```
<br/>
### 熱力圖 heatmap
```=
sns.heatmap(tips.corr())
```
PS 如果有不能計算的欄位，先轉換為數字
```=
tips['sex'] = tips['sex'].map({'Female': 0, 'Male': 1})
tips['smoker'] = tips['smoker'].map({'No': 0, 'Yes': 1})
day_mapping = {'Thur': 1, 'Fri': 2, 'Sat': 3, 'Sun': 4}
tips['day'] = tips['day'].map(day_mapping)
time_mapping = {'Lunch': 1, 'Dinner': 2}
tips['time'] = tips['time'].map(time_mapping)
```
或是只看數字欄位
```=
tips_2 = tips[['total_bill', 'tip', 'size']]
sns.heatmap(tips_2.corr())
```
加上 相關性係數的標籤
```=
sns.heatmap(tips_2.corr(),cmap='coolwarm',annot=True)
```
<br/>
### 補充: .set_style(' ')
```=
sns.set_style('white')
sns.countplot(x='sex',data=tips)
```
圖的四個邊緣，添加座標軸刻度線
```=
sns.set_style('ticks')
sns.countplot(x='sex',data=tips,palette='deep')
```
.despine() 移除Seaborn繪圖中的上、右邊框線，使得圖形更加清晰
```=
sns.countplot(x='sex',data=tips)
sns.despine()
# <> sns.despine(left=True)
```
<br/>
### 數據透視表 pivot_table
```=
flights.pivot_table(values='passengers',index='month',columns='year')
```
加上熱力圖
```=
pvflights = flights.pivot_table(values='passengers',index='month',columns='year')
sns.heatmap(pvflights)
```
```=
# map='magma' 顏色映射
# 'viridis'、'plasma'、'inferno'、'cividis'、'coolwarm'、'RdBu'、'YlGnBu'
sns.heatmap(pvflights, cmap='magma',linecolor='white',linewidths=1)
```
<br/>
### 聚類函數 clustermap
```=
sns.clustermap(pvflights)
```
### 關聯性網格圖 sns.FacetGrid
```=
g = sns.FacetGrid(tips, col="time", row="smoker")
g = g.map(plt.hist, "total_bill")
```
hue 分組
```=
g = sns.FacetGrid(tips, col="time", row="smoker",hue='sex')
g = g.map(plt.scatter, "total_bill", "tip")
```
等同sns.jointplot(x='total_bill',y='tip',data=tips,kind='reg')
```=
g = sns.JointGrid(x="total_bill", y="tip", data=tips)
g = g.plot(sns.regplot, sns.distplot)
```
### 線性回歸模型 lmplot
```=
sns.lmplot(x='total_bill',y='tip',data=tips)
```
hue 分組
```=
sns.lmplot(x='total_bill',y='tip',data=tips,hue='sex',palette='coolwarm')
```
catter_k 散點的大小 100
```=
sns.lmplot(x='total_bill',y='tip',data=tips,hue='sex',palette='seismic',
markers=['o','v'],scatter_kws={'s':100})
```
分組分圖
```=
sns.lmplot(x='total_bill', y='tip', data=tips, col='sex')
```
```=
sns.lmplot(x='total_bill', y='tip', data=tips, row='sex')
```
```=
sns.lmplot(x="total_bill", y="tip", data=tips, row="sex", col="time")
```
```=
sns.lmplot(x='total_bill', y='tip', data=tips, col='day', hue='sex', palette='coolwarm', aspect=0.6, height=8)
```
<br/>
## Plotly
### 初始化
- 參考
https://plotly.com/python/
- 申請api
https://chart-studio.plotly.com/settings/api#/
- 先安裝
```=
pip install plotly
pip install cufflinks
pip install chart_studio
```
- 導入模組
```=
# 離線使用, jupyter notebook 初始化, html file, jupyter notebook 繪圖
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
# Pandas DataFrame 轉為 plotly
import cufflinks as cf
# 連接Plotly Chart Studio、上傳 Plotly 圖表到 Plotly Chart Studio
import chart_studio
import chart_studio.plotly as py
```
- 導入api，填入前面申請到的 api key
```=
chart_studio.tools.set_credentials_file(username='你的帳號', api_key='your_api_key')
```
- 初始化Plotly以進入離線模式
```=
init_notebook_mode(connected=True)
```
- 先創兩個dataframe
df
```=
df = pd.DataFrame(np.random.randn(100,4),columns='A B C D'.split())
df
```
df2
```=
df2 = pd.DataFrame({'Category':['A','B','C'],'Values':[32,43,50]})
df2.head()
```
<br/>
### 散佈圖 scatter
```=
df.iplot(kind='scatter', x='A', y='B', mode='markers', size=10)
```
```=
import plotly.offline as pyo
import plotly.graph_objs as go
import numpy as np
np.random.seed(50)
x_values = np.linspace(0, 1, 100)
y_values = np.random.randn(100)
trace0 = go.Scatter(
x = x_values,
y = y_values+5,
mode = 'markers',
name = 'markers'
)
trace1 = go.Scatter(
x = x_values,
y = y_values,
mode = 'lines+markers',
name = 'lines+markers'
)
trace2 = go.Scatter(
x = x_values,
y = y_values-5,
mode = 'lines',
name = 'lines'
)
data = [trace0, trace1, trace2]
layout = go.Layout(
title = 'three different modes of scatter'
)
fig = go.Figure(data=data,layout=layout)
# pyo.plot(fig, filename='scatter2.html')
pyo.iplot(fig)
```
```=
import plotly.offline as pyo
import plotly.graph_objs as go
import numpy as np
np.random.seed(42)
random_x = np.random.randint(1,200,100)
random_y = np.random.randint(1,300,100)
data = [go.Scatter(
x = random_x,
y = random_y,
mode = 'markers',
marker=dict( # marker style
size = 12,
color = 'rgb(50,300,100)',
symbol = 'pentagon',
line = dict(
width = 2,
)
)
)]
layout = go.Layout(
title = 'Random Data Scatterplot',
xaxis = dict(title = 'random x-values'),
yaxis = dict(title = 'random y-values'),
hovermode ='closest'
)
fig = go.Figure(data=data, layout=layout)
# pyo.plot(fig, filename='scatter2.html')
pyo.iplot(fig)
```
<br/>
### 直方圖 hist
```=
df['A'].iplot(kind='hist',bins=25)
```
<br/>
### 分布圖 distplot
```=
import plotly.offline as pyo
import plotly.figure_factory as ff
import numpy as np
x = np.random.randn(1000)
hist_data = [x]
group_labels = ['distplot']
fig = ff.create_distplot(hist_data, group_labels)
pyo.iplot(fig)
```
```=
import plotly.offline as pyo
import plotly.figure_factory as ff
import numpy as np
x1 = np.random.randn(200)-2
x2 = np.random.randn(200)
x3 = np.random.randn(200)+2
x4 = np.random.randn(200)+4
hist_data = [x1,x2,x3,x4]
group_labels = ['Group1','Group2','Group3','Group4']
fig = ff.create_distplot(hist_data, group_labels)
pyo.iplot(fig)
```
```=
import plotly.offline as pyo
import plotly.figure_factory as ff
snodgrass = [.209,.205,.196,.210,.202,.207,.224,.223,.220,.201]
twain = [.225,.262,.217,.240,.230,.229,.235,.217]
hist_data = [snodgrass,twain]
group_labels = ['Snodgrass','Twain']
fig = ff.create_distplot(hist_data, group_labels, bin_size=[.005,.005])
pyo.iplot(fig)
```
<br/>
### 直條圖 barplot
```=
df2.iplot(kind='bar',x='Category',y='Values')
```
<br/>
### 橫條圖 barhplot
```=
df2.iplot(kind='barh', x='Category', y='Values')
```
```=
import plotly.graph_objs as go
import plotly.offline as pyo
data = [go.Bar(
x=titanic['alive'].value_counts().index,
y=titanic['alive'].value_counts().values,
marker=dict(color='blue')
)]
layout = go.Layout(
title='Survival Count',
xaxis=dict(title='Survived'),
yaxis=dict(title='Count')
)
fig = go.Figure(data=data, layout=layout)
pyo.iplot(fig)
```
```=
total_count = len(titanic)
titanic['no_survived'] = total_count - df['survived']
trace1 = go.Bar(
x=df['sex'],
y=df['survived'],
name='Survived',
marker=dict(color='#FFD700')
)
trace2 = go.Bar(
x=df['sex'],
y=df['no_survived'],
name='Not Survived',
marker=dict(color='#9EA0A1')
)
data = [trace1, trace2]
layout = go.Layout(
title='Survival by Gender'
)
fig = go.Figure(data=data, layout=layout)
pyo.iplot(fig)
```
```=
total_count = len(titanic)
titanic['no_survived'] = total_count - df['survived']
trace1 = go.Bar(
x=df['sex'],
y=df['survived'],
name='Survived',
marker=dict(color='#FFD700')
)
trace2 = go.Bar(
x=df['sex'],
y=df['no_survived'],
name='Not Survived',
marker=dict(color='#9EA0A1')
)
data = [trace1, trace2]
layout = go.Layout(
title='Survival by Gender',
barmode='stack' # 堆叠
)
fig = go.Figure(data=data, layout=layout)
pyo.iplot(fig)
```
<br/>
### 圓餅圖 pie
```=
import plotly.express as px
import pandas as pd
# 建立交叉表，計算不同性別和生存狀態的人數
cross_tab = pd.crosstab(titanic['sex'], titanic['alive'])
# 重命名列
cross_tab.columns = ['Not Survived', 'Survived']
# 指定順序、顏色
cross_tab['color'] = ['female', 'male']
colors = ['red', 'blue']
fig1 = px.pie(
values=cross_tab['Survived'],
names=cross_tab.index,
title='Survival by Gender',
color=cross_tab['color'],
color_discrete_sequence=colors
)
fig2 = px.pie(
values=cross_tab['Not Survived'],
names=cross_tab.index,
title='Not Survival by Gender',
color=cross_tab['color'],
color_discrete_sequence=colors
)
fig1.show()
fig2.show()
```
<br/>
### 箱型圖 boxplot
```=
df.iplot(kind='box')
```
```=
import plotly.offline as pyo
import plotly.graph_objs as go
snodgrass = [.209,.205,.196,.210,.202,.207,.224,.223,.220,.201]
twain = [.225,.262,.217,.240,.230,.229,.235,.217]
data = [
go.Box(
y=snodgrass,
name='QCS'
),
go.Box(
y=twain,
name='MT'
)
]
layout = go.Layout(
title = ''
)
fig = go.Figure(data=data, layout=layout)
pyo.iplot(fig)
```
<br/>
### 3D圖
```=
df3 = pd.DataFrame({'x':[1,2,3,4,5],'y':[10,20,30,20,10],'z':[5,4,3,2,1]})
df3.iplot(kind='surface',colorscale='rdylbu')
```
<br/>
### 氣泡圖 bubble
```=
df.iplot(kind='bubble',x='A',y='B',size='C')
```
<br/>
### 熱力圖 choropleth
```=
import chart_studio.plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
data = dict(type = 'choropleth',
locations = ['AZ','CA','NY'],
locationmode = 'USA-states',
colorscale= 'Portland',
text= ['text1','text2','text3'],
z=[1.0,2.0,3.0],
colorbar = {'title':'Colorbar Title'})
```
```=
layout = dict(geo = {'scope':'usa'})
choromap = go.Figure(data = [data],layout = layout)
iplot(choromap)
```
假設有一個df
```=
data = dict(
type = 'choropleth',
colorscale = 'viridis',
locations = df['CODE'],
z = df['GDP (BILLIONS)'],
text = df['COUNTRY'],
colorbar = {'title' : 'GDP Billions US'},
)
layout = dict(
title = '2014 Global GDP',
geo = dict(
scope='world',
showframe = False,
projection = {'type':'stereographic'} # 類型可換 https://plotly.com/python/reference/
)
)
choromap = go.Figure(data = [data],layout = layout)
iplot(choromap)
```
<br/>
### 熱力圖 heatmap
```=
import plotly.offline as pyo
import plotly.graph_objs as go
import pandas as pd
import numpy as np
np.random.seed(42)
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
times = [f'{i:02d}:00' for i in range(24)]
data = {
'DAY': np.random.choice(days, size=100),
'LST_TIME': np.random.choice(times, size=100),
'T_HR_AVG': np.random.uniform(60, 90, size=100)
}
df = pd.DataFrame(data)
heatmap_data = [go.Heatmap(
x=df['DAY'],
y=df['LST_TIME'],
z=df['T_HR_AVG'],
colorscale='Jet'
)]
layout = go.Layout(
title='Hourly Temperatures, June 1-7, 2010 in<br>\
Santa Barbara, CA USA'
)
fig = go.Figure(data=heatmap_data, layout=layout)
pyo.iplot(fig)
```
PS 假設要顯示不同地區的檔案
```=
import plotly.offline as pyo
import plotly.graph_objs as go
import pandas as pd
df = sns.load_dataset('flights')
data = [go.Heatmap(
x=df['month'],
y=df['year'],
z=df['passengers']
)]
layout = go.Layout(
title='Flights'
)
fig = go.Figure(data=data, layout=layout)
pyo.iplot(fig)
```
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