Python 視覺化：Matplotlib、Seaborn、Plotly 熱力圖與互動圖

PS 程式碼後面沒有print()、plt.show()，是因為用jupyter notebook

Contents 目錄

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()

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()

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

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()

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()

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()

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()

補充import matplotlib.pyplot as pltfig, 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()

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])

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')

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()

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()

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()

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()

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')

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()

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()

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'])

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')

面積圖 area

tips_2[['total_bill','tip']][0:30].plot.area(alpha=0.4)

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')

密度圖 density、kde

tips.plot.density()

tips.plot.kde()

tips['total_bill'].plot.kde(lw=5,ls='--')

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)

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')

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)

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()

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’

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()

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()

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")

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)

計數圖 countplot

sns.countplot(x='sex',data=tips)

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')

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'])

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")

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')

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)

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)

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")

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)

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)

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)

聚類函數 clustermap

sns.clustermap(pvflights)

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)

Plotly

初始化

參考
https://plotly.com/python/

申請api key
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()

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)

直方圖 hist

df['A'].iplot(kind='hist',bins=25)

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)

直條圖 barplot

df2.iplot(kind='bar',x='Category',y='Values')

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)

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()

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)

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')

氣泡圖 bubble

df.iplot(kind='bubble',x='A',y='B',size='C')

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)

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)

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 (改欄/行的數量，就能增加子圖數量)

創建圖中圖的一種方式

散佈圖 scatter

直方圖 hist

橫條圖 barh

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)

histplot

displot

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

聚類函數 clustermap

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 hist

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

Catalina

相關文章

• 韓文補習班新課程商業分析 Korean Language School Business Analysis

• 潛力產品、店家銷售分析 Potential Products and Store Sales Analysis

• Microsoft Power BI 資料分析與視覺化筆記

• Colab 讀取資料 : Github、Google Drive、本地端

發佈留言取消回覆

目前趨勢