設定jupyter notebook

import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
print("Setup Complete")

Setup Complete

讀取資料和顯示資料

# Path of the file to read
iris_filepath = "./iris.csv"

# Read the file into a variable iris_data
iris_data = pd.read_csv(iris_filepath, index_col="Id")

# Print the first 5 rows of the data
iris_data.head()

直方圖

可以使用sns.distplot來繪製直方圖

# Histogram
sns.distplot(a=iris_data['Petal Length (cm)'], kde=False)

/opt/conda/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)





<AxesSubplot:xlabel='Petal Length (cm)'>

在sns.distplot中有兩個參數

• a=選擇一個plot(在這個case中，我們選擇Petal Length (cm))
• kde=False在繪製直方圖的時候，通常會繪製另一個圖表(密度圖)，不過這邊暫時不需要

密度圖

下一個圖表是密度圖，可以當作是比較緩和的直方圖

# KDE plot
sns.kdeplot(data=iris_data['Petal Length (cm)'], shade=True)

<AxesSubplot:xlabel='Petal Length (cm)', ylabel='Density'>

• data=代表說需要用到的資料
• shade=代表說要在圖表下方加上顏色

二維密度圖(2D KDE plots)

我們並不限制於單一columns的密度圖，也可以創造二維的密度圖

# 2D KDE plot
sns.jointplot(x=iris_data['Petal Length (cm)'], y=iris_data['Sepal Width (cm)'], kind="kde", shade=True)

<seaborn.axisgrid.JointGrid at 0x7f405a194710>

彩色編碼圖

在這個部分我們要將不同種類的圖加在一起

先將資料分為三個部分讀取

# Paths of the files to read
iris_set_filepath = "./iris_setosa.csv"
iris_ver_filepath = "./iris_versicolor.csv"
iris_vir_filepath = "./iris_virginica.csv"

# Read the files into variables 
iris_set_data = pd.read_csv(iris_set_filepath, index_col="Id")
iris_ver_data = pd.read_csv(iris_ver_filepath, index_col="Id")
iris_vir_data = pd.read_csv(iris_vir_filepath, index_col="Id")

# Print the first 5 rows of the Iris versicolor data
iris_ver_data.head()

我們使用sns.distplot三次來顯示三種資料

並且用label=來將資料的名稱顯示瑜說明欄

# Histograms for each species
sns.distplot(a=iris_set_data['Petal Length (cm)'], label="Iris-setosa", kde=False)
sns.distplot(a=iris_ver_data['Petal Length (cm)'], label="Iris-versicolor", kde=False)
sns.distplot(a=iris_vir_data['Petal Length (cm)'], label="Iris-virginica", kde=False)

# Add title
plt.title("Histogram of Petal Lengths, by Species")

# Force legend to appear
plt.legend()

<matplotlib.legend.Legend at 0x7f405a5ab9b0>

為了要讓說明欄顯示，要加上plt.legend()

我們也可以使用kdeplot來呈現多個圖表

# KDE plots for each species
sns.kdeplot(data=iris_set_data['Petal Length (cm)'], label="Iris-setosa", shade=True)
sns.kdeplot(data=iris_ver_data['Petal Length (cm)'], label="Iris-versicolor", shade=True)
sns.kdeplot(data=iris_vir_data['Petal Length (cm)'], label="Iris-virginica", shade=True)

# Add title
plt.title("Distribution of Petal Lengths, by Species")

Text(0.5, 1.0, 'Distribution of Petal Lengths, by Species')