[Section7] Data Visualization with Seaborn

해당 내용은 Udemy 강의 'Pandas 및 Python을 이용한 데이터 분석:마스터 클래스'를 수강 후 정리한 내용입니다.

https://www.udemy.com/course/best-pandas-python

Section1

SEABORN SCATTERPLOT & COUNTPLOT

# Seaborn is a visualization library that sits on top of matplotlib
# Seaborn offers enhanced features compared to matplotlib

Example gallery — seaborn 0.13.2 documentation

# import libraries 
import pandas as pd # Import Pandas for data manipulation using dataframes
import numpy as np # Import Numpy for data statistical analysis 
import matplotlib.pyplot as plt # Import matplotlib for data visualisation
import seaborn as sns # Statistical data visualization

# Import Cancer data 

cancer_df = pd.read_csv('cancer.csv')

# Plot scatter plot between mean area and mean smoothness

sns.scatterplot(x='mean area', y='mean smoothness', hue='target', data=cancer_df)

# Let's print out countplot to know how many samples belong to class #0 and #1

sns.countplot(x='target', data=cancer_df, palette='Set1')

MINI CHALLENGE #1:

• Plot the scatterplot between the mean radius and mean area. Comment on the plot

sns.scatterplot(x='mean radius', y='mean area', hue='target', data=cancer_df)

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Section2

SEABORN PAIRPLOT, DISPLOT, AND HEATMAPS/CORRELATIONS

# Plot the pairplot

sns.pairplot(cancer_df, hue='target',
             vars=['mean radius', 'mean texture', 'mean area', 'mean perimeter', 'mean smoothness'])

vars=[''] 에 들어있는 변수에 대해 pairplot을 그려준다.

# Strong correlation between the mean radius and mean perimeter, mean area and mean primeter

sns.heatmap(cancer_df.corr())

plt.figure(figsize=(20, 10))

sns.heatmap(cancer_df.corr(), annot=True)

annot=True를 지정해줌으로써 상관계수 값을 표기할 수 있다.

# plot the distplot 
# Displot combines matplotlib histogram function with kdeplot() (Kernel density estimate)
# KDE is used to plot the Probability Density of a continuous variable. 

sns.distplot(cancer_df['mean radius'], bins=5, color='blue')

MINI CHALLENGE #2:

• Plot two separate distplot for each target class #0 and target class #1

class_0_df = cancer_df[cancer_df['target']==0]
class_0_df

class_1_df = cancer_df[cancer_df['target']==1]
class_1_df

# Plot the distplot for both classes
plt.figure(figsize=(10, 7))
sns.distplot(class_0_df['mean radius'], bins = 25, color = 'blue')
sns.distplot(class_1_df['mean radius'], bins = 25, color = 'red')
plt.grid()