[kaggle] Data Visualization 수료과정

0. 목차 및 내용

 1) Hello, Seaborn

  - notebook 설명, csv 읽기, lineplot

plt.figure(figsize=(16,6))
sns.lineplot(data=fifa_data)

 2) Line Charts

  - title, xlabel, label

plt.figure(figsize=(14,6))
plt.title("Daily Global Streams of Popular Songs in 2017-2018")
sns.lineplot(data=spotify_data['Shape of You'], label="Shape of You")
sns.lineplot(data=spotify_data['Despacito'], label="Despacito")
plt.xlabel("Date")

 3) Bar Charts and Heatmaps

  - barplot, heatmap

plt.figure(figsize=(10,6))
plt.title("Average Arrival Delay for Spirit Airlines Flights, by Month")
sns.barplot(x=flight_data.index, y=flight_data['NK'])
plt.ylabel("Arrival delay (in minutes)")

plt.figure(figsize=(14,7))
plt.title("Average Arrival Delay for Each Airline, by Month")
sns.heatmap(data=flight_data, annot=True)
plt.xlabel("Airline")

 4) Scatter Plots

  - scatterplot, regplot, lmplot, swarmplot

sns.scatterplot(x=insurance_data['bmi'], y=insurance_data['charges'], hue=insurance_data['smoker'])

sns.regplot(x=insurance_data['bmi'], y=insurance_data['charges'])

sns.lmplot(x="bmi", y="charges", hue="smoker", data=insurance_data)

sns.swarmplot(x=insurance_data['smoker'], y=insurance_data['charges'])

 5) Distributions

  - distplot, kdeplot, jointplot

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

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

sns.jointplot(x=iris_data['Petal Length (cm)'], y=iris_data['Sepal Width (cm)'], kind="kde")

 6) Choosing Plot Types and Custom Styles

  - 상황에 따른 플롯 설정, set을 이용한 그래프 수정

 7) Final Project

  - 캐글에서 데이터 추가하는 방법

 8) Creating Your Own Notebooks

  - 캐글시작하기

1. 수료증

2. 참고

 - Data Visualization