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Python으로 시계열 데이터 시각화

중급기술 수준

업데이트됨 2022. 3.

시계열 데이터의 계절성, 추세 등 다양한 패턴을 시각화합니다.

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PythonData Visualization

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강의 설명

시계열 데이터는 Data Science 전반에서 매우 흔합니다. 비즈니스 트렌드를 분석하거나, 회사 매출을 예측하거나, 고객 행동을 탐색할 때 등 데이터 과학자라면 업무 중 언젠가는 시계열 데이터를 접하게 마련이죠. 이 강의는 Python을 사용해 시계열 데이터를 시각화하는 실무 지식을 제공하여, 시계열 데이터 작업을 시작하실 수 있도록 도와드립니다.

선수 조건

Introduction to Data Visualization with Matplotlib Manipulating Time Series Data in Python

1

Line Plots

You will learn how to leverage basic plottings tools in Python, and how to annotate and personalize your time series plots. By the end of this chapter, you will be able to take any static dataset and produce compelling plots of your data.

Welcome to the course!

Load your time series data

Test whether your data is of the correct type

Plot your first time series

Your first plot!

Specify plot styles

Display and label plots

Customize your time series plot

Subset time series data

Add vertical and horizontal markers

Add shaded regions to your plot

2

Summary Statistics and Diagnostics

In this chapter, you will gain a deeper understanding of your time series data by computing summary statistics and plotting aggregated views of your data.

Clean your time series data

Find missing values

Handle missing values

Plot aggregates of your data

Display rolling averages

Display aggregated values

Summarize the values in your time series data

Compute numerical summaries

Boxplots and Histograms

Density plots

3

Seasonality, Trend and Noise

You will go beyond summary statistics by learning about autocorrelation and partial autocorrelation plots. You will also learn how to automatically detect seasonality, trend and noise in your time series data.

Autocorrelation and Partial autocorrelation

Autocorrelation in time series data

Interpret autocorrelation plots

Partial autocorrelation in time series data

Interpret partial autocorrelation plots

Seasonality, trend and noise in time series data

Time series decomposition

Plot individual components

A quick review

Visualize the airline dataset

Analyze the airline dataset

Time series decomposition of the airline dataset

4

Work with Multiple Time Series

In the field of Data Science, it is common to be involved in projects where multiple time series need to be studied simultaneously. In this chapter, we will show you how to plot multiple time series at once, and how to discover and describe relationships between multiple time series.

Working with more than one time series

Load multiple time series

Visualize multiple time series

Statistical summaries of multiple time series

Plot multiple time series

Define the color palette of your plots

Add summary statistics to your time series plot

Plot your time series on individual plots

Find relationships between multiple time series

Compute correlations between time series

Visualize correlation matrices

Clustered heatmaps

5

Case Study: Unemployment Rate

This chapter will give you a chance to practice all the concepts covered in the course. You will visualize the unemployment rate in the US from 2000 to 2010.

Apply your knowledge to a new dataset

Explore the Jobs dataset

Describe time series data with boxplots

Beyond summary statistics

Plot all the time series in your dataset

Annotate significant events in time series data

Plot monthly and yearly trends

Decompose time series data

Apply time series decomposition to your dataset

Visualize the seasonality of multiple time series

Compute correlations between time series

Correlations between multiple time series

Interpret correlations

Congratulations!

Python으로 시계열 데이터 시각화

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