Welcome to the course!

Visualizzare dati di serie temporali in Python

Thomas Vincent

Head of Data Science, Getty Images

Prerequisites

Time series in the field of Data Science

Time series are a fundamental way to store and analyze many types of data
Financial, weather and device data are all best handled as time series

Time series in the field of Data Science

An example of time series data

Course overview

Chapter 1: Getting started and personalizing your first time series plot
Chapter 2: Summarizing and describing time series data
Chapter 3: Advanced time series analysis
Chapter 4: Working with multiple time series
Chapter 5: Case Study

Reading data with Pandas

import pandas as pd

df = pd.read_csv('ch2_co2_levels.csv')
print(df)

       datestamp    co2
0     1958-03-29  316.1
1     1958-04-05  317.3
2     1958-04-12  317.6
...
...
...
2281  2001-12-15  371.2
2282  2001-12-22  371.3
2283  2001-12-29  371.5

Preview data with Pandas

print(df.head(n=5))

    datestamp    co2
0  1958-03-29  316.1
1  1958-04-05  317.3
2  1958-04-12  317.6
3  1958-04-19  317.5
4  1958-04-26  316.4

print(df.tail(n=5))

       datestamp    co2
2279  2001-12-01  370.3
2280  2001-12-08  370.8
2281  2001-12-15  371.2
2282  2001-12-22  371.3
2283  2001-12-29  371.5

Check data types with Pandas

print(df.dtypes)

datestamp     object
co2          float64
dtype: object

Working with dates

To work with time series data in pandas, your date columns needs to be of the datetime64 type.

pd.to_datetime(['2009/07/31', 'test'])

ValueError: Unknown string format

pd.to_datetime(['2009/07/31', 'test'], errors='coerce')

DatetimeIndex(['2009-07-31', 'NaT'], 
    dtype='datetime64[ns]', freq=None)

Let's get started!

Visualizzare dati di serie temporali in Python