Working with more than one time series

Visualizing Time Series Data in Python

Thomas Vincent

Head of Data Science, Getty Images

Working with multiple time series

An isolated time series

date	ts1
1949-01	112
1949-02	118
1949-03	132

A file with multiple time series

date	ts1	ts2	ts3	ts4	ts5	ts6	ts7
2012-01-01	2113.8	10.4	1987.0	12.1	3091.8	43.2	476.7
2012-02-01	2009.0	9.8	1882.9	12.3	2954.0	38.8	466.8
2012-03-01	2159.8	10.0	1987.9	14.3	3043.7	40.1	502.1

The Meat production dataset

import pandas as pd
meat = pd.read_csv("meat.csv")
print(meat.head(5))

         date   beef   veal    pork  lamb_and_mutton  broilers   
0  1944-01-01  751.0   85.0  1280.0             89.0       NaN 
1  1944-02-01  713.0   77.0  1169.0             72.0       NaN 
2  1944-03-01  741.0   90.0  1128.0             75.0       NaN 
3  1944-04-01  650.0   89.0   978.0             66.0       NaN 
4  1944-05-01  681.0  106.0  1029.0             78.0       NaN 

   other_chicken  turkey  
0            NaN     NaN  
1            NaN     NaN  
2            NaN     NaN  
3            NaN     NaN  
4            NaN     NaN

Summarizing and plotting multiple time series

import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
ax = df.plot(figsize=(12, 4), fontsize=14)

plt.show()

The Meat production dataset

Area charts

import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
ax = df.plot.area(figsize=(12, 4), fontsize=14)

plt.show()

The Meat production dataset as an Area Chart

Let's practice!

Visualizing Time Series Data in Python