Plotting time-series data

Introduction to Data Visualization with Matplotlib

Ariel Rokem

Data Scientist

Time-series data

Climate change time-series

date,co2,relative_temp
1958-03-06,315.71,0.1
1958-04-06,317.45,0.01
1958-05-06,317.5,0.08
1958-06-06,-99.99,-0.05
1958-07-06,315.86,0.06
1958-08-06,314.93,-0.06
...
2016-08-06,402.27,0.98
2016-09-06,401.05,0.87
2016-10-06,401.59,0.89
2016-11-06,403.55,0.93
2016-12-06,404.45,0.81

import pandas as pd
climate_change = pd.read_csv('climate_change.csv', parse_dates=["date"], 
                             index_col="date")

DateTimeIndex

climate_change.index

DatetimeIndex(['1958-03-06', '1958-04-06', '1958-05-06', '1958-06-06',
               '1958-07-06', '1958-08-06', '1958-09-06', '1958-10-06',
               '1958-11-06', '1958-12-06',
               ...
               '2016-03-06', '2016-04-06', '2016-05-06', '2016-06-06',
               '2016-07-06', '2016-08-06', '2016-09-06', '2016-10-06',
               '2016-11-06', '2016-12-06'],
              dtype='datetime64[ns]', name='date', length=706, freq=None)

Time-series data

climate_change['relative_temp']

0      0.10
1      0.01
2      0.08
3     -0.05
4      0.06
5     -0.06
6     -0.03
7      0.04
      ...
701    0.98
702    0.87
703    0.89
704    0.93
705    0.81
Name:co2, Length: 706, dtype: float64

climate_change['co2']

0      315.71
1      317.45
2      317.50
3         NaN
4      315.86
5      314.93
6      313.20
7         NaN
      ...
701    402.27
702    401.05
703    401.59
704    403.55
705    404.45
Name:co2, Length: 706, dtype: float64

Plotting time-series data

import matplotlib.pyplot as plt
fig, ax = plt.subplots()

ax.plot(climate_change.index, climate_change['co2'])
ax.set_xlabel('Time')
ax.set_ylabel('CO2 (ppm)')
plt.show()

Zooming in on a decade

sixties = climate_change["1960-01-01":"1969-12-31"]

fig, ax = plt.subplots()
ax.plot(sixties.index, sixties['co2'])
ax.set_xlabel('Time')
ax.set_ylabel('CO2 (ppm)')
plt.show()

Zooming in on one year

sixty_nine = climate_change["1969-01-01":"1969-12-31"]

fig, ax = plt.subplots()
ax.plot(sixty_nine.index, sixty_nine['co2'])
ax.set_xlabel('Time')
ax.set_ylabel('CO2 (ppm)')
plt.show()

Let's practice time-series plotting!

Introduction to Data Visualization with Matplotlib