Zeitreihen mit verschiedenen Variablen darstellen

Einführung in die Datenvisualisierung mit Matplotlib

Ariel Rokem

Data Scientist

Zwei Zeitreihen zusammen darstellen

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

climate_change

              co2  relative_temp
date
1958-03-06  315.71           0.10
1958-04-06  317.45           0.01
1958-07-06  315.86           0.06
...            ...            ...
2016-11-06  403.55           0.93
2016-12-06  404.45           0.81

[706 rows x 2 columns]

Zwei Zeitreihen zusammen darstellen

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(climate_change.index, climate_change["co2"])

ax.plot(climate_change.index, climate_change["relative_temp"])

ax.set_xlabel('Time')
ax.set_ylabel('CO2 (ppm) / Relative temperature')
plt.show()

![(https://assets.datacamp.com/production/repositories/3634/datasets/bf275a536052f7cf5c2b6e97ced7d4df5d8eee6b/timeseries5.png =40)

Zwillings-Achsen

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

ax2 = ax.twinx()

ax2.plot(climate_change.index, climate_change["relative_temp"])
ax2.set_ylabel('Relative temperature (Celsius)')
plt.show()

![(https://assets.datacamp.com/production/repositories/3634/datasets/2dac71c6faa1e2482e352ca2e259b3a3bbc87ee6/timeseries6.png =40)

Variablen nach Farbe trennen

fig, ax = plt.subplots()
ax.plot(climate_change.index, climate_change["co2"], color='blue')
ax.set_xlabel('Time')
ax.set_ylabel('CO2 (ppm)', color='blue')

ax2 = ax.twinx()
ax2.plot(climate_change.index, climate_change["relative_temp"], 
         color='red')
ax2.set_ylabel('Relative temperature (Celsius)', color='red')
plt.show()

![(https://assets.datacamp.com/production/repositories/3634/datasets/cc9db6b6a79cb1394f5b87c4b47cbb8e0aba010c/timeseries7.png =30)

Trennstriche einfärben

fig, ax = plt.subplots()
ax.plot(climate_change.index, climate_change["co2"], 
        color='blue')
ax.set_xlabel('Time')
ax.set_ylabel('CO2 (ppm)', color='blue')

ax.tick_params('y', colors='blue')

ax2 = ax.twinx()
ax2.plot(climate_change.index, 
         climate_change["relative_temp"], 
         color='red')
ax2.set_ylabel('Relative temperature (Celsius)', 
color='red')

ax2.tick_params('y', colors='red')

plt.show()

Trennstriche einfärben

Eine Funktion, die Zeitreihen darstellt

def plot_timeseries(axes, x, y, color, xlabel, ylabel):
  axes.plot(x, y, color=color)
  axes.set_xlabel(xlabel)
  axes.set_ylabel(ylabel, color=color)
  axes.tick_params('y', colors=color)

Unsere Funktion nutzen

fig, ax = plt.subplots()
plot_timeseries(ax, climate_change.index, climate_change['co2'],
               'blue', 'Time', 'CO2 (ppm)')

ax2 = ax.twinx()
plot_timeseries(ax2, climate_change.index,
                climate_change['relative_temp'],
               'red', 'Time', 'Relative temperature (Celsius)')

plt.show()

![(https://assets.datacamp.com/production/repositories/3634/datasets/67a401c6326b809b647d18a8aedb2af8a29e7246/timeseries9.png =40)

Erstelle deine eigene Funktion!

Einführung in die Datenvisualisierung mit Matplotlib