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Plotting time - series data IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB Ariel Rokem Data Scientist Time - series data INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB Climate change time - series date,co2,relative_temp

  1. Plotting time - series data IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB Ariel Rokem Data Scientist

  2. Time - series data INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  3. 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 INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  4. DateTimeInde x 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) INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  5. Time - series data climate_change['relative_temp'] climate_change['co2'] 0 0.10 0 315.71 1 0.01 1 317.45 2 0.08 2 317.50 3 -0.05 3 NaN 4 0.06 4 315.86 5 -0.06 5 314.93 6 -0.03 6 313.20 7 0.04 7 NaN ... ... 701 0.98 701 402.27 702 0.87 702 401.05 703 0.89 703 401.59 704 0.93 704 403.55 705 0.81 705 404.45 Name:co2, Length: 706, dtype: float64 Name:co2, Length: 706, dtype: float64 INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  6. 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() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  7. 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() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  8. Zooming in on one y ear 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() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  9. Let ' s practice time - series plotting ! IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB

  10. Plotting time - series w ith different v ariables IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB Ariel Rokem Data Scientist

  11. Plotting t w o time - series together 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] INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  12. Plotting t w o time - series together 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() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  13. Using t w in a x es 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() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  14. Separating v ariables b y color 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() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  15. Coloring the ticks 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() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  16. Coloring the ticks INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  17. A f u nction that plots time - series 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) INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  18. Using o u r f u nction fig, ax = plt.subplots() plot_timeseries(ax, climate_change.index, climate_change['co2'], 'blue', 'Time', 'CO2 (ppm)') ax2 = ax.twinx() plot_timeseries(ax, climate_change.index, climate_change['relative_temp'], 'red', 'Time', 'Relative temperature (Celsius)') plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  19. Create y o u r o w n f u nction ! IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB

  20. Annotating time - series data IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB Ariel Rokem Data Scientist

  21. Time - series data INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  22. Annotation 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)') ax2.annotate(">1 degree", xy=[pd.TimeStamp("2015-10-06"), 1]) plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  23. Positioning the te x t ax2.annotate(">1 degree", xy=(pd.Timestamp('2015-10-06'), 1), xytext=(pd.Timestamp('2008-10-06'), -0.2)) INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  24. Adding arro w s to annotation ax2.annotate(">1 degree", xy=(pd.Timestamp('2015-10-06'), 1), xytext=(pd.Timestamp('2008-10-06'), -0.2), arrowprops={}) INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  25. C u stomi z ing arro w properties ax2.annotate(">1 degree", xy=(pd.Timestamp('2015-10-06'), 1), xytext=(pd.Timestamp('2008-10-06'), -0.2), arrowprops={"arrowstyle":"->", "color":"gray"}) INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  26. C u stomi z ing annotations h � ps :// matplotlib . org /u sers / annotations . html INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  27. Practice annotating plots ! IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB

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