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Vis u ali z ing time series IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON Br y an Van de Ven Core De v eloper of Bokeh Datetimes & time series type(weather), type(weather.index) (pandas.core.frame.DataFrame,

  1. Vis u ali z ing time series IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON Br y an Van de Ven Core De v eloper of Bokeh

  2. Datetimes & time series type(weather), type(weather.index) (pandas.core.frame.DataFrame, pandas.tseries.index.DatetimeIndex) Date Temperat u re De w Point Press u re 2010-01-01 00:00:00 AM 46.2 37.5 1 2010-01-01 01:00:00 AM 44.6 37.1 1 2010-01-01 02:00:00 AM 44.1 36.9 1 ... ... ... ... INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  3. Plotting DataFrames plt.plot(weather) plt.show() INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  4. Time series pandas time series : datetime as inde x Datetime : represents periods or time - stamps Datetime inde x: speciali z ed slicing weather['2010-07-04'] weather['2010-03':'2010-04'] weather['2010-05'] etc . INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  5. Slicing time series temperature = weather['Temperature'] march_apr = temperature['2010-03':'2010-04'] # data of March & April 2010 only march_apr.shape (1463,) march_apr.iloc[-4:] #extract last 4 entries from time series Date 2010-04-30 20:00:00 73.3 2010-04-30 21:00:00 71.3 2010-04-30 22:00:00 69.7 2010-04-30 23:00:00 68.5 Name: Temperature, dtype: float64 INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  6. Plotting time series slices INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  7. Plotting time series slices plt.plot(temperature['2010-01'], color='red', label='Temperature') dew point = weather['DewPoint'] plt.plot(dewpoint['2010-01'], color='blue', label='Dewpoint') plt.legend(loc='upper right') plt.xticks(rotation=60) plt.show() INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  8. Selecting & formatting dates jan = temperature['2010-01'] dates = jan.index[::96] # Pick every 4th day print(dates) DatetimeIndex(['2010-01-01', '2010-01-05', '2010-01-09', '2010-01-13','2010-01-17', '2010-01-21', '2010-01-25', '2010-01-29'], dtype='datetime64[ns]', name='Date', freq=None) labels = dates.strftime('%b %d') # Make formatted labels print(labels) ['Jan 01' 'Jan 05' 'Jan 09' 'Jan 13' 'Jan 17' 'Jan 21' 'Jan 25' 'Jan 29'] INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  9. Cleaning u p ticks on a x is INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  10. Cleaning u p ticks on a x is plt.plot(temperature['2010-01'], color='red', label='Temperature') plt.plot(dewpoint['2010-01'], color='blue', label='Dewpoint') plt.xticks(dates, labels, rotation=60) plt.legend(loc='upper right') plt.show() INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  11. Let ' s practice ! IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON

  12. Time series w ith mo v ing w indo w s IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON Br y an Van de Ven Core De v eloper of Bokeh

  13. Ho u rl y data o v er a y ear INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  14. Mo v ing w indo w s and time series Mo v ing w indo w calc u lations A v erages Medians Standard de v iations E x tracts information on longer time scales See pandas co u rses on ho w to comp u te INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  15. Mo v ing a v erages # smoothed computing using moving averages print(smoothed.iloc[:3,:]) smoothed.info() 14d 1d 3d 7d Temperatur <class 'pandas.core.frame.DataFrame'> Date DatetimeIndex: 8759 entries, 2010-01-01 00:00:00 NaN NaN NaN NaN 46. 2010-01-01 00:00:00 to 2010-12-31 23:00:00 2010-01-01 01:00:00 NaN NaN NaN NaN 44. Data columns (total 5 columns): 2010-01-01 02:00:00 NaN NaN NaN NaN 44. 14d 8424 non-null float64 1d 8736 non-null float64 3d 8688 non-null float64 7d 8592 non-null float64 dtypes: float64(5) memory usage: 410.6 KB INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  16. Vie w ing 24 ho u r a v erages # moving average over 24 hours plt.plot(smoothed['1d']) plt.title('Temperature (2010)') plt.xticks(rotation=60) plt.show() INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  17. Vie w ing all mo v ing a v erages INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  18. Vie w ing all mo v ing a v erages # plot DataFrame for January plt.plot(smoothed['2010-01']) plt.legend(smoothed.columns) plt.title('Temperature (Jan. 2010)') plt.xticks(rotation=60) plt.show() INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  19. Mo v ing standard de v iations INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  20. Mo v ing standard de v iations plt.plot(variances['2010-01']) plt.legend(variances.columns) plt.title('Temperature deviations') plt.xticks(rotation=60) plt.show() INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  21. Let ' s practice ! IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON

  22. Histogram eq u ali z ation in images IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON Br y an Van de Ven Core De v eloper of Bokeh

  23. Original image INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  24. Eq u ali z ed image INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  25. Image histograms INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  26. Image histograms orig = plt.imread('low-contrast-moon.jpg') pixels = orig.flatten() plt.hist(pixels, bins=256, range=(0,256), normed=True, color='blue', alpha=0.3) plt.show() minval, maxval = orig.min(), orig.max() print(minval, maxval) 125 244 INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  27. Rescaling the image minval, maxval = orig.min(), orig.max() print(minval, maxval) 125 244 rescaled = (255/(maxval-minval)) * (pixels - minval) print(rescaled.min(), rescaled.max()) 0.0 255.0 plt.imshow(rescaled) plt.axis('off') plt.show() INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  28. Rescaled image INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  29. Original and rescaled histograms INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  30. Original and rescaled histograms plt.hist(orig.flatten(), bins=256, range=(0,255), normed=True, color='blue', alpha=0.2)) plt.hist(rescaled.flatten(), bins=256, range=(0,255), normed=True, color='green', alpha=0.2)) plt.legend(['original', 'rescaled']) plt.show() INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  31. Image histogram & CDF INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  32. Image histogram & CDF plt.hist(pixels, bins=256, range=(0,256), normed=True, color='blue', alpha=0.3) plt.twinx() orig_cdf, bins, patches = plt.hist(pixels, cumulative=True, bins=256, range=(0,256), normed=True, color='red', alpha=0.3) plt.title('Image histogram and CDF') plt.xlim((0, 255)) plt.show() INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  33. Eq u ali z ing intensit y v al u es new_pixels = np.interp(pixels,bins[:-1], orig_cdf*255 new = new_pixels.reshape(orig.shape) plt.imshow(new) plt.axis('off') plt.title('Equalized image') plt.show() 1 , INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  34. Eq u ali z ed histogram & CDF plt.hist(new_pixels, bins=256, range=(0,256), normed=True, color='blue', alpha=0.3) plt.twinx() plt.hist(new_pixels, bins=256, range=(0,256), normed=True, cumulative=True, color='red', alpha=0.1) plt.title('Equalized image histogram and CDF') plt.xlim((0, 255)) plt.show() INTRODUCTION TO DATA VISUALIZATION IN PYTHON

  35. Let ' s practice ! IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON

  36. Congrat u lations ! IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON Br y an Van de Ven Core De v eloper of Bokeh

  37. Congrat u lations ! IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON

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