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Looking at the farmers market data IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON Nick Stra y er Instr u ctor First e x plorations of a dataset Take a broad v ie w Sho w as m u ch info as possible Don ' t f u ss o v er appearances


  1. Looking at the farmers market data IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON Nick Stra y er Instr u ctor

  2. First e x plorations of a dataset Take a broad v ie w Sho w as m u ch info as possible Don ' t f u ss o v er appearances IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  3. Using y o u r head () pollution.head() IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  4. # Just show median pollution.describe(percentiles=[0.5] # Describe all columns include='all') IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  5. pd.plotting.scatter_matrix(pollution, alpha = 0.2); IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  6. IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

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  8. markets.head() IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  9. Let ' s e x plore o u r data IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON

  10. E x ploring the patterns IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON Nick Stra y er Instr u ctor

  11. Digging in deeper In v estigating correlations Are correlations dri v en b y confo u nding ? An y thing s u rprising ? IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  12. Target a u diences Shared w ith peers Be smart abo u t design decisions Remember the y aren ' t as familiar w ith data IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  13. sns.regplot('NO2', 'CO', ci=False, data=pollution, # Lower opacity of points scatter_kws={'alpha':0.2, 'color':'grey'} ) IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  14. Profiling patterns Fo u nd interesting pa � ern in data Ho w to q u ickl y e x plore and e x plain the pa � ern ? Use te x t ! IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  15. Using te x t scatters to id o u tliers IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  16. g = sns.scatterplot("SO2","CO", data=long_beach_avgs) # Iterate over the rows of our data for _, row in long_beach_avgs.iterrows(): # Unpack columns from row month, SO2, CO = row # Draw annotation in correct place g.annotate(month, (SO2,CO)) plt.title('Long Beach avg SO2 by CO') IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  17. IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  18. Let ' s dig in IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON

  19. Making y o u r v is u ali z ations efficient IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON Nick Stra y er Instr u ctor

  20. What is efficient ? Red u ce the e � ort needed to see stor y Re - organi z e plots to keep foc u s Impro v e ' ink ' to info ratio Don ' t compromise the message IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  21. IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  22. # Create a subplot w/ one row & two columns. f, (ax1, ax2) = plt.subplots(1, 2) # Pass each axes to respective plot sns.lineplot('month', 'NO2', 'year', ax=ax1, data=pol_by_month) sns.barplot('year', 'count', ax=ax2, data=obs_by_year) IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  23. Clear u nnecessar y legends IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  24. sns.lineplot('month', 'NO2', 'year', ax=ax1, data=pol_by_month, palette='RdBu',) sns.barplot('year', 'count', 'year', ax=ax2, data=obs_by_year, palette='RdBu', dodge=False) # Remove legends for both plots ax1.legend_.remove() ax2.legend_.remove() IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  25. Let ' s practice IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON

  26. T w eaking y o u r plots IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON Nick Stra y er Instr u ctor

  27. Looking at the small things P u t y o u rself into the v ie w er ' s shoes IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  28. Is the aesthetic appropriate ? Is the aesthetic appropriate for the conte x t ? IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  29. Font - si z es Is e v er y thing legible ? IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

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  34. IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  35. Remo v ing spines from plots IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  36. Remo v ing spines from plots IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  37. IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  38. Let ' s t w eak some plots IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON

  39. Wrap - Up IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON Nick Stra y er Instr u ctor

  40. IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  41. Using color responsibl y IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  42. IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  43. IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  44. Going f u rther Blogs Flo w ing data C u rated list of data v is u ali z ations . Data w rapper Blog Articles that dig deep into v is u ali z ation techniq u es and mistakes . T w i � er # data v is An ongoing stream of cool projects and inspiration . IMPROVING YOUR DATA VISUALIZATIONS IN PYTHON

  45. Thank y o u! IMP R OVIN G YOU R DATA VISU AL IZATION S IN P YTH ON

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