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Q u antitati v e comparisons : bar - charts IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB Ariel Rokem Data Scientist Ol y mpic medals ,Gold, Silver, Bronze United States, 137, 52, 67 Germany, 47, 43, 67 Great Britain, 64,

  1. Q u antitati v e comparisons : bar - charts IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB Ariel Rokem Data Scientist

  2. Ol y mpic medals ,Gold, Silver, Bronze United States, 137, 52, 67 Germany, 47, 43, 67 Great Britain, 64, 55, 26 Russia, 50, 28, 35 China, 44, 30, 35 France, 20, 55, 21 Australia, 23, 34, 25 Italy, 8, 38, 24 Canada, 4, 4, 61 Japan, 17, 13, 34 INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  3. Ol y mpic medals : v is u ali z ing the data medals = pd.read_csv('medals_by_country_2016.csv', index_col=0) fig, ax = plt.subplots() ax.bar(medals.index, medals["Gold"]) plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  4. Interl u de : rotate the tick labels fig, ax = plt.subplots() ax.bar(medals.index, medals["Gold"]) ax.set_xticklabels(medals.index, rotation=90) ax.set_ylabel("Number of medals") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  5. Ol y mpic medals : v is u ali z ing the other medals fig, ax = plt.subplots ax.bar(medals.index, medals["Gold"]) ax.bar(medals.index, medals["Silver"], bottom=medals["Gold"]) ax.set_xticklabels(medals.index, rotation=90) ax.set_ylabel("Number of medals") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  6. Ol y mpic medals : v is u ali z ing all three fig, ax = plt.subplots ax.bar(medals.index, medals["Gold"]) ax.bar(medals.index, medals["Silver"], bottom=medals["Gold"]) ax.bar(medals.index, medals["Bronze"], bottom=medals["Gold"] + medals["Silver"]) ax.set_xticklabels(medals.index, rotation=90) ax.set_ylabel("Number of medals") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  7. Stacked bar chart INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  8. Adding a legend fig, ax = plt.subplots ax.bar(medals.index, medals["Gold"]) ax.bar(medals.index, medals["Silver"], bottom=medals["Gold"]) ax.bar(medals.index, medals["Bronze"], bottom=medals["Gold"] + medals["Silver"]) ax.set_xticklabels(medals.index, rotation=90) ax.set_ylabel("Number of medals") INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  9. Adding a legend fig, ax = plt.subplots ax.bar(medals.index, medals["Gold"], label="Gold") ax.bar(medals.index, medals["Silver"], bottom=medals["Gold"], label="Silver") ax.bar(medals.index, medals["Bronze"], bottom=medals["Gold"] + medals["Silver"], label="Bronze") ax.set_xticklabels(medals.index, rotation=90) ax.set_ylabel("Number of medals") ax.legend() plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  10. Stacked bar chart w ith legend INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  11. Create a bar chart ! IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB

  12. Q u antitati v e comparisons : histograms IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB Ariel Rokem Data Scientist

  13. Histograms INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  14. A bar chart again fig, ax = plt.subplots() ax.bar("Rowing", mens_rowing["Height"].mean()) ax.bar("Gymnastics", mens_gymnastics["Height"].mean()) ax.set_ylabel("Height (cm)") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  15. Introd u cing histograms fig, ax = plt.subplots() ax.hist(mens_rowing["Height"]) ax.hist(mens_gymnastic["Height"]) ax.set_xlabel("Height (cm)") ax.set_ylabel("# of observations") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  16. Labels are needed ax.hist(mens_rowing["Height"], label="Rowing") ax.hist(mens_gymnastic["Height"], label="Gymnastics") ax.set_xlabel("Height (cm)") ax.set_ylabel("# of observations") ax.legend() plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  17. C u stomi z ing histograms : setting the n u mber of bins ax.hist(mens_rowing["Height"], label="Rowing", bins=5) ax.hist(mens_gymnastic["Height"], label="Gymnastics", bins=5) ax.set_xlabel("Height (cm)") ax.set_ylabel("# of observations") ax.legend() plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  18. C u stomi z ing histograms : setting bin bo u ndaries ax.hist(mens_rowing["Height"], label="Rowing", bins=[150, 160, 170, 180, 190, 200, 210]) ax.hist(mens_gymnastic["Height"], label="Gymnastics", bins=[150, 160, 170, 180, 190, 200, 210]) ax.set_xlabel("Height (cm)") ax.set_ylabel("# of observations") ax.legend() plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  19. C u stomi z ing histograms : transparenc y ax.hist(mens_rowing["Height"], label="Rowing", bins=[150, 160, 170, 180, 190, 200, 210], histtype="step") ax.hist(mens_gymnastic["Height"], label="Gymnastics", bins=[150, 160, 170, 180, 190, 200, 210], histtype="step") ax.set_xlabel("Height (cm)") ax.set_ylabel("# of observations") ax.legend() plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  20. Histogram w ith a histt y pe of step INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

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

  22. Statistical plotting IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB Ariel Rokem Data Scientist

  23. Adding error bars to bar charts fig, ax = plt.subplots() ax.bar("Rowing", mens_rowing["Height"].mean(), yerr=mens_rowing["Height"].std()) ax.bar("Gymnastics", mens_gymnastics["Height"].mean(), yerr=mens_gymnastics["Height"].std()) ax.set_ylabel("Height (cm)") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  24. Error bars in a bar chart INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  25. Adding error bars to plots fig, ax = plt.subplots() ax.errorbar(seattle_weather["MONTH"], seattle_weather["MLY-TAVG-NORMAL"], yerr=seattle_weather["MLY-TAVG-STDDEV"]) ax.errorbar(austin_weather["MONTH"], austin_weather["MLY-TAVG-NORMAL"], yerr=austin_weather["MLY-TAVG-STDDEV"]) ax.set_ylabel("Temperature (Fahrenheit)") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  26. Error bars in plots INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  27. Adding bo x plots fig, ax = plt.subplots() ax.boxplot([mens_rowing["Height"], mens_gymnastics["Height"]]) ax.set_xticklabels(["Rowing", "Gymnastics"]) ax.set_ylabel("Height (cm)") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  28. Interpreting bo x plots INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  29. Tr y it y o u rself ! IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB

  30. Q u antitati v e comparisons : scatter plots IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB Ariel Rokem Data Scientist

  31. Introd u cing scatter plots fig, ax = plt.subplots() ax.scatter(climate_change["co2"], climate_change["relative_temp"]) ax.set_xlabel("CO2 (ppm)") ax.set_ylabel("Relative temperature (Celsius)") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  32. C u stomi z ing scatter plots eighties = climate_change["1980-01-01":"1989-12-31"] nineties = climate_change["1990-01-01":"1999-12-31"] fig, ax = plt.subplots() ax.scatter(eighties["co2"], eighties["relative_temp"], color="red", label="eighties") ax.scatter(nineties["co2"], nineties["relative_temp"], color="blue", label="nineties") ax.legend() ax.set_xlabel("CO2 (ppm)") ax.set_ylabel("Relative temperature (Celsius)") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  33. Encoding a comparison b y color INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  34. Encoding a third v ariable b y color fig, ax = plt.subplots() ax.scatter(climate_change["co2"], climate_change["relative_temp"], c=climate_change.index) ax.set_xlabel("CO2 (ppm)") ax.set_ylabel("Relative temperature (Celsius)") plt.show() INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  35. Encoding time in color INTRODUCTION TO DATA VISUALIZATION WITH MATPLOTLIB

  36. Practice making y o u r o w n scatter plots ! IN TR OD U C TION TO DATA VISU AL IZATION W ITH MATP L OTL IB

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