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Does time of da y affect arrest rate ? AN ALYZIN G P OL IC E AC TIVITY W ITH PAN DAS Ke v in Markham Fo u nder , Data School Anal yz ing datetime data apple price volume date_and_time 0 174.35 20567800 2018-01-08 16:00:00 1

  1. Does time of da y affect arrest rate ? AN ALYZIN G P OL IC E AC TIVITY W ITH PAN DAS Ke v in Markham Fo u nder , Data School

  2. Anal yz ing datetime data apple price volume date_and_time 0 174.35 20567800 2018-01-08 16:00:00 1 174.33 21584000 2018-01-09 16:00:00 2 155.15 54390500 2018-02-08 16:00:00 3 156.41 70672600 2018-02-09 16:00:00 4 176.94 23774100 2018-03-08 16:00:00 5 179.98 32185200 2018-03-09 16:00:00 ANALYZING POLICE ACTIVITY WITH PANDAS

  3. Accessing datetime attrib u tes (1) apple.dtypes price float64 volume int64 date_and_time datetime64[ns] apple.date_and_time.dt.month 0 1 1 1 2 2 3 2 ... ANALYZING POLICE ACTIVITY WITH PANDAS

  4. Accessing datetime attrib u tes (2) apple.set_index('date_and_time', inplace=True) apple.index DatetimeIndex(['2018-01-08 16:00:00', '2018-01-09 16:00:00', '2018-02-08 16:00:00', '2018-02-09 16:00:00', '2018-03-08 16:00:00', '2018-03-09 16:00:00'], dtype='datetime64[ns]', name='date_and_time', freq=Non apple.index.month Int64Index([1, 1, 2, 2, 3, 3], dtype='int64', name='date_and_time') dt accessor is not u sed w ith a DatetimeInde x ANALYZING POLICE ACTIVITY WITH PANDAS

  5. Calc u lating the monthl y mean price apple.price.mean() 169.52666666666667 apple.groupby(apple.index.month).price.mean() date_and_time 1 174.34 2 155.78 3 178.46 Name: price, dtype: float64 monthly_price = apple.groupby(apple.index.month).price.mean() ANALYZING POLICE ACTIVITY WITH PANDAS

  6. Plotting the monthl y mean price import matplotlib.pyplot as plt monthly_price.plot() Line plot : Series inde x on x- a x is , Series v al u es on y- a x is plt.xlabel('Month') plt.ylabel('Price') plt.title('Monthly mean stock price for Apple') plt.show() ANALYZING POLICE ACTIVITY WITH PANDAS

  7. ANALYZING POLICE ACTIVITY WITH PANDAS

  8. Let ' s practice ! AN ALYZIN G P OL IC E AC TIVITY W ITH PAN DAS

  9. Are dr u g - related stops on the rise ? AN ALYZIN G P OL IC E AC TIVITY W ITH PAN DAS Ke v in Markham Fo u nder , Data School

  10. Resampling the price apple.groupby(apple.index.month).price.mean() date_and_time 1 174.34 2 155.78 3 178.46 apple.price.resample('M').mean() date_and_time 2018-01-31 174.34 2018-02-28 155.78 2018-03-31 178.46 ANALYZING POLICE ACTIVITY WITH PANDAS

  11. Resampling the v ol u me apple date_and_time price volume 2018-01-08 16:00:00 174.35 20567800 2018-01-09 16:00:00 174.33 21584000 2018-02-08 16:00:00 155.15 54390500 ... ... ... apple.volume.resample('M').mean() date_and_time 2018-01-31 21075900 2018-02-28 62531550 2018-03-31 27979650 ANALYZING POLICE ACTIVITY WITH PANDAS

  12. Concatenating price and v ol u me monthly_price = apple.price.resample('M').mean() monthly_volume = apple.volume.resample('M').mean() pd.concat([monthly_price, monthly_volume], axis='columns') date_and_time price volume 2018-01-31 174.34 21075900 2018-02-28 155.78 62531550 2018-03-31 178.46 27979650 monthly = pd.concat([monthly_price, monthly_volume], axis='columns') ANALYZING POLICE ACTIVITY WITH PANDAS

  13. Plotting price and v ol u me (1) monthly.plot() plt.show() ANALYZING POLICE ACTIVITY WITH PANDAS

  14. Plotting price and v ol u me (2) monthly.plot(subplots=True) plt.show() ANALYZING POLICE ACTIVITY WITH PANDAS

  15. Let ' s practice ! AN ALYZIN G P OL IC E AC TIVITY W ITH PAN DAS

  16. What v iolations are ca u ght in each district ? AN ALYZIN G P OL IC E AC TIVITY W ITH PAN DAS Ke v in Markham Fo u nder , Data School

  17. Comp u ting a freq u enc y table pd.crosstab(ri.driver_race, ri[(ri.driver_race == 'Asian') & ri.driver_gender) (ri.driver_gender == 'F') ].shape driver_gender F M driver_race (551, 14) Asian 551 1838 Black 2681 9604 driver_race is along the Hispanic 1953 7774 inde x, driver_gender is Other 53 212 White 18536 43334 along the col u mns table = pd.crosstab( ri.driver_race, Freq u enc y table : Tall y of ri.driver_gender) ho w man y times each combination of v al u es occ u rs ANALYZING POLICE ACTIVITY WITH PANDAS

  18. Selecting a DataFrame slice .loc[] accessor : Select table.loc['Asian':'Hispanic'] from a DataFrame b y label driver_gender F M table driver_race Asian 551 1838 driver_gender F M Black 2681 9604 driver_race Hispanic 1953 7774 Asian 551 1838 Black 2681 9604 table = Hispanic 1953 7774 table.loc['Asian':'Hispanic'] Other 53 212 White 18536 43334 ANALYZING POLICE ACTIVITY WITH PANDAS

  19. Creating a line plot table.plot() plt.show() ANALYZING POLICE ACTIVITY WITH PANDAS

  20. Creating a bar plot table.plot(kind='bar') plt.show() ANALYZING POLICE ACTIVITY WITH PANDAS

  21. Stacking the bars table.plot(kind='bar', stacked=True) plt.show() ANALYZING POLICE ACTIVITY WITH PANDAS

  22. Let ' s practice ! AN ALYZIN G P OL IC E AC TIVITY W ITH PAN DAS

  23. Ho w long might y o u be stopped for a v iolation ? AN ALYZIN G P OL IC E AC TIVITY W ITH PAN DAS Ke v in Markham Fo u nder , Data School

  24. Anal yz ing an object col u mn apple date_and_time price volume change 2018-01-08 16:00:00 174.35 20567800 down ... ... ... ... 2018-03-09 16:00:00 179.98 32185200 up apple.change.dtype Create a Boolean col u mn : True if the price w ent u p , and False other w ise dtype('O') Calc u late ho w o � en the .astype() can ' t be u sed in price w ent u p b y taking the this case col u mn mean ANALYZING POLICE ACTIVITY WITH PANDAS

  25. Mapping one set of v al u es to another Dictionar y maps the v al u es y o u ha v e to the v al u es y o u w ant mapping = {'up':True, 'down':False} apple['is_up'] = apple.change.map(mapping) apple date_and_time price volume change is_up 2018-01-08 16:00:00 174.35 20567800 down False ... ... ... ... ... 2018-03-09 16:00:00 179.98 32185200 up True apple.is_up.mean() 0.5 ANALYZING POLICE ACTIVITY WITH PANDAS

  26. Calc u lating the search rate Vis u ali z e ho w o � en searches w ere done a � er each v iolation t y pe ri.groupby('violation').search_conducted.mean() violation Equipment 0.064280 Moving violation 0.057014 Other 0.045362 Registration/plates 0.093438 Seat belt 0.031513 Speeding 0.021560 search_rate = ri.groupby('violation').search_conducted.mean ANALYZING POLICE ACTIVITY WITH PANDAS

  27. Creating a bar plot search_rate.plot(kind='bar') plt.show() ANALYZING POLICE ACTIVITY WITH PANDAS

  28. Ordering the bars (1) Order the bars from le � to right b y si z e search_rate.sort_values() violation Speeding 0.021560 Seat belt 0.031513 Other 0.045362 Moving violation 0.057014 Equipment 0.064280 Registration/plates 0.093438 Name: search_conducted, dtype: float64 ANALYZING POLICE ACTIVITY WITH PANDAS

  29. Ordering the bars (2) search_rate.sort_values().plot(kind='bar') plt.show() ANALYZING POLICE ACTIVITY WITH PANDAS

  30. Rotating the bars search_rate.sort_values().plot(kind='barh') plt.show() ANALYZING POLICE ACTIVITY WITH PANDAS

  31. Let ' s practice ! AN ALYZIN G P OL IC E AC TIVITY W ITH PAN DAS

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