![]() Hint: group the data in by the column corresponding to the hours of the day, and save the resuling grouped data to a variable, for example, today_by_hour. Further divide today's trip data by hours. Save the resulting data to a variable, say, today. Hint: see question 1-(c) for how to get desired data. We already create a list containing all the days for you: Your loop can just loop over days_of week. ![]() Create a for -loop to go through each day of the week. Here is a brief ste-by-step instruction if you have no clue how to do it: - Step 1. In addition to the 7 lines, your plot must include a title, axis labels, and the legend. There are 7 days per week, so there will be 7 lines in the plot. We now want to create a similar plot with x-axis being the hours of a day (in 24 hours format) and y-axis being the number of trips in each hour. Recall the spaghetti plot in HW 4 and lesson 4 of module 2. Think carefully which array should be x and which should be y in your regression line. Access the 'TOTAL' column and and the slope into Save the Hints: 1. Save the y-intercept into a variable named regression line object to linefit. Use the polyfit function to calculate the linear regression coefficients. Access the 'MILEAGE' column of the cleaned_data dataframe and extract the values into a NumPy array called extract the values into an array called final price. subplots(figsize = ( 15, 15 )) BBox = (( − 77.112057, − 76.910928, 38.813473, 38.993565 )) pyplot.rc ('font', family='serif', size= '18') pyplot.rc ('lines', color=' b ', markersize=2) ax.set x lim ( BB o x, BB o x ) ax.set ylim(BBox, BB o x ) ax.imshow(ruh m, zorder = 0, extent = BBox) \# YOUR CODE HERE raise NotImplementedError() (d) Use NumPy's linear regression to get a line object that can predict the final price based on the trip distance. ruh_m = pyplot,imread ('Map_DC_Final,png') fig, ax = pyplot,subplots(figsize = ( 15, 15 )) BBox = ((-77.112057, − 76.910928, 38.813473, 38.993565 )) pyplot.rc('font', family='serif', size= 1 8 ′ ) pyplot.rc('lines', color='b', markersize = 2 ) ax.set,xlim(BBox, BB o x ) ax.set ylim(BBox, BB o x ) ax.imshow(ruh_m, zorder = 0, extent = BBox) \# YOUR CODE HERE raise NotImplementedError() ruh_m = pyplot.imread ( Map_DC_Final.png') fig, ax = pyplot. Save the drop-off longitude and latitudes into the variables end longitude and end latitude. Now, similarly to the previous point, create a new plot for the distribution on the map of drop off locations in red. ⋅ imread ( Map DC Final.png') fig, ax = pyplot.subplots(figsize = ( 15, 15 )) BBOX = (( − 77.112057, − 76.910928, 38.813473, 38.993565 )) pyplot.rc('font', family='serif', size=' 1 8 ′ ') pyplot.rc ('lines', c = ' b ', markersize=2) ax.set x lim(BBox, BBox ) ax.set ylim(BBox, BB o x ) ax.imshow(ruh_m, zorder = 0, extent = BBox) \# YOUR CODE HERE raise NotImplementedError() ![]() ![]() This plot is showing the distribution on the map of the starting point of all the taxi rides in DC in September 2022 ! url = '' urlretrieve(url, 'Map_pC_Final.png') ruh_m = pyplot.imread ('Map_DC_Final.png') fig, ax = pyplot,subplots(figsize = ( 15, 15 )) BBox = (( − 77.112057, − 76.910928, 38.813473, 38.993565 )) pyplot.rc('font', family='serif', size='18') pyplot.rc('lines', c='b', markersize=2) ax.set_xlim(BBox, BB o x ) ax.set ylim(BBox,BBox ) ax.imshow(ruh_m, zorder=0, extent = BBox) \# YOuR CoDE HERE raise NotImplementedError() url = 'https: // tinyurl ⋅ com / xneaambr' urlretrieve(url, 'Map_DC_Final.png') ruh m = pyplot. Your plot must include a title and axes labels. \# YOUR CODE HERE raise NotimplementedError() (b) Create a scatter plot with longitude as the x axis and latitude as the y axis. (a) Access the orig_longitude and the orig_latitude column of the cleaned_data dataframe and extract the values into two NumPy arrays called start_longitude and start_latitude. However, we will focus on analysis and visualization. Analysis and visualization with taxi data In this problem, you will play with the same taxi dataset.
0 Comments
Leave a Reply. |