lab4

lab4 April 26, 2024 [1]: import numpy as np import pandas as pd import otter grader = otter . Notebook() 0.1 Lab 4: Keep going in pandas This lab is for more practice using pandas . Below are some functions you may find useful for these exercises. pd.Series.value_counts pd.DataFrame.groupby pd.crosstab pd.DataFrame.sort_values We will be summarizing and manipulating information coming from a history of weather conditions. [2]: weather = pd . read_csv( "weatherHistory.csv" ) weather . head() [2]: Formatted Date Summary Precip Type Temperature (C) \ 0 2006-04-01 00:00:00.000 +0200 Partly Cloudy rain 9.472222 1 2006-04-01 01:00:00.000 +0200 Partly Cloudy rain 9.355556 2 2006-04-01 02:00:00.000 +0200 Mostly Cloudy rain 9.377778 3 2006-04-01 03:00:00.000 +0200 Partly Cloudy rain 8.288889 4 2006-04-01 04:00:00.000 +0200 Mostly Cloudy rain 8.755556 Apparent Temperature (C) Humidity Wind Speed (km/h) \ 0 7.388889 0.89 14.1197 1 7.227778 0.86 14.2646 2 9.377778 0.89 3.9284 3 5.944444 0.83 14.1036 4 6.977778 0.83 11.0446 Wind Bearing (degrees) Visibility (km) Loud Cover Pressure (millibars) \ 1

0 251.0 15.8263 0.0 1015.13 1 259.0 15.8263 0.0 1015.63 2 204.0 14.9569 0.0 1015.94 3 269.0 15.8263 0.0 1016.41 4 259.0 15.8263 0.0 1016.51 Daily Summary 0 Partly cloudy throughout the day. 1 Partly cloudy throughout the day. 2 Partly cloudy throughout the day. 3 Partly cloudy throughout the day. 4 Partly cloudy throughout the day. Question 1 Subset the weather data for just the Summary , Temperature (C) and Humidity columns. [3]: weather_sub = weather[[ 'Summary' , 'Temperature (C)' , 'Humidity' ]] [4]: grader . check( "q1_1" ) [4]: q1_1 results: All test cases passed! Question 2 How many total days in the data were “Clear”? [7]: clear_days = weather[ 'Precip Type' ] == 'Clear' clear_days [7]: [0 False 1 False 2 False 3 False 4 False … 96448 False 96449 False 96450 False 96451 False 96452 False Name: Precip Type, Length: 96453, dtype: bool] [8]: grader . check( "q1_2" ) [8]: q1_2 results: q1_2 - 1 result: ￿ Test case failed Trying: 5000 < clear_days < 15000 2

Question 5 Add a new column to weather called Temperature (F) which represents the tem- perature in Celsius converted to Fahrenheit. 𝐹 = 𝐶 ∗ 1.8 + 32 [ ]: ... [ ]: grader . check( "q1_5" ) Question 6 What is the difference in average temperature (F) between rain and snow Precip Type ? [ ]: temp_diff = ... temp_diff [ ]: grader . check( "q1_6" ) Question 7 What was the lowest temperature (F) recorded in the data in snow precipitation and foggy conditions. [ ]: ... lowest_temp = ... lowest_temp [ ]: grader . check( "q1_7" ) Question 8 Create a cross-tabulation of precipitation type and weather type (Summary), re- turning the counts of each type of weather for each precipitation type. [ ]: precip_tab = ... precip_tab [ ]: grader . check( "q1_8" ) Question 9 Which precipitation type (rain or snow), has the highest incidence of Overcast weather as a proportion of all their weather, and what is that proportion? [ ]: rain_overcast = ... print (rain_overcast) snow_overcast = ... print (snow_overcast) most_overcast = "snow" [ ]: grader . check( "q1_9" ) 4

Question 10 Pivot weather_sub such that humidity and temperature represent rows and values of summary are the columns. pd.DataFrame.pivot_table [ ]: weather_sub . head() [ ]: weather_sub_pivot = ... weather_sub_pivot [ ]: grader . check( "q1_10" ) Be sure to run the tests and verify that they all pass, then Save your changes, then Download your file to your host machine (if you are using jupyterhub), then submit your file to the Lab 4 Canvas assignment by 11:59pm on the due date. [ ]: 5