jupyter Module One Discussion Last Checkpoint: 09/1 1/2019 (autosaved) File Edit View Insert Cell Kernel Widgets Help Not Trusted | Python 3 0 B + x 4 6 + + HRun C H Markdown v Step 1: Preparing the dataset This block of Python code will generate the sample data in a Python dataframe that will be used in the calculations later. Your task is to use a website to collect the daily maximum temperature data for your city or zip code for the past fourteen days. Then, input your data in the code below by filling in the Python list. After entering your data in the Python list, click the code section below and hit the Run button above. In [ ]: # This module will be used to prepare a pandas dataframe and calculate descriptive statistics import pandas as pd input your data in the Python list below. For example, if your temperature data iss 81, 79, 80, 85, 83, 85, 87, 84, 84, 85, 85, 87 then the step below should be set as: temperatures = (81, 79, 80, 85, 83, 85, 87, 84, 84, 88, 85, 87] temperatures = [Enter your data here] prepare a dataframe for temperatures. temperatures_df - pd. DataFrame ( temperatures, columns=[ ' temperature' ] ) print temperatures dataframe print ( temperatures_df) Step 2: Calculating descriptive statistics The block of code below will calculate descriptive statistics for the data set you entered. The pandas dataframe has several methods that calculate descriptive statistics. Each method has a comment telling you what that method calculates. Click the code section below and hit the Run button above. In [ ]: # Pandas dataframe has several methods that calculate descriptive statistics. mean mean - temperatures_df[ ' temperature' ]. mean( ) print ( "Means", round (mean, 2) ) median median = temperatures_of[ 'temperature' ]. median( ) print ( "Medians", round (median, 2) ) variance variance = temperatures_of[ 'temperature' ]. var( ) print ( "Variances", round(variance, 2) ) standard deviation stdeviation = temperatures_of[ 'temperature' ]. std( ) print ( "Standard Deviations", round(stdeviation, 2) ) describe - a useful function that calculates several different descriptive statistics statistics = temperatures_df [ ' temperature' ] . describe ( ) print( "") print ( "Describe method" ) print (statistics)