matplotlib

Assignment-10 Scipy: We have the min and max temperatures in a city In India for each months of the year. We would like to find a function to describe this and show it graphically, the dataset given below. Task: 1. fitting it to the periodic function 2. plot the fit Data Max = 39, 41, 43, 47, 49, 51, 45, 38, 37, 29, 27, 25 Min = 21, 23, 27, 28, 32, 35, 31, 28, 21, 19, 17, 18 1. fitting it to the periodic function #defining periodic function def yearly_temps(times, avg, ampl, time_offset): return (avg + ampl * np.cos((times + time_offset) * 1.8 * np.pi / times.max())) #identifying the curve for plot using scipy res_max, cov_max = optimize.curve_fit(yearly_temps, months,temp_max, [40, 20, 0]) res_min, cov_min = optimize.curve_fit(yearly_temps, months,temp_min, [-40, 20, 0]) print(res_max) print(res_min) [ 39.10062829 -11.0872493 2.10332481] [24.87112259 -7.89899664 1.60673368] #2. plot the fit days = np.linspace(0, 12, num=365) plt.figure() plt.plot(months,Max, 'ro') plt.plot(days, yearly_temps(days, *res_max), 'r-') plt.plot(months,Min, 'bo') plt.plot(days, yearly_temps(days, *res_min), 'b-') plt.xlabel('Month') plt.ylabel('Temperature (°C)') plt.show() Matplotlib: This assignment is for visualization using matplotlib: data to use: url= https://raw.githubusercontent.com/Geoyi/Cleaning-Titanic-Data/master/titanic_original.csv titanic = pd.read_csv(url) Charts to plot: 1. Create a pie chart presenting the male/female proportion 2. Create a scatterplot with the Fare paid and the Age, differ the plot color by gender import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline data = pd.read_csv('https://raw.githubusercontent.com/Geoyi/Cleaning-Titanic-Data/master/titanic_original.csv') data.head(5) pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home.dest 0 1.0 1.0 Allen, Miss. Elisabeth Walton female 29.0000 0.0 0.0 24160 211.3375 B5 S 2 NaN St Louis, MO 1 1.0 1.0 Allison, Master. Hudson Trevor male 0.9167 1.0 2.0 113781 151.5500 C22 C26 S 11 NaN Montreal, PQ / Chesterville, ON 2 1.0 0.0 Allison, Miss. Helen Loraine female 2.0000 1.0 2.0 113781 151.5500 C22 C26 S NaN NaN Montreal, PQ / Chesterville, ON 3 1.0 0.0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1.0 2.0 113781 151.5500 C22 C26 S NaN 135.0 Montreal, PQ / Chesterville, ON 4 1.0 0.0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1.0 2.0 113781 151.5500 C22 C26 S NaN NaN Montreal, PQ / Chesterville, ON #1. Create a pie chart presenting the male/female proportion s=round((data['sex'].value_counts())/len(data)*100,2) pd.DataFrame(s) sex male 64.35 female 35.57 labels = ['male','female'] sizes = data.sex.value_counts() fig1, ax1 = plt.subplots() ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True, colors = ['Blue','Red']) #ax1.axis('equal') plt.show() 2. Create a scatterplot with the Fare paid and the Age, differ the plot color by gender