Question

The purpose of this is to plot data using Matplotlib.

Description

complete the Jupyter notebook named main.ipynb that reads in the file diamonds.csv into a Pandas DataFrame. Information about the file can be found here:

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diamonds

R Documentation

Prices of over 50,000 round cut diamonds

Description

A dataset containing the prices and other attributes of almost 54,000 diamonds. The variables are as follows:

Usage

diamonds

Format

A data frame with 53940 rows and 10 variables:

price

price in US dollars (\$326–\$18,823)

carat

weight of the diamond (0.2–5.01)

cut

quality of the cut (Fair, Good, Very Good, Premium, Ideal)

color

diamond colour, from D (best) to J (worst)

clarity

a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))

x

length in mm (0–10.74)

y

width in mm (0–58.9)

z

depth in mm (0–31.8)

depth

total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43–79)

table

width of top of diamond relative to widest point (43–95)

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There are two figures that you need to create:

Figure 1:

• normalized histogram with 30 bins using the Fair cut diamond prices
• a line plot of the normal distribution using the mean and standard deviation of the Fair cut diamond prices
• appropriate labels on the both the x and y axes
• appropriate title
• appropriate legend

Figure 2:

• appropriate title

There are two figures that you need to create:

Figure 1:

• normalized histogram with 30 bins using the Fair cut diamond prices
• a line plot of the normal distribution using the mean and standard deviation of the Fair cut diamond prices
• appropriate labels on the both the x and y axes
• appropriate title
• appropriate legend

Figure 2:

• horizontal bar chart of the mean prices of the diamond cuts
• ten evenly spaced tick marks on the x axis from 0 to the maximum mean price
• appropriate labels on the x and y axes
• appropriate title

main.ipynb

is

Setup

The following code imports the required libraries and loads a dataset containing information about diamonds into a Pandas DataFrame. Information about the dataset can be found here.

In [ ]:import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline

def normal_distribution(x, mu, sigma):
return 1/(sigma * np.sqrt(2*np.pi)) * np.exp(-(x-mu)**2/(2*sigma**2))

df = pd.read_csv('diamonds.csv')
df.pop('Unnamed: 0');

df = pd.read_csv('diamonds.csv')
df.pop('Unnamed: 0');

Bar chart of average price per cut

Make a plot the meets the following criteria:

• horizontal bar chart of the mean prices of the diamond cuts
• ten evenly spaced tick marks on the x axis from 0 to the maximum mean price
• appropriate labels on the x and y axes
• appropriate title

diamonds.csv is

https://forge.scilab.org/index.php/p/rdataset/source/tree/master/csv/ggplot2/diamonds.csv

Answer 1

1. The required source-code is given below:-

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib

def normal_distribution(x, mu, sigma):
        return 1/(sigma * np.sqrt(2*np.pi)) * np.exp(-(x-mu)**2/(2*sigma**2))

def plotFirst(df):
        data=df['price'].where(df['cut']=='Fair').dropna().tolist()
        # Calculating Normal Distribution 
        mean=np.mean(data)
        stdv=np.std(data)
        arr = []
        for num in data:
                a = normal_distribution(num,mean,stdv)
                arr.append(a)
        # Plotting the Graph
        fig, axs = plt.subplots(1, 1, figsize=(20,20))
        hist = axs.hist(arr, np.arange(min(arr),max(arr),(max(arr)-min(arr))/30))       
        axs.set_ylabel("Norm. Distrib. of fair-cut Diamonds")
        axs.set_xlabel("Bins")
        plt.show()
        
def plotSecond(df):
        distinct=df['cut'].unique().tolist()
        # Finding the Averages
        arr = []
        for cut in distinct:
                avg = df['price'].where(df['cut']==cut).mean()
                arr.append(avg)
        # Plotting the Graph    
        b = (distinct,arr)
        plt.bar(*b)
        plt.show()

if __name__=="__main__":
        df = pd.read_csv('diamonds.csv')
        df.pop('Unnamed: 0')
        plotFirst(df)
        plotSecond(df)

2. Screenshots of the output are below:-