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# -*- coding: utf-8 -*-
"""DiamondEconomicData.159
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1S_CVJWdykN_6LSpjdHLcSQhC6UVUcFDe
"""
!pip install ydata-profiling
import pandas as pd
import numpy as np
import matplotlib as plt
import seaborn as sns
import tensorflow as tf
from ydata_profiling import ProfileReport
df = pd.read_csv('/content/M6_T2_V1_Diamonds.csv')
df.sample(5)
print(df.head())
df.info()
df.isnull().sum
df.describe()
df.duplicated().sum()
df.head()
df_numeric = df.select_dtypes(include=[np.number])
df_numeric
df_numeric.corr()['price']
df['cut'].value_counts().plot(kind='bar')
df['color'].value_counts().plot(kind='bar')
df['clarity'].value_counts().plot(kind='bar')
df['cut'].value_counts().plot(kind='pie', autopct='%.2f')
df['color'].value_counts().plot(kind='pie', autopct='%.2f')
df['clarity'].value_counts().plot(kind='pie', autopct='%.2f')
sns.histplot(df['price'])
sns.histplot(df['x'], bins=10)
sns.histplot(df['y'], bins=50)
sns.histplot(df['z'], bins=50)
sns.distplot(df['price'])
sns.distplot(df['x'])
sns.distplot(df['y'])
sns.distplot(df['z'])
sns.boxplot(df['price'])
sns.boxplot(df['x'])
sns.boxplot(df['y'])
sns.boxplot(df['z'])
sns.pairplot(df)
prof = ProfileReport(df)
prof.to_file(output_file='output.html')
from IPython.core.display import display, HTML
with open('/content/output.html', 'r') as file:
html_content = file.read()
display(HTML(html_content))
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