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# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
import os
import sys
import operator
import numpy as np
import pandas as pd
import scipy
from scipy import sparse
from sklearn import model_selection, preprocessing, ensemble
from sklearn.metrics import log_loss
import re
'''Plotly visualization .'''

import matplotlib.pyplot as plt
import seaborn as sns
color = sns.color_palette()

%matplotlib inline
import plotly.offline as py
import plotly.graph_objs as go
import plotly.tools as tls

'''Display markdown formatted output like bold, italic bold etc.'''
from IPython.display import Markdown
def bold(string):

'''Ignore deprecation and future, and user warnings.'''
import warnings as wrn
wrn.filterwarnings('ignore', category = DeprecationWarning) 
wrn.filterwarnings('ignore', category = FutureWarning) 
wrn.filterwarnings('ignore', category = UserWarning) 
file ="../input/quant-ori/Quantmetry.csv"
df = pd.read_csv(file)
#Variable Description
def description(df):
    print(f"Dataset Shape: {df.shape}")
    summary = pd.DataFrame(df.dtypes,columns=['dtypes'])
    summary = summary.reset_index()
    summary['Name'] = summary['index']
    summary = summary[['Name','dtypes']]
    summary['Missing'] = df.isnull().sum().values    
    summary['Uniques'] = df.nunique().values
    summary['First Value'] = df.iloc[0].values
    summary['Second Value'] = df.iloc[1].values
    summary['Third Value'] = df.iloc[2].values
    return summary

Dataset Shape: (20000, 13)