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!pip install Faker
from faker import Faker

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#BONUS
#To push your GC plot to Plotly Studio automatically
#!pip install chart_studio
import chart_studio 
import chart_studio.plotly as py

username = '#REPLACE YOUR USERNAME HERE#' # your username
api_key = '#REPLACE YOUR API KEY HERE#' # your api key - go to profile > settings > regenerate key
chart_studio.tools.set_credentials_file(username=username, api_key=api_key)
py.plot(fig, filename = 'cmc_treemap', auto_open=True)

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#!pip install --upgrade plotly
import plotly.graph_objects as go
import plotly.express as px

fig = px.treemap(df_final, 
                 path=['name'], 
                 values='USD_market_cap',
                 color_continuous_scale='RdYlGn',
                 color='USD_percent_change_24h',            
                 )

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import matplotlib
import matplotlib.pyplot as plt
#!pip install squarify
import squarify 

#load data
sizes=df_final["USD_market_cap"]
label=df_final["name"]

# color scale on the price development 

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import pandas as pd

#normalize the data into dataframe format
df = pd.json_normalize(data["data"])
cols_to_keep = ['name','symbol','cmc_rank','quote.USD.price','quote.USD.percent_change_24h','quote.USD.market_cap',]
df_final = df[cols_to_keep]
#rename columns
df_final.columns = ['name','symbol','cmc_rank','USD_price','USD_percent_change_24h','USD_market_cap',]
#uncomment below to print the table
#df_final

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  #This example uses Python 2.7 and the python-request library.

from requests import Request, Session
from requests.exceptions import ConnectionError, Timeout, TooManyRedirects
import json

url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest'
parameters = {
  'start':'1',
  'limit':'10',

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# import initial libraries
from collections import Counter
from sklearn.datasets import make_classification
from imblearn.under_sampling import NearMiss
from matplotlib import pyplot
from numpy import where
# create dataset - imbalanced in the ratio of 90:10
X, y = make_classification(n_samples=1000, n_features=20, n_redundant=1,
	n_clusters_per_class=1, weights=[0.9], flip_y=0.3, random_state=1)
counter = Counter(y)

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# import necessary libraries 
import numpy as np
from sklearn.svm import SVC
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score
from sklearn.metrics import roc_auc_score
from numpy import mean

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#PART 1
# import sampling and other necessary libraries 
from collections import Counter
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
from sklearn.svm import SVC

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#PART 1
# import SMOTE oversampling and other necessary libraries 
from collections import Counter
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)