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from sklearn.decomposition import PCA

import io

import numpy as np

import numpy as np

import pandas as pd

import pandas as pd

import requests

import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)

import numpy as np
data = data.replace('?', np.NaN)

data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())

data2 = data.dropna()

Z = (data2 - data2.mean()) / data2.std()
Z[20:25]

dups = data.duplicated()
data.loc[[11, 28]]

data2 = data.drop_duplicates()

#modified to load from remote URL, Ye 2021
import requests
import io
url = "https://cgi.luddy.indiana.edu/~yye/b565/data/DTW_prec.csv"
s=requests.get(url).content
daily = pd.read_csv(io.StringIO(s.decode('utf-8')), header='infer')
daily.index = pd.to_datetime(daily['DATE'])
daily = daily['PRCP']
ax = daily.plot(kind='line',figsize=(15,3))
ax.set_title('Daily Precipitation (variance = %.4f)' % (daily.var()))

monthly = daily.groupby(pd.Grouper(freq='M')).sum()
ax = monthly.plot(kind='line',figsize=(15,3))
ax.set_title('Monthly Precipitation (variance = %.4f)' % (monthly.var()))

annual = daily.groupby(pd.Grouper(freq='Y')).sum()
ax = annual.plot(kind='line',figsize=(15,3))
ax.set_title('Annual Precipitation (variance = %.4f)' % (annual.var()))

sample = data.sample(n=3)
sample

sample = data.sample(frac=0.01, random_state=1)
sample

sample = data.sample(frac=0.01, replace=True, random_state=1)
sample

bins = pd.cut(data['Clump Thickness'], 4)
bins.value_counts(sort=False)

bins = pd.qcut(data['Clump Thickness'], 4)
bins.value_counts(sort=False)

import pandas as pd
from sklearn.decomposition import PCA
numComponents = 2
pca = PCA(n_components=numComponents)
pca.fit(imgData)
projected = pca.transform(imgData)
projected = pd.DataFrame(projected, columns=['pc1', 'pc2'], index=range(1, numImages + 1))
projected['food'] = ['burger', 'burger', 'burger', 'burger', 'drink', 'drink', 'drink', 'drink', 'pasta', 'pasta', 'pasta', 'pasta', 'chicken', 'chicken', 'chicken', 'chicken']
projected