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dtomlinson
2019-07-10 20:18:31 +01:00
parent 13c0e9cb4d
commit e3ac390e8b
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.gitignore
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./bayes-learning/packages/matplotlib/*
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bayes-learning/class.py
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import pandas as pd
import os
# path = os.getcwd()
# data = pd.read_csv(path + str('/data/Social_Network_Ads.csv'),
# engine='python')
# df = pd.DataFrame(data)
# train_size = int(0.75 * df.shape[0])
# test_size = int(0.25 * df.shape[0])
# print('Training set size {}, Testing set size {}'.format(train_size,
# test_size))
class bayesClassifer(object):
"""initial implmentation of a bayes classifer"""
path = os.getcwd()
def __init__(self, data_file):
super(bayesClassifer, self).__init__()
self.data_file = data_file
def createDataFrame(self):
return pd.read_csv(self.path + self.data_file, engine='python')
def print_debug(self):
print('{0} rows, {1} columns'.format(self.df.shape[0],
self.df.shape[1]))
print(self.df[1:7])
def trainData(dataframe):
train_size = int(0.75 * dataframe.shape[0])
test_size = int(0.25 * dataframe.shape[0])
return(train_size, test_size)
bayesClassifer('/data/Social_Network_Ads.csv')
bayesClassifer.createDataFrame()
bayes-learning/data/Social_Network_Ads.csv
+401
View File
@@ -0,0 +1,401 @@
User ID,Gender,Age,EstimatedSalary,Purchased
15624510,Male,19,19000,0
15810944,Male,35,20000,0
15668575,Female,26,43000,0
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15728773,Male,27,58000,0
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15600575,Male,25,33000,0
15727311,Female,35,65000,0
15570769,Female,26,80000,0
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15746139,Male,20,86000,0
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15628972,Male,18,82000,0
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15617482,Male,45,26000,1
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15736760,Female,47,49000,1
15714658,Male,48,41000,1
15599081,Female,45,22000,1
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15633531,Female,47,30000,1
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15669656,Male,31,18000,0
15581198,Male,31,74000,0
15729054,Female,27,137000,1
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15724858,Male,27,90000,0
15713144,Male,35,27000,0
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15764419,Female,27,17000,0
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15715541,Female,28,84000,0
15639277,Male,23,20000,0
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15727696,Male,30,135000,1
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15764195,Female,18,44000,0
15744919,Female,29,83000,0
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15775562,Female,23,48000,0
15807481,Male,28,79000,0
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15789109,Female,32,117000,0
15814004,Male,27,20000,0
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15595135,Female,23,66000,0
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15718071,Male,24,58000,0
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15577178,Female,22,63000,0
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15726358,Female,24,27000,0
15595228,Female,20,23000,0
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15595917,Male,30,80000,0
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15682268,Male,35,50000,0
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15699284,Female,29,28000,0
15786993,Female,29,83000,0
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15710257,Female,35,25000,0
15582492,Male,28,123000,1
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15766289,Male,27,88000,0
15593014,Male,28,59000,0
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15675949,Female,33,149000,1
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15801658,Male,21,72000,0
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15789863,Male,27,89000,0
15720943,Male,26,86000,0
15697997,Female,38,80000,0
15665416,Female,39,71000,0
15660200,Female,37,71000,0
15619653,Male,38,61000,0
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15739160,Male,42,80000,0
15689237,Male,40,57000,0
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15701962,Male,41,59000,0
15811613,Female,36,75000,0
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15724423,Female,40,75000,0
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15610801,Male,42,65000,0
15745232,Male,26,32000,0
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15792102,Female,26,84000,0
15675185,Male,31,58000,0
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15725660,Male,30,87000,0
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15800061,Female,28,55000,0
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15687491,Male,30,107000,1
15610403,Female,28,59000,0
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15617134,Male,35,59000,0
15783029,Male,30,89000,0
15622833,Female,34,25000,0
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15750839,Female,27,96000,1
15749130,Female,41,30000,0
15779862,Male,29,61000,0
15767871,Male,20,74000,0
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15699247,Male,31,76000,0
15619087,Female,36,50000,0
15605327,Male,40,47000,0
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15762605,Male,26,30000,0
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15744279,Male,32,100000,1
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15599533,Female,37,33000,0
15757837,Male,35,38000,0
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15762228,Female,22,55000,0
15614827,Female,35,71000,0
15789815,Male,29,148000,1
15579781,Female,29,47000,0
15587013,Male,21,88000,0
15570932,Male,34,115000,0
15794661,Female,26,118000,0
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15644296,Female,34,72000,0
15614420,Female,23,28000,0
15609653,Female,35,47000,0
15594577,Male,25,22000,0
15584114,Male,24,23000,0
15673367,Female,31,34000,0
15685576,Male,26,16000,0
15774727,Female,31,71000,0
15694288,Female,32,117000,1
15603319,Male,33,43000,0
15759066,Female,33,60000,0
15814816,Male,31,66000,0
15724402,Female,20,82000,0
15571059,Female,33,41000,0
15674206,Male,35,72000,0
15715160,Male,28,32000,0
15730448,Male,24,84000,0
15662067,Female,19,26000,0
15779581,Male,29,43000,0
15662901,Male,19,70000,0
15689751,Male,28,89000,0
15667742,Male,34,43000,0
15738448,Female,30,79000,0
15680243,Female,20,36000,0
15745083,Male,26,80000,0
15708228,Male,35,22000,0
15628523,Male,35,39000,0
15708196,Male,49,74000,0
15735549,Female,39,134000,1
15809347,Female,41,71000,0
15660866,Female,58,101000,1
15766609,Female,47,47000,0
15654230,Female,55,130000,1
15794566,Female,52,114000,0
15800890,Female,40,142000,1
15697424,Female,46,22000,0
15724536,Female,48,96000,1
15735878,Male,52,150000,1
15707596,Female,59,42000,0
15657163,Male,35,58000,0
15622478,Male,47,43000,0
15779529,Female,60,108000,1
15636023,Male,49,65000,0
15582066,Male,40,78000,0
15666675,Female,46,96000,0
15732987,Male,59,143000,1
15789432,Female,41,80000,0
15663161,Male,35,91000,1
15694879,Male,37,144000,1
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15575002,Female,35,60000,0
15622171,Male,37,53000,0
15795224,Female,36,126000,1
15685346,Male,56,133000,1
15691808,Female,40,72000,0
15721007,Female,42,80000,1
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15694453,Male,39,42000,0
15813113,Male,40,107000,1
15614187,Male,49,86000,1
15619407,Female,38,112000,0
15646227,Male,46,79000,1
15660541,Male,40,57000,0
15753874,Female,37,80000,0
15617877,Female,46,82000,0
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15701537,Male,42,149000,1
15736228,Male,38,59000,0
15780572,Female,50,88000,1
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15586996,Female,41,72000,0
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15638003,Female,35,50000,0
15775590,Female,57,122000,1
15730688,Male,41,52000,0
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15810075,Female,44,39000,0
15723373,Male,37,52000,0
15795298,Female,48,134000,1
15584320,Female,37,146000,1
15724161,Female,50,44000,0
15750056,Female,52,90000,1
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15569641,Female,58,95000,1
15815236,Female,45,131000,1
15811177,Female,35,77000,0
15680587,Male,36,144000,1
15672821,Female,55,125000,1
15767681,Female,35,72000,0
15600379,Male,48,90000,1
15801336,Female,42,108000,1
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15746203,Female,47,144000,1
15583137,Male,40,61000,0
15680752,Female,43,133000,0
15688172,Female,59,76000,1
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15589449,Male,39,106000,1
15692819,Female,57,26000,1
15727467,Male,57,74000,1
15734312,Male,38,71000,0
15764604,Male,49,88000,1
15613014,Female,52,38000,1
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15685536,Male,35,61000,0
15750447,Male,37,70000,1
15663249,Female,52,21000,1
15638646,Male,48,141000,0
15734161,Female,37,93000,1
15631070,Female,37,62000,0
15761950,Female,48,138000,1
15649668,Male,41,79000,0
15713912,Female,37,78000,1
15586757,Male,39,134000,1
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15625395,Male,55,39000,1
15760570,Male,37,77000,0
15566689,Female,35,57000,0
15725794,Female,36,63000,0
15673539,Male,42,73000,1
15705298,Female,43,112000,1
15675791,Male,45,79000,0
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15736397,Female,58,38000,1
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15720745,Female,37,137000,1
15637593,Male,37,79000,1
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15733973,Female,47,113000,1
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15733964,Female,38,50000,0
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15623502,Male,36,60000,0
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15577806,Male,41,87000,1
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1 User ID Gender Age EstimatedSalary Purchased
2 15624510 Male 19 19000 0
3 15810944 Male 35 20000 0
4 15668575 Female 26 43000 0
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7 15728773 Male 27 58000 0
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62 15814004 Male 27 20000 0
63 15673619 Male 25 87000 0
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bayes-learning/lesson1-rewrite.py
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import pandas as pd
import os
from sklearn.preprocessing import StandardScaler
from matplotlib.colors import ListedColormap
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['backend'] = 'TkAgg'
class getData(object):
"""docstring for getData"""
def __init__(self, file):
super(getData, self).__init__()
self.file = file
class Bayes(getData):
"""docstring for Bayes"""
def __init__(self, file):
super(Bayes, self).__init__()
self.file = file
def get_path(self):
path = os.getcwd()
file_path = path + self.file
return file_path
bayes = Bayes(file='data/Social_Network_Ads.csv')
print(bayes.get_path())
bayes-learning/lesson1.py
+152
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import pandas as pd
import os
from sklearn.preprocessing import Normalizer, StandardScaler
from matplotlib.colors import ListedColormap
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix, classification_report
matplotlib.rcParams['backend'] = 'TkAgg'
plt.style.use('seaborn-dark-palette')
# path = os.getcwd()
path = '/home/dtomlinson/projects/bayes-learning'
data = pd.read_csv(path + str('/data/Social_Network_Ads.csv'), engine='python')
df = pd.DataFrame(data)
print('{0} rows, {1} columns'.format(df.shape[0], df.shape[1]))
# print(df[1:7])
train_size = int(0.75 * df.shape[0])
test_size = int(0.25 * df.shape[0])
print('Training set size {}, Testing set size {}'.format(train_size,
test_size))
df = df.sample(frac=1).reset_index(drop=True)
print(df[0:5])
X = df.iloc[:, [2, 3]].values
y = df.iloc[:, 4].values
normalizer = StandardScaler(copy=False).fit(X)
X = normalizer.fit_transform(X)
X_train = X[0:train_size, :]
y_train = y[0:train_size]
X_test = X[train_size:, :]
y_test = y[train_size:]
X_set, y_set = X_train, y_train
# ind = np.argsort(X_set[:, 0])
# X_set = X_set[ind]
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c=ListedColormap(('red', 'blue'))(i),
label=j, marker='.')
plt.title('Training Set')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
def generate_data(class_data_dic, X_train, y_train):
first_one = True
first_zero = True
for i in range(y_train.shape[0]):
X_temp = X_train[i, :].reshape(X_train[i, :].shape[0], 1)
if y_train[i] == 1:
if first_one is True:
class_data_dic[1] = X_temp
first_one = False
else:
class_data_dic[1] = np.append(class_data_dic[1], X_temp,
axis=1)
elif y_train[i] == 0:
if first_zero is True:
class_data_dic[0] = X_temp
first_zero = False
else:
class_data_dic[0] = np.append(class_data_dic[0], X_temp,
axis=1)
return class_data_dic
class_data_dic = generate_data(class_data_dic={}, X_train=X_train,
y_train=y_train)
"""find the mean (2x1) for each column. 0 and 1 are the values for having 0
and 1 seperately"""
mean_0 = np.mean(class_data_dic[0], axis=1)
mean_1 = np.mean(class_data_dic[1], axis=1)
std_0 = np.std(class_data_dic[0], axis=1)
std_1 = np.std(class_data_dic[1], axis=1)
print('mean_0={}, std_0={}, mean_1={}, std_1={}'.format(
mean_0, mean_1, std_0, std_1))
# plt.show()
"""define the likelyhood function (the pdf of the norm dist) """
def likelyhood(x, mean, sigma):
return np.exp(-(x - mean)**2 / (2 * sigma ** 2)) * (1 / (np.sqrt(2 * np.pi) * sigma ** 2))
""" the posterior function times together all the likelihoods for each row
of X_test here we are working out the likelihood func for each row of X_test
with their corresponding mean and stdev """
"""we then times this by the prior-prob-func to find the posterior func"""
def posterior(X, X_train_class, mean_, std_):
product = np.prod(likelyhood(X, mean_, std_), axis=1)
product = product * (X_train_class.shape[0] / X.shape[0])
return product
""" we test the posterior fun with the test data to find the probs"""
p_1 = posterior(X_test, class_data_dic[1], mean_1, std_1)
p_0 = posterior(X_test, class_data_dic[0], mean_0, std_0)
y_pred = 1 * (p_1 > p_0)
print(X_test.shape)
print(class_data_dic[0].shape)
print(p_1.shape)
tp = len([i for i in range(0, y_test.shape[0])
if y_test[i] == 0 and y_pred[i] == 0])
tn = len([i for i in range(0, y_test.shape[0])
if y_test[i] == 0 and y_pred[i] == 1])
fp = len([i for i in range(0, y_test.shape[0])
if y_test[i] == 1 and y_pred[i] == 0])
fn = len([i for i in range(0, y_test.shape[0])
if y_test[i] == 1 and y_pred[i] == 1])
confusion_matrix_alg = np.array([[tp, tn], [fp, fn]])
print(confusion_matrix_alg)
classifer = GaussianNB()
classifer.fit(X_train, y_train)
y_pred = classifer.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
report = classification_report(y_test, y_pred)
print(cm)
print(report)
bayes-learning/output.csv
+3
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bayes-learning/packages/matplotlib
Submodule
+1
Submodule bayes-learning/packages/matplotlib added at fdc7a8c384
bayes-learning/seaborn-descr.py
+84
View File
@@ -0,0 +1,84 @@
import pandas as pd
import os
import matplotlib
import matplotlib.pyplot as plt
# from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
from scipy.stats import trim_mean, kurtosis
from scipy.stats.mstats import mode, gmean, hmean
def linebreak():
"""prints a line break to split up functions"""
print('\n ============================================== \n')
matplotlib.rcParams['backend'] = 'TkAgg'
plt.style.use('seaborn-dark-palette')
path = os.getcwd()
data_file = str('/data/Social_Network_Ads.csv')
df = pd.read_csv(path + data_file)
# df = pd.DataFrame(df)
df = df.sample(frac=1).reset_index(drop=True)
print('{} rows. {} cols.'.format(df.shape[0], df.shape[1]))
linebreak()
print(df.iloc[0:10, :])
linebreak()
X = df[['Age', 'EstimatedSalary']]
y = df['Purchased'].to_frame()
print('X equals:')
print(X.iloc[0:5])
linebreak()
print('y equals:')
print(y[0:5])
linebreak()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
description = df.describe().drop(columns=['User ID'])
description_grouped = df.groupby(['Purchased'])
description_grouped_split = description_grouped['Age', 'EstimatedSalary']\
.describe().unstack()
description_grouped_mode = description_grouped['Age'].apply(mode, axis=None)
df_quartile_slary = df.groupby('Purchased')['EstimatedSalary']\
.quantile([.1, .5, .9])
df_quartile_age = df.groupby('Purchased')['Age'].quantile([.1, .5, .9])
df_trimmed_mean = description_grouped['Age', 'EstimatedSalary'].\
aggregate(trim_mean, .1)
df_summary = description_grouped['Age', 'EstimatedSalary']\
.aggregate([np.median, np.std, np.mean, gmean, hmean])
df_var = description_grouped['Age', 'EstimatedSalary'].var()
df_null = df.isna().sum()
print(description)
linebreak()
print(description_grouped_split)
linebreak()
print(description_grouped_mode)
linebreak()
print(df_quartile_slary)
print(df_quartile_age)
linebreak()
print(df_trimmed_mean)
linebreak()
print(df_summary)
linebreak()
print(df_var)
linebreak()
print(df_null)
bayes-learning/seaborn-graphing.py
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import pandas as pd
import numpy as np
import os
import matplotlib
import matplotlib.pyplot as plt
from scipy.stats import trim_mean
from scipy.stats.mstats import mode, gmean, hmean
from sklearn.model_selection import train_test_split
import seaborn as sns
def linebreak():
"""prints a line break to split up functions"""
print('\n ============================================== \n')
matplotlib.rcParams['backend'] = 'TkAgg'
# plt.style.use('seaborn-dark-palette')
path = os.getcwd()
data_file = str('/data/Social_Network_Ads.csv')
df = pd.read_csv(path + data_file)
df = df.sample(frac=1).reset_index(drop=True)
print(df[0:5])
X = df[['Age', 'EstimatedSalary']]
y = df['Purchased']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
# ax1 = df.plot.scatter(x='Age', y='EstimatedSalary', c='DarkBlue')
# ax2 = df.query('Age < 30').plot.scatter(x='Age', y='EstimatedSalary',
# c='DarkBlue')
# figure_1 = df.query('Age < 35').plot(kind='scatter', x='Age',
# y='EstimatedSalary')
df_purchased_sum = df['Purchased'].value_counts()
# figure_2 = plt.plot(df_purchased_sum)
# cp = sns.countplot(data=df, y='Purchased')
# pal = dict(1="seagreen", 0="gray")
fig, axs = plt.subplots(ncols=2)
sns.countplot(data=df, x='Age', hue='Purchased', ax=axs[0])
cp = sns.countplot(data=df, x='Purchased', ax=axs[1])
plt.show()
# print(df_purchased_sum)
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https://medium.com/@urvashilluniya/why-data-normalization-is-necessary-for-machine-learning-models-681b65a05029
visualise data
https://towardsdatascience.com/the-art-of-effective-visualization-of-multi-dimensional-data-6c7202990c57
https://www.marsja.se/pandas-python-descriptive-statistics/
https://www.marsja.se/explorative-data-analysis-with-pandas-scipy-and-seaborn/
deep learning https://towardsdatascience.com/detecting-malaria-with-deep-learning-9e45c1e34b60
likelihood functions https://stats.stackexchange.com/questions/2641/what-is-the-difference-between-likelihood-and-probability
plotting with df and matplotlib
https://nbviewer.jupyter.org/urls/gist.github.com/fonnesbeck/5850463/raw/a29d9ffb863bfab09ff6c1fc853e1d5bf69fe3e4/3.+Plotting+and+Visualization.ipynb
https://towardsdatascience.com/a-guide-to-pandas-and-matplotlib-for-data-exploration-56fad95f951c
seaborne
vis the dist
https://seaborn.pydata.org/tutorial/distributions.html
pdfs
https://stats.stackexchange.com/questions/14483/intuitive-explanation-for-density-of-transformed-variable
kernel
https://mathisonian.github.io/kde/
https://chemicalstatistician.wordpress.com/2013/06/09/exploratory-data-analysis-kernel-density-estimation-in-r-on-ozone-pollution-data-in-new-york-and-ozonopolis/
https://www.quora.com/What-is-kernel-density-estimation
choose bandwith so that it minimses the mean integrated square error so ban = min(MSIE)
pandas plotting
https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html
class
https://jeffknupp.com/blog/2017/03/27/improve-your-python-python-classes-and-object-oriented-programming/
https://realpython.com/python3-object-oriented-programming/
errors
https://doughellmann.com/blog/2009/06/19/python-exception-handling-techniques/
public + private members
https://www.tutorialsteacher.com/python/private-and-protected-access-modifiers-in-python
properties
https://www.programiz.com/python-programming/property
generators
https://pythontips.com/2013/09/29/the-python-yield-keyword-explained/
https://www.programiz.com/python-programming/generator
class methods with inheritence
https://stackoverflow.com/questions/5738470/whats-an-example-use-case-for-a-python-classmethod
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from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y , train_size = 0.7, random_state = 90)
normalizing methods : https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/normalize-data
https://en.wikipedia.org/wiki/Feature_scaling
we apply the normalizing methods to the training data first, then seperately to the test data to compare!
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import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors
x = np.linspace(-3, 3)
X, Y = np.meshgrid(x, x)
Z = np.exp(-(X**2 + Y**2))
fig, (ax, ax2) = plt.subplots(ncols=2)
colors = ["red", "orange", "gold", "limegreen", "k",
"#550011", "purple", "seagreen"]
ax.set_title("contour with color list")
contour = ax.contourf(X, Y, Z, colors=colors)
ax2.set_title("contour with colormap")
cmap = matplotlib.colors.ListedColormap(colors)
contour = ax2.contourf(X, Y, Z, cmap=cmap)
fig.colorbar(contour)
plt.show()
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2019-07-01 22:12:55,531 - INFO - no path entered, using current directory /home/dtomlinson/projects/boto3/blocking_suggestions/
2019-07-01 22:12:55,833 - INFO - opening history file
2019-07-01 22:12:55,833 - INFO - attempting to clear current files
2019-07-01 22:12:55,834 - INFO - no files to remove
2019-07-01 22:12:55,834 - INFO - ========= SCRIPT FINISHED =========
2019-07-01 22:13:07,271 - INFO - ========= SCRIPT STARTED =========
2019-07-01 22:13:07,272 - INFO - no path entered, using current directory /home/dtomlinson/projects/boto3/blocking_suggestions/
2019-07-01 22:13:07,571 - INFO - opening history file
2019-07-01 22:13:07,572 - INFO - attempting to clear current files
2019-07-01 22:13:07,572 - INFO - no files to remove
2019-07-01 22:13:08,239 - INFO - downloaded blocking_suggestions_2019-07-01T00.csv
2019-07-01 22:13:08,373 - INFO - downloaded blocking_suggestions_2019-07-01T02.csv
2019-07-01 22:13:08,522 - INFO - downloaded blocking_suggestions_2019-07-01T04.csv
2019-07-01 22:13:08,636 - INFO - downloaded blocking_suggestions_2019-07-01T06.csv
2019-07-01 22:13:08,816 - INFO - downloaded blocking_suggestions_2019-07-01T08.csv
2019-07-01 22:13:08,949 - INFO - downloaded blocking_suggestions_2019-07-01T10.csv
2019-07-01 22:13:09,075 - INFO - downloaded blocking_suggestions_2019-07-01T12.csv
2019-07-01 22:13:09,558 - INFO - downloaded blocking_suggestions_2019-07-01T14.csv
2019-07-01 22:13:09,814 - INFO - downloaded blocking_suggestions_2019-07-01T16.csv
2019-07-01 22:13:10,020 - INFO - downloaded blocking_suggestions_2019-07-01T18.csv
2019-07-01 22:13:10,020 - INFO - ========= SCRIPT FINISHED =========
boto3/pull.py
+249
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import boto3
import botocore
import os
from datetime import date
import sys
import logging
import argparse
import glob
# Set Global Variables
log_location = 'pull.log'
remote_folder = ['bot_predictions']
remote_file_prefix = ['blocking_suggestions_']
append_date = [True]
date_format = ['%Y-%m-%d']
bucket = ['td-ingest-storage-williamhill']
access_key = ['AKIAYJXVWMRHQ2OGNHLA']
secret_key = ['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']
class downloadFiles(object):
"""docstring for downloadFiles"""
today = date.today()
def __init__(self,
client,
resource,
bucket,
remote_folder,
remote_file_prefix,
local_path,
append_date=False,
date_format=''):
super(downloadFiles, self).__init__()
self.client = client
self.resource = resource
self.bucket = bucket
self.append_date = append_date
self.date_format = date_format
self.remote_folder = self._folder_fixer(remote_folder)
self.dest = f'{self.remote_folder!s}{remote_file_prefix!s}'
self.local_path = local_path
self.remote_list, self.local_list, self.local_file_list = \
(list() for _ in range(3))
@staticmethod
def generate_date(date_format):
date = downloadFiles.today.strftime(date_format)
return date
@staticmethod
def _folder_fixer(folder):
try:
if folder[-1] != '/':
folder = f'{folder}/'
except IndexError:
folder = ''
return folder
def get_path(self):
if self.local_path:
self.local_path = self._folder_fixer(self.local_path)
logger.info(f'path entered is {self.local_path}')
return self
else:
self.local_path = os.getcwd()
self.local_path = self._folder_fixer(self.local_path)
self.local_path = f'{self.local_path}blocking_suggestions/'
logger.info(f'no path entered, using current directory '
f'{self.local_path}')
return self
def get_files(self):
if self.append_date:
date = f'{self.generate_date(self.date_format)!s}'
else:
date = ''
self.dest = f'{self.dest!s}{date!s}'
paginator = self.client.get_paginator('list_objects')
iterator = paginator.paginate(Bucket=self.bucket, Prefix=self.dest)
self.filtered = iterator.search('Contents[*].Key')
for i in self.filtered:
try:
self.remote_list.append(i)
self.local_list.append(
f'{self.local_path}{i[len(self.remote_folder):]}'
)
self.local_file_list.append(
f'{i[len(self.remote_folder):]}'
)
except TypeError:
logger.info('no files available to download -- exiting')
raise SystemExit
logger.debug(f'remote files are {self.remote_list}')
logger.debug(f'saving files locally to {self.local_list}')
return self
def get_history(self):
self.history_file = f'{self.local_path}.history.txt'
try:
logger.info('opening history file')
open(self.history_file, 'a').close()
pass
except FileNotFoundError:
logger.critical('history file cannot be found or created'
' - check permissions of the folder.')
raise
self.history_list = \
[line.rstrip('\n') for line in open(self.history_file)]
return self
def remove_files(self):
logger.info('attempting to clear current files')
current_files = glob.glob(f'{self.local_path}[!history.txt]*')
if current_files:
for i in current_files:
try:
os.remove(i)
logger.info(f'removed {i}')
except OSError:
logger.exception('Error:')
else:
logger.info('no files to remove')
return self
def download_files(self):
for remote_file, local_file_with_path, local_file in zip(
self.remote_list, self.local_list, self.local_file_list):
if local_file not in self.history_list:
with open(local_file_with_path, 'wb'), \
open(self.history_file, 'a') as hist:
try:
self.resource.Bucket(self.bucket).download_file(
remote_file, local_file_with_path)
hist.write(f'\n{local_file}')
logger.info(f'downloaded {local_file}')
except botocore.exceptions.ClientError as e:
if e.response['Error']['Code'] == '404':
print(f'The object {remote_file} does not exist.')
else:
raise
if local_file in self.history_list:
logger.debug(f'{local_file} already downloaded - skipping')
return self
def _call():
global args, debug
parser = argparse.ArgumentParser(description="""
downloads any new files for the current day from an S3 bucket. \
uses a local history file to track what has been \
previously downloaded in the download path.
""")
parser.add_argument('--path', type=str,
help='enter pull path to download to. if left \
blank will use the same location as the script.',
default='')
parser.add_argument('--debug', action='store_true', default=False,
help='Use this to log DEBUG information.')
args = parser.parse_args()
debug = vars(args)['debug']
if debug:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
main(_clients=_clients,
_resources=_resources,
_buckets=_buckets,
remote_folder=remote_folder,
remote_file_prefix=remote_file_prefix,
append_date=append_date,
date_format=date_format,
**vars(args))
def main(*args,
_clients={'client0': ''},
_resources={'resource0': ''},
_buckets={'bucket0': ''},
remote_folder=[''],
remote_file_prefix=[''],
append_date=['True'],
date_format=[''],
path='',
**kwargs):
logger.info('========= SCRIPT STARTED =========')
instance = downloadFiles(client=_clients['client0'],
resource=_resources['resource0'],
bucket=_buckets['bucket0'],
remote_folder=remote_folder[0],
remote_file_prefix=remote_file_prefix[0],
local_path=path,
append_date=append_date[0],
date_format=date_format[0])
instance.get_path().get_files().get_history().remove_files()\
.download_files()
logger.info('========= SCRIPT FINISHED =========')
if __name__ == '__main__':
args, debug = '', ''
# define logging
logger = logging.getLogger(__name__)
c_handler = logging.StreamHandler(sys.stdout)
f_handler = logging.FileHandler(log_location)
c_format = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
f_format = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
c_handler.setFormatter(c_format)
f_handler.setFormatter(f_format)
logger.addHandler(c_handler)
logger.addHandler(f_handler)
_clients = {}
_resources = {}
_buckets = {}
for i in range(0, len(bucket)):
_clients[f'client{i}'] =\
boto3.client('s3',
aws_access_key_id=f'{access_key[i]}',
aws_secret_access_key=f'{secret_key[i]}')
_resources[f'resource{i}'] =\
boto3.resource('s3',
aws_access_key_id=f'{access_key[i]}',
aws_secret_access_key=f'{secret_key[i]}')
_buckets[f'bucket{i}'] = f'{bucket[i]}'
try:
_length = len(remote_folder)
if _length == 0:
remote_folder = ['']
elif remote_folder[0] == 'root':
remote_folder = ['']
else:
pass
except NameError:
remote_folder = ['']
_call()
learning/class-and-object-orianted-python/lesson1.py
+23
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#!~/.virtualenvs/learning/bin/python
class Customer(object):
"""example class defining a customer deposit and withdraw methods
for a bank.
Attributes:
name: A string represnting the customer's name
balance: A float tracking the current balance of the customer."""
def __init__(self, name, balance, list):
super(Customer, self).__init__()
self.name = name
self.balance = balance
self.list = list
print(self.name)
print(self.balance)
for i in range(len(self.list)):
print(self.list[i])
Customer('Homer', 200, ['100', '102', '103'])
learning/class-and-object-orianted-python/links.txt
+1
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https://jeffknupp.com/blog/2017/03/27/improve-your-python-python-classes-and-object-oriented-programming/
learning/decorators/__init__.py
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learning/decorators/classes_decorater.py
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import sys
import os
sys.path.append(os.getcwd()) # noqa E402
from decorator import timer, debug, repeatN, repeat_partial, count_calls, \
Counter, Slow, slowDown
from dataclasses import dataclass
class TimeWaster(object):
@debug
def __init__(self, max_num: int):
super(TimeWaster, self).__init__()
self.max_num = max_num
@timer
def waste_time(self, num_times: int):
for _ in range(num_times):
sum([i ** 2 for i in range(self.max_num)])
@timer
@dataclass
class PlayingCard(object):
rank: str
suit: str
@repeatN
def say_hello(name: str):
print(f'Hello, {name}.')
@repeatN(num=5)
def say_hi(name: str):
print(f'Hi, {name}.')
@repeat_partial(num=10)
def say_yo(name: str):
print(f'Yo, {name}!')
@count_calls
def say_whee():
print('Whee!')
@Counter
def say_howdy():
print('Howdy!')
@slowDown()
def count_down(num: int):
if not isinstance(num, int):
raise TypeError("Must input an integer.")
if num >= 1:
print(num)
count_down(num - 1)
else:
print('Liftoff!')
learning/decorators/classes_with_args.py
+72
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import sys
import os
sys.path.append(os.getcwd()) # noqa E402
import decorator
from itertools import repeat
# @slowDown(1)
# def count_down(num: int):
# if not isinstance(num, int):
# raise TypeError("Must input an integer.")
# if num >= 1:
# print(num)
# count_down(num - 1)
# else:
# print('Liftoff!')
# @Counter
# def say_howdy():
# print('Howdy!')
class tester(object):
@decorator.slowDown()
def count(self, num):
if not isinstance(num, int):
raise TypeError("Must input an integer.")
if num >= 1:
print(num)
self.count(num - 1)
else:
print('Liftoff!')
# print()
# var = tester()
# var.count(1)
# @decorator.slowDown()
# def count(num):
# if not isinstance(num, int):
# raise TypeError("Must input an integer.")
# if num >= 1:
# print(num)
# count(num - 1)
# else:
# print('Liftoff!')
# print()
# count(3)
# @decorator.slowDown
# def count(num):
# if not isinstance(num, int):
# raise TypeError("Must input an integer.")
# if num >= 1:
# print(num)
# count(num - 1)
# else:
# print('Liftoff!')
# @slowDown
# def countme(num):
# if not isinstance(num, int):
# raise TypeError("Must input an integer.")
# if num >= 1:
# print(num)
# countme(num - 1)
# else:
# print('Liftoff!')
learning/decorators/decorator.py
Executable
+204
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import itertools
import functools
from time import perf_counter, sleep
import sys
def do_twice(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
for _ in itertools.repeat(None, 2):
func(*args, **kwargs)
return func(*args, **kwargs)
return wrapper
def timer(func):
@functools.wraps(func)
def wrapper_timer(*args, **kwargs):
start_time = perf_counter()
value = func(*args, **kwargs)
end_time = perf_counter()
run_time = end_time - start_time
print(f'Finished {func.__name__!r} in {run_time:.4f} secs')
return value
return wrapper_timer
def debug(func):
""" Print the function signature and return the value """
@functools.wraps(func)
def wrapper_debug(*args, **kwargs):
args_repr = [repr(a) for a in args]
kwargs_repr = [f'{k}={v!r}' for k, v in kwargs.items()]
signature = ', '.join(args_repr + kwargs_repr)
print(f'Calling {func.__name__}({signature})')
value = func(*args, **kwargs)
print(f'{func.__name__!r} returned {value!r}')
return value
return wrapper_debug
def slow_down(func):
@functools.wraps(func)
def wrapper_slow_down(*args, **kwargs):
sleep(1)
return func(*args, **kwargs)
return wrapper_slow_down
def repeat(num: int):
def decorator_repeat(func: callable):
@functools.wraps(func)
def wrapper_repeat(*args: list, **kwargs: dict):
for _ in range(num):
value = func(*args, **kwargs)
return value
return wrapper_repeat
return decorator_repeat
def repeatN(_func=None, *, num=2):
def decorator_repeatN(func):
@functools.wraps(func)
def wrapper_repeatN(*args, **kwargs):
for _ in range(num):
value = func(*args, **kwargs)
return value
return wrapper_repeatN
if _func is None:
return decorator_repeatN
else:
return decorator_repeatN(_func)
def repeat_partial(func=None, *, num=2):
if func is None:
return functools.partial(repeat_partial, num=num)
@functools.wraps(func)
def repeat_partial_wrapper(*args, **kwargs):
for _ in range(num):
value = func(*args, **kwargs)
return value
return repeat_partial_wrapper
# def count_calls(func: callable):
# @functools.wraps(func)
# def wrapper_count_calls(*args, **kwargs):
# wrapper_count_calls.num_calls += 1
# print(f'Call {wrapper_count_calls.num_calls} of {func.__name__!r}')
# return func(*args, **kwargs)
# wrapper_count_calls.num_calls = 0
# print(wrapper_count_calls.num_calls)
# return wrapper_count_calls
# def count_calls(func: callable):
# num_calls = 0
# print(num_calls)
# @functools.wraps(func)
# def wrapper_count_calls(*args, **kwargs):
# nonlocal num_calls
# num_calls += 1
# print(f'Call {num_calls} of {func.__name__}')
# return func(*args, **kwargs)
# return wrapper_count_calls
def count_calls(func=None, *, num=0):
if func is None:
return functools.partial(count_calls, num=num)
@functools.wraps(func)
def wrapper_count_calls(*args, **kwargs):
wrapper_count_calls.num += 1
print(f'Call {wrapper_count_calls.num} of {func.__name__}')
return func(*args, **kwargs)
wrapper_count_calls.num = num
return wrapper_count_calls
class Counter(object):
"""docstring for Counter"""
def __init__(self, func):
print('start init')
super(Counter, self).__init__()
functools.update_wrapper(self, func)
self.func = func
self.num_calls = 0
print('finished init')
def __call__(self, *args, **kwargs):
print('start call')
self.num_calls += 1
print(f'Call {self.num_calls} of {self.func.__name__!r}')
print('finished call')
return self.func(*args, **kwargs)
# class slowDown(object):
# """docstring for slowDown"""
# def __init__(self, rate):
# if callable(rate):
# self.func = rate
# self.rate = 1
# else:
# self.rate = rate
# def __get__(self, obj, type=None):
# return functools.partial(self, obj)
# def __call__(self, *args, **kwargs):
# if not hasattr(self, 'func'):
# self.func = args[0]
# return self
# sleep(self.rate)
# self.func(*args, **kwargs)
class slowDown(object):
"""docstring for Slow_Down"""
def __init__(self, rate=1):
print('init')
self.rate = rate
def __call__(self, func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
print('wrapper')
sleep(self.rate)
func(*args, **kwargs)
print('call finished')
print(self, func)
return wrapper
# class slowDown(object):
# """docstring for slowDown"""
# def __init__(self, rate):
# if callable(rate):
# self.func = rate
# self.rate = 1
# print(f'no args, {locals()}, func={self.func}, rate={self.rate},\
# self={self}')
# else:
# self.rate = rate
# print(f'args set, rate={self.rate}')
# def __get__(self, obj, type=None):
# print(f'get called, self={self}, obj={obj}, type={type}')
# return functools.partial(self, obj)
# def __call__(self, *args, **kwargs):
# print(f'call called, rate={self.rate}, args={args}'
# f', kwargs={kwargs} ,self={self}')
# print(f'locals = {locals()}')
# if not hasattr(self, 'func'):
# self.func = args[0]
# print(f'args set, setting self.func to {self.func}')
# return self
# sleep(self.rate)
# self.func(*args, **kwargs)
learning/decorators/function_decorators.py
+58
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import sys
import os
sys.path.append(os.getcwd()) # noqa E402
from decorator import timer, debug, slow_down
import math
import random
PLUGINS = dict()
@timer
def waste_time(num):
for _ in range(num):
sum([i ** 2 for i in range(10000)])
@debug
def make_greeting(first_name, age: int):
return f'Hello {first_name}, you are {age}!'
math.factorial = debug(math.factorial)
def approximate_e(terms):
return sum(1 / math.factorial(n) for n in range(terms))
@slow_down
def countdown(num: int):
if num < 1:
print('Liftoff!')
else:
print(num)
countdown(num - 1)
def register(func):
"""Register a function as a plug-in"""
PLUGINS[func.__name__] = func
return func
@register
def say_hello(name: str) -> str:
return f'Hello {name}'
@register
def be_awesome(name: str) -> str:
return f'Yo {name}, together we are awesome!'
def randomly_greet(name):
greeter, greeter_func = random.choice(list(PLUGINS.items()))
print(f'Using {greeter!r}')
return greeter_func(name)
learning/decorators/test.py
+13
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import sys
import os
sys.path.append(os.getcwd()) # noqa E402
import decorator
import functools
@functools.lru_cache(maxsize=4)
@decorator.count_calls
def fib(num):
if num < 2:
return num
return fib(num - 1) + fib(num - 2)
learning/descriptors/descriptors.py
+18
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class Person(object):
"""docstring for Person"""
def __init__(self, name, age):
super(Person, self).__init__()
self.name = name
self.age = age
@property
def age(self):
print('old enough')
return self.__age
@age.setter
def age(self, value):
if value < 21:
raise Exception(f'{self.name} is not old enough.')
else:
self.__age = value
learning/descriptors/descriptors2.py
+37
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from weakref import WeakKeyDictionary
class Drinker:
def __init__(self):
self.req_age = 21
self.age = dict()
print('init')
def __get__(self, instance_obj, objtype):
print(f'{instance_obj}, {objtype}, {self.age}')
return self.age.get(instance_obj, self.req_age)
def __set__(self, instance, new_age):
if new_age < 21:
msg = '{name} is too young to legally imbibe'
raise Exception(msg.format(name=instance.name))
self.age[instance] = new_age
print('{name} can legally drink in the USA'.format(
name=instance.name))
def __delete__(self, instance):
del self.age[instance]
class Person:
drinker_age = Drinker()
def __init__(self, name, age):
self.name = name
self.drinker_age = age
p = Person('Miguel', 30)
p1 = Person('Bob', 50)
p = Person('Niki', 13)
learning/descriptors/test.py
+16
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class P(object):
def __init__(self, x):
self._x = x
@property
def x(self):
return self._x
@x.setter
def x(self, x):
if x < 0:
self._x = 0
elif x > 1000:
self._x = 1000
else:
self._x = x
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2019-06-13 22:23:19,920 - CRITICAL - Critical message
2019-06-13 22:26:08,285 - DEBUG - Critical message
2019-06-13 22:26:12,263 - DEBUG - Critical message
2019-06-13 22:26:13,688 - DEBUG - Critical message
2019-06-13 22:26:14,120 - DEBUG - Critical message
2019-06-13 22:26:14,499 - DEBUG - Critical message
2019-06-13 22:26:14,990 - DEBUG - Critical message
2019-06-13 22:26:15,361 - DEBUG - Critical message
2019-06-13 22:27:00,510 - ERROR - Critical message
2019-06-13 22:27:45,847 - ERROR - Critical message
2019-06-13 22:27:46,242 - ERROR - Critical message
2019-06-13 22:27:46,513 - ERROR - Critical message
2019-06-13 22:27:46,770 - ERROR - Critical message
2019-06-13 22:27:47,018 - ERROR - Critical message
2019-06-13 22:27:47,534 - ERROR - Critical message
2019-06-13 22:27:52,098 - CRITICAL - Critical message
2019-06-13 22:27:52,678 - CRITICAL - Critical message
2019-06-13 22:27:52,918 - CRITICAL - Critical message
2019-06-13 22:28:24,770 - CRITICAL - Critical message
2019-06-13 22:28:25,180 - CRITICAL - Critical message
2019-06-13 22:28:25,487 - CRITICAL - Critical message
2019-06-13 22:28:25,760 - CRITICAL - Critical message
2019-06-13 22:28:26,184 - CRITICAL - Critical message
2019-06-13 22:28:26,538 - CRITICAL - Critical message
2019-06-13 22:28:26,932 - CRITICAL - Critical message
2019-06-13 22:28:27,301 - CRITICAL - Critical message
2019-06-13 22:28:27,690 - CRITICAL - Critical message
2019-06-13 22:28:33,666 - ERROR - Critical message
2019-06-13 22:28:34,203 - ERROR - Critical message
2019-06-13 22:28:34,414 - ERROR - Critical message
2019-06-13 22:28:58,465 - ERROR - Critical message
2019-06-13 22:28:58,917 - ERROR - Critical message
2019-06-13 22:28:59,138 - ERROR - Critical message
2019-06-13 22:29:02,335 - INFO - Critical message
learning/import-args/script1.py
+14
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@@ -0,0 +1,14 @@
def my_decorator(func):
def wrapper():
print('Something happening before the func is called.')
func()
print('Something happening after the func is called.')
return wrapper
@my_decorator
def say_whee():
print('Whee!')
say_whee()
learning/import-args/script2.py
+23
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@@ -0,0 +1,23 @@
import argparse
def main(folder, *args, **kwargs):
my_path = folder
print(my_path)
print(f"script2 kwargs {kwargs}")
print('end of script2.py')
def call():
global arguments
parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, default='')
parser.add_argument('--folder', type=str, default='test-folder')
arguments = parser.parse_args()
main(**vars(arguments))
if __name__ == '__main__':
arguments = ''
call()
# print(f"final {arguments}")
learning/import-args/script3.py
+21
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@@ -0,0 +1,21 @@
import script1
import argparse
def main(**kwargs):
# print(vars(arguments))
script1.main(**vars(arguments))
def call():
global arguments
parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, default='test-path')
parser.add_argument('--folder', type=str, default='test-folder')
arguments = parser.parse_args()
main(**vars(arguments))
if __name__ == '__main__':
arguments = ''
call()
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learning/property/property.py
+13
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class Celsius(object):
"""docstring for Celsius"""
def __init__(self, temperature=0):
super(Celsius, self).__init__()
self.temperature = temperature
@classmethod
def from_fahrenheit(cls, temperature=32):
temperature = (temperature - 32) / (9 / 5)
return cls(temperature)
def to_fahrenheit(self):
return (self.temperature * 1.8) + 32
learning/property/property1.py
+26
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@@ -0,0 +1,26 @@
class P:
def __init__(self, x):
print('init')
self._x = x
@property
def x(self):
print('get')
return self._x
@x.setter
def x(self, x):
print('set')
if x < 0:
self._x = 0
elif x > 1000:
self._x = 1000
else:
self._x = x
def double(self):
return self.x * 2
def overwrite(self):
self.x = 20
learning/property/property2.py
+32
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@@ -0,0 +1,32 @@
class Robot:
def __init__(self, name, build_year, lk=0.5,
lp=0.5):
self.name = name
self.build_year = build_year
self.__potential_physical = lk
self.__potential_psychic = lp
@property
def condition(self):
s = self.__potential_physical + self.__potential_psychic
if s <= -1:
return "I feel miserable!"
elif s <= 0:
return "I feel bad!"
elif s <= 0.5:
return "Could be worse!"
elif s <= 1:
return "Seems to be okay!"
else:
return "Great!"
def printer(self):
return self.condition
def __get__(self, instance, owner):
return 'get'
x = Robot("Marvin", 1979, 0.2, 0.4)
y = Robot("Caliban", 1993, -0.4, 0.3)
learning/singletons/__pycache__/db.cpython-37.pyc
BIN
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learning/singletons/client_a.py
+6
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@@ -0,0 +1,6 @@
mygenerator = (x * x for x in range(3))
for i in mygenerator:
print(i)
for i in mygenerator:
print(i)
learning/singletons/client_b.py
+4
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@@ -0,0 +1,4 @@
import db
if (db.db_name == 'mongo'):
db.db_name = None
learning/singletons/db.py
+19
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@@ -0,0 +1,19 @@
import sys
import os
sys.path.append(os.getcwd()) # noqa E402
this = sys.modules[__name__]
print(this)
this.db_name = None
print(this.db_name)
def initialize_db(name):
if (this.db_name is None):
this.db_name = name
# db_name = "Local Variable"
else:
msg = f"Database is already initialized to {name}"
raise RuntimeError(msg.format(this.db_name))
learning/super/class_1.py
+32
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@@ -0,0 +1,32 @@
class Celsius:
def __init__(self, temperature=0):
print('initalising')
self.temperature = temperature
def __repr__(self):
return(f'current temperature is {self.temperature} degrees C')
def to_fahrenheit(self):
return (self.temperature * 1.8) + 32
def get_temperature(self):
print("Getting value")
return self._temperature
def set_temperature(self, value):
if value < -273:
raise ValueError("Temperature below -273 is not possible")
print("Setting value")
self._temperature = value
return value
temperature = property(get_temperature, set_temperature)
# c = Celsius(20)
# print(Celsius(20).get_temperature)
# print(Celsius(20).set_temperature(25))
# Celsius(20).get_temperature()
c = Celsius()
print(c.to_fahrenheit())
learning/super/notes.txt
+33
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@@ -0,0 +1,33 @@
children precede their parents and the order of appearance in __bases__ is respected.
why not to use super in object classes: The problem with incompatible signatures https://www.artima.com/weblogs/viewpost.jsp?thread=281127
why (swap the print and super in each command)
class First(object):
def __init__(self):
super(First, self).__init__()
print("first")
class Second(object):
def __init__(self):
super(Second, self).__init__()
print("second")
class Third(First, Second):
def __init__(self):
super(Third, self).__init__()
print("third")
Third()
print(Third.__mro__)
second
first
third
(<class '__main__.Third'>, <class '__main__.First'>, <class '__main__.Second'>, <class 'object'>)
[Finished in 0.0s]
https://www.datacamp.com/community/data-science-cheatsheets
https://www.datacamp.com/community/tutorials/decorators-python - functions returning other functions?
learning/super/rectangle-square.py
+97
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@@ -0,0 +1,97 @@
#!~/.virtualenvs/learning/bin/python
class Rectangle(object):
"""calculates the area and perimeter of a rectangle"""
def __init__(self, length, width, **kwargs):
super(Rectangle, self).__init__(**kwargs)
self.length = length
self.width = width
def area(self):
return(self.length * self.width)
def perimeter(self):
return(2 * self.length + 2 * self.width)
# Here we can declare the superclass for the subclass Square
class Square(Rectangle):
"""calculates the area and perimeter of a square"""
def __init__(self, length, **kwargs):
super(Square, self).__init__(length=length, width=length, **kwargs)
class Cube(Square):
"""calculates the surface area and volume of a cube"""
def __init__(self, length, **kwargs):
super(Cube, self).__init__(length=length, **kwargs)
self.length = length
def surface_area(self):
face_area = super(Cube, self).area()
return(6 * face_area)
def volume(self):
face_area = super(Cube, self).area()
return(face_area * self.length)
def area(self):
return(self.length * self.length + 1)
# return(super(Cube, self))
class Triangle(object):
"""calculates the area of a triangle"""
def __init__(self, base, height, **kwargs):
self.base = base
self.height = height
super(Triangle, self).__init__(**kwargs)
def tri_area(self):
return 0.5 * self.base * self.height
class RightPyramid(Square, Triangle):
"""calculates the surface area of a right pyramid"""
def __init__(self, base, slant_height, **kwargs):
kwargs['height'] = slant_height
kwargs['length'] = base
super(RightPyramid, self).__init__(base=base, **kwargs)
self.base = base
self.slant_height = slant_height
def area(self):
base_area = super(RightPyramid, self).area()
perimeter = super(RightPyramid, self).perimeter()
return(0.5 * perimeter * self.slant_height + base_area)
def area_2(self):
base_area = super(RightPyramid, self).area()
triangle_area = super(RightPyramid, self).tri_area()
return(triangle_area * 4 + base_area)
class new_cube(Cube):
"""docstring for new_cube"""
def __init__(self, new_length):
super(new_cube, self).__init__(length=new_length)
self.new_length = new_length
# print(RightPyramid.__mro__)
pyramid = RightPyramid(2, 4)
print(pyramid.area())
print(pyramid.area_2())
nc = new_cube(4)
print(nc.surface_area())
square = Square(3)
print(square.perimeter())
learning/super/temp.py
View File
learning/super/triangle.py
+20
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@@ -0,0 +1,20 @@
# ~/.virtualenvs/learning/python
def convert_to_uppercase(function):
""" will convert to uppercase """
def wrapper():
func = function()
make_uppercase = func.upper()
return make_uppercase
# @convert_to_uppercase
def hello():
""" print hello world """
return 'hello world'
say_HELLO = convert_to_uppercase(hello)
print(say_HELLO())
learning/test.py
+14
View File
@@ -0,0 +1,14 @@
replace_chars = ['[', ']', '\'', ',']
list_of_ips = []
new_list = []
def split_ips(list):
for sub_list in list:
for ip in sub_list.split():
for i in range(len(replace_chars)):
ip = ip.replace(replace_chars[i], '')
new_list.append(ip)
split_ips(list_of_ips)
random_sampling/sampling.py
+9
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@@ -0,0 +1,9 @@
import numpy as np
import pandas as pd
np.random.seed(127)
df = pd.DataFrame(np.random.uniform(1000, high=1500, size=(30, 1)),
columns=['bets']).round(0)
print(df)
random_sampling/test.py
+64
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@@ -0,0 +1,64 @@
import numpy as np
import pandas as pd
import itertools
import seaborn as sns
import matplotlib.pyplot as plt
# np.random.seed(127)
low = 1000
high = 1200
repeat = 120
anom_perc = 0.2
sns.set()
sns.set_style("darkgrid")
def generator(low: float, high: float, repeat: int):
for i in range(repeat):
num = np.random.uniform(low, high)
curve = float(i) ** 2
num = num + curve
if num > high * 1.2:
low = num - 10
high = num + 5
elif num < low:
low = num - 5
high = num + 10
else:
pass
yield num
mygenerator = generator(low, high, repeat)
df = pd.DataFrame([])
for i, j in zip(mygenerator, np.arange(repeat)):
df = df.append([pd.DataFrame({'time': j, 'bets': i, },
index=[0])]).round(0)
def create_anom(dataframe, number):
for _ in itertools.repeat(None, number):
rows = dataframe.shape[0]
anomaly = f"{float(dataframe.iloc[-1]['bets']) * (1 - anom_perc)}"
anomaly = float(anomaly)
dataframe = dataframe.append([pd.DataFrame(
{'time': rows, 'bets': anomaly},
index=[0])])
return dataframe
df_anom = create_anom(df, 3)
f, axes = plt.subplots(2, 1)
for ax in axes:
ax.set_ylim([low * 0.5, high * 1.5])
sns.lineplot(x='time', y='bets', data=df, ax=axes[0])
sns.lineplot(x='time', y='bets', data=df_anom, ax=axes[1])
plt.show()
reddit/comment_scrape.py
+19
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@@ -0,0 +1,19 @@
import praw
# from praw.models import MoreComments
r = praw.Reddit(client_id='rDpKg8v-jw4QoQ',
client_secret='IUcwsp_1_zkmgGMY-X5fvXKyMWs',
password='IjNSZQdtQAdl',
user_agent='test-script',
username='adhesiveduck')
print(r.user.me())
submission = r.submission(url='https://old.reddit.com/r/AskReddit/comments/'
'cbg7m1/what_movie_do_you_consider_perfect/')
submission.comments.replace_more(limit=20)
# print(type(submission))
for top_level_comment in submission.comments:
print(top_level_comment.body)
temp/__pycache__/temp_1.cpython-37.pyc
BIN
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Binary file not shown.
temp/markov.py
+175
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import numpy as np
import itertools
import functools
""" Define our data """
# The Statespace
states = np.array(['L', 'w', 'W'])
# Possible sequences of events
transitionName = np.array([['LL', 'Lw', 'LW'],
['wL', 'ww', 'wW'],
['WL', 'Ww', 'WW']])
# Probabilities Matrix (transition matrix)
transitionMatrix = np.array([[0.8, 0.15, 0.05],
[0.8, 0.15, 0.05],
[0.8, 0.15, 0.05]])
# Starting state
startingState = 'L'
# Steps to run
stepTime = 2
# End state you want to find probabilites of
endState = 'L'
""" Set our parameters """
# Should we seed the results?
setSeed = False
seedNum = 27
""" Simulation parameters """
# Should we simulate more than once?
setSim = True
simNum = 10
# A class that implements the Markov chain to forecast the state/mood:
class markov(object):
"""simulates a markov chain given its states, current state and
transition matrix.
Parameters:
states: 1-d array containing all the possible states
transitionName: 2-d array containing a list
of the all possible state directions
transitionMatrix: 2-d array containing all
the probabilites of moving to each state
currentState: a string indicating the starting state
steps: an integer determining how many steps (or times) to simulate"""
def __init__(self, states: np.array, transitionName: np.array,
transitionMatrix: np.array, currentState: str,
steps: int):
super(markov, self).__init__()
self.states = states
self.list = list
self.transitionName = transitionName
self.transitionMatrix = transitionMatrix
self.currentState = currentState
self.steps = steps
@staticmethod
def setSeed(num: int):
return np.random.seed(num)
@functools.lru_cache(maxsize=128)
def forecast(self):
print(f'Start state: {self.currentState}')
# Shall store the sequence of states taken
self.stateList = [self.currentState]
i = 0
# To calculate the probability of the stateList
self.prob = 1
while i != self.steps:
if self.currentState == 'L':
self.change = np.random.choice(self.transitionName[0],
replace=True,
p=transitionMatrix[0])
if self.change == 'LL':
self.prob = self.prob * 0.8
self.stateList.append('L')
pass
elif self.change == 'Lw':
self.prob = self.prob * 0.15
self.currentState = 'w'
self.stateList.append('w')
else:
self.prob = self.prob * 0.05
self.currentState = "W"
self.stateList.append("W")
elif self.currentState == "w":
self.change = np.random.choice(self.transitionName[1],
replace=True,
p=transitionMatrix[1])
if self.change == "ww":
self.prob = self.prob * 0.15
self.stateList.append("w")
pass
elif self.change == "wL":
self.prob = self.prob * 0.8
self.currentState = "L"
self.stateList.append("L")
else:
self.prob = self.prob * 0.05
self.currentState = "W"
self.stateList.append("W")
elif self.currentState == "W":
self.change = np.random.choice(self.transitionName[2],
replace=True,
p=transitionMatrix[2])
if self.change == "WW":
self.prob = self.prob * 0.05
self.stateList.append("W")
pass
elif self.change == "WL":
self.prob = self.prob * 0.8
self.currentState = "L"
self.stateList.append("L")
else:
self.prob = self.prob * 0.15
self.currentState = "w"
self.stateList.append("w")
i += 1
print(f'Possible states: {self.stateList}')
print(f'End state after {self.steps} steps: {self.currentState}')
print(f'Probability of all the possible sequence of states:'
f' {self.prob}')
return self.stateList
def main(*args, **kwargs):
try:
simNum = kwargs['simNum']
except KeyError:
pass
sumTotal = 0
# Check validity of transitionMatrix
for i in range(len(transitionMatrix)):
sumTotal += sum(transitionMatrix[i])
if i != len(states) and i == len(transitionMatrix):
raise ValueError('Probabilities should add to 1')
# Set the seed so we can repeat with the same results
if setSeed:
markov.setSeed(seedNum)
# Save our simulations:
list_state = []
count = 0
# Simulate Multiple Times
if setSim:
for _ in itertools.repeat(None, simNum):
markovChain = markov(states, transitionName,
transitionMatrix, startingState,
stepTime)
list_state.append(markovChain.forecast())
else:
for _ in range(1, 2):
list_state.append(markov(states, transitionName,
transitionMatrix, startingState,
stepTime).forecast())
for list in list_state:
if(list[-1] == f'{endState!s}'):
print(True, list)
count += 1
else:
print(False, list)
if setSim is False:
simNum = 1
print(f'\nThe probability of starting in {startingState} and finishing'
f' in {endState} after {stepTime} steps is {(count / simNum):.2%}')
if __name__ == '__main__':
main(simNum=simNum)
temp/numpy_test.py
+172
View File
@@ -0,0 +1,172 @@
import numpy as np
import itertools
import functools
""" Define our data """
# The Statespace
states = np.array(['Bonus', 'Ten', 'Fifty', 'Hundred', 'Five-Hundred'])
# Possible sequences of events
transitionName = np.array([['BB', 'BT', 'SI'],
['RS', 'RR', 'RI'],
['IS', 'IR', 'II']])
# Probabilities Matrix (transition matrix)
transitionMatrix = np.array([[0.2, 0.6, 0.2],
[0.1, 0.6, 0.3],
[0.2, 0.7, 0.1]])
# Starting state
startingState = 'Sleep'
# Steps to run
stepTime = 1
# End state you want to find probabilites of
endState = 'Run'
""" Set our parameters """
# Should we seed the results?
setSeed = False
seedNum = 27
""" Simulation parameters """
# Should we simulate more than once?
setSim = False
simNum = 100000
# A class that implements the Markov chain to forecast the state/mood:
class markov(object):
"""simulates a markov chain given its states, current state and
transition matrix.
Parameters:
states: list containing all the possible states
transitionName: a matrix (nested list in a list) containing a list
of the all possible state directions
transitionMatrix: a matrix (nested list in a list) containing all
the probabilites of moving to each state
currentState: a string indicating the starting state
days: an integer determining how many days (or times) to simulate"""
def __init__(self, states: np.array, transitionName: np.array,
transitionMatrix: np.array, currentState: str,
days: int):
super(markov, self).__init__()
self.states = states
self.list = list
self.transitionName = transitionName
self.transitionMatrix = transitionMatrix
self.currentState = currentState
self.days = days
@staticmethod
def setSeed(num: int):
return np.random.seed(num)
@functools.lru_cache(maxsize=128)
def forecast(self):
print(f'Start state: {self.currentState}')
# Shall store the sequence of states taken
self.stateList = [self.currentState]
i = 0
# To calculate the probability of the stateList
self.prob = 1
while i != self.days:
if self.currentState == 'Sleep':
self.change = np.random.choice(self.transitionName[0],
replace=True,
p=transitionMatrix[0])
if self.change == 'SS':
self.prob = self.prob * 0.2
self.stateList.append('Sleep')
pass
elif self.change == 'SR':
self.prob = self.prob * 0.6
self.currentState = 'Run'
self.stateList.append('Run')
else:
self.prob = self.prob * 0.2
self.currentState = "Icecream"
self.stateList.append("Icecream")
elif self.currentState == "Run":
self.change = np.random.choice(self.transitionName[1],
replace=True,
p=transitionMatrix[1])
if self.change == "RR":
self.prob = self.prob * 0.6
self.stateList.append("Run")
pass
elif self.change == "RS":
self.prob = self.prob * 0.1
self.currentState = "Sleep"
self.stateList.append("Sleep")
else:
self.prob = self.prob * 0.3
self.currentState = "Icecream"
self.stateList.append("Icecream")
elif self.currentState == "Icecream":
self.change = np.random.choice(self.transitionName[2],
replace=True,
p=transitionMatrix[2])
if self.change == "II":
self.prob = self.prob * 0.1
self.stateList.append("Icecream")
pass
elif self.change == "IS":
self.prob = self.prob * 0.2
self.currentState = "Sleep"
self.stateList.append("Sleep")
else:
self.prob = self.prob * 0.7
self.currentState = "Run"
self.stateList.append("Run")
i += 1
print(f'Possible states: {self.stateList}')
print(f'End state after {self.days} steps: {self.currentState}')
print(f'Probability of all the possible sequence of states:'
f' {self.prob}')
return self.stateList
def main(*args, **kwargs):
try:
simNum = kwargs['simNum']
except KeyError:
pass
sumTotal = 0
# Check validity of transitionMatrix
for i in range(len(transitionMatrix)):
sumTotal += sum(transitionMatrix[i])
if i != len(states) and i == len(transitionMatrix):
raise ValueError('Probabilities should add to 1')
# Set the seed so we can repeat with the same results
if setSeed:
markov.setSeed(seedNum)
# Save our simulations:
list_state = []
count = 0
# Simulate Multiple Times
if setSim:
for _ in itertools.repeat(None, simNum + 1):
markovChain = markov(states, transitionName,
transitionMatrix, startingState,
stepTime)
list_state.append(markovChain.forecast())
else:
for _ in range(1, 2):
list_state.append(markov(states, transitionName,
transitionMatrix, startingState,
stepTime).forecast())
for list in list_state:
if(list[-1] == f'{endState!s}'):
count += 1
if setSim is False:
simNum = 1
print(f'\nThe probability of starting in {startingState} and finishing'
f' in {endState} after {stepTime} steps is {(count / simNum):.2%}')
if __name__ == '__main__':
main()
temp/temp.sublime-workspace
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temp/temp_1.py
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# import pandas
def function():
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print("test")
class testclass(object):
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def __init__(self, arg):
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temp/test.py
+17
View File
@@ -0,0 +1,17 @@
#!/usr/bin/python
replace_chars = ['[', ']', '\'', ',']
list_of_ips = ['list-items-in-here']
new_list = []
def split_ips(list):
for sub_list in list:
for ip in sub_list.split():
for i in range(len(replace_chars)):
ip = ip.replace(replace_chars[i], '')
new_list.append(ip)
split_ips(list_of_ips)