infontology

infontology

#Korrelationsmatrisen
corr = df_cancer.corr()

def correlation_columns (correlation_matrix, threshold):
    all_cols = correlation_matrix.columns
    cols = [all_cols[correlation_matrix[column]>threshold] for column in correlation_matrix]
    cols = [col.tolist() for col in cols]
    result = [(all_cols[index], col) for index, col in enumerate(cols)]
    return result

infontology

infontology

import pickle
import requests

#Skapa picklad fil från np.array (db_contents)
with open('pick.npy', 'wb') as pickle_file:
    pickle.dump(db_contents, pickle_file)
    
#Läsa in filen lokalt.
with open('pick.npy', 'rb') as pickle_file:
    groda = pickle.load(pickle_file) #groda blir snällt en np.array igen.

infontology

infontology

pattern = re.compile('(?<=\s\S)\s(?!\S\S)', re.S)
re.sub(pattern, '', text)

infontology

import os
import shutil

raw_ext = '.NEF'
jpg_ext = '.JPG'
destination = 'raws'

for filename in os.listdir('.'):
    (shortname, extension) = os.path.splitext(filename)

infontology

infontology

conn = sqlite3.connect('../livs.db')  # Create db and establish connection
conn.row_factory = sqlite3.Row
curs = conn.cursor()
result = []
rows = curs.execute('select * from livs limit 4000')
for row in rows:
        result.append(row)

db_contents = np.array(result)
print (db_contents)

infontology