We will compare the two groups using Fisher’s exact test. (Of course, the data used for the test is hypothetical.)

This post makes it easy to digest principal component analysis

from scipy import signal 

f0, t0, Zxx=signal.stft(original_wave, fs=1 )
ax.plot(f0, Zxx, lw=2.5)

simple_salesforce allows us to do it

from simple_salesforce import Salesforce
import requests
import pandas as pd
import csv
import requests
from io import StringIO
import getpass
import json
import datetime
sf0=Salesforce(username=USERNAME, password=PASSWORD, security_token=SECURITY_TOKEN)
message_a="Awesome! A message was posted at " + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
user_id=USER_ID
body0 = { "body":
{"messageSegments": [
{"type": "Text", "text": message_a},
{"type": "Mention", "id": user_id}
]},
"feedElementType": "FeedItem", "subjectId": "me"
}
zz_rest0=sf0.restful(r'chatter/feed-elements/', method='POST', data=json.dumps(body0))
import MeCab
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
num_topics=6print(len(text0))
cnt_vec…

T Miyamoto

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