# TALKPAY¶

In [137]:
import tweepy
import json
import re
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
sns.set(style='ticks', palette='Set2')
%matplotlib inline

consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''

# OAuth process, using the keys and tokens
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

# Creation of the actual interface, using authentication
api = tweepy.API(auth)

In [ ]:
c = tweepy.Cursor(api.search, q='talkpay')
c_iter = c.items()
all_text = []
for tweet in c_iter:
all_text.append(tweet.text)


I do probably the most naive approach to extracting the data I want. I just pull out the digits that fall in between \$and k. For example,$125k returns 125. This obviously doesn't get all the data and even gets some data we might not care about. For example, some people put multiple salaries over time, for which we maybe only want the most recent. For the sake of quickness and getting more data, I include everything my naive approach finds.

In [152]:
thous_dollars_raw = [re.findall(r'\$(\d*)[k|K]', t, re.DOTALL | re.MULTILINE) for t in all_text] thous_dollars = [] for dollar in thous_dollars_raw: if len(dollar) > 0: for d in dollar: thous_dollars.append(int(d)) thous_dollars = np.array(thous_dollars)  In [154]: plt.figure(figsize=(10,5)) plt.hist(thous_dollars, bins=50) plt.xlabel("Thousands of Dollars") plt.ylabel("Number of People") plt.title("#talkpay tweets") sns.despine()  In [142]: pd_series = pd.Series(thous_dollars) pd_series.describe()  Out[142]: count 315.000000 mean 96.530159 std 69.642089 min 2.000000 25% 42.000000 50% 84.000000 75% 135.000000 max 500.000000 dtype: float64 It looks like we got 315 salaries from our approach - not too bad. Also, the median salary comes out to be \$84k with the mean being pulled up to \$96.5k from the few large outliers we have. Virtually all of our data falls below \$200k and 75 percent of it below \\$135k.

So - it is kind of interesting to look at this data. I would be very hesitant to really infer much given the nature of the data and my naive way of extracting it. But still a fun, quick side project :)