HW5 -- Artificial Artificial Intelligence¶
In [60]:
import pandas as pd
import numpy as np
neg = pd.read_csv('AMT_neg.csv')
pos = pd.read_csv('AMT_pos.csv')
Initial EDA¶
In [61]:
neg[:3]
Out[61]:
| HITId | HITTypeId | Title | Description | Keywords | Reward | CreationTime | MaxAssignments | RequesterAnnotation | AssignmentDurationInSeconds | ... | RejectionTime | RequesterFeedback | WorkTimeInSeconds | LifetimeApprovalRate | Last30DaysApprovalRate | Last7DaysApprovalRate | Input.text | Answer.sentiment.label | Approve | Reject | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3IQ9O0AYW6ZI3GD740H32KGG2SWITJ | 3N0K7CX2I27L2NR2L8D93MF8LIRA5J | Sentiment analysis | Sentiment analysis | sentiment, text | $0.02 | Fri Nov 01 12:08:17 PDT 2019 | 3 | BatchId:3821423;OriginalHitTemplateId:928390909; | 10800 | ... | NaN | NaN | 44 | 0% (0/0) | 0% (0/0) | 0% (0/0) | Missed Opportunity\nI had been very excited to... | Neutral | NaN | NaN |
| 1 | 3IQ9O0AYW6ZI3GD740H32KGG2SWITJ | 3N0K7CX2I27L2NR2L8D93MF8LIRA5J | Sentiment analysis | Sentiment analysis | sentiment, text | $0.02 | Fri Nov 01 12:08:17 PDT 2019 | 3 | BatchId:3821423;OriginalHitTemplateId:928390909; | 10800 | ... | NaN | NaN | 7 | 0% (0/0) | 0% (0/0) | 0% (0/0) | Missed Opportunity\nI had been very excited to... | Negative | NaN | NaN |
| 2 | 3IQ9O0AYW6ZI3GD740H32KGG2SWITJ | 3N0K7CX2I27L2NR2L8D93MF8LIRA5J | Sentiment analysis | Sentiment analysis | sentiment, text | $0.02 | Fri Nov 01 12:08:17 PDT 2019 | 3 | BatchId:3821423;OriginalHitTemplateId:928390909; | 10800 | ... | NaN | NaN | 449 | 0% (0/0) | 0% (0/0) | 0% (0/0) | Missed Opportunity\nI had been very excited to... | Positive | NaN | NaN |
3 rows × 31 columns
In [62]:
pos[:3]
Out[62]:
| HITId | HITTypeId | Title | Description | Keywords | Reward | CreationTime | MaxAssignments | RequesterAnnotation | AssignmentDurationInSeconds | ... | RejectionTime | RequesterFeedback | WorkTimeInSeconds | LifetimeApprovalRate | Last30DaysApprovalRate | Last7DaysApprovalRate | Input.text | Answer.sentiment.label | Approve | Reject | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3VMV5CHJZ8F47P7CECH0H830NF4GTP | 3N0K7CX2I27L2NR2L8D93MF8LIRA5J | Sentiment analysis | Sentiment analysis | sentiment, text | $0.02 | Fri Nov 01 12:11:19 PDT 2019 | 3 | BatchId:3821427;OriginalHitTemplateId:928390909; | 10800 | ... | NaN | NaN | 355 | 0% (0/0) | 0% (0/0) | 0% (0/0) | funny like a clown\nGreetings again from the d... | Positive | NaN | NaN |
| 1 | 3VMV5CHJZ8F47P7CECH0H830NF4GTP | 3N0K7CX2I27L2NR2L8D93MF8LIRA5J | Sentiment analysis | Sentiment analysis | sentiment, text | $0.02 | Fri Nov 01 12:11:19 PDT 2019 | 3 | BatchId:3821427;OriginalHitTemplateId:928390909; | 10800 | ... | NaN | NaN | 487 | 0% (0/0) | 0% (0/0) | 0% (0/0) | funny like a clown\nGreetings again from the d... | Neutral | NaN | NaN |
| 2 | 3VMV5CHJZ8F47P7CECH0H830NF4GTP | 3N0K7CX2I27L2NR2L8D93MF8LIRA5J | Sentiment analysis | Sentiment analysis | sentiment, text | $0.02 | Fri Nov 01 12:11:19 PDT 2019 | 3 | BatchId:3821427;OriginalHitTemplateId:928390909; | 10800 | ... | NaN | NaN | 1052 | 0% (0/0) | 0% (0/0) | 0% (0/0) | funny like a clown\nGreetings again from the d... | Positive | NaN | NaN |
3 rows × 31 columns
In [63]:
neg.columns.tolist()
Out[63]:
['HITId', 'HITTypeId', 'Title', 'Description', 'Keywords', 'Reward', 'CreationTime', 'MaxAssignments', 'RequesterAnnotation', 'AssignmentDurationInSeconds', 'AutoApprovalDelayInSeconds', 'Expiration', 'NumberOfSimilarHITs', 'LifetimeInSeconds', 'AssignmentId', 'WorkerId', 'AssignmentStatus', 'AcceptTime', 'SubmitTime', 'AutoApprovalTime', 'ApprovalTime', 'RejectionTime', 'RequesterFeedback', 'WorkTimeInSeconds', 'LifetimeApprovalRate', 'Last30DaysApprovalRate', 'Last7DaysApprovalRate', 'Input.text', 'Answer.sentiment.label', 'Approve', 'Reject']
How many unique turkers worked on each dataframe?¶
In [64]:
def get_unique(df, column):
unique = np.unique(df[column], return_counts=True)
df = pd.DataFrame(zip(unique[0], unique[1]))
return len(unique[0]), unique, df
num_neg, unique_neg, u_neg_df = get_unique(neg, 'WorkerId')
num_pos, unique_pos, u_pos_df = get_unique(pos, 'WorkerId')
print(num_neg, 'Turkers worked on NEG batch')
print(num_pos, 'Turkers worked on POS batch')
53 Turkers worked on NEG batch 38 Turkers worked on POS batch
How many HITS did each unique turker do?¶
In [65]:
u_neg_df.plot(kind='bar',x=0,y=1)
Out[65]:
<matplotlib.axes._subplots.AxesSubplot at 0x11aa920b8>
In [66]:
u_pos_df.plot(kind='bar',x=0,y=1)
Out[66]:
<matplotlib.axes._subplots.AxesSubplot at 0x11c0be898>
What's the max and min HIT for unique turkers¶
In [67]:
print('For {}, the min was: {} and the max was: {}'.format('neg', unique_neg[1].min(), unique_neg[1].max()))
print('For {}, the min was: {} and the max was: {}'.format('pos', unique_pos[1].min(), unique_pos[1].max()))
For neg, the min was: 1 and the max was: 37 For pos, the min was: 1 and the max was: 40
Did a specitic Sentiment take longer for turkers to assess?¶
In [68]:
import seaborn as sns
import matplotlib.pyplot as plt
sns.catplot(x="Answer.sentiment.label",
y="WorkTimeInSeconds",
kind="bar",
order=['Negative', 'Neutral', 'Positive'],
data=neg);
plt.title('Negative')
Out[68]:
Text(0.5, 1, 'Negative')
In [69]:
sns.catplot(x="Answer.sentiment.label",
y="WorkTimeInSeconds",
kind="bar",
order=['Negative', 'Neutral', 'Positive'],
data=pos)
plt.title('Positive')
Out[69]:
Text(0.5, 1, 'Positive')
How many turkers had less than 10 second response time?¶
In [70]:
response_time = neg[neg['WorkTimeInSeconds'] < 10]
response_time_check = neg[neg['WorkTimeInSeconds'] > 10]
In [71]:
len(response_time)
Out[71]:
48
In [72]:
len(response_time_check)
Out[72]:
312
Checking for potential bots¶
Did anyone have a consistent average low response time?¶
In [73]:
count = pos.groupby(['WorkerId'])['HITId'].count()
work_time = pos.groupby(['WorkerId'])['WorkTimeInSeconds'].mean()
new_df = pd.DataFrame([work_time, count]).T
new_df[:5]
Out[73]:
| WorkTimeInSeconds | HITId | |
|---|---|---|
| WorkerId | ||
| A13CLN8L5HFT46 | 7.230769 | 13.0 |
| A18WFPSLFV4FKY | 47.000000 | 2.0 |
| A1IQV3QUWRA8G1 | 22.000000 | 1.0 |
| A1N1ULK71RHVMM | 10.000000 | 3.0 |
| A1S2MN0E9BHPVA | 173.444444 | 27.0 |
Did anyone have a consistent average high response time?¶
In [74]:
new_df['WorkTimeInMin'] = new_df['WorkTimeInSeconds']/60
new_df[:5]
Out[74]:
| WorkTimeInSeconds | HITId | WorkTimeInMin | |
|---|---|---|---|
| WorkerId | |||
| A13CLN8L5HFT46 | 7.230769 | 13.0 | 0.120513 |
| A18WFPSLFV4FKY | 47.000000 | 2.0 | 0.783333 |
| A1IQV3QUWRA8G1 | 22.000000 | 1.0 | 0.366667 |
| A1N1ULK71RHVMM | 10.000000 | 3.0 | 0.166667 |
| A1S2MN0E9BHPVA | 173.444444 | 27.0 | 2.890741 |
In [75]:
count = pos.groupby(['WorkerId', 'Answer.sentiment.label'])['Answer.sentiment.label'].count()
# count = pos.groupby(['WorkerId'])['Answer.sentiment.label'].count()
count
Out[75]:
WorkerId Answer.sentiment.label
A13CLN8L5HFT46 Neutral 2
Positive 11
A18WFPSLFV4FKY Positive 2
A1IQV3QUWRA8G1 Positive 1
A1N1ULK71RHVMM Negative 1
..
AMC42JMQA8A5U Positive 1
AO2WNSGOXAX52 Neutral 3
Positive 1
AOMFEAWQHU3D8 Neutral 1
Positive 6
Name: Answer.sentiment.label, Length: 74, dtype: int64
Did anyone answer ONLY pos/neg/neutral?¶
In [76]:
pnn = pd.DataFrame()
pnn['Neutral'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Neutral').sum())
pnn['Positive'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Positive').sum())
pnn['Negative'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Negative').sum())
pnn['Total'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: x.count())
pnn[:5]
Out[76]:
| Neutral | Positive | Negative | Total | |
|---|---|---|---|---|
| WorkerId | ||||
| A13CLN8L5HFT46 | 2 | 11 | 0 | 13 |
| A18WFPSLFV4FKY | 0 | 2 | 0 | 2 |
| A1IQV3QUWRA8G1 | 0 | 1 | 0 | 1 |
| A1N1ULK71RHVMM | 0 | 2 | 1 | 3 |
| A1S2MN0E9BHPVA | 2 | 21 | 4 | 27 |
This is getting a little confusing, let's just look at our top performers¶
In [77]:
top = pnn.sort_values(by=['Total'], ascending=False)
In [78]:
top[:10]
Out[78]:
| Neutral | Positive | Negative | Total | |
|---|---|---|---|---|
| WorkerId | ||||
| A681XM15AN28F | 13 | 20 | 7 | 40 |
| A1Y66T7FKJ8PJA | 5 | 23 | 7 | 35 |
| A33ENZVC1XB4BA | 0 | 34 | 0 | 34 |
| A1S2MN0E9BHPVA | 2 | 21 | 4 | 27 |
| A37L5E8MHHQGZM | 6 | 13 | 3 | 22 |
| AE03LUY7RH400 | 4 | 10 | 7 | 21 |
| A2G44A4ZPWRPXU | 4 | 12 | 2 | 18 |
| A1YK1IKACUJMV4 | 0 | 15 | 0 | 15 |
| A3AW887GI0NLKF | 3 | 10 | 2 | 15 |
| A3HAEQW13YPT6A | 0 | 14 | 0 | 14 |
Interesting!! Looking from here, we have three workers who ONLY chose positive.
Let's look at their response time to see if we can determine if they are a bot!!
In [79]:
top['Avg_WorkTimeInSeconds'] = pos.groupby('WorkerId')['WorkTimeInSeconds'].apply(lambda x: x.mean())
top['Avg_WorkTimeInMin'] = pos.groupby('WorkerId')['WorkTimeInSeconds'].apply(lambda x: x.mean()/60)
top['Min_WorkTimeInMin'] = pos.groupby('WorkerId')['WorkTimeInSeconds'].apply(lambda x: x.min()/60)
top['Max_WorkTimeInMin'] = pos.groupby('WorkerId')['WorkTimeInSeconds'].apply(lambda x: x.max()/60)
In [80]:
top[:10]
Out[80]:
| Neutral | Positive | Negative | Total | Avg_WorkTimeInSeconds | Avg_WorkTimeInMin | Min_WorkTimeInMin | Max_WorkTimeInMin | |
|---|---|---|---|---|---|---|---|---|
| WorkerId | ||||||||
| A681XM15AN28F | 13 | 20 | 7 | 40 | 13.575000 | 0.226250 | 0.100000 | 0.833333 |
| A1Y66T7FKJ8PJA | 5 | 23 | 7 | 35 | 695.857143 | 11.597619 | 0.216667 | 22.000000 |
| A33ENZVC1XB4BA | 0 | 34 | 0 | 34 | 366.647059 | 6.110784 | 0.616667 | 9.916667 |
| A1S2MN0E9BHPVA | 2 | 21 | 4 | 27 | 173.444444 | 2.890741 | 0.400000 | 4.983333 |
| A37L5E8MHHQGZM | 6 | 13 | 3 | 22 | 346.272727 | 5.771212 | 2.150000 | 8.283333 |
| AE03LUY7RH400 | 4 | 10 | 7 | 21 | 102.238095 | 1.703968 | 0.100000 | 3.433333 |
| A2G44A4ZPWRPXU | 4 | 12 | 2 | 18 | 221.277778 | 3.687963 | 0.383333 | 7.383333 |
| A1YK1IKACUJMV4 | 0 | 15 | 0 | 15 | 593.600000 | 9.893333 | 1.716667 | 11.000000 |
| A3AW887GI0NLKF | 3 | 10 | 2 | 15 | 269.400000 | 4.490000 | 1.616667 | 7.216667 |
| A3HAEQW13YPT6A | 0 | 14 | 0 | 14 | 442.928571 | 7.382143 | 0.866667 | 11.100000 |
Even more interesting! These two don't appear to be bots, based on our current metric which is time variability.
HOWEVER, worker A681XM15AN28F appears to only work for an average of 13 seconds per review which doesn't seem like enough time to read and judge a review...
PART 2: Second submission to AMT¶
TOO MANY REVIEWERS!
Here is when we realized that doing a kappa score with over 30 individual reviewers would be tricky, so we rusubmitted to AMT and required the turkers to be 'Master' in the hopes that this additional barrier-to-entry would help reduce the amount of turkers working on the project
In [81]:
v2 = pd.read_csv('HW5_amt_v2.csv')
v2[:5]
len(v2)
Out[81]:
293
This time, I didn't separate the df into pos and neg before submitting to AMT, so we have to reimport the labels.
In [82]:
labels = pd.read_csv('all_JK_extremes_labeled.csv')
In [83]:
len(labels)
Out[83]:
98
Oops! That's right, we replicated each review * 3 so three separate people could look at each review
In [84]:
labels2 = labels.append([labels] * 2, ignore_index=True)
In [85]:
len(labels2)
Out[85]:
294
In [86]:
labels2.sort_values(by='0')
Out[86]:
| 0 | PoN | |
|---|---|---|
| 76 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P |
| 174 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P |
| 272 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P |
| 116 | A 'Triumph of the Will' for Nihilists\n'Joker... | N |
| 18 | A 'Triumph of the Will' for Nihilists\n'Joker... | N |
| ... | ... | ... |
| 227 | lose of both time and money\nThis was one of ... | N |
| 31 | lose of both time and money\nThis was one of ... | N |
| 207 | poor plot\nPoor plot. i find no reason for jo... | N |
| 11 | poor plot\nPoor plot. i find no reason for jo... | N |
| 109 | poor plot\nPoor plot. i find no reason for jo... | N |
294 rows × 2 columns
Shoot! I realized I had to delete some emojis for the csv to be accepted by AMT, so the reviews themselves won't actually be matching... solution: Create two 'for-matching' columns made up of the first 5 words of each review
In [87]:
v2['for_matching'] = v2.apply(lambda x: x['Input.text'].split()[:5], axis=1)
In [88]:
labels2['for_matching'] = labels2.apply(lambda x: x['0'].split()[:5], axis=1)
Annnnnd why did I do that when I could just sort the df and apply the PoN
In [89]:
sorted_labels = labels2.sort_values(by='0')
sorted_labels[:6]
Out[89]:
| 0 | PoN | for_matching | |
|---|---|---|---|
| 76 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P | [#LetRottenTomatoesRotSquad, I, am, a, simple] |
| 174 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P | [#LetRottenTomatoesRotSquad, I, am, a, simple] |
| 272 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P | [#LetRottenTomatoesRotSquad, I, am, a, simple] |
| 116 | A 'Triumph of the Will' for Nihilists\n'Joker... | N | [A, 'Triumph, of, the, Will'] |
| 18 | A 'Triumph of the Will' for Nihilists\n'Joker... | N | [A, 'Triumph, of, the, Will'] |
| 214 | A 'Triumph of the Will' for Nihilists\n'Joker... | N | [A, 'Triumph, of, the, Will'] |
In [90]:
sorted_v2 = v2.sort_values(by='Input.text')
sorted_v2[sorted_v2.columns[-5:]][:6]
Out[90]:
| Input.text | Answer.sentiment.label | Approve | Reject | for_matching | |
|---|---|---|---|---|---|
| 229 | #LetRottenTomatoesRotSquad\nI am a simple guy... | Positive | NaN | NaN | [#LetRottenTomatoesRotSquad, I, am, a, simple] |
| 228 | #LetRottenTomatoesRotSquad\nI am a simple guy... | Positive | NaN | NaN | [#LetRottenTomatoesRotSquad, I, am, a, simple] |
| 227 | #LetRottenTomatoesRotSquad\nI am a simple guy... | Positive | NaN | NaN | [#LetRottenTomatoesRotSquad, I, am, a, simple] |
| 53 | A 'Triumph of the Will' for Nihilists\n'Joker... | Neutral | NaN | NaN | [A, 'Triumph, of, the, Will'] |
| 55 | A 'Triumph of the Will' for Nihilists\n'Joker... | Negative | NaN | NaN | [A, 'Triumph, of, the, Will'] |
| 54 | A 'Triumph of the Will' for Nihilists\n'Joker... | Negative | NaN | NaN | [A, 'Triumph, of, the, Will'] |
In [91]:
all_df = sorted_v2.copy()
# all_df['PoN'] = sorted_labels['PoN'].tolist()
# THIS DIDN'T WORK BECAUSE I DIDN'T WAIT UNTIL ALL WERE DONE FROM AMT. RESEARCHER ERROR BUT OMG I HATE MYSELF
In [92]:
len(all_df)
Out[92]:
293
In [93]:
293/3
Out[93]:
97.66666666666667
Confirming that YEP. 293 isn't divisible by 3, meaning I didn't wait until the last turker finished. omg.
Reuploading now -- WITH BETTER CODE AND BETTER VARIABLE NAMES!¶
In [94]:
turker = pd.read_csv('HW5_amt_294.csv')
print(len(turker))
turker[turker.columns[-5:]][:5]
294
Out[94]:
| Last7DaysApprovalRate | Input.text | Answer.sentiment.label | Approve | Reject | |
|---|---|---|---|---|---|
| 0 | 0% (0/0) | Everyone praised an overrated movie.\nOverrat... | Negative | NaN | NaN |
| 1 | 0% (0/0) | Everyone praised an overrated movie.\nOverrat... | Negative | NaN | NaN |
| 2 | 0% (0/0) | Everyone praised an overrated movie.\nOverrat... | Negative | NaN | NaN |
| 3 | 0% (0/0) | What idiotic FIlm\nI can say that Phoenix is ... | Negative | NaN | NaN |
| 4 | 0% (0/0) | What idiotic FIlm\nI can say that Phoenix is ... | Negative | NaN | NaN |
In [95]:
# Getting labels...
labels = pd.read_csv('all_JK_extremes_labeled.csv')
# X3
labels = labels.append([labels] * 2, ignore_index=True)
print(len(labels))
labels[:5]
294
Out[95]:
| 0 | PoN | |
|---|---|---|
| 0 | Everyone praised an overrated movie.\nOverrat... | N |
| 1 | What idiotic FIlm\nI can say that Phoenix is ... | N |
| 2 | Terrible\nThe only thing good about this movi... | N |
| 3 | Watch Taxi Driver instead\nThis is a poor att... | N |
| 4 | I learned one thing.\nIt borrows a lot of ele... | N |
NOW, TO SORT!¶
In [96]:
sorted_labels = labels.sort_values(by=['0'])
sorted_turker = turker.sort_values(by=['Input.text'])
In [97]:
sorted_labels[:5]
Out[97]:
| 0 | PoN | |
|---|---|---|
| 76 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P |
| 174 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P |
| 272 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P |
| 116 | A 'Triumph of the Will' for Nihilists\n'Joker... | N |
| 18 | A 'Triumph of the Will' for Nihilists\n'Joker... | N |
In [98]:
sorted_turker['Input.text'][:5]
Out[98]:
228 #LetRottenTomatoesRotSquad\nI am a simple guy... 229 #LetRottenTomatoesRotSquad\nI am a simple guy... 230 #LetRottenTomatoesRotSquad\nI am a simple guy... 56 A 'Triumph of the Will' for Nihilists\n'Joker... 55 A 'Triumph of the Will' for Nihilists\n'Joker... Name: Input.text, dtype: object
OMG HOORAY HOORAY HOORAY!!
NOTE: FUN FACT!! I can type here and then hit the esc key to turn this cell into markdown!!
In [99]:
# YUCK THIS IS SO AGGRIVATING!! This line below doens't work because it still uses indexes.
# So the P and N didn't match up
# sorted_turker['PoN'] = sorted_labels['PoN']
sorted_turker['PoN'] = sorted_labels['PoN'].tolist()
sorted_turker[sorted_turker.columns[-5:]][:5]
Out[99]:
| Input.text | Answer.sentiment.label | Approve | Reject | PoN | |
|---|---|---|---|---|---|
| 228 | #LetRottenTomatoesRotSquad\nI am a simple guy... | Positive | NaN | NaN | P |
| 229 | #LetRottenTomatoesRotSquad\nI am a simple guy... | Positive | NaN | NaN | P |
| 230 | #LetRottenTomatoesRotSquad\nI am a simple guy... | Positive | NaN | NaN | P |
| 56 | A 'Triumph of the Will' for Nihilists\n'Joker... | Negative | NaN | NaN | N |
| 55 | A 'Triumph of the Will' for Nihilists\n'Joker... | Negative | NaN | NaN | N |
PART 3: ANALYZE¶
First, let's clean ALL the things
In [100]:
all_df = sorted_turker[['Input.text', 'WorkerId', 'Answer.sentiment.label', 'PoN']]
In [101]:
all_df[:5]
Out[101]:
| Input.text | WorkerId | Answer.sentiment.label | PoN | |
|---|---|---|---|---|
| 228 | #LetRottenTomatoesRotSquad\nI am a simple guy... | A681XM15AN28F | Positive | P |
| 229 | #LetRottenTomatoesRotSquad\nI am a simple guy... | A2XFO0X6RCS98M | Positive | P |
| 230 | #LetRottenTomatoesRotSquad\nI am a simple guy... | AURYD2FH3FUOQ | Positive | P |
| 56 | A 'Triumph of the Will' for Nihilists\n'Joker... | A1T79J0XQXDDGC | Negative | N |
| 55 | A 'Triumph of the Will' for Nihilists\n'Joker... | A2XFO0X6RCS98M | Negative | N |
In [102]:
all_df_all = all_df.copy()
all_df_all['APoN'] = all_df_all.apply(lambda x: x['Answer.sentiment.label'][0], axis=1)
In [103]:
all_df_all
Out[103]:
| Input.text | WorkerId | Answer.sentiment.label | PoN | APoN | |
|---|---|---|---|---|---|
| 228 | #LetRottenTomatoesRotSquad\nI am a simple guy... | A681XM15AN28F | Positive | P | P |
| 229 | #LetRottenTomatoesRotSquad\nI am a simple guy... | A2XFO0X6RCS98M | Positive | P | P |
| 230 | #LetRottenTomatoesRotSquad\nI am a simple guy... | AURYD2FH3FUOQ | Positive | P | P |
| 56 | A 'Triumph of the Will' for Nihilists\n'Joker... | A1T79J0XQXDDGC | Negative | N | N |
| 55 | A 'Triumph of the Will' for Nihilists\n'Joker... | A2XFO0X6RCS98M | Negative | N | N |
| ... | ... | ... | ... | ... | ... |
| 265 | Venice 76 review\nI have just watched the Joke... | ARLGZWN6W91WD | Positive | N | P |
| 266 | Venice 76 review\nI have just watched the Joke... | A38DC3BG1ZCVZ2 | Positive | N | P |
| 93 | lose of both time and money\nThis was one of t... | A2XFO0X6RCS98M | Negative | N | N |
| 94 | lose of both time and money\nThis was one of t... | A3EZ0H07TSDAPW | Negative | N | N |
| 95 | lose of both time and money\nThis was one of t... | ASB8T0H7L99RF | Negative | N | N |
294 rows × 5 columns
In [104]:
all_df_all['agree'] = all_df_all.apply(lambda x: x['PoN'] == x['APoN'], axis=1)
In [105]:
all_df_all[-10:]
Out[105]:
| Input.text | WorkerId | Answer.sentiment.label | PoN | APoN | agree | |
|---|---|---|---|---|---|---|
| 38 | This is extremely bad...\nThis whole film make... | A3EZ0H07TSDAPW | Negative | N | N | True |
| 216 | Took my 65 year old mother to see it.\nI saw t... | A3EZ0H07TSDAPW | Positive | N | P | False |
| 217 | Took my 65 year old mother to see it.\nI saw t... | A2XFO0X6RCS98M | Positive | N | P | False |
| 218 | Took my 65 year old mother to see it.\nI saw t... | AKSJ3C5O3V9RB | Positive | N | P | False |
| 264 | Venice 76 review\nI have just watched the Joke... | A3EZ0H07TSDAPW | Positive | N | P | False |
| 265 | Venice 76 review\nI have just watched the Joke... | ARLGZWN6W91WD | Positive | N | P | False |
| 266 | Venice 76 review\nI have just watched the Joke... | A38DC3BG1ZCVZ2 | Positive | N | P | False |
| 93 | lose of both time and money\nThis was one of t... | A2XFO0X6RCS98M | Negative | N | N | True |
| 94 | lose of both time and money\nThis was one of t... | A3EZ0H07TSDAPW | Negative | N | N | True |
| 95 | lose of both time and money\nThis was one of t... | ASB8T0H7L99RF | Negative | N | N | True |
Lets see how many agree!¶
In [106]:
agree_df = pd.DataFrame(all_df_all.groupby(['Input.text','PoN'])['agree'].mean())
agree_df = agree_df.reset_index()
agree_df[:5]
Out[106]:
| Input.text | PoN | agree | |
|---|---|---|---|
| 0 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P | 1.000000 |
| 1 | A 'Triumph of the Will' for Nihilists\n'Joker... | N | 1.000000 |
| 2 | A Breath of Fresh Cinema\nBursting with emoti... | P | 1.000000 |
| 3 | A MASTERPIECE\nJoaquin Phoenix's performance ... | N | 0.333333 |
| 4 | A brilliant movie\nThis movie is slow but nev... | P | 1.000000 |
OK so this actually gave us something we want... BUT PLEASE TELL ME THE BETTER WAY!!
In [107]:
def return_agreement(num):
if num == 0:
return 'agree_wrong'
if num == 1:
return 'agree'
if (num/1) !=0:
return 'disparity'
agree_df['agree_factor'] = agree_df.apply(lambda x: return_agreement(x['agree']), axis=1)
agree_df
Out[107]:
| Input.text | PoN | agree | agree_factor | |
|---|---|---|---|---|
| 0 | #LetRottenTomatoesRotSquad\nI am a simple guy... | P | 1.000000 | agree |
| 1 | A 'Triumph of the Will' for Nihilists\n'Joker... | N | 1.000000 | agree |
| 2 | A Breath of Fresh Cinema\nBursting with emoti... | P | 1.000000 | agree |
| 3 | A MASTERPIECE\nJoaquin Phoenix's performance ... | N | 0.333333 | disparity |
| 4 | A brilliant movie\nThis movie is slow but nev... | P | 1.000000 | agree |
| ... | ... | ... | ... | ... |
| 93 | The mirror of society\nActing 10/10\nActors 10... | N | 0.000000 | agree_wrong |
| 94 | This is extremely bad...\nThis whole film make... | N | 1.000000 | agree |
| 95 | Took my 65 year old mother to see it.\nI saw t... | N | 0.000000 | agree_wrong |
| 96 | Venice 76 review\nI have just watched the Joke... | N | 0.000000 | agree_wrong |
| 97 | lose of both time and money\nThis was one of t... | N | 1.000000 | agree |
98 rows × 4 columns
In [122]:
df1 = agree_df.groupby(['agree_factor']).count()
df1.reset_index(inplace=True)
df1
Out[122]:
| agree_factor | Input.text | PoN | agree | |
|---|---|---|---|---|
| 0 | agree | 33 | 33 | 33 |
| 1 | agree_wrong | 31 | 31 | 31 |
| 2 | disparity | 34 | 34 | 34 |
In [123]:
sns.barplot(x=['Agreed', 'Disagreed'],
y= [64,34],
data = df1);
plt.title('How many turkers agreed on sentiment?')
Out[123]:
Text(0.5, 1.0, 'How many turkers agreed on sentiment?')
In [129]:
sns.barplot(x="agree_factor", y="agree", data=df1);
plt.title('How many turkers agreed on sentiment, but were wrong?')
Out[129]:
Text(0.5, 1.0, 'How many turkers agreed on sentiment, but were wrong?')
In [130]:
df2 = agree_df.groupby(['agree_factor', 'PoN']).count()
df2.reset_index(inplace=True)
In [131]:
sns.barplot(x="agree_factor",
y="agree",
hue="PoN",
data=df2);
plt.title("What was the pos/neg split for the turkers?")
Out[131]:
Text(0.5, 1.0, 'What was the pos/neg split for the turkers?')
What was the kappa score for the turkers?¶
In [132]:
# Example code
from sklearn.metrics import cohen_kappa_score
y1 = [0,1,2,3,4,0,1,2,3,4,0,1,2,3,4]
y2 = [0,1,2,2,4,1,2,3,0,0,0,2,2,4,4]
cohen_kappa_score(y1,y2)
Out[132]:
0.33333333333333337
In [136]:
pnn = pd.DataFrame()
# pnn['Neutral'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Neutral').sum())
# pnn['Positive'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Positive').sum())
# pnn['Negative'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: (x=='Negative').sum())
# pnn['Total'] = pos.groupby('WorkerId')['Answer.sentiment.label'].apply(lambda x: x.count())
# pnn[:5]
In [137]:
Out[137]:
| Input.text | WorkerId | Answer.sentiment.label | PoN | |
|---|---|---|---|---|
| 228 | #LetRottenTomatoesRotSquad\nI am a simple guy... | A681XM15AN28F | Positive | P |
| 229 | #LetRottenTomatoesRotSquad\nI am a simple guy... | A2XFO0X6RCS98M | Positive | P |
| 230 | #LetRottenTomatoesRotSquad\nI am a simple guy... | AURYD2FH3FUOQ | Positive | P |
| 56 | A 'Triumph of the Will' for Nihilists\n'Joker... | A1T79J0XQXDDGC | Negative | N |
| 55 | A 'Triumph of the Will' for Nihilists\n'Joker... | A2XFO0X6RCS98M | Negative | N |
| ... | ... | ... | ... | ... |
| 265 | Venice 76 review\nI have just watched the Joke... | ARLGZWN6W91WD | Positive | N |
| 266 | Venice 76 review\nI have just watched the Joke... | A38DC3BG1ZCVZ2 | Positive | N |
| 93 | lose of both time and money\nThis was one of t... | A2XFO0X6RCS98M | Negative | N |
| 94 | lose of both time and money\nThis was one of t... | A3EZ0H07TSDAPW | Negative | N |
| 95 | lose of both time and money\nThis was one of t... | ASB8T0H7L99RF | Negative | N |
294 rows × 4 columns
In [138]:
top[:10]
Out[138]:
| Neutral | Positive | Negative | Total | Avg_WorkTimeInSeconds | Avg_WorkTimeInMin | Min_WorkTimeInMin | Max_WorkTimeInMin | |
|---|---|---|---|---|---|---|---|---|
| WorkerId | ||||||||
| A681XM15AN28F | 13 | 20 | 7 | 40 | 13.575000 | 0.226250 | 0.100000 | 0.833333 |
| A1Y66T7FKJ8PJA | 5 | 23 | 7 | 35 | 695.857143 | 11.597619 | 0.216667 | 22.000000 |
| A33ENZVC1XB4BA | 0 | 34 | 0 | 34 | 366.647059 | 6.110784 | 0.616667 | 9.916667 |
| A1S2MN0E9BHPVA | 2 | 21 | 4 | 27 | 173.444444 | 2.890741 | 0.400000 | 4.983333 |
| A37L5E8MHHQGZM | 6 | 13 | 3 | 22 | 346.272727 | 5.771212 | 2.150000 | 8.283333 |
| AE03LUY7RH400 | 4 | 10 | 7 | 21 | 102.238095 | 1.703968 | 0.100000 | 3.433333 |
| A2G44A4ZPWRPXU | 4 | 12 | 2 | 18 | 221.277778 | 3.687963 | 0.383333 | 7.383333 |
| A1YK1IKACUJMV4 | 0 | 15 | 0 | 15 | 593.600000 | 9.893333 | 1.716667 | 11.000000 |
| A3AW887GI0NLKF | 3 | 10 | 2 | 15 | 269.400000 | 4.490000 | 1.616667 | 7.216667 |
| A3HAEQW13YPT6A | 0 | 14 | 0 | 14 | 442.928571 | 7.382143 | 0.866667 | 11.100000 |
In [141]:
newdf = pd.DataFrame(turker.groupby(['HITId', 'WorkerId']))
In [142]:
newdf
Out[142]:
| 0 | 1 | |
|---|---|---|
| 0 | (302OLP89DZ7MBHSY6QU0WCST11GACJ, A1T79J0XQXDDGC) | HITId ... |
| 1 | (302OLP89DZ7MBHSY6QU0WCST11GACJ, A2XFO0X6RCS98M) | HITId ... |
| 2 | (302OLP89DZ7MBHSY6QU0WCST11GACJ, A681XM15AN28F) | HITId ... |
| 3 | (3087LXLJ6MGXDGEQ5QN8FC1JPSW0FT, A1L8RL58MYU4NC) | HITId ... |
| 4 | (3087LXLJ6MGXDGEQ5QN8FC1JPSW0FT, A1T79J0XQXDDGC) | HITId ... |
| ... | ... | ... |
| 289 | (3ZLW647WALV9TE1B0IQKXR51J0B327, A38DC3BG1ZCVZ2) | HITId ... |
| 290 | (3ZLW647WALV9TE1B0IQKXR51J0B327, ARLGZWN6W91WD) | HITId ... |
| 291 | (3ZRKL6Z1E833SPUXPCCA737ELZESG6, A1L8RL58MYU4NC) | HITId ... |
| 292 | (3ZRKL6Z1E833SPUXPCCA737ELZESG6, A38DC3BG1ZCVZ2) | HITId ... |
| 293 | (3ZRKL6Z1E833SPUXPCCA737ELZESG6, A681XM15AN28F) | HITId ... |
294 rows × 2 columns
In [147]:
turker.columns
Out[147]:
Index(['HITId', 'HITTypeId', 'Title', 'Description', 'Keywords', 'Reward',
'CreationTime', 'MaxAssignments', 'RequesterAnnotation',
'AssignmentDurationInSeconds', 'AutoApprovalDelayInSeconds',
'Expiration', 'NumberOfSimilarHITs', 'LifetimeInSeconds',
'AssignmentId', 'WorkerId', 'AssignmentStatus', 'AcceptTime',
'SubmitTime', 'AutoApprovalTime', 'ApprovalTime', 'RejectionTime',
'RequesterFeedback', 'WorkTimeInSeconds', 'LifetimeApprovalRate',
'Last30DaysApprovalRate', 'Last7DaysApprovalRate', 'Input.text',
'Answer.sentiment.label', 'Approve', 'Reject'],
dtype='object')
In [148]:
turker_clean = turker[['HITId', 'WorkerId', 'Answer.sentiment.label', 'Input.text']]
In [149]:
# turker_clean.groupby
Out[149]:
| HITId | WorkerId | Answer.sentiment.label | Input.text | |
|---|---|---|---|---|
| 0 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | AH5A86OLRZWCS | Negative | Everyone praised an overrated movie.\nOverrat... |
| 1 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | A2HGRSPR50ENHL | Negative | Everyone praised an overrated movie.\nOverrat... |
| 2 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | AKSJ3C5O3V9RB | Negative | Everyone praised an overrated movie.\nOverrat... |
| 3 | 37MQ8Z1JQEWA9HYZP3JANL1ES162YC | ARLGZWN6W91WD | Negative | What idiotic FIlm\nI can say that Phoenix is ... |
| 4 | 37MQ8Z1JQEWA9HYZP3JANL1ES162YC | AKSJ3C5O3V9RB | Negative | What idiotic FIlm\nI can say that Phoenix is ... |
| ... | ... | ... | ... | ... |
| 289 | 3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH | A3EZ0H07TSDAPW | Negative | Oscar for Phoenix\nI will stop watching movie... |
| 290 | 3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH | A38DC3BG1ZCVZ2 | Positive | Oscar for Phoenix\nI will stop watching movie... |
| 291 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A194R45ACMQEOR | Positive | Joker > Endgame\nNeed I say more? Everything ... |
| 292 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A1L8RL58MYU4NC | Positive | Joker > Endgame\nNeed I say more? Everything ... |
| 293 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A1T79J0XQXDDGC | Positive | Joker > Endgame\nNeed I say more? Everything ... |
294 rows × 4 columns
In [150]:
turker_clean.WorkerId.value_counts()
Out[150]:
ARLGZWN6W91WD 46 A681XM15AN28F 37 A1T79J0XQXDDGC 34 A2XFO0X6RCS98M 33 A3EZ0H07TSDAPW 33 A1L8RL58MYU4NC 28 A38DC3BG1ZCVZ2 22 AKSJ3C5O3V9RB 21 ASB8T0H7L99RF 10 AE03LUY7RH400 6 A37JENVKZQ56U6 5 A194R45ACMQEOR 5 AH5A86OLRZWCS 4 A2HG1N3BVQO6I 4 AURYD2FH3FUOQ 2 AMC42JMQA8A5U 2 ATHS9GUME1XCA 1 A2HGRSPR50ENHL 1 Name: WorkerId, dtype: int64
In [161]:
turker1 = turker_clean[turker_clean['WorkerId'] == 'ARLGZWN6W91WD']
turker2 = turker_clean[turker_clean['WorkerId'] == 'A681XM15AN28F']
turker3 = turker_clean[turker_clean['WorkerId'] == 'A1T79J0XQXDDGC']
turker4 = turker_clean[turker_clean['WorkerId'] == 'A2XFO0X6RCS98M']
turker5 = turker_clean[turker_clean['WorkerId'] == 'A3EZ0H07TSDAPW']
In [162]:
turker1.reset_index(drop=True, inplace=True)
turker2.reset_index(drop=True, inplace=True)
turker3.reset_index(drop=True, inplace=True)
turker4.reset_index(drop=True, inplace=True)
turker5.reset_index(drop=True, inplace=True)
In [165]:
merged_df = pd.concat([turker1, turker2, turker3, turker4, turker5], axis=0, sort=False)
In [168]:
merged_df.reset_index(drop=True, inplace=True)
In [196]:
merged_df.sort_values(by='Input.text')
Out[196]:
| HITId | WorkerId | Answer.sentiment.label | Input.text | |
|---|---|---|---|---|
| 79 | 3AQN9REUTFGXCRWFMS3RJ4SIPSUYDG | A681XM15AN28F | Positive | #LetRottenTomatoesRotSquad\nI am a simple guy... |
| 142 | 3AQN9REUTFGXCRWFMS3RJ4SIPSUYDG | A2XFO0X6RCS98M | Positive | #LetRottenTomatoesRotSquad\nI am a simple guy... |
| 122 | 3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5 | A2XFO0X6RCS98M | Negative | A 'Triumph of the Will' for Nihilists\n'Joker... |
| 55 | 3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5 | A681XM15AN28F | Neutral | A 'Triumph of the Will' for Nihilists\n'Joker... |
| 87 | 3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5 | A1T79J0XQXDDGC | Negative | A 'Triumph of the Will' for Nihilists\n'Joker... |
| ... | ... | ... | ... | ... |
| 175 | 3J9UN9O9J3SDII0MOGETUATBIZD0JW | A3EZ0H07TSDAPW | Positive | Took my 65 year old mother to see it.\nI saw t... |
| 43 | 31ODACBENUFU5EOBS8HM1HBGRMNSQ1 | ARLGZWN6W91WD | Positive | Venice 76 review\nI have just watched the Joke... |
| 180 | 31ODACBENUFU5EOBS8HM1HBGRMNSQ1 | A3EZ0H07TSDAPW | Positive | Venice 76 review\nI have just watched the Joke... |
| 162 | 3M93N4X8HKNDJRKYXIXD4GZUDRVSJA | A3EZ0H07TSDAPW | Negative | lose of both time and money\nThis was one of t... |
| 127 | 3M93N4X8HKNDJRKYXIXD4GZUDRVSJA | A2XFO0X6RCS98M | Negative | lose of both time and money\nThis was one of t... |
183 rows × 4 columns
In [171]:
merged_df2 = pd.concat([turker1, turker2], axis=0, sort=False)
In [195]:
merged_df2.sort_values(by='Input.text')
Out[195]:
| HITId | WorkerId | Answer.sentiment.label | Input.text | |
|---|---|---|---|---|
| 33 | 3AQN9REUTFGXCRWFMS3RJ4SIPSUYDG | A681XM15AN28F | Positive | #LetRottenTomatoesRotSquad\nI am a simple guy... |
| 9 | 3IVKZBIBJ09HSLP89IUSS3JF0ZRSH5 | A681XM15AN28F | Neutral | A 'Triumph of the Will' for Nihilists\n'Joker... |
| 36 | 39O0SQZVJN78YHJJHK8BBGPP0UD7RV | ARLGZWN6W91WD | Positive | A Breath of Fresh Cinema\nBursting with emoti... |
| 30 | 334ZEL5JX6FRK2BVDVPICCGGCL5SOT | A681XM15AN28F | Positive | A brilliant movie\nThis movie is slow but nev... |
| 31 | 3DWGDA5POF4MG2LY1OWCB3NFIEPV1E | ARLGZWN6W91WD | Positive | A clean masterpiece!\nWhat I loved the most a... |
| ... | ... | ... | ... | ... |
| 7 | 3D17ECOUOEV24TJFHEQ6S8VWRUX31Q | ARLGZWN6W91WD | Negative | Overhyped and not everyone joker performance i... |
| 6 | 3G3AJKPCXLSKCVDMTH2YG0YCCF1Y43 | A681XM15AN28F | Neutral | Ridiculous well acted Trash\nSaw the movie Jok... |
| 17 | 3JAOYN9IHL2YEWXU4I4PG1ATPEB33I | A681XM15AN28F | Neutral | The king has no clothes\nRead the reviews- the... |
| 38 | 3J5XXLQDHMBIQ5ZDOSAVZW2CGY3V36 | ARLGZWN6W91WD | Positive | The mirror of society\nActing 10/10\nActors 10... |
| 43 | 31ODACBENUFU5EOBS8HM1HBGRMNSQ1 | ARLGZWN6W91WD | Positive | Venice 76 review\nI have just watched the Joke... |
83 rows × 4 columns
In [191]:
# merged_df2['Input.text'].value_counts()
# df = pd.DataFrame(merged_df2.groupby('HITId'))
# df.set_index([turker1, turker2]).unstack(level=0)
In [203]:
# grouped = turker_clean.groupby(['HITId','WorkerId'])
# grouped.set_index(['HITId', 'WorkerId']).mean().unstack(level=0)
df = merged_df.drop('Input.text', axis=1)
df
Out[203]:
| HITId | WorkerId | Answer.sentiment.label | |
|---|---|---|---|
| 0 | 37MQ8Z1JQEWA9HYZP3JANL1ES162YC | ARLGZWN6W91WD | Negative |
| 1 | 3I7SHAD35MWH116RCCCUPHVFU7E7M7 | ARLGZWN6W91WD | Negative |
| 2 | 3XUSYT70IT10FW0UEKSIRCYYDFG0DI | ARLGZWN6W91WD | Negative |
| 3 | 3SD15I2WD2UXBFKCNK2NN4MDZ5D63R | ARLGZWN6W91WD | Negative |
| 4 | 3P7QK0GJ3TLAE784LPLT1SAGYVA2Z3 | ARLGZWN6W91WD | Negative |
| ... | ... | ... | ... |
| 178 | 39KV3A5D187KZWJWW98G1QULMWW7SJ | A3EZ0H07TSDAPW | Neutral |
| 179 | 35F6NGNVM8JLEWWBL9D6BVQ7OFA7T8 | A3EZ0H07TSDAPW | Positive |
| 180 | 31ODACBENUFU5EOBS8HM1HBGRMNSQ1 | A3EZ0H07TSDAPW | Positive |
| 181 | 3PN6H8C9R4QWG9YC6MPBGIABM1SDAM | A3EZ0H07TSDAPW | Neutral |
| 182 | 3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH | A3EZ0H07TSDAPW | Negative |
183 rows × 3 columns
In [213]:
df = pd.DataFrame({'Turker': merged_df['WorkerId'].tolist(),
'REVIEW': merged_df['Answer.sentiment.label'].tolist(),
'SENTIMENT': merged_df['HITId'].tolist() })
grouped = df.groupby('Turker')
values = grouped['SENTIMENT'].agg('sum')
id_df = grouped['REVIEW'].apply(lambda x: pd.Series(x.values)).unstack()
id_df = id_df.rename(columns={i: 'REVIEW{}'.format(i + 1) for i in range(id_df.shape[1])})
result = pd.concat([id_df, values], axis=1)
result_df = pd.DataFrame(result)
print(result_df)
REVIEW1 REVIEW2 REVIEW3 REVIEW4 REVIEW5 REVIEW6 \
Turker
A1T79J0XQXDDGC Positive Negative Positive Positive Negative Negative
A2XFO0X6RCS98M Negative Negative Negative Negative Positive Negative
A3EZ0H07TSDAPW Positive Neutral Positive Negative Negative Positive
A681XM15AN28F Negative Positive Positive Positive Positive Negative
ARLGZWN6W91WD Negative Negative Negative Negative Negative Negative
REVIEW7 REVIEW8 REVIEW9 REVIEW10 ... REVIEW38 \
Turker ...
A1T79J0XQXDDGC Negative Positive Negative Negative ... NaN
A2XFO0X6RCS98M Negative Negative Negative Negative ... NaN
A3EZ0H07TSDAPW Negative Positive Positive Negative ... NaN
A681XM15AN28F Neutral Neutral Neutral Neutral ... NaN
ARLGZWN6W91WD Negative Negative Negative Negative ... Positive
REVIEW39 REVIEW40 REVIEW41 REVIEW42 REVIEW43 REVIEW44 \
Turker
A1T79J0XQXDDGC NaN NaN NaN NaN NaN NaN
A2XFO0X6RCS98M NaN NaN NaN NaN NaN NaN
A3EZ0H07TSDAPW NaN NaN NaN NaN NaN NaN
A681XM15AN28F NaN NaN NaN NaN NaN NaN
ARLGZWN6W91WD Positive Positive Positive Negative Positive Positive
REVIEW45 REVIEW46 \
Turker
A1T79J0XQXDDGC NaN NaN
A2XFO0X6RCS98M NaN NaN
A3EZ0H07TSDAPW NaN NaN
A681XM15AN28F NaN NaN
ARLGZWN6W91WD Positive Positive
SENTIMENT
Turker
A1T79J0XQXDDGC 302OLP89DZ7MBHSY6QU0WCST11GACJ32LAQ1JNT9PNC787...
A2XFO0X6RCS98M 3I7SHAD35MWH116RCCCUPHVFU7E7M73XUSYT70IT10FW0U...
A3EZ0H07TSDAPW 38O9DZ0A62N8QXOTJKOI4UHLTRD62G3I7SHAD35MWH116R...
A681XM15AN28F 3SD15I2WD2UXBFKCNK2NN4MDZ5D63R302OLP89DZ7MBHSY...
ARLGZWN6W91WD 37MQ8Z1JQEWA9HYZP3JANL1ES162YC3I7SHAD35MWH116R...
[5 rows x 47 columns]
In [216]:
df = pd.DataFrame({'Turker': merged_df['WorkerId'].tolist(),
'SENTIMENT': merged_df['Answer.sentiment.label'].tolist(),
'REVIEW': merged_df['HITId'].tolist() })
grouped = df.groupby('Turker')
values = grouped['REVIEW'].agg('sum')
id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()
id_df = id_df.rename(columns={i: 'SENTIMENT{}'.format(i + 1) for i in range(id_df.shape[1])})
result = pd.concat([id_df, values], axis=1)
result_df = pd.DataFrame(result)
print(result_df.T)
Turker A1T79J0XQXDDGC \ SENTIMENT1 Positive SENTIMENT2 Negative SENTIMENT3 Positive SENTIMENT4 Positive SENTIMENT5 Negative SENTIMENT6 Negative SENTIMENT7 Negative SENTIMENT8 Positive SENTIMENT9 Negative SENTIMENT10 Negative SENTIMENT11 Negative SENTIMENT12 Negative SENTIMENT13 Negative SENTIMENT14 Negative SENTIMENT15 Positive SENTIMENT16 Positive SENTIMENT17 Positive SENTIMENT18 Positive SENTIMENT19 Positive SENTIMENT20 Positive SENTIMENT21 Positive SENTIMENT22 Positive SENTIMENT23 Positive SENTIMENT24 Positive SENTIMENT25 Positive SENTIMENT26 Positive SENTIMENT27 Positive SENTIMENT28 Positive SENTIMENT29 Positive SENTIMENT30 Negative SENTIMENT31 Positive SENTIMENT32 Positive SENTIMENT33 Positive SENTIMENT34 Positive SENTIMENT35 NaN SENTIMENT36 NaN SENTIMENT37 NaN SENTIMENT38 NaN SENTIMENT39 NaN SENTIMENT40 NaN SENTIMENT41 NaN SENTIMENT42 NaN SENTIMENT43 NaN SENTIMENT44 NaN SENTIMENT45 NaN SENTIMENT46 NaN REVIEW 302OLP89DZ7MBHSY6QU0WCST11GACJ32LAQ1JNT9PNC787... Turker A2XFO0X6RCS98M \ SENTIMENT1 Negative SENTIMENT2 Negative SENTIMENT3 Negative SENTIMENT4 Negative SENTIMENT5 Positive SENTIMENT6 Negative SENTIMENT7 Negative SENTIMENT8 Negative SENTIMENT9 Negative SENTIMENT10 Negative SENTIMENT11 Negative SENTIMENT12 Negative SENTIMENT13 Negative SENTIMENT14 Negative SENTIMENT15 Positive SENTIMENT16 Negative SENTIMENT17 Negative SENTIMENT18 Positive SENTIMENT19 Positive SENTIMENT20 Positive SENTIMENT21 Positive SENTIMENT22 Positive SENTIMENT23 Positive SENTIMENT24 Positive SENTIMENT25 Positive SENTIMENT26 Positive SENTIMENT27 Positive SENTIMENT28 Positive SENTIMENT29 Positive SENTIMENT30 Positive SENTIMENT31 Positive SENTIMENT32 Positive SENTIMENT33 Positive SENTIMENT34 NaN SENTIMENT35 NaN SENTIMENT36 NaN SENTIMENT37 NaN SENTIMENT38 NaN SENTIMENT39 NaN SENTIMENT40 NaN SENTIMENT41 NaN SENTIMENT42 NaN SENTIMENT43 NaN SENTIMENT44 NaN SENTIMENT45 NaN SENTIMENT46 NaN REVIEW 3I7SHAD35MWH116RCCCUPHVFU7E7M73XUSYT70IT10FW0U... Turker A3EZ0H07TSDAPW \ SENTIMENT1 Positive SENTIMENT2 Neutral SENTIMENT3 Positive SENTIMENT4 Negative SENTIMENT5 Negative SENTIMENT6 Positive SENTIMENT7 Negative SENTIMENT8 Positive SENTIMENT9 Positive SENTIMENT10 Negative SENTIMENT11 Neutral SENTIMENT12 Negative SENTIMENT13 Negative SENTIMENT14 Neutral SENTIMENT15 Neutral SENTIMENT16 Positive SENTIMENT17 Negative SENTIMENT18 Negative SENTIMENT19 Neutral SENTIMENT20 Neutral SENTIMENT21 Neutral SENTIMENT22 Positive SENTIMENT23 Positive SENTIMENT24 Neutral SENTIMENT25 Positive SENTIMENT26 Positive SENTIMENT27 Positive SENTIMENT28 Positive SENTIMENT29 Neutral SENTIMENT30 Positive SENTIMENT31 Positive SENTIMENT32 Neutral SENTIMENT33 Negative SENTIMENT34 NaN SENTIMENT35 NaN SENTIMENT36 NaN SENTIMENT37 NaN SENTIMENT38 NaN SENTIMENT39 NaN SENTIMENT40 NaN SENTIMENT41 NaN SENTIMENT42 NaN SENTIMENT43 NaN SENTIMENT44 NaN SENTIMENT45 NaN SENTIMENT46 NaN REVIEW 38O9DZ0A62N8QXOTJKOI4UHLTRD62G3I7SHAD35MWH116R... Turker A681XM15AN28F \ SENTIMENT1 Negative SENTIMENT2 Positive SENTIMENT3 Positive SENTIMENT4 Positive SENTIMENT5 Positive SENTIMENT6 Negative SENTIMENT7 Neutral SENTIMENT8 Neutral SENTIMENT9 Neutral SENTIMENT10 Neutral SENTIMENT11 Positive SENTIMENT12 Positive SENTIMENT13 Negative SENTIMENT14 Positive SENTIMENT15 Neutral SENTIMENT16 Neutral SENTIMENT17 Neutral SENTIMENT18 Neutral SENTIMENT19 Positive SENTIMENT20 Negative SENTIMENT21 Neutral SENTIMENT22 Positive SENTIMENT23 Neutral SENTIMENT24 Neutral SENTIMENT25 Negative SENTIMENT26 Neutral SENTIMENT27 Negative SENTIMENT28 Positive SENTIMENT29 Negative SENTIMENT30 Neutral SENTIMENT31 Positive SENTIMENT32 Negative SENTIMENT33 Positive SENTIMENT34 Positive SENTIMENT35 Negative SENTIMENT36 Neutral SENTIMENT37 Positive SENTIMENT38 NaN SENTIMENT39 NaN SENTIMENT40 NaN SENTIMENT41 NaN SENTIMENT42 NaN SENTIMENT43 NaN SENTIMENT44 NaN SENTIMENT45 NaN SENTIMENT46 NaN REVIEW 3SD15I2WD2UXBFKCNK2NN4MDZ5D63R302OLP89DZ7MBHSY... Turker ARLGZWN6W91WD SENTIMENT1 Negative SENTIMENT2 Negative SENTIMENT3 Negative SENTIMENT4 Negative SENTIMENT5 Negative SENTIMENT6 Negative SENTIMENT7 Negative SENTIMENT8 Negative SENTIMENT9 Negative SENTIMENT10 Negative SENTIMENT11 Negative SENTIMENT12 Negative SENTIMENT13 Negative SENTIMENT14 Negative SENTIMENT15 Negative SENTIMENT16 Negative SENTIMENT17 Negative SENTIMENT18 Negative SENTIMENT19 Neutral SENTIMENT20 Negative SENTIMENT21 Negative SENTIMENT22 Negative SENTIMENT23 Positive SENTIMENT24 Positive SENTIMENT25 Positive SENTIMENT26 Positive SENTIMENT27 Positive SENTIMENT28 Positive SENTIMENT29 Positive SENTIMENT30 Positive SENTIMENT31 Positive SENTIMENT32 Positive SENTIMENT33 Positive SENTIMENT34 Positive SENTIMENT35 Positive SENTIMENT36 Positive SENTIMENT37 Positive SENTIMENT38 Positive SENTIMENT39 Positive SENTIMENT40 Positive SENTIMENT41 Positive SENTIMENT42 Negative SENTIMENT43 Positive SENTIMENT44 Positive SENTIMENT45 Positive SENTIMENT46 Positive REVIEW 37MQ8Z1JQEWA9HYZP3JANL1ES162YC3I7SHAD35MWH116R...
In [225]:
t1 = result_df.T['A3EZ0H07TSDAPW'].tolist()
len(t1)
Out[225]:
47
In [245]:
t2 = result_df.T['A2XFO0X6RCS98M'].tolist()
len(t2)
t3 = result_df.T['A681XM15AN28F'].tolist()
len(t3)
t4 = result_df.T['ARLGZWN6W91WD'].tolist()
In [246]:
t1[:-1]
Out[246]:
['Positive', 'Neutral', 'Positive', 'Negative', 'Negative', 'Positive', 'Negative', 'Positive', 'Positive', 'Negative', 'Neutral', 'Negative', 'Negative', 'Neutral', 'Neutral', 'Positive', 'Negative', 'Negative', 'Neutral', 'Neutral', 'Neutral', 'Positive', 'Positive', 'Neutral', 'Positive', 'Positive', 'Positive', 'Positive', 'Neutral', 'Positive', 'Positive', 'Neutral', 'Negative', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
In [247]:
t2[:-1]
Out[247]:
['Negative', 'Negative', 'Negative', 'Negative', 'Positive', 'Negative', 'Negative', 'Negative', 'Negative', 'Negative', 'Negative', 'Negative', 'Negative', 'Negative', 'Positive', 'Negative', 'Negative', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', 'Positive', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
In [248]:
t3
Out[248]:
['Negative', 'Positive', 'Positive', 'Positive', 'Positive', 'Negative', 'Neutral', 'Neutral', 'Neutral', 'Neutral', 'Positive', 'Positive', 'Negative', 'Positive', 'Neutral', 'Neutral', 'Neutral', 'Neutral', 'Positive', 'Negative', 'Neutral', 'Positive', 'Neutral', 'Neutral', 'Negative', 'Neutral', 'Negative', 'Positive', 'Negative', 'Neutral', 'Positive', 'Negative', 'Positive', 'Positive', 'Negative', 'Neutral', 'Positive', nan, nan, nan, nan, nan, nan, nan, nan, nan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
In [251]:
from sklearn.metrics import cohen_kappa_score
y1 = t1[:-1]
y2 = t2[:-1]
cohen_kappa_score(y1,y2)
Out[251]:
0.43974358974358974
In [252]:
from sklearn.metrics import cohen_kappa_score
y3 = t3[:-1]
y4 = t4[:-1]
cohen_kappa_score(y3,y4)
Out[252]:
-0.07585335018963324
In [272]:
# turker_clean
turker_clean_test = turker_clean.copy()
turker_clean_test.reset_index(inplace=True)
id_dict = {}
id_num = 1
def return_new_id(old_id,):
if old_id in id_dict.keys():
return id_dict[old_id]
else:
id_num = id_num + 1
id_dict.update({ old_id: id_num })
return num
# turker_clean_test['ReviewID'] = turker_clean_test.apply(lambda x: return_new_id(x['HITId']), axis=1)
# turker_clean_test
turker_clean_test
# import Counter
# Counter(K)
new_ids = pd.factorize(turker_clean_test['HITId'].tolist())
new_ids[0]
turker_clean_test['ReviewID'] = new_ids[0]
In [273]:
turker_clean_test
Out[273]:
| index | HITId | WorkerId | Answer.sentiment.label | Input.text | ReviewID | |
|---|---|---|---|---|---|---|
| 0 | 0 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | AH5A86OLRZWCS | Negative | Everyone praised an overrated movie.\nOverrat... | 0 |
| 1 | 1 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | A2HGRSPR50ENHL | Negative | Everyone praised an overrated movie.\nOverrat... | 0 |
| 2 | 2 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | AKSJ3C5O3V9RB | Negative | Everyone praised an overrated movie.\nOverrat... | 0 |
| 3 | 3 | 37MQ8Z1JQEWA9HYZP3JANL1ES162YC | ARLGZWN6W91WD | Negative | What idiotic FIlm\nI can say that Phoenix is ... | 1 |
| 4 | 4 | 37MQ8Z1JQEWA9HYZP3JANL1ES162YC | AKSJ3C5O3V9RB | Negative | What idiotic FIlm\nI can say that Phoenix is ... | 1 |
| ... | ... | ... | ... | ... | ... | ... |
| 289 | 289 | 3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH | A3EZ0H07TSDAPW | Negative | Oscar for Phoenix\nI will stop watching movie... | 96 |
| 290 | 290 | 3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH | A38DC3BG1ZCVZ2 | Positive | Oscar for Phoenix\nI will stop watching movie... | 96 |
| 291 | 291 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A194R45ACMQEOR | Positive | Joker > Endgame\nNeed I say more? Everything ... | 97 |
| 292 | 292 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A1L8RL58MYU4NC | Positive | Joker > Endgame\nNeed I say more? Everything ... | 97 |
| 293 | 293 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A1T79J0XQXDDGC | Positive | Joker > Endgame\nNeed I say more? Everything ... | 97 |
294 rows × 6 columns
In [274]:
new_turker_ids = pd.factorize(turker_clean_test['WorkerId'].tolist())
In [276]:
t_ids = ['T_' + str(id) for id in new_turker_ids[0]]
In [277]:
t_ids
Out[277]:
['T_0', 'T_1', 'T_2', 'T_3', 'T_2', 'T_4', 'T_5', 'T_6', 'T_7', 'T_8', 'T_5', 'T_3', 'T_5', 'T_3', 'T_8', 'T_5', 'T_3', 'T_9', 'T_10', 'T_9', 'T_8', 'T_3', 'T_9', 'T_4', 'T_7', 'T_10', 'T_2', 'T_9', 'T_7', 'T_4', 'T_9', 'T_3', 'T_2', 'T_11', 'T_0', 'T_9', 'T_4', 'T_8', 'T_5', 'T_5', 'T_9', 'T_12', 'T_5', 'T_3', 'T_10', 'T_8', 'T_10', 'T_13', 'T_9', 'T_3', 'T_2', 'T_3', 'T_9', 'T_4', 'T_9', 'T_8', 'T_10', 'T_3', 'T_13', 'T_4', 'T_10', 'T_5', 'T_8', 'T_2', 'T_9', 'T_10', 'T_3', 'T_5', 'T_10', 'T_3', 'T_9', 'T_10', 'T_5', 'T_8', 'T_7', 'T_9', 'T_12', 'T_8', 'T_10', 'T_5', 'T_3', 'T_4', 'T_8', 'T_2', 'T_3', 'T_14', 'T_5', 'T_15', 'T_12', 'T_4', 'T_4', 'T_3', 'T_9', 'T_8', 'T_5', 'T_6', 'T_2', 'T_8', 'T_9', 'T_10', 'T_4', 'T_9', 'T_8', 'T_3', 'T_7', 'T_10', 'T_3', 'T_9', 'T_2', 'T_5', 'T_3', 'T_8', 'T_9', 'T_2', 'T_9', 'T_8', 'T_3', 'T_7', 'T_5', 'T_16', 'T_4', 'T_3', 'T_8', 'T_4', 'T_10', 'T_9', 'T_0', 'T_4', 'T_10', 'T_5', 'T_7', 'T_8', 'T_9', 'T_3', 'T_13', 'T_5', 'T_4', 'T_2', 'T_5', 'T_3', 'T_0', 'T_4', 'T_11', 'T_2', 'T_4', 'T_3', 'T_5', 'T_9', 'T_3', 'T_5', 'T_4', 'T_2', 'T_6', 'T_7', 'T_10', 'T_6', 'T_10', 'T_9', 'T_5', 'T_5', 'T_9', 'T_10', 'T_5', 'T_3', 'T_9', 'T_3', 'T_4', 'T_11', 'T_17', 'T_5', 'T_7', 'T_9', 'T_3', 'T_12', 'T_5', 'T_16', 'T_3', 'T_8', 'T_10', 'T_12', 'T_3', 'T_10', 'T_7', 'T_8', 'T_3', 'T_4', 'T_10', 'T_6', 'T_8', 'T_7', 'T_3', 'T_6', 'T_3', 'T_7', 'T_2', 'T_3', 'T_4', 'T_9', 'T_2', 'T_9', 'T_8', 'T_9', 'T_10', 'T_8', 'T_9', 'T_8', 'T_10', 'T_10', 'T_4', 'T_9', 'T_9', 'T_3', 'T_16', 'T_3', 'T_12', 'T_9', 'T_5', 'T_8', 'T_2', 'T_3', 'T_8', 'T_4', 'T_6', 'T_3', 'T_10', 'T_2', 'T_3', 'T_5', 'T_9', 'T_8', 'T_14', 'T_10', 'T_3', 'T_4', 'T_8', 'T_4', 'T_5', 'T_3', 'T_8', 'T_10', 'T_8', 'T_10', 'T_13', 'T_8', 'T_10', 'T_9', 'T_3', 'T_7', 'T_10', 'T_7', 'T_9', 'T_2', 'T_6', 'T_9', 'T_7', 'T_5', 'T_3', 'T_2', 'T_2', 'T_3', 'T_7', 'T_5', 'T_4', 'T_10', 'T_5', 'T_3', 'T_7', 'T_6', 'T_8', 'T_7', 'T_10', 'T_3', 'T_4', 'T_6', 'T_10', 'T_7', 'T_7', 'T_15', 'T_8', 'T_2', 'T_8', 'T_4', 'T_10', 'T_5', 'T_16', 'T_3', 'T_11', 'T_7', 'T_11', 'T_5', 'T_7', 'T_13', 'T_4', 'T_10']
In [278]:
turker_clean_test['T_ID'] = t_ids
turker_clean_test
Out[278]:
| index | HITId | WorkerId | Answer.sentiment.label | Input.text | ReviewID | T_ID | |
|---|---|---|---|---|---|---|---|
| 0 | 0 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | AH5A86OLRZWCS | Negative | Everyone praised an overrated movie.\nOverrat... | 0 | T_0 |
| 1 | 1 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | A2HGRSPR50ENHL | Negative | Everyone praised an overrated movie.\nOverrat... | 0 | T_1 |
| 2 | 2 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | AKSJ3C5O3V9RB | Negative | Everyone praised an overrated movie.\nOverrat... | 0 | T_2 |
| 3 | 3 | 37MQ8Z1JQEWA9HYZP3JANL1ES162YC | ARLGZWN6W91WD | Negative | What idiotic FIlm\nI can say that Phoenix is ... | 1 | T_3 |
| 4 | 4 | 37MQ8Z1JQEWA9HYZP3JANL1ES162YC | AKSJ3C5O3V9RB | Negative | What idiotic FIlm\nI can say that Phoenix is ... | 1 | T_2 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 289 | 289 | 3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH | A3EZ0H07TSDAPW | Negative | Oscar for Phoenix\nI will stop watching movie... | 96 | T_5 |
| 290 | 290 | 3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH | A38DC3BG1ZCVZ2 | Positive | Oscar for Phoenix\nI will stop watching movie... | 96 | T_7 |
| 291 | 291 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A194R45ACMQEOR | Positive | Joker > Endgame\nNeed I say more? Everything ... | 97 | T_13 |
| 292 | 292 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A1L8RL58MYU4NC | Positive | Joker > Endgame\nNeed I say more? Everything ... | 97 | T_4 |
| 293 | 293 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A1T79J0XQXDDGC | Positive | Joker > Endgame\nNeed I say more? Everything ... | 97 | T_10 |
294 rows × 7 columns
In [281]:
turker_clean_test['sentiment'] = turker_clean_test.apply(lambda x: x['Answer.sentiment.label'][0], axis=1)
In [282]:
turker_clean_test
Out[282]:
| index | HITId | WorkerId | Answer.sentiment.label | Input.text | ReviewID | T_ID | sentiment | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | AH5A86OLRZWCS | Negative | Everyone praised an overrated movie.\nOverrat... | 0 | T_0 | N |
| 1 | 1 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | A2HGRSPR50ENHL | Negative | Everyone praised an overrated movie.\nOverrat... | 0 | T_1 | N |
| 2 | 2 | 338GLSUI43BXEPY2ES6SPI72KKESF7 | AKSJ3C5O3V9RB | Negative | Everyone praised an overrated movie.\nOverrat... | 0 | T_2 | N |
| 3 | 3 | 37MQ8Z1JQEWA9HYZP3JANL1ES162YC | ARLGZWN6W91WD | Negative | What idiotic FIlm\nI can say that Phoenix is ... | 1 | T_3 | N |
| 4 | 4 | 37MQ8Z1JQEWA9HYZP3JANL1ES162YC | AKSJ3C5O3V9RB | Negative | What idiotic FIlm\nI can say that Phoenix is ... | 1 | T_2 | N |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 289 | 289 | 3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH | A3EZ0H07TSDAPW | Negative | Oscar for Phoenix\nI will stop watching movie... | 96 | T_5 | N |
| 290 | 290 | 3PUV2Q8SV441ZJ34C0P7BTUH4JDDBH | A38DC3BG1ZCVZ2 | Positive | Oscar for Phoenix\nI will stop watching movie... | 96 | T_7 | P |
| 291 | 291 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A194R45ACMQEOR | Positive | Joker > Endgame\nNeed I say more? Everything ... | 97 | T_13 | P |
| 292 | 292 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A1L8RL58MYU4NC | Positive | Joker > Endgame\nNeed I say more? Everything ... | 97 | T_4 | P |
| 293 | 293 | 3FO95NVK5C0UHF3B5N6M67LLN8PSR2 | A1T79J0XQXDDGC | Positive | Joker > Endgame\nNeed I say more? Everything ... | 97 | T_10 | P |
294 rows × 8 columns
In [283]:
even_cleaner_df = turker_clean_test[['ReviewID', 'T_ID', 'sentiment']]
In [300]:
In [301]:
df
Out[301]:
| 0 | 1 | |
|---|---|---|
| 0 | 0 | 0 N 1 N 2 N Name: sentiment, dtype: o... |
| 1 | 1 | 3 N 4 N 5 N Name: sentiment, dtype: o... |
| 2 | 2 | 6 P 7 N 8 N Name: sentiment, dtype: o... |
| 3 | 3 | 9 N 10 N 11 N Name: sentiment, dtype... |
| 4 | 4 | 12 P 13 N 14 N Name: sentiment, dtype... |
| ... | ... | ... |
| 93 | 93 | 279 P 280 P 281 P Name: sentiment, dt... |
| 94 | 94 | 282 P 283 N 284 P Name: sentiment, dt... |
| 95 | 95 | 285 P 286 P 287 P Name: sentiment, dt... |
| 96 | 96 | 288 N 289 N 290 P Name: sentiment, dt... |
| 97 | 97 | 291 P 292 P 293 P Name: sentiment, dt... |
98 rows × 2 columns
In [305]:
df = pd.DataFrame({'Turker': even_cleaner_df['T_ID'].tolist(),
'SENTIMENT': even_cleaner_df['sentiment'].tolist(),
'REVIEW': even_cleaner_df['ReviewID'].tolist() })
grouped = df.groupby('Turker')
values = grouped['REVIEW'].agg('sum')
id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()
id_df = id_df.rename(columns={i: 'REVIEW{}'.format(i + 1) for i in range(id_df.shape[1])})
result = pd.concat([id_df, values], axis=1)
result_df = pd.DataFrame(result)
print(result_df.T)
Turker T_0 T_1 T_10 T_11 T_12 T_13 T_14 T_15 T_16 T_17 T_2 T_3 T_4 \ REVIEW1 N N P N N N N N N P N N N REVIEW2 N NaN N N N N P N P NaN N N N REVIEW3 N NaN P P N N NaN NaN P NaN N N N REVIEW4 N NaN P P N P NaN NaN P NaN N N N REVIEW5 NaN NaN N N P P NaN NaN NaN NaN N N N REVIEW6 NaN NaN N NaN P NaN NaN NaN NaN NaN N N N REVIEW7 NaN NaN N NaN NaN NaN NaN NaN NaN NaN N N N REVIEW8 NaN NaN P NaN NaN NaN NaN NaN NaN NaN N N P REVIEW9 NaN NaN N NaN NaN NaN NaN NaN NaN NaN N N N REVIEW10 NaN NaN N NaN NaN NaN NaN NaN NaN NaN N N N REVIEW11 NaN NaN N NaN NaN NaN NaN NaN NaN NaN N N N REVIEW12 NaN NaN N NaN NaN NaN NaN NaN NaN NaN N N N REVIEW13 NaN NaN N NaN NaN NaN NaN NaN NaN NaN P N N REVIEW14 NaN NaN N NaN NaN NaN NaN NaN NaN NaN P N N REVIEW15 NaN NaN P NaN NaN NaN NaN NaN NaN NaN P N N REVIEW16 NaN NaN P NaN NaN NaN NaN NaN NaN NaN P N P REVIEW17 NaN NaN P NaN NaN NaN NaN NaN NaN NaN P N P REVIEW18 NaN NaN P NaN NaN NaN NaN NaN NaN NaN P N P REVIEW19 NaN NaN P NaN NaN NaN NaN NaN NaN NaN N N P REVIEW20 NaN NaN P NaN NaN NaN NaN NaN NaN NaN P N P REVIEW21 NaN NaN P NaN NaN NaN NaN NaN NaN NaN P N P REVIEW22 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN N P REVIEW23 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P P REVIEW24 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P P REVIEW25 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P P REVIEW26 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P P REVIEW27 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P P REVIEW28 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P P REVIEW29 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW30 NaN NaN N NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW31 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW32 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW33 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW34 NaN NaN P NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW35 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW36 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW37 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW38 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW39 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW40 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW41 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW42 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN N NaN REVIEW43 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW44 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW45 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW46 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN P NaN REVIEW 99 0 1797 304 254 255 104 121 261 56 954 2177 1342 Turker T_5 T_6 T_7 T_8 T_9 REVIEW1 P N N N N REVIEW2 N N N N P REVIEW3 P P N N P REVIEW4 N P N N P REVIEW5 N P N P P REVIEW6 P P N N N REVIEW7 N P N N N REVIEW8 P P P N N REVIEW9 P P P N N REVIEW10 N P P N N REVIEW11 N NaN P N P REVIEW12 N NaN P N P REVIEW13 N NaN P N N REVIEW14 N NaN P N P REVIEW15 N NaN P P N REVIEW16 P NaN P N N REVIEW17 N NaN P N N REVIEW18 N NaN P P N REVIEW19 N NaN P P P REVIEW20 N NaN P P N REVIEW21 N NaN P P N REVIEW22 P NaN P P P REVIEW23 P NaN NaN P N REVIEW24 N NaN NaN P N REVIEW25 P NaN NaN P N REVIEW26 P NaN NaN P N REVIEW27 P NaN NaN P N REVIEW28 P NaN NaN P P REVIEW29 N NaN NaN P N REVIEW30 P NaN NaN P N REVIEW31 P NaN NaN P P REVIEW32 N NaN NaN P N REVIEW33 N NaN NaN P P REVIEW34 NaN NaN NaN NaN P REVIEW35 NaN NaN NaN NaN N REVIEW36 NaN NaN NaN NaN N REVIEW37 NaN NaN NaN NaN P REVIEW38 NaN NaN NaN NaN NaN REVIEW39 NaN NaN NaN NaN NaN REVIEW40 NaN NaN NaN NaN NaN REVIEW41 NaN NaN NaN NaN NaN REVIEW42 NaN NaN NaN NaN NaN REVIEW43 NaN NaN NaN NaN NaN REVIEW44 NaN NaN NaN NaN NaN REVIEW45 NaN NaN NaN NaN NaN REVIEW46 NaN NaN NaN NaN NaN REVIEW 1458 597 1339 1605 1536
In [306]:
df = pd.DataFrame(result_df.T)
In [310]:
df
Out[310]:
| Turker | T_0 | T_1 | T_10 | T_11 | T_12 | T_13 | T_14 | T_15 | T_16 | T_17 | T_2 | T_3 | T_4 | T_5 | T_6 | T_7 | T_8 | T_9 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| REVIEW1 | N | N | P | N | N | N | N | N | N | P | N | N | N | P | N | N | N | N |
| REVIEW2 | N | NaN | N | N | N | N | P | N | P | NaN | N | N | N | N | N | N | N | P |
| REVIEW3 | N | NaN | P | P | N | N | NaN | NaN | P | NaN | N | N | N | P | P | N | N | P |
| REVIEW4 | N | NaN | P | P | N | P | NaN | NaN | P | NaN | N | N | N | N | P | N | N | P |
| REVIEW5 | NaN | NaN | N | N | P | P | NaN | NaN | NaN | NaN | N | N | N | N | P | N | P | P |
| REVIEW6 | NaN | NaN | N | NaN | P | NaN | NaN | NaN | NaN | NaN | N | N | N | P | P | N | N | N |
| REVIEW7 | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | N | N | N | P | N | N | N |
| REVIEW8 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | N | P | P | P | P | N | N |
| REVIEW9 | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | N | N | P | P | P | N | N |
| REVIEW10 | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | N | N | N | P | P | N | N |
| REVIEW11 | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | N | N | N | NaN | P | N | P |
| REVIEW12 | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | N | N | N | NaN | P | N | P |
| REVIEW13 | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | N | N | N | NaN | P | N | N |
| REVIEW14 | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | N | N | N | NaN | P | N | P |
| REVIEW15 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | N | N | N | NaN | P | P | N |
| REVIEW16 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | N | P | P | NaN | P | N | N |
| REVIEW17 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | N | P | N | NaN | P | N | N |
| REVIEW18 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | N | P | N | NaN | P | P | N |
| REVIEW19 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | N | P | N | NaN | P | P | P |
| REVIEW20 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | N | P | N | NaN | P | P | N |
| REVIEW21 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | N | P | N | NaN | P | P | N |
| REVIEW22 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | P | P | NaN | P | P | P |
| REVIEW23 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | P | P | NaN | NaN | P | N |
| REVIEW24 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | P | N | NaN | NaN | P | N |
| REVIEW25 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | P | P | NaN | NaN | P | N |
| REVIEW26 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | P | P | NaN | NaN | P | N |
| REVIEW27 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | P | P | NaN | NaN | P | N |
| REVIEW28 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | P | P | NaN | NaN | P | P |
| REVIEW29 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | N | NaN | NaN | P | N |
| REVIEW30 | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | P | NaN | NaN | P | N |
| REVIEW31 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | P | NaN | NaN | P | P |
| REVIEW32 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | N | NaN | NaN | P | N |
| REVIEW33 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | N | NaN | NaN | P | P |
| REVIEW34 | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | P |
| REVIEW35 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | N |
| REVIEW36 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | N |
| REVIEW37 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | P |
| REVIEW38 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW39 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW40 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW41 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW42 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW43 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW44 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW45 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW46 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | P | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW | 99 | 0 | 1797 | 304 | 254 | 255 | 104 | 121 | 261 | 56 | 954 | 2177 | 1342 | 1458 | 597 | 1339 | 1605 | 1536 |
That is obviously wrong because only THREE people commented on Review1¶
In [311]:
df = pd.DataFrame({'Turker': even_cleaner_df['T_ID'].tolist(),
'SENTIMENT': even_cleaner_df['ReviewID'].tolist(),
'REVIEW': even_cleaner_df['sentiment'].tolist() })
grouped = df.groupby('Turker')
values = grouped['REVIEW'].agg('sum')
id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()
id_df = id_df.rename(columns={i: 'REVIEW{}'.format(i + 1) for i in range(id_df.shape[1])})
result = pd.concat([id_df, values], axis=1)
result_df = pd.DataFrame(result)
print(result_df.T)
Turker T_0 T_1 T_10 T_11 T_12 T_13 \ REVIEW1 0 0 6 11 13 15 REVIEW2 11 NaN 8 47 25 19 REVIEW3 42 NaN 14 55 29 44 REVIEW4 46 NaN 15 95 57 80 REVIEW5 NaN NaN 18 96 59 97 REVIEW6 NaN NaN 20 NaN 71 NaN REVIEW7 NaN NaN 21 NaN NaN NaN REVIEW8 NaN NaN 22 NaN NaN NaN REVIEW9 NaN NaN 23 NaN NaN NaN REVIEW10 NaN NaN 26 NaN NaN NaN REVIEW11 NaN NaN 33 NaN NaN NaN REVIEW12 NaN NaN 35 NaN NaN NaN REVIEW13 NaN NaN 41 NaN NaN NaN REVIEW14 NaN NaN 42 NaN NaN NaN REVIEW15 NaN NaN 51 NaN NaN NaN REVIEW16 NaN NaN 52 NaN NaN NaN REVIEW17 NaN NaN 53 NaN NaN NaN REVIEW18 NaN NaN 59 NaN NaN NaN REVIEW19 NaN NaN 60 NaN NaN NaN REVIEW20 NaN NaN 62 NaN NaN NaN REVIEW21 NaN NaN 67 NaN NaN NaN REVIEW22 NaN NaN 68 NaN NaN NaN REVIEW23 NaN NaN 69 NaN NaN NaN REVIEW24 NaN NaN 74 NaN NaN NaN REVIEW25 NaN NaN 77 NaN NaN NaN REVIEW26 NaN NaN 79 NaN NaN NaN REVIEW27 NaN NaN 80 NaN NaN NaN REVIEW28 NaN NaN 81 NaN NaN NaN REVIEW29 NaN NaN 82 NaN NaN NaN REVIEW30 NaN NaN 87 NaN NaN NaN REVIEW31 NaN NaN 90 NaN NaN NaN REVIEW32 NaN NaN 91 NaN NaN NaN REVIEW33 NaN NaN 94 NaN NaN NaN REVIEW34 NaN NaN 97 NaN NaN NaN REVIEW35 NaN NaN NaN NaN NaN NaN REVIEW36 NaN NaN NaN NaN NaN NaN REVIEW37 NaN NaN NaN NaN NaN NaN REVIEW38 NaN NaN NaN NaN NaN NaN REVIEW39 NaN NaN NaN NaN NaN NaN REVIEW40 NaN NaN NaN NaN NaN NaN REVIEW41 NaN NaN NaN NaN NaN NaN REVIEW42 NaN NaN NaN NaN NaN NaN REVIEW43 NaN NaN NaN NaN NaN NaN REVIEW44 NaN NaN NaN NaN NaN NaN REVIEW45 NaN NaN NaN NaN NaN NaN REVIEW46 NaN NaN NaN NaN NaN NaN REVIEW NNNN N PNPPNNNPNNNNNNPPPPPPPPPPPPPPPNPPPP NNPPN NNNNPP NNNPP Turker T_14 T_15 T_16 T_17 T_2 \ REVIEW1 28 29 39 56 0 REVIEW2 76 92 58 NaN 1 REVIEW3 NaN NaN 70 NaN 8 REVIEW4 NaN NaN 94 NaN 10 REVIEW5 NaN NaN NaN NaN 16 REVIEW6 NaN NaN NaN NaN 21 REVIEW7 NaN NaN NaN NaN 27 REVIEW8 NaN NaN NaN NaN 32 REVIEW9 NaN NaN NaN NaN 36 REVIEW10 NaN NaN NaN NaN 37 REVIEW11 NaN NaN NaN NaN 45 REVIEW12 NaN NaN NaN NaN 47 REVIEW13 NaN NaN NaN NaN 50 REVIEW14 NaN NaN NaN NaN 64 REVIEW15 NaN NaN NaN NaN 66 REVIEW16 NaN NaN NaN NaN 72 REVIEW17 NaN NaN NaN NaN 75 REVIEW18 NaN NaN NaN NaN 83 REVIEW19 NaN NaN NaN NaN 85 REVIEW20 NaN NaN NaN NaN 86 REVIEW21 NaN NaN NaN NaN 93 REVIEW22 NaN NaN NaN NaN NaN REVIEW23 NaN NaN NaN NaN NaN REVIEW24 NaN NaN NaN NaN NaN REVIEW25 NaN NaN NaN NaN NaN REVIEW26 NaN NaN NaN NaN NaN REVIEW27 NaN NaN NaN NaN NaN REVIEW28 NaN NaN NaN NaN NaN REVIEW29 NaN NaN NaN NaN NaN REVIEW30 NaN NaN NaN NaN NaN REVIEW31 NaN NaN NaN NaN NaN REVIEW32 NaN NaN NaN NaN NaN REVIEW33 NaN NaN NaN NaN NaN REVIEW34 NaN NaN NaN NaN NaN REVIEW35 NaN NaN NaN NaN NaN REVIEW36 NaN NaN NaN NaN NaN REVIEW37 NaN NaN NaN NaN NaN REVIEW38 NaN NaN NaN NaN NaN REVIEW39 NaN NaN NaN NaN NaN REVIEW40 NaN NaN NaN NaN NaN REVIEW41 NaN NaN NaN NaN NaN REVIEW42 NaN NaN NaN NaN NaN REVIEW43 NaN NaN NaN NaN NaN REVIEW44 NaN NaN NaN NaN NaN REVIEW45 NaN NaN NaN NaN NaN REVIEW46 NaN NaN NaN NaN NaN REVIEW NP NN NPPP P NNNNNNNNNNNNPPPPPPNPP Turker T_3 \ REVIEW1 1 REVIEW2 3 REVIEW3 4 REVIEW4 5 REVIEW5 7 REVIEW6 10 REVIEW7 14 REVIEW8 16 REVIEW9 17 REVIEW10 19 REVIEW11 22 REVIEW12 23 REVIEW13 26 REVIEW14 28 REVIEW15 30 REVIEW16 34 REVIEW17 35 REVIEW18 36 REVIEW19 38 REVIEW20 40 REVIEW21 44 REVIEW22 46 REVIEW23 48 REVIEW24 49 REVIEW25 54 REVIEW26 55 REVIEW27 57 REVIEW28 58 REVIEW29 60 REVIEW30 61 REVIEW31 63 REVIEW32 64 REVIEW33 65 REVIEW34 70 REVIEW35 71 REVIEW36 73 REVIEW37 74 REVIEW38 75 REVIEW39 77 REVIEW40 79 REVIEW41 82 REVIEW42 85 REVIEW43 86 REVIEW44 88 REVIEW45 90 REVIEW46 95 REVIEW NNNNNNNNNNNNNNNNNNNNNNPPPPPPPPPPPPPPPPPPPNPPPP Turker T_4 T_5 \ REVIEW1 1 2 REVIEW2 7 3 REVIEW3 9 4 REVIEW4 12 5 REVIEW5 17 12 REVIEW6 19 13 REVIEW7 27 14 REVIEW8 29 20 REVIEW9 30 22 REVIEW10 33 24 REVIEW11 40 26 REVIEW12 41 28 REVIEW13 42 31 REVIEW14 45 36 REVIEW15 47 39 REVIEW16 48 43 REVIEW17 50 45 REVIEW18 55 46 REVIEW19 61 48 REVIEW20 65 49 REVIEW21 69 52 REVIEW22 73 53 REVIEW23 77 54 REVIEW24 78 56 REVIEW25 87 58 REVIEW26 90 72 REVIEW27 93 75 REVIEW28 97 78 REVIEW29 NaN 85 REVIEW30 NaN 87 REVIEW31 NaN 88 REVIEW32 NaN 94 REVIEW33 NaN 96 REVIEW34 NaN NaN REVIEW35 NaN NaN REVIEW36 NaN NaN REVIEW37 NaN NaN REVIEW38 NaN NaN REVIEW39 NaN NaN REVIEW40 NaN NaN REVIEW41 NaN NaN REVIEW42 NaN NaN REVIEW43 NaN NaN REVIEW44 NaN NaN REVIEW45 NaN NaN REVIEW46 NaN NaN REVIEW NNNNNNNPNNNNNNNPPPPPPPPPPPPP PNPNNPNPPNNNNNNPNNNNNPPNPPPPNPPNN Turker T_6 T_7 \ REVIEW1 2 2 REVIEW2 31 8 REVIEW3 50 9 REVIEW4 51 24 REVIEW5 62 34 REVIEW6 63 39 REVIEW7 74 43 REVIEW8 84 51 REVIEW9 89 56 REVIEW10 91 60 REVIEW11 NaN 63 REVIEW12 NaN 64 REVIEW13 NaN 82 REVIEW14 NaN 83 REVIEW15 NaN 84 REVIEW16 NaN 86 REVIEW17 NaN 88 REVIEW18 NaN 89 REVIEW19 NaN 91 REVIEW20 NaN 92 REVIEW21 NaN 95 REVIEW22 NaN 96 REVIEW23 NaN NaN REVIEW24 NaN NaN REVIEW25 NaN NaN REVIEW26 NaN NaN REVIEW27 NaN NaN REVIEW28 NaN NaN REVIEW29 NaN NaN REVIEW30 NaN NaN REVIEW31 NaN NaN REVIEW32 NaN NaN REVIEW33 NaN NaN REVIEW34 NaN NaN REVIEW35 NaN NaN REVIEW36 NaN NaN REVIEW37 NaN NaN REVIEW38 NaN NaN REVIEW39 NaN NaN REVIEW40 NaN NaN REVIEW41 NaN NaN REVIEW42 NaN NaN REVIEW43 NaN NaN REVIEW44 NaN NaN REVIEW45 NaN NaN REVIEW46 NaN NaN REVIEW NNPPPPPPPP NNNNNNNPPPPPPPPPPPPPPP Turker T_8 \ REVIEW1 3 REVIEW2 4 REVIEW3 6 REVIEW4 12 REVIEW5 15 REVIEW6 18 REVIEW7 20 REVIEW8 24 REVIEW9 25 REVIEW10 27 REVIEW11 31 REVIEW12 32 REVIEW13 34 REVIEW14 37 REVIEW15 38 REVIEW16 40 REVIEW17 43 REVIEW18 59 REVIEW19 61 REVIEW20 62 REVIEW21 66 REVIEW22 67 REVIEW23 68 REVIEW24 72 REVIEW25 73 REVIEW26 76 REVIEW27 78 REVIEW28 79 REVIEW29 80 REVIEW30 81 REVIEW31 89 REVIEW32 92 REVIEW33 93 REVIEW34 NaN REVIEW35 NaN REVIEW36 NaN REVIEW37 NaN REVIEW38 NaN REVIEW39 NaN REVIEW40 NaN REVIEW41 NaN REVIEW42 NaN REVIEW43 NaN REVIEW44 NaN REVIEW45 NaN REVIEW46 NaN REVIEW NNNNPNNNNNNNNNPNNPPPPPPPPPPPPPPPP Turker T_9 REVIEW1 5 REVIEW2 6 REVIEW3 7 REVIEW4 9 REVIEW5 10 REVIEW6 11 REVIEW7 13 REVIEW8 16 REVIEW9 17 REVIEW10 18 REVIEW11 21 REVIEW12 23 REVIEW13 25 REVIEW14 30 REVIEW15 32 REVIEW16 33 REVIEW17 35 REVIEW18 37 REVIEW19 38 REVIEW20 41 REVIEW21 44 REVIEW22 49 REVIEW23 52 REVIEW24 53 REVIEW25 54 REVIEW26 57 REVIEW27 65 REVIEW28 66 REVIEW29 67 REVIEW30 68 REVIEW31 69 REVIEW32 70 REVIEW33 71 REVIEW34 76 REVIEW35 81 REVIEW36 83 REVIEW37 84 REVIEW38 NaN REVIEW39 NaN REVIEW40 NaN REVIEW41 NaN REVIEW42 NaN REVIEW43 NaN REVIEW44 NaN REVIEW45 NaN REVIEW46 NaN REVIEW NPPPPNNNNNPPNPNNNNPNNPNNNNNPNNPNPPNNP
In [312]:
df = pd.DataFrame(result_df.T)
In [313]:
df
Out[313]:
| Turker | T_0 | T_1 | T_10 | T_11 | T_12 | T_13 | T_14 | T_15 | T_16 | T_17 | T_2 | T_3 | T_4 | T_5 | T_6 | T_7 | T_8 | T_9 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| REVIEW1 | 0 | 0 | 6 | 11 | 13 | 15 | 28 | 29 | 39 | 56 | 0 | 1 | 1 | 2 | 2 | 2 | 3 | 5 |
| REVIEW2 | 11 | NaN | 8 | 47 | 25 | 19 | 76 | 92 | 58 | NaN | 1 | 3 | 7 | 3 | 31 | 8 | 4 | 6 |
| REVIEW3 | 42 | NaN | 14 | 55 | 29 | 44 | NaN | NaN | 70 | NaN | 8 | 4 | 9 | 4 | 50 | 9 | 6 | 7 |
| REVIEW4 | 46 | NaN | 15 | 95 | 57 | 80 | NaN | NaN | 94 | NaN | 10 | 5 | 12 | 5 | 51 | 24 | 12 | 9 |
| REVIEW5 | NaN | NaN | 18 | 96 | 59 | 97 | NaN | NaN | NaN | NaN | 16 | 7 | 17 | 12 | 62 | 34 | 15 | 10 |
| REVIEW6 | NaN | NaN | 20 | NaN | 71 | NaN | NaN | NaN | NaN | NaN | 21 | 10 | 19 | 13 | 63 | 39 | 18 | 11 |
| REVIEW7 | NaN | NaN | 21 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 27 | 14 | 27 | 14 | 74 | 43 | 20 | 13 |
| REVIEW8 | NaN | NaN | 22 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 32 | 16 | 29 | 20 | 84 | 51 | 24 | 16 |
| REVIEW9 | NaN | NaN | 23 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 36 | 17 | 30 | 22 | 89 | 56 | 25 | 17 |
| REVIEW10 | NaN | NaN | 26 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 37 | 19 | 33 | 24 | 91 | 60 | 27 | 18 |
| REVIEW11 | NaN | NaN | 33 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 45 | 22 | 40 | 26 | NaN | 63 | 31 | 21 |
| REVIEW12 | NaN | NaN | 35 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 47 | 23 | 41 | 28 | NaN | 64 | 32 | 23 |
| REVIEW13 | NaN | NaN | 41 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 50 | 26 | 42 | 31 | NaN | 82 | 34 | 25 |
| REVIEW14 | NaN | NaN | 42 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 64 | 28 | 45 | 36 | NaN | 83 | 37 | 30 |
| REVIEW15 | NaN | NaN | 51 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 66 | 30 | 47 | 39 | NaN | 84 | 38 | 32 |
| REVIEW16 | NaN | NaN | 52 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 72 | 34 | 48 | 43 | NaN | 86 | 40 | 33 |
| REVIEW17 | NaN | NaN | 53 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 75 | 35 | 50 | 45 | NaN | 88 | 43 | 35 |
| REVIEW18 | NaN | NaN | 59 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 83 | 36 | 55 | 46 | NaN | 89 | 59 | 37 |
| REVIEW19 | NaN | NaN | 60 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 85 | 38 | 61 | 48 | NaN | 91 | 61 | 38 |
| REVIEW20 | NaN | NaN | 62 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 86 | 40 | 65 | 49 | NaN | 92 | 62 | 41 |
| REVIEW21 | NaN | NaN | 67 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 93 | 44 | 69 | 52 | NaN | 95 | 66 | 44 |
| REVIEW22 | NaN | NaN | 68 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 46 | 73 | 53 | NaN | 96 | 67 | 49 |
| REVIEW23 | NaN | NaN | 69 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 48 | 77 | 54 | NaN | NaN | 68 | 52 |
| REVIEW24 | NaN | NaN | 74 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 49 | 78 | 56 | NaN | NaN | 72 | 53 |
| REVIEW25 | NaN | NaN | 77 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 54 | 87 | 58 | NaN | NaN | 73 | 54 |
| REVIEW26 | NaN | NaN | 79 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 55 | 90 | 72 | NaN | NaN | 76 | 57 |
| REVIEW27 | NaN | NaN | 80 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 57 | 93 | 75 | NaN | NaN | 78 | 65 |
| REVIEW28 | NaN | NaN | 81 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 58 | 97 | 78 | NaN | NaN | 79 | 66 |
| REVIEW29 | NaN | NaN | 82 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 60 | NaN | 85 | NaN | NaN | 80 | 67 |
| REVIEW30 | NaN | NaN | 87 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 61 | NaN | 87 | NaN | NaN | 81 | 68 |
| REVIEW31 | NaN | NaN | 90 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 63 | NaN | 88 | NaN | NaN | 89 | 69 |
| REVIEW32 | NaN | NaN | 91 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 64 | NaN | 94 | NaN | NaN | 92 | 70 |
| REVIEW33 | NaN | NaN | 94 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 65 | NaN | 96 | NaN | NaN | 93 | 71 |
| REVIEW34 | NaN | NaN | 97 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 70 | NaN | NaN | NaN | NaN | NaN | 76 |
| REVIEW35 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 71 | NaN | NaN | NaN | NaN | NaN | 81 |
| REVIEW36 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 73 | NaN | NaN | NaN | NaN | NaN | 83 |
| REVIEW37 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 74 | NaN | NaN | NaN | NaN | NaN | 84 |
| REVIEW38 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 75 | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW39 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 77 | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW40 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 79 | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW41 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 82 | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW42 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 85 | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW43 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 86 | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW44 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 88 | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW45 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 90 | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW46 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 95 | NaN | NaN | NaN | NaN | NaN | NaN |
| REVIEW | NNNN | N | PNPPNNNPNNNNNNPPPPPPPPPPPPPPPNPPPP | NNPPN | NNNNPP | NNNPP | NP | NN | NPPP | P | NNNNNNNNNNNNPPPPPPNPP | NNNNNNNNNNNNNNNNNNNNNNPPPPPPPPPPPPPPPPPPPNPPPP | NNNNNNNPNNNNNNNPPPPPPPPPPPPP | PNPNNPNPPNNNNNNPNNNNNPPNPPPPNPPNN | NNPPPPPPPP | NNNNNNNPPPPPPPPPPPPPPP | NNNNPNNNNNNNNNPNNPPPPPPPPPPPPPPPP | NPPPPNNNNNPPNPNNNNPNNPNNNNNPNNPNPPNNP |
In [317]:
df = pd.DataFrame({'Turker': even_cleaner_df['T_ID'].tolist(),
'SENTIMENT': even_cleaner_df['ReviewID'].tolist(),
'REVIEW': even_cleaner_df['sentiment'].tolist() })
grouped = df.groupby('Turker')
print(grouped.tolist())
# values = grouped['REVIEW'].agg('sum')
# id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack()
# id_df = id_df.rename(columns={i: 'REVIEW{}'.format(i + 1) for i in range(id_df.shape[1])})
# result = pd.concat([id_df, values], axis=1)
# result_df = pd.DataFrame(result)
# print(result_df.T)
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-317-1bc38d4e9879> in <module> 4 5 grouped = df.groupby('Turker') ----> 6 print(grouped.tolist()) 7 # values = grouped['REVIEW'].agg('sum') 8 # id_df = grouped['SENTIMENT'].apply(lambda x: pd.Series(x.values)).unstack() /usr/local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in __getattr__(self, attr) 564 565 raise AttributeError( --> 566 "%r object has no attribute %r" % (type(self).__name__, attr) 567 ) 568 AttributeError: 'DataFrameGroupBy' object has no attribute 'tolist'
I want every review on the left side and I want all 46 turkers on the top
In [319]:
df = pd.DataFrame({ 'review': even_cleaner_df['ReviewID']})
In [342]:
def get_array_of_reviews(turker, df):
a = [0]*98
df = even_cleaner_df[even_cleaner_df['T_ID'] == turker]
t_reviews = df['ReviewID'].tolist()
t_sentiment = df['sentiment'].tolist()
for index,review in enumerate(t_reviews):
a[review] = t_sentiment[index]
print(t_reviews)
return a
sparse_df = even_cleaner_df.copy()
sparse_df['big_array'] = sparse_df.apply(lambda x: get_array_of_reviews(x['T_ID'], even_cleaner_df), axis=1)
# t0 = even_cleaner_df[even_cleaner_df['T_ID'] == 'T_0']
[0, 11, 42, 46] [0] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [2, 31, 50, 51, 62, 63, 74, 84, 89, 91] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [11, 47, 55, 95, 96] [0, 11, 42, 46] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [13, 25, 29, 57, 59, 71] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [15, 19, 44, 80, 97] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [15, 19, 44, 80, 97] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [13, 25, 29, 57, 59, 71] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [28, 76] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [29, 92] [13, 25, 29, 57, 59, 71] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [2, 31, 50, 51, 62, 63, 74, 84, 89, 91] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 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23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [39, 58, 70, 94] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [13, 25, 29, 57, 59, 71] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [2, 31, 50, 51, 62, 63, 74, 84, 89, 91] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [28, 76] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [15, 19, 44, 80, 97] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [2, 31, 50, 51, 62, 63, 74, 84, 89, 91] [5, 6, 7, 9, 10, 11, 13, 16, 17, 18, 21, 23, 25, 30, 32, 33, 35, 37, 38, 41, 44, 49, 52, 53, 54, 57, 65, 66, 67, 68, 69, 70, 71, 76, 81, 83, 84] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [2, 31, 50, 51, 62, 63, 74, 84, 89, 91] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [2, 31, 50, 51, 62, 63, 74, 84, 89, 91] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [29, 92] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [0, 1, 8, 10, 16, 21, 27, 32, 36, 37, 45, 47, 50, 64, 66, 72, 75, 83, 85, 86, 93] [3, 4, 6, 12, 15, 18, 20, 24, 25, 27, 31, 32, 34, 37, 38, 40, 43, 59, 61, 62, 66, 67, 68, 72, 73, 76, 78, 79, 80, 81, 89, 92, 93] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [39, 58, 70, 94] [1, 3, 4, 5, 7, 10, 14, 16, 17, 19, 22, 23, 26, 28, 30, 34, 35, 36, 38, 40, 44, 46, 48, 49, 54, 55, 57, 58, 60, 61, 63, 64, 65, 70, 71, 73, 74, 75, 77, 79, 82, 85, 86, 88, 90, 95] [11, 47, 55, 95, 96] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [11, 47, 55, 95, 96] [2, 3, 4, 5, 12, 13, 14, 20, 22, 24, 26, 28, 31, 36, 39, 43, 45, 46, 48, 49, 52, 53, 54, 56, 58, 72, 75, 78, 85, 87, 88, 94, 96] [2, 8, 9, 24, 34, 39, 43, 51, 56, 60, 63, 64, 82, 83, 84, 86, 88, 89, 91, 92, 95, 96] [15, 19, 44, 80, 97] [1, 7, 9, 12, 17, 19, 27, 29, 30, 33, 40, 41, 42, 45, 47, 48, 50, 55, 61, 65, 69, 73, 77, 78, 87, 90, 93, 97] [6, 8, 14, 15, 18, 20, 21, 22, 23, 26, 33, 35, 41, 42, 51, 52, 53, 59, 60, 62, 67, 68, 69, 74, 77, 79, 80, 81, 82, 87, 90, 91, 94, 97]
In [334]:
t0
Out[334]:
| ReviewID | T_ID | sentiment | |
|---|---|---|---|
| 0 | 0 | T_0 | N |
| 34 | 11 | T_0 | N |
| 126 | 42 | T_0 | N |
| 140 | 46 | T_0 | N |
In [343]:
sparse_df
Out[343]:
| ReviewID | T_ID | sentiment | big_array | |
|---|---|---|---|---|
| 0 | 0 | T_0 | N | [N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ... |
| 1 | 0 | T_1 | N | [N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... |
| 2 | 0 | T_2 | N | [N, N, 0, 0, 0, 0, 0, 0, N, 0, N, 0, 0, 0, 0, ... |
| 3 | 1 | T_3 | N | [0, N, 0, N, N, N, 0, N, 0, 0, N, 0, 0, 0, N, ... |
| 4 | 1 | T_2 | N | [N, N, 0, 0, 0, 0, 0, 0, N, 0, N, 0, 0, 0, 0, ... |
| ... | ... | ... | ... | ... |
| 289 | 96 | T_5 | N | [0, 0, P, N, P, N, 0, 0, 0, 0, 0, 0, N, P, N, ... |
| 290 | 96 | T_7 | P | [0, 0, N, 0, 0, 0, 0, 0, N, N, 0, 0, 0, 0, 0, ... |
| 291 | 97 | T_13 | P | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... |
| 292 | 97 | T_4 | P | [0, N, 0, 0, 0, 0, 0, N, 0, N, 0, 0, N, 0, 0, ... |
| 293 | 97 | T_10 | P | [0, 0, 0, 0, 0, 0, P, 0, N, 0, 0, 0, 0, 0, P, ... |
294 rows × 4 columns
In [344]:
t0 = sparse_df[sparse_df['T_ID'] == 'T_0']
In [346]:
t0
Out[346]:
| ReviewID | T_ID | sentiment | big_array | |
|---|---|---|---|---|
| 0 | 0 | T_0 | N | [N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ... |
| 34 | 11 | T_0 | N | [N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ... |
| 126 | 42 | T_0 | N | [N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ... |
| 140 | 46 | T_0 | N | [N, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N, 0, 0, 0, ... |
In [352]:
sparse_df['big_array'].unique()
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-352-49ad7af36cba> in <module> ----> 1 sparse_df['big_array'].unique() /usr/local/lib/python3.7/site-packages/pandas/core/series.py in unique(self) 1986 Categories (3, object): [a < b < c] 1987 """ -> 1988 result = super().unique() 1989 return result 1990 /usr/local/lib/python3.7/site-packages/pandas/core/base.py in unique(self) 1403 from pandas.core.algorithms import unique1d 1404 -> 1405 result = unique1d(values) 1406 1407 return result /usr/local/lib/python3.7/site-packages/pandas/core/algorithms.py in unique(values) 403 404 table = htable(len(values)) --> 405 uniques = table.unique(values) 406 uniques = _reconstruct_data(uniques, dtype, original) 407 return uniques pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.unique() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable._unique() TypeError: unhashable type: 'list'
In [354]:
even_cleaner_df['T_ID'].unique()
Out[354]:
array(['T_0', 'T_1', 'T_2', 'T_3', 'T_4', 'T_5', 'T_6', 'T_7', 'T_8',
'T_9', 'T_10', 'T_11', 'T_12', 'T_13', 'T_14', 'T_15', 'T_16',
'T_17'], dtype=object)
In [ ]: