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stringlengths 13
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sequencelengths 1
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|---|---|---|---|
312349/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))
plt.title('Number of locations reported - Top 30') | code |
312349/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
age_groups = ('confirmed_age_under_1', 'confirmed_age_1-4', 'confirmed_age_5-9', 'confirmed_age_10-14', 'confirmed_age_15-19', 'confirmed_age_20-24', 'confirmed_age_25-34', 'confirmed_age_35-49', 'confirmed_age_50-59', 'confirmed_age_60-64', 'confirmed_age_60_plus')
for i, age_group in enumerate(age_groups):
print(age_group)
print(df[df.data_field == age_group].value)
print('') | code |
312349/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sbn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
312349/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
age_groups = ('confirmed_age_under_1', 'confirmed_age_1-4', 'confirmed_age_5-9', 'confirmed_age_10-14', 'confirmed_age_15-19', 'confirmed_age_20-24', 'confirmed_age_25-34', 'confirmed_age_35-49', 'confirmed_age_50-59', 'confirmed_age_60-64', 'confirmed_age_60_plus')
symptoms = ['confirmed_fever', 'confirmed_acute_fever', 'confirmed_arthralgia', 'confirmed_arthritis', 'confirmed_rash', 'confirmed_conjunctivitis', 'confirmed_eyepain', 'confirmed_headache', 'confirmed_malaise']
fig = plt.figure(figsize=(13, 13))
for symptom in symptoms:
df[df.data_field == symptom].value.plot()
plt.legend(symptoms, loc='best')
plt.title('Understanding symptoms of zika virus') | code |
312349/cell_3 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.head(3) | code |
312349/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df[df.data_field == 'confirmed_male'].value.plot()
df[df.data_field == 'confirmed_female'].value.plot().legend(('Male', 'Female'), loc='best')
plt.title('Confirmed Male vs Female cases') | code |
316827/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
data.head() | code |
316827/cell_30 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
conf_interval('comments')
print(compare_means('comments')) | code |
316827/cell_33 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
conf_interval('msg_len')
print(compare_means('msg_len')) | code |
316827/cell_20 | [
"text_plain_output_1.png"
] | from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
conf_interval('likes') | code |
316827/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
sns.pairplot(data, hue='gid') | code |
316827/cell_24 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
conf_interval('likes')
print(compare_means('likes')) | code |
316827/cell_27 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
conf_interval('shares')
print(compare_means('shares')) | code |
316827/cell_37 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
shared = data[data.shares > data.shares.quantile(0.98)][data.shares > data.likes * 10][['msg', 'shares']]
top = 10
print('top %d out of %d' % (top, shared.shape[0]))
sorted_data = shared.sort_values(by='shares', ascending=False)[:top]
for i in sorted_data.index.values:
print('shares:', sorted_data.shares[i], '\n', 'message:', sorted_data.msg[i][:200], '\n') | code |
309674/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_csv('../input/like.csv')
members = pd.read_csv('../input/member.csv')
posts = pd.read_csv('../input/post.csv')
likeResponse = pd.merge(likes.loc[likes['gid'] == 117291968282998], posts.loc[posts['gid'] == 117291968282998, ['pid', 'name']], left_on='pid', right_on='pid')
result = likeResponse.groupby(['name_y', 'name_x'])['response'].count()
finalResult = pd.DataFrame(result.index.values, columns=['NameCombo'])
finalResult['Weight'] = result.values
finalResult['From'] = finalResult['NameCombo'].map(lambda x: x[0])
finalResult['To'] = finalResult['NameCombo'].map(lambda x: x[1])
del finalResult['NameCombo']
g = nx.Graph()
plt.figure()
g.add_edges_from([(row['From'], row['To']) for index, row in finalResult.iterrows()])
d = nx.degree(g)
spring_pos = nx.spring_layout(g)
plt.axis('off')
nx.draw_networkx(g, spring_pos, with_labels=False, nodelist=d.keys(), node_size=[v * 10 for v in d.values()])
plt.savefig('LIKE_PLOT_GROUP1.png')
plt.clf() | code |
311500/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df[df.data_field == 'confirmed_male'].value.plot()
df[df.data_field == 'confirmed_female'].value.plot().legend(('Male', 'Female'), loc='best')
plt.title('Confirmed Male vs Female cases') | code |
311500/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.data_field.unique() | code |
311500/cell_2 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.head(3) | code |
311500/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sbn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
311500/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.data_field.unique()
age_groups = ('confirmed_age_under_1', 'confirmed_age_1-4', 'confirmed_age_5-9', 'confirmed_age_10-14', 'confirmed_age_15-19', 'confirmed_age_20-24', 'confirmed_age_25-34', 'confirmed_age_35-49', 'confirmed_age_50-59', 'confirmed_age_60-64', 'confirmed_age_60_plus')
for i, age_group in enumerate(age_groups):
print(age_group)
print(df[df.data_field == age_group].value)
print('') | code |
311500/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))
plt.title('Number of locations reported - Top 30') | code |
306027/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import sqlite3
import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
scripts = pd.read_sql_query('\n\nSELECT s.Id,\n\n cv.Title,\n\n COUNT(DISTINCT vo.Id) NumVotes,\n\n COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END) NumNonSelfVotes,\n\n CASE WHEN COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END)>0 THEN 1 ELSE 0 END HasNonSelfVotes,\n\n COUNT(DISTINCT v.Id) NumVersions,\n\n SUM(CASE WHEN r.WorkerStatus=2 THEN 1 ELSE 0 END) NumSuccessfulRuns,\n\n SUM(CASE WHEN r.WorkerStatus=3 THEN 1 ELSE 0 END) NumErroredRuns,\n\n SUM(CASE WHEN v.IsChange=1 THEN 1 ELSE 0 END) NumChangedVersions,\n\n SUM(v.LinesInsertedFromPrevious-v.LinesDeletedFromPrevious) Lines,\n\n SUM(v.LinesInsertedFromPrevious+v.LinesChangedFromPrevious) LinesAddedOrChanged,\n\n l.Name\n\nFROM Scripts s\n\nINNER JOIN ScriptVersions v ON v.ScriptId=s.Id\n\nINNER JOIN ScriptVersions cv ON s.CurrentScriptVersionId=cv.Id\n\nINNER JOIN ScriptRuns r ON r.ScriptVersionId=v.Id\n\nINNER JOIN ScriptLanguages l ON v.ScriptLanguageId=l.Id\n\nLEFT OUTER JOIN ScriptVotes vo ON vo.ScriptVersionId=v.Id\n\nWHERE r.WorkerStatus != 4\n\n AND r.WorkerStatus != 5\n\nGROUP BY s.Id,\n\n cv.Title,\n\n cv.Id,\n\n l.Name\n\nORDER BY cv.Id DESC\n\n', con)
scripts
pd.read_sql_query('\nSELECT *\nFROM ScriptLanguages\nLIMIT 100\n', con) | code |
306027/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import sqlite3
import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
scripts = pd.read_sql_query('\nSELECT s.Id,\n cv.Title,\n COUNT(DISTINCT vo.Id) NumVotes,\n COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END) NumNonSelfVotes,\n CASE WHEN COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END)>0 THEN 1 ELSE 0 END HasNonSelfVotes,\n COUNT(DISTINCT v.Id) NumVersions,\n SUM(CASE WHEN r.WorkerStatus=2 THEN 1 ELSE 0 END) NumSuccessfulRuns,\n SUM(CASE WHEN r.WorkerStatus=3 THEN 1 ELSE 0 END) NumErroredRuns,\n SUM(CASE WHEN v.IsChange=1 THEN 1 ELSE 0 END) NumChangedVersions,\n SUM(v.LinesInsertedFromPrevious-v.LinesDeletedFromPrevious) Lines,\n SUM(v.LinesInsertedFromPrevious+v.LinesChangedFromPrevious) LinesAddedOrChanged,\n l.Name\nFROM Scripts s\nINNER JOIN ScriptVersions v ON v.ScriptId=s.Id\nINNER JOIN ScriptVersions cv ON s.CurrentScriptVersionId=cv.Id\nINNER JOIN ScriptRuns r ON r.ScriptVersionId=v.Id\nINNER JOIN ScriptLanguages l ON v.ScriptLanguageId=l.Id\nLEFT OUTER JOIN ScriptVotes vo ON vo.ScriptVersionId=v.Id\nWHERE r.WorkerStatus != 4\n AND r.WorkerStatus != 5\nGROUP BY s.Id,\n cv.Title,\n cv.Id,\n l.Name\nORDER BY cv.Id DESC\n', con)
scripts | code |
306027/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline, FeatureUnion
import pandas as pd
import sqlite3
import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
scripts = pd.read_sql_query('\n\nSELECT s.Id,\n\n cv.Title,\n\n COUNT(DISTINCT vo.Id) NumVotes,\n\n COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END) NumNonSelfVotes,\n\n CASE WHEN COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END)>0 THEN 1 ELSE 0 END HasNonSelfVotes,\n\n COUNT(DISTINCT v.Id) NumVersions,\n\n SUM(CASE WHEN r.WorkerStatus=2 THEN 1 ELSE 0 END) NumSuccessfulRuns,\n\n SUM(CASE WHEN r.WorkerStatus=3 THEN 1 ELSE 0 END) NumErroredRuns,\n\n SUM(CASE WHEN v.IsChange=1 THEN 1 ELSE 0 END) NumChangedVersions,\n\n SUM(v.LinesInsertedFromPrevious-v.LinesDeletedFromPrevious) Lines,\n\n SUM(v.LinesInsertedFromPrevious+v.LinesChangedFromPrevious) LinesAddedOrChanged,\n\n l.Name\n\nFROM Scripts s\n\nINNER JOIN ScriptVersions v ON v.ScriptId=s.Id\n\nINNER JOIN ScriptVersions cv ON s.CurrentScriptVersionId=cv.Id\n\nINNER JOIN ScriptRuns r ON r.ScriptVersionId=v.Id\n\nINNER JOIN ScriptLanguages l ON v.ScriptLanguageId=l.Id\n\nLEFT OUTER JOIN ScriptVotes vo ON vo.ScriptVersionId=v.Id\n\nWHERE r.WorkerStatus != 4\n\n AND r.WorkerStatus != 5\n\nGROUP BY s.Id,\n\n cv.Title,\n\n cv.Id,\n\n l.Name\n\nORDER BY cv.Id DESC\n\n', con)
scripts
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
class RawColumnExtractor:
def __init__(self, column):
self.column = column
def fit(self, *_):
return self
def transform(self, data):
return data[[self.column]]
features = FeatureUnion([('NumSuccessfulRuns', RawColumnExtractor('NumSuccessfulRuns')), ('NumChangedVersions', RawColumnExtractor('NumChangedVersions'))])
pipeline = Pipeline([('feature_union', features), ('predictor', RandomForestClassifier())])
train = scripts
target_name = 'HasNonSelfVotes'
x_train, x_test, y_train, y_test = train_test_split(train, train[target_name], test_size=0.4, random_state=0)
pipeline.fit(x_train, y_train)
score = pipeline.score(x_test, y_test)
print('Score %f' % score) | code |
311174/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))
plt.title('Number of locations reported - Top 30') | code |
311174/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sbn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
311174/cell_3 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.head(3) | code |
311174/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df[df.data_field == 'confirmed_male'].value.plot()
df[df.data_field == 'confirmed_female'].value.plot().legend(('Male', 'Female'), loc='best')
plt.title('Confirmed Male vs Female cases') | code |
311188/cell_4 | [
"text_plain_output_1.png"
] | 50 + 100 | code |
311188/cell_6 | [
"text_plain_output_1.png"
] | 100 + 200 | code |
311188/cell_1 | [
"text_plain_output_1.png"
] | 1 + 1 | code |
311188/cell_3 | [
"text_plain_output_1.png"
] | 20 + 30 | code |
311188/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.0, 2.0, 0.01)
s = np.sin(2 * np.pi * t)
plt.plot(t, s)
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.title('About as simple as it gets, folks')
plt.grid(True)
plt.savefig('test.png')
plt.show() | code |
309683/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_csv('../input/like.csv')
members = pd.read_csv('../input/member.csv')
posts = pd.read_csv('../input/post.csv')
likeResponse = pd.merge(likes.loc[likes['gid'] == 117291968282998], posts.loc[posts['gid'] == 117291968282998, ['pid', 'name']], left_on='pid', right_on='pid')
result = likeResponse.groupby(['name_y', 'name_x'])['response'].count()
finalResult = pd.DataFrame(result.index.values, columns=['NameCombo'])
finalResult['Weight'] = result.values
finalResult['From'] = finalResult['NameCombo'].map(lambda x: x[0])
finalResult['To'] = finalResult['NameCombo'].map(lambda x: x[1])
del finalResult['NameCombo']
g = nx.Graph()
g.add_edges_from([(row['From'], row['To']) for index, row in finalResult.iterrows()])
d = nx.degree(g)
spring_pos = nx.spring_layout(g)
plt.axis('off')
plt.clf()
g.number_of_nodes()
spring_pos = nx.spring_layout(g, scale=2)
nx.draw(g, spring_pos, with_labels=False, nodelist=d.keys(), node_size=[v * 5 for v in d.values()]) | code |
309683/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_csv('../input/like.csv')
members = pd.read_csv('../input/member.csv')
posts = pd.read_csv('../input/post.csv')
likeResponse = pd.merge(likes.loc[likes['gid'] == 117291968282998], posts.loc[posts['gid'] == 117291968282998, ['pid', 'name']], left_on='pid', right_on='pid')
result = likeResponse.groupby(['name_y', 'name_x'])['response'].count()
finalResult = pd.DataFrame(result.index.values, columns=['NameCombo'])
finalResult['Weight'] = result.values
finalResult['From'] = finalResult['NameCombo'].map(lambda x: x[0])
finalResult['To'] = finalResult['NameCombo'].map(lambda x: x[1])
del finalResult['NameCombo']
g = nx.Graph()
plt.figure()
g.add_edges_from([(row['From'], row['To']) for index, row in finalResult.iterrows()])
d = nx.degree(g)
spring_pos = nx.spring_layout(g)
plt.axis('off')
nx.draw_networkx(g, spring_pos, with_labels=False, nodelist=d.keys(), node_size=[v * 10 for v in d.values()])
plt.savefig('LIKE_PLOT_GROUP1.png')
plt.clf() | code |
309683/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_csv('../input/like.csv')
members = pd.read_csv('../input/member.csv')
posts = pd.read_csv('../input/post.csv')
likeResponse = pd.merge(likes.loc[likes['gid'] == 117291968282998], posts.loc[posts['gid'] == 117291968282998, ['pid', 'name']], left_on='pid', right_on='pid')
result = likeResponse.groupby(['name_y', 'name_x'])['response'].count()
finalResult = pd.DataFrame(result.index.values, columns=['NameCombo'])
finalResult['Weight'] = result.values
finalResult['From'] = finalResult['NameCombo'].map(lambda x: x[0])
finalResult['To'] = finalResult['NameCombo'].map(lambda x: x[1])
del finalResult['NameCombo']
g = nx.Graph()
g.add_edges_from([(row['From'], row['To']) for index, row in finalResult.iterrows()])
d = nx.degree(g)
spring_pos = nx.spring_layout(g)
plt.axis('off')
plt.clf()
f = open('g.json', 'w')
f.write('{"nodes":[')
str1 = ''
for i in finalResult['From'].unique():
str1 += '{"name":"' + str(i) + '","group":' + str(1) + '},'
f.write(str1[:-1])
f.write('],"links":[')
str1 = ''
for i in range(len(finalResult)):
str1 += '{"source":' + str(finalResult['From'][i]) + ',"target":' + str(finalResult['To'][i]) + ',"value":' + str(finalResult['Weight'][i]) + '},'
f.write(str1[:-1])
f.write(']}')
f.close
h1 = '\n<!DOCTYPE html>\n<meta charset="utf-8">\n<style>\n.link {stroke: #ccc;}\n.node text {pointer-events: none; font: 10px sans-serif;}\n</style>\n<body>\n<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js"></script>\n<script>\nvar width = 800, height = 800;\nvar color = d3.scale.category20();\nvar force = d3.layout.force()\n .charge(-120)\n .linkDistance(80)\n .size([width, height]);\nvar svg = d3.select("body").append("svg")\n .attr("width", width)\n .attr("height", height);\nd3.json("g.json", function(error, graph) {\n if (error) throw error;\n\tforce.nodes(graph.nodes)\n\t .links(graph.links)\n\t .start();\n\tvar link = svg.selectAll(".link")\n\t .data(graph.links)\n\t .enter().append("line")\n\t .attr("class", "link")\n\t .style("stroke-width", function (d) {return Math.sqrt(d.value);});\n\tvar node = svg.selectAll(".node")\n\t .data(graph.nodes)\n\t .enter().append("g")\n\t .attr("class", "node")\n\t .call(force.drag);\n\tnode.append("circle")\n\t .attr("r", 8)\n\t .style("fill", function (d) {return color(d.group);})\n\tnode.append("text")\n\t .attr("dx", 10)\n\t .attr("dy", ".35em")\n\t .text(function(d) { return d.name });\n\tforce.on("tick", function () {\n\t link.attr("x1", function (d) {return d.source.x;})\n\t\t.attr("y1", function (d) {return d.source.y;})\n\t\t.attr("x2", function (d) {return d.target.x;})\n\t\t.attr("y2", function (d) {return d.target.y;});\n\t d3.selectAll("circle").attr("cx", function (d) {return d.x;})\n\t\t.attr("cy", function (d) {return d.y;});\n\t d3.selectAll("text").attr("x", function (d) {return d.x;})\n\t\t.attr("y", function (d) {return d.y;});\n });\n});\n</script>\n'
f = open('output.html', 'w')
f.write(h1)
f.close | code |
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