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|---|---|---|---|
303338\cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import seaborn as sns
sns.set_style('whitegrid')
zika = pd.read_csv('../input/cdc_zika.csv')
zika.groupby('location').size().reset_index().rename(columns={0: 'count'}) | 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 |
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 |
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 |
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_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_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 |
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 | [
"application_vnd.jupyter.stderr_output_1.png",
"text_html_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_4 | [
"image_output_1.png",
"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.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))
plt.title('Number of locations reported - Top 30') | code |
311174\cell_5 | [
"image_output_1.png",
"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[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_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_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"text_html_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_3 | [
"image_output_1.png",
"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.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))
plt.title('Number of locations reported - Top 30') | code |
311500\cell_4 | [
"image_output_1.png",
"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[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_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 |
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_3 | [
"application_vnd.jupyter.stderr_output_1.png",
"text_html_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_4 | [
"image_output_1.png",
"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.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))
plt.title('Number of locations reported - Top 30') | code |
312349\cell_5 | [
"image_output_1.png",
"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[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 |
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_8 | [
"image_output_1.png",
"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')
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 |
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_16 | [
"image_output_1.png",
"text_plain_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_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_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_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_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 |
316827\cell_40 | [
"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
sorted_data = shared.sort_values(by='shares', ascending=False)[:top]
likes = data[data.likes > data.likes.quantile(0.98)][data.likes > data.shares * 100][['msg', 'likes']]
print('top %d out of %d' % (top, likes.shape[0]))
sorted_data = likes.sort_values(by='likes', ascending=False)[:top]
for i in sorted_data.index.values:
print('likes:', sorted_data.likes[i], '\n', 'message:', sorted_data.msg[i][:300], '\n') | code |
318069\cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
print('%s is ranked %.0f with %d attacks resulting to %d deaths and %d injuries' % (country, country_rank, country_attacks, country_killed, country_injured)) | code |
318069\cell_14 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.City.value_counts().plot(kind='bar', figsize=(17, 7))
plt.title('Number of attacks by city') | code |
318069\cell_17 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.groupby('City').sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True) | code |
318069\cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.groupby('City').sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True)
most_victim = dfc.sort_values(by='Victims', ascending=False).head(1)
print("Attack with most victims happened on %s on %s with %d killed, %d injuries with a total of %d victims with the following article: \n'%s' \n" % (most_victim.City.values[0], most_victim.index.strftime('%B %d,%Y')[0], most_victim.Killed, most_victim.Injured, most_victim.Victims, '%s' % most_victim.Description.values[0]))
most_killed = dfc.sort_values(by='Killed', ascending=False).head(1)
print("Attack with the most deaths happened on %s on %s with %d killed, %d injuries with a total of %d victims with the following article: \n'%s' \n" % (most_killed.City.values[0], most_killed.index.strftime('%B %d,%Y')[0], most_killed.Killed, most_killed.Injured, most_killed.Victims, '%s' % most_killed.Description.values[0]))
most_injuries = dfc.sort_values(by='Injured', ascending=False).head(1)
print("Attack with the most injuries happened on %s on %s with %d killed, %d injuries with a total of %d victims with the following article: \n'%s' \n" % (most_injuries.City.values[0], most_injuries.index.strftime('%B %d,%Y')[0], most_injuries.Killed, most_injuries.Injured, most_injuries.Victims, '%s' % most_injuries.Description.values[0])) | code |
318069\cell_23 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.groupby('City').sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True)
most_victim = dfc.sort_values(by='Victims', ascending=False).head(1)
most_killed = dfc.sort_values(by='Killed', ascending=False).head(1)
most_injuries = dfc.sort_values(by='Injured', ascending=False).head(1)
dfc.groupby(dfc.index.year).sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=False) | code |
318069\cell_26 | [
"image_output_1.png",
"image_output_2.png",
"text_plain_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.groupby('City').sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True)
most_victim = dfc.sort_values(by='Victims', ascending=False).head(1)
most_killed = dfc.sort_values(by='Killed', ascending=False).head(1)
most_injuries = dfc.sort_values(by='Injured', ascending=False).head(1)
dfc.groupby(dfc.index.year).sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=False)
killedbyday = dfc.groupby([dfc.index.map(lambda x: x.weekday), dfc.index.year], sort=True).agg({'Killed': 'sum'})
rcParams['figure.figsize'] = (20, 10)
killedbyday.unstack(level=0).plot(kind='bar', subplots=False)
killedbyday.unstack(level=1).plot(kind='bar', subplots=False) | code |
318069\cell_27 | [
"image_output_1.png",
"image_output_2.png",
"text_plain_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.groupby('City').sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True)
most_victim = dfc.sort_values(by='Victims', ascending=False).head(1)
most_killed = dfc.sort_values(by='Killed', ascending=False).head(1)
most_injuries = dfc.sort_values(by='Injured', ascending=False).head(1)
dfc.groupby(dfc.index.year).sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=False)
killedbyday = dfc.groupby([dfc.index.map(lambda x: x.weekday), dfc.index.year], sort=True).agg({'Killed': 'sum'})
rcParams['figure.figsize'] = (20, 10)
killedbymonth = dfc.groupby([dfc.index.map(lambda x: x.month), dfc.index.year], sort=True).agg({'Killed': 'sum'})
rcParams['figure.figsize'] = (20, 10)
killedbymonth.unstack(level=0).plot(kind='bar', subplots=False)
killedbymonth.unstack(level=1).plot(kind='bar', subplots=False) | code |
318221\cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
rmember.head(3) | code |
318221\cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
comment['gid'].value_counts() | code |
318221\cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
comment[(comment.gid == '117291968282998') & (comment.rid == '')]['name'].value_counts().head(10) | code |
318221\cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
comment[(comment.gid == '117291968282998') & (comment.rid != '')]['rname'].value_counts().head(10) | code |
318372\cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
comment.head(4) | code |
318372\cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
comment.gid = comment.gid.map({'117291968282998': 'EPH', '25160801076': 'UCT', '1443890352589739': 'FSZ'})
comment['gid'].value_counts() | code |
318372\cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
comment.gid = comment.gid.map({'117291968282998': 'EPH', '25160801076': 'UCT', '1443890352589739': 'FSZ'})
comment['gid'].value_counts()
comment[(comment.gid == 'EPH') & (comment.rid == '')]['name'].value_counts().head(10) | code |
318372\cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
comment.gid = comment.gid.map({'117291968282998': 'EPH', '25160801076': 'UCT', '1443890352589739': 'FSZ'})
comment['gid'].value_counts()
comment[(comment.gid == 'EPH') & (comment.rid != '')]['rname'].value_counts().head(10) | code |
320866\cell_3 | [
"image_output_1.png",
"text_plain_output_1.png"
] | from dateutil.parser import parse
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
data = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
import matplotlib.pyplot as plt
from dateutil.parser import parse
years = []
for i in range(len(data)):
years.append(parse(data.Date[i]).year)
data.Fatalities = data.Fatalities.fillna(data.Fatalities.mean())
temp = zip(years, data.Fatalities)
temp = [(x, y) for (x, y) in temp if y > 50]
temp = pd.DataFrame(temp, columns=['massive_years', 'Fatalities'])
counts = temp.massive_years.value_counts()
plt.figure(figsize=(11, 7))
plt.bar(counts.index, counts.values)
plt.ylabel('Number of Massive Crashes(fatalities>50)', fontsize=15)
plt.xlabel('Year', fontsize=15)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15) | code |
320908\cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
teams = pd.read_sql_query('select * from Teams', conn)
users = pd.read_sql_query('select * from Users', conn)
teammembers = pd.read_sql_query('select * from TeamMemberships', conn)
teams_q = teammembers.groupby('TeamId').UserId.count()
teams_q = teams_q[teams_q > 1].reset_index()
teammembers_cut = teammembers.merge(teams_q, on='TeamId')
users_q = teammembers_cut.groupby('UserId_x').TeamId.count().reset_index()
teammembers_cut = teammembers_cut.merge(users_q, left_on='UserId_x', right_on='UserId_x')
teammembers_cut = teammembers_cut.merge(teams, left_on='TeamId_x', right_on='Id')
teammembers_cut = teammembers_cut.merge(users, left_on='UserId_x', right_on='Id')
tm4graph = teammembers_cut[['TeamId_x', 'UserId_x']]
tm4graph['TeamId_x'] = 'Team_' + tm4graph['TeamId_x'].astype('str')
tm4graph['UserId_x'] = 'User_' + tm4graph['UserId_x'].astype('str') | code |
320908\cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import spdiags, coo_matrix
import networkx as nx
import numpy as np
import numpy as np
import pandas as pd
import plotly
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
teams = pd.read_sql_query('select * from Teams', conn)
users = pd.read_sql_query('select * from Users', conn)
teammembers = pd.read_sql_query('select * from TeamMemberships', conn)
teams_q = teammembers.groupby('TeamId').UserId.count()
teams_q = teams_q[teams_q > 1].reset_index()
teammembers_cut = teammembers.merge(teams_q, on='TeamId')
users_q = teammembers_cut.groupby('UserId_x').TeamId.count().reset_index()
teammembers_cut = teammembers_cut.merge(users_q, left_on='UserId_x', right_on='UserId_x')
teammembers_cut = teammembers_cut.merge(teams, left_on='TeamId_x', right_on='Id')
teammembers_cut = teammembers_cut.merge(users, left_on='UserId_x', right_on='Id')
tm4graph = teammembers_cut[['TeamId_x', 'UserId_x']]
tm4graph['TeamId_x'] = 'Team_' + tm4graph['TeamId_x'].astype('str')
tm4graph['UserId_x'] = 'User_' + tm4graph['UserId_x'].astype('str')
from scipy.sparse import spdiags, coo_matrix
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
def forceatlas2_layout(G, iterations=10, linlog=False, pos=None, nohubs=False, kr=0.001, k=None, dim=2):
"""
Options values are
g The graph to layout
iterations Number of iterations to do
linlog Whether to use linear or log repulsion
random_init Start with a random position
If false, start with FR
avoidoverlap Whether to avoid overlap of points
degreebased Degree based repulsion
"""
for n in G:
G.node[n]['prevcs'] = 0
G.node[n]['currcs'] = 0
A = nx.to_scipy_sparse_matrix(G, dtype='f')
(nnodes, _) = A.shape
try:
A = A.tolil()
except Exception as e:
A = coo_matrix(A).tolil()
if pos is None:
pos = np.asarray(np.random.random((nnodes, dim)), dtype=A.dtype)
else:
pos = pos.astype(A.dtype)
if k is None:
k = np.sqrt(1.0 / nnodes)
t = 0.1
dt = t / float(iterations + 1)
displacement = np.zeros((dim, nnodes))
for iteration in range(iterations):
displacement *= 0
for i in range(A.shape[0]):
delta = (pos[i] - pos).T
distance = np.sqrt((delta ** 2).sum(axis=0))
distance = np.where(distance < 0.01, 0.01, distance)
Ai = np.asarray(A.getrowview(i).toarray())
Dist = k * k / distance ** 2
if nohubs:
Dist = Dist / float(Ai.sum(axis=1) + 1)
if linlog:
Dist = np.log(Dist + 1)
displacement[:, i] += (delta * (Dist - Ai * distance / k)).sum(axis=1)
length = np.sqrt((displacement ** 2).sum(axis=0))
length = np.where(length < 0.01, 0.01, length)
pos += (displacement * t / length).T
t -= dt
return dict(zip(G, pos))
axis = dict(showline=False, zeroline=False, showgrid=False, showticklabels=False, title='')
layout = Layout(title='Kaggle teams/users universe', font=Font(size=12), showlegend=True, autosize=False, width=800, height=800, xaxis=XAxis(axis), yaxis=YAxis(axis), margin=Margin(l=40, r=40, b=85, t=100), hovermode='closest', annotations=Annotations([Annotation(showarrow=False, text='', xref='paper', yref='paper', x=0, y=-0.1, xanchor='left', yanchor='bottom', font=Font(size=14))]))
edges_to_use = 22000
G = nx.Graph()
G.add_edges_from(tm4graph.values[0:edges_to_use])
pos = forceatlas2_layout(G, iterations=300, nohubs=True)
N = G.number_of_nodes()
E = G.edges()
labels = G.nodes()
Xv_teams = [pos[k][0] for k in labels if 'Team' in k]
Yv_teams = [pos[k][1] for k in labels if 'Team' in k]
Xv_users = [pos[k][0] for k in labels if 'User' in k]
Yv_users = [pos[k][1] for k in labels if 'User' in k]
labels_team = [teammembers_cut.iloc[0:edges_to_use, :].loc[teammembers_cut.TeamId_x == int(k.replace('Team_', '')), 'TeamName'].values[0] for k in labels if 'Team' in k]
labels_users = [teammembers_cut.iloc[0:edges_to_use, :].loc[teammembers_cut.UserId_x == int(k.replace('User_', '')), 'DisplayName'].values[0] for k in labels if 'User' in k]
Xed = []
Yed = []
for edge in E:
Xed += [pos[edge[0]][0], pos[edge[1]][0], None]
Yed += [pos[edge[0]][1], pos[edge[1]][1], None]
trace3 = Scatter(x=Xed, y=Yed, mode='lines', line=Line(color='rgb(200,200,200)', width=2), name='Links', hoverinfo='none')
trace4 = Scatter(x=Xv_teams, y=Yv_teams, mode='markers', name='Teams', marker=Marker(symbol='dot', size=[teammembers_cut.iloc[0:edges_to_use, :].loc[teammembers_cut.TeamId_x == int(k.replace('Team_', '')), 'UserId_y'].values[0] for k in labels if 'Team' in k], color='rgb(146,209,81)', line=Line(color='rgb(50,50,50)', width=0.5)), text=map(lambda x: ['Team: ' + u''.join(x[0]).encode('utf8').strip() + '<br>Users: ' + str(','.join(x[1]).encode('utf8')) + '<br>'], zip(labels_team, [teammembers_cut.iloc[0:edges_to_use, :].loc[teammembers_cut.TeamId_x == int(k.replace('Team_', '')), 'DisplayName'].values.tolist() for k in labels if 'Team' in k])), hoverinfo='text')
trace5 = Scatter(x=Xv_users, y=Yv_users, mode='markers', name='Users', marker=Marker(symbol='dot', size=[teammembers_cut.iloc[0:edges_to_use, :].loc[teammembers_cut.UserId_x == int(k.replace('User_', '')), 'TeamId_y'].values[0] * 0.5 for k in labels if 'User' in k], color='#000000', line=Line(color='rgb(50,50,50)', width=0.5)), text=map(lambda x: ['User: ' + u''.join(x[0]).encode('utf8').strip() + '<br>Teams: ' + str(','.join(x[1]).encode('utf8')) + '<br>'], zip(labels_users, [teammembers_cut.iloc[0:edges_to_use, :].loc[teammembers_cut.UserId_x == int(k.replace('User_', '')), 'TeamName'].values.tolist() for k in labels if 'User' in k])), hoverinfo='text')
data1 = Data([trace3, trace4, trace5])
fig1 = Figure(data=data1, layout=layout)
plotly.offline.iplot(fig1) | code |
322662\cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import datetime
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import datetime
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
def dateparse(x):
try:
print('Inside DateParse')
return pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
except TypeError as err:
print('My exception occurred, value:', err.value)
return None
d = pd.read_csv('../input/trainView.csv', header=0, names=['train_id', 'status', 'next_station', 'service', 'dest', 'lon', 'lat', 'source', 'track_change', 'track', 'date', 'timeStamp0', 'timeStamp1'], dtype={'train_id': str, 'status': str, 'next_station': str, 'service': str, 'dest': str, 'lon': str, 'lat': str, 'source': str, 'track_change': str, 'track': str, 'date': str, 'timeStamp0': datetime.datetime, 'timeStamp1': datetime.datetime}) | code |
322662\cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import datetime
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import datetime
from subprocess import check_output
def dateparse(x):
try:
return pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
except TypeError as err:
return None
d = pd.read_csv('../input/trainView.csv', header=0, names=['train_id', 'status', 'next_station', 'service', 'dest', 'lon', 'lat', 'source', 'track_change', 'track', 'date', 'timeStamp0', 'timeStamp1'], dtype={'train_id': str, 'status': str, 'next_station': str, 'service': str, 'dest': str, 'lon': str, 'lat': str, 'source': str, 'track_change': str, 'track': str, 'date': str, 'timeStamp0': datetime.datetime, 'timeStamp1': datetime.datetime})
d.head()
d['timeStamp0'] = pd.to_datetime(d['timeStamp0'], format='%Y-%m-%d %H:%M:%S')
d['timeStamp1'] = pd.to_datetime(d['timeStamp1'], format='%Y-%m-%d %H:%M:%S', errors='coerce')
d.head() | code |
322662\cell_3 | [
"text_plain_output_1.png"
] | """
def getDeltaTime(x):
r=(x[1] - x[0]).total_seconds()
return r
# It might make sense to add delta_s to the next version
d['delta_s']=d[['timeStamp0','timeStamp1']].apply(getDeltaTime, axis=1)
""" | code |
322963\cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
322963\cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
zika_df = pd.read_csv(os.path.join('..', 'input', 'cdc_zika.csv'), low_memory=False)
keep_rows = pd.notnull(zika_df['report_date'])
zika_df = zika_df[keep_rows]
print('Removed {:d} out of {:d} rows with missing report_date.'.format(len(keep_rows) - sum(keep_rows), len(keep_rows)))
zika_df.index = pd.to_datetime([d.replace('_', '-') for d in zika_df['report_date']], format='%Y-%m-%d')
zika_df.sort_index(inplace=True)
zika_df.index.rename('report_date', inplace=True)
zika_df.drop('report_date', axis=1, inplace=True) | code |
322985\cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_set.loc[full_data_set.PlayType == 'Pass']
Sack_Plays = full_data_set.loc[full_data_set.PlayType == 'Sack']
P_S_data = pd.concat([Pass_Plays, Sack_Plays])
good_columns = ['Drive', 'qtr', 'down', 'TimeUnder', 'TimeSecs', 'PlayTimeDiff', 'yrdline100', 'ydstogo']
good_columns += ['ScoreDiff', 'PosTeamScore', 'DefTeamScore']
good_columns += ['Sack']
uncleaned_data = P_S_data[good_columns]
uncleaned_data.head() | code |
322985\cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_set.loc[full_data_set.PlayType == 'Pass']
Sack_Plays = full_data_set.loc[full_data_set.PlayType == 'Sack']
P_S_data = pd.concat([Pass_Plays, Sack_Plays])
good_columns = ['Drive', 'qtr', 'down', 'TimeUnder', 'TimeSecs', 'PlayTimeDiff', 'yrdline100', 'ydstogo']
good_columns += ['ScoreDiff', 'PosTeamScore', 'DefTeamScore']
good_columns += ['Sack']
uncleaned_data = P_S_data[good_columns]
uncleaned_data.qtr.unique() | code |
322985\cell_5 | [
"application_vnd.jupyter.stderr_output_1.png",
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_set.loc[full_data_set.PlayType == 'Pass']
Sack_Plays = full_data_set.loc[full_data_set.PlayType == 'Sack']
P_S_data = pd.concat([Pass_Plays, Sack_Plays])
good_columns = ['Drive', 'qtr', 'down', 'TimeUnder', 'TimeSecs', 'PlayTimeDiff', 'yrdline100', 'ydstogo']
good_columns += ['ScoreDiff', 'PosTeamScore', 'DefTeamScore']
good_columns += ['Sack']
uncleaned_data = P_S_data[good_columns]
uncleaned_data.qtr.unique()
def quarter_binary(df, name, number):
df[name] = np.where(df['qtr'] == number, 1, 0)
return df
for x in [['qt1', 1], ['qt2', 2], ['qt3', 3], ['qt4', 4], ['qt5', 5]]:
uncleaned_data = quarter_binary(uncleaned_data, x[0], x[1])
del uncleaned_data['qtr']
uncleaned_data.head() | code |
323155\cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn.linear_model as sk
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
from sklearn import preprocessing
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_set.loc[full_data_set.PlayType == 'Pass']
Sack_Plays = full_data_set.loc[full_data_set.PlayType == 'Sack']
P_S_data = pd.concat([Pass_Plays, Sack_Plays])
good_columns = ['Drive', 'qtr', 'down', 'TimeUnder', 'TimeSecs', 'PlayTimeDiff', 'yrdline100', 'ydstogo']
good_columns += ['ScoreDiff', 'PosTeamScore', 'DefTeamScore']
good_columns += ['Sack']
uncleaned_data = P_S_data[good_columns]
uncleaned_data.qtr.unique()
def quarter_binary(df, name, number):
df[name] = np.where(df['qtr'] == number, 1, 0)
return df
for x in [['qt1', 1], ['qt2', 2], ['qt3', 3], ['qt4', 4], ['qt5', 5]]:
uncleaned_data = quarter_binary(uncleaned_data, x[0], x[1])
del uncleaned_data['qtr']
logreg = sk.LogisticRegressionCV()
logreg.fit(X_all, y_all)
coef_array = np.abs(logreg.coef_)
x = np.arange(1, coef_array.shape[1] + 1, 1)
plt.scatter(x, coef_array, marker='x', color='r')
plt.axhline(0, color='b') | code |
323155\cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
from sklearn import preprocessing
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_set.loc[full_data_set.PlayType == 'Pass']
Sack_Plays = full_data_set.loc[full_data_set.PlayType == 'Sack']
P_S_data = pd.concat([Pass_Plays, Sack_Plays])
good_columns = ['Drive', 'qtr', 'down', 'TimeUnder', 'TimeSecs', 'PlayTimeDiff', 'yrdline100', 'ydstogo']
good_columns += ['ScoreDiff', 'PosTeamScore', 'DefTeamScore']
good_columns += ['Sack']
uncleaned_data = P_S_data[good_columns]
uncleaned_data.qtr.unique()
def quarter_binary(df, name, number):
df[name] = np.where(df['qtr'] == number, 1, 0)
return df
for x in [['qt1', 1], ['qt2', 2], ['qt3', 3], ['qt4', 4], ['qt5', 5]]:
uncleaned_data = quarter_binary(uncleaned_data, x[0], x[1])
del uncleaned_data['qtr']
cleaned_data = uncleaned_data.dropna()
explanatory_variables = cleaned_data.columns
explanatory_variables[1] | code |
323155\cell_6 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
from sklearn import preprocessing
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_set.loc[full_data_set.PlayType == 'Pass']
Sack_Plays = full_data_set.loc[full_data_set.PlayType == 'Sack']
P_S_data = pd.concat([Pass_Plays, Sack_Plays])
good_columns = ['Drive', 'qtr', 'down', 'TimeUnder', 'TimeSecs', 'PlayTimeDiff', 'yrdline100', 'ydstogo']
good_columns += ['ScoreDiff', 'PosTeamScore', 'DefTeamScore']
good_columns += ['Sack']
uncleaned_data = P_S_data[good_columns]
uncleaned_data.qtr.unique() | code |
323155\cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
from sklearn import preprocessing
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_set.loc[full_data_set.PlayType == 'Pass']
Sack_Plays = full_data_set.loc[full_data_set.PlayType == 'Sack']
P_S_data = pd.concat([Pass_Plays, Sack_Plays])
good_columns = ['Drive', 'qtr', 'down', 'TimeUnder', 'TimeSecs', 'PlayTimeDiff', 'yrdline100', 'ydstogo']
good_columns += ['ScoreDiff', 'PosTeamScore', 'DefTeamScore']
good_columns += ['Sack']
uncleaned_data = P_S_data[good_columns]
uncleaned_data.qtr.unique()
def quarter_binary(df, name, number):
df[name] = np.where(df['qtr'] == number, 1, 0)
return df
for x in [['qt1', 1], ['qt2', 2], ['qt3', 3], ['qt4', 4], ['qt5', 5]]:
uncleaned_data = quarter_binary(uncleaned_data, x[0], x[1])
del uncleaned_data['qtr'] | code |
323429\cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"text_plain_output_3.png",
"text_plain_output_4.png",
"text_plain_output_5.png",
"text_plain_output_6.png"
] | from subprocess import check_output
import sqlite3
import numpy as np
import pandas as pd
import sqlite3
import nltk
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
import scipy
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
con = sqlite3.connect('../input/database.sqlite')
cur = con.cursor()
sqlString = ' \n SELECT complaint_id, product, consumer_complaint_narrative, company\n FROM consumer_complaints\n WHERE product = "Mortgage" AND \n consumer_complaint_narrative != ""\n '
cur.execute(sqlString)
complaints = cur.fetchall()
con.close() | code |
323429\cell_4 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import sqlite3
import numpy as np
import pandas as pd
import sqlite3
import nltk
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
import scipy
from subprocess import check_output
con = sqlite3.connect('../input/database.sqlite')
cur = con.cursor()
sqlString = ' \n SELECT complaint_id, product, consumer_complaint_narrative, company\n FROM consumer_complaints\n WHERE product = "Mortgage" AND \n consumer_complaint_narrative != ""\n '
cur.execute(sqlString)
complaints = cur.fetchall()
con.close()
complaint_list = []
for i in range(len(complaints)):
complaint_list.append(complaints[i][2]) | code |
324023\cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
324023\cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv') | code |
324023\cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv')
data.columns.values | code |
324276\cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import colorsys
plt.style.use('seaborn-talk')
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', sep=',') | code |
324276\cell_12 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05,1))
plt.title("Gender")
plt.show()
N = len(df.JobRoleInterest.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.JobRoleInterest.value_counts().index
colors = ['OliveDrab', 'Orange', 'OrangeRed', 'DarkCyan', 'Salmon', 'Sienna', 'Maroon', 'LightSlateGrey', 'DimGray']
patches, texts = plt.pie(df.JobRoleInterest.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.25, 1))
plt.title("Job Role Interest")
plt.show()
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.EmploymentField.value_counts().index
(patches, texts) = plt.pie(df.EmploymentField.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.3, 1))
plt.title('Employment Field')
plt.show() | code |
324276\cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
df.Age.hist(bins=100)
plt.xlabel('Age')
plt.title('Distribution of Age')
plt.show() | code |
324276\cell_6 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
(patches, texts) = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05, 1))
plt.title('Gender')
plt.show() | code |
324276\cell_9 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05,1))
plt.title("Gender")
plt.show()
N = len(df.JobRoleInterest.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.JobRoleInterest.value_counts().index
colors = ['OliveDrab', 'Orange', 'OrangeRed', 'DarkCyan', 'Salmon', 'Sienna', 'Maroon', 'LightSlateGrey', 'DimGray']
(patches, texts) = plt.pie(df.JobRoleInterest.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.25, 1))
plt.title('Job Role Interest')
plt.show() | code |
324293\cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import colorsys
plt.style.use('seaborn-talk')
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', sep=',') | code |
324293\cell_12 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05,1))
plt.title("Gender")
plt.show()
N = len(df.JobRoleInterest.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.JobRoleInterest.value_counts().index
colors = ['OliveDrab', 'Orange', 'OrangeRed', 'DarkCyan', 'Salmon', 'Sienna', 'Maroon', 'LightSlateGrey', 'DimGray']
patches, texts = plt.pie(df.JobRoleInterest.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.25, 1))
plt.title("Job Role Interest")
plt.show()
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.EmploymentField.value_counts().index
(patches, texts) = plt.pie(df.EmploymentField.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.3, 1))
plt.title('Employment Field')
plt.show() | code |
324293\cell_15 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
import pandas as pd
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05,1))
plt.title("Gender")
plt.show()
N = len(df.JobRoleInterest.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.JobRoleInterest.value_counts().index
colors = ['OliveDrab', 'Orange', 'OrangeRed', 'DarkCyan', 'Salmon', 'Sienna', 'Maroon', 'LightSlateGrey', 'DimGray']
patches, texts = plt.pie(df.JobRoleInterest.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.25, 1))
plt.title("Job Role Interest")
plt.show()
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.EmploymentField.value_counts().index
patches, texts = plt.pie(df.EmploymentField.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.3, 1))
plt.title("Employment Field")
plt.show()
df_ageranges = df.copy()
bins = [0, 20, 30, 40, 50, 60, 100]
df_ageranges['AgeRanges'] = pd.cut(df_ageranges['Age'], bins, labels=['< 20', '20-30', '30-40', '40-50', '50-60', '< 60'])
df2 = pd.crosstab(df_ageranges.AgeRanges, df_ageranges.JobPref).apply(lambda r: r / r.sum(), axis=1)
N = len(df_ageranges.AgeRanges.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
ax1 = df2.plot(kind='bar', stacked=True, color=RGB_tuples, title='Job preference per Age')
(lines, labels) = ax1.get_legend_handles_labels()
ax1.legend(lines, labels, bbox_to_anchor=(1.51, 1)) | code |
324293\cell_18 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
import pandas as pd
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05,1))
plt.title("Gender")
plt.show()
N = len(df.JobRoleInterest.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.JobRoleInterest.value_counts().index
colors = ['OliveDrab', 'Orange', 'OrangeRed', 'DarkCyan', 'Salmon', 'Sienna', 'Maroon', 'LightSlateGrey', 'DimGray']
patches, texts = plt.pie(df.JobRoleInterest.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.25, 1))
plt.title("Job Role Interest")
plt.show()
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.EmploymentField.value_counts().index
patches, texts = plt.pie(df.EmploymentField.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.3, 1))
plt.title("Employment Field")
plt.show()
df_ageranges = df.copy()
bins=[0, 20, 30, 40, 50, 60, 100]
df_ageranges['AgeRanges'] = pd.cut(df_ageranges['Age'], bins, labels=["< 20", "20-30", "30-40", "40-50", "50-60", "< 60"])
df2 = pd.crosstab(df_ageranges.AgeRanges,df_ageranges.JobPref).apply(lambda r: r/r.sum(), axis=1)
N = len(df_ageranges.AgeRanges.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
ax1 = df2.plot(kind="bar", stacked=True, color= RGB_tuples, title="Job preference per Age")
lines, labels = ax1.get_legend_handles_labels()
ax1.legend(lines,labels, bbox_to_anchor=(1.51, 1))
df4 = pd.crosstab(df_ageranges.EmploymentField, df_ageranges.IsUnderEmployed).apply(lambda r: r / r.sum(), axis=1)
df4 = df4.sort_values(by=1.0)
N = len(df_ageranges.EmploymentField.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
ax1 = df4.plot(kind='bar', stacked=True, color=RGB_tuples, title='Under-employed per Employment Field')
(lines, labels) = ax1.get_legend_handles_labels()
ax1.legend(lines, ['No', 'Yes'], bbox_to_anchor=(1.51, 1)) | code |
324293\cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
df.Age.hist(bins=100)
plt.xlabel('Age')
plt.title('Distribution of Age')
plt.show() | code |
324293\cell_6 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
(patches, texts) = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05, 1))
plt.title('Gender')
plt.show() | code |
324293\cell_9 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05,1))
plt.title("Gender")
plt.show()
N = len(df.JobRoleInterest.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.JobRoleInterest.value_counts().index
colors = ['OliveDrab', 'Orange', 'OrangeRed', 'DarkCyan', 'Salmon', 'Sienna', 'Maroon', 'LightSlateGrey', 'DimGray']
(patches, texts) = plt.pie(df.JobRoleInterest.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.25, 1))
plt.title('Job Role Interest')
plt.show() | code |
324947\cell_10 | [
"text_plain_output_1.png"
] | import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n for (i, n) in zip(ids, names)}
id_league
ids = [i[0] for i in c.execute('SELECT id FROM Country').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM Country').fetchall()]
id_country = {i: n for (i, n) in zip(ids, names)}
c.execute('PRAGMA TABLE_INFO(Player_Stats)').fetchall() | code |
324947\cell_13 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n for (i, n) in zip(ids, names)}
id_league
ids = [i[0] for i in c.execute('SELECT id FROM Country').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM Country').fetchall()]
id_country = {i: n for (i, n) in zip(ids, names)}
c.execute('PRAGMA TABLE_INFO(Player_Stats)').fetchall()
cols = ', '.join(['home_player_Y' + str(i) for i in range(1, 12)])
c.execute('SELECT {0:s} FROM Match'.format(cols))
Y_array = c.fetchall()
Y = np.array([a for row in Y_array for a in row])
from collections import Counter
print('Player Y value: # of instances in database (home players)')
Counter(Y) | code |
324947\cell_16 | [
"text_plain_output_1.png",
"text_plain_output_2.png"
] | from collections import Counter
import datetime as dt
import numpy as np
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n for (i, n) in zip(ids, names)}
id_league
ids = [i[0] for i in c.execute('SELECT id FROM Country').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM Country').fetchall()]
id_country = {i: n for (i, n) in zip(ids, names)}
c.execute('PRAGMA TABLE_INFO(Player_Stats)').fetchall()
cols = ', '.join(['home_player_Y' + str(i) for i in range(1, 12)])
c.execute('SELECT {0:s} FROM Match'.format(cols))
Y_array = c.fetchall()
Y = np.array([a for row in Y_array for a in row])
from collections import Counter
Counter(Y)
EA_stats = {'player': ', '.join(['overall_rating']), 'goalie': ', '.join(['gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning', 'gk_reflexes'])}
def getTeamScores(match_id, team, EA_stats, printout=False, group='forward_mid_defense_goalie'):
""" Return the cumulative average team scores for
a given EA Sports FIFA statistic. If particular EA
stats are not in the database that stat is taken as
the overall player rating. If any positional stat is
unavailable (i.e. no goalie information) that stat is
taken as the average of the others for that team.
team : str
'home' or 'away'
EA_stat : dict
Names of statistics to cumulate for goalie and players.
e.g. {'player': 'overall_rating, heading_accuracy',
'goalie': 'gk_diving, gk_handling'}
printout : boolean
Option to print out debug information,
defaults to False.
group : str
How to group scores:
'forward_mid_defense_goalie': output 4 values
'all': output 1 value (currently not implemented)
"""
if team == 'home':
player_cols = ', '.join(['home_player_' + str(i) for i in range(1, 12)])
player_Y_cols = np.array(['home_player_Y' + str(i) for i in range(1, 12)])
elif team == 'away':
player_cols = ', '.join(['away_player_' + str(i) for i in range(1, 12)])
player_Y_cols = np.array(['away_player_Y' + str(i) for i in range(1, 12)])
c.execute('SELECT {0:s} FROM Match WHERE id={1:d}'.format(player_cols, match_id))
player_api_id = np.array(c.fetchall()[0])
if False not in [p == 0 or p == None for p in player_api_id]:
return {'F': np.array([np.nan]), 'M': np.array([np.nan]), 'D': np.array([np.nan]), 'G': np.array([np.nan])}
empty_mask = player_api_id != np.array(None)
player_api_id = player_api_id[empty_mask]
player_Y_cols = ', '.join(player_Y_cols[empty_mask])
c.execute('SELECT {0:s} FROM Match WHERE id={1:d}'.format(player_Y_cols, match_id))
player_Y = c.fetchall()[0]
def givePosition(Y):
""" Input the Y position of the player (as opposed
to the lateral X position) and return the categorical
position. """
if Y == 1:
return 'G'
elif Y == 3:
return 'D'
elif Y == 5 or Y == 6 or Y == 7:
return 'M'
elif Y == 8 or Y == 9 or Y == 10 or (Y == 11):
return 'F'
else:
return 'NaN'
player_pos = np.array([givePosition(Y) for Y in player_Y])
def toDatetime(datetime):
""" Convert string date to datetime object. """
return dt.datetime.strptime(datetime, '%Y-%m-%d %H:%M:%S')
c.execute('SELECT date FROM Match WHERE id={}'.format(match_id))
match_date = toDatetime(c.fetchall()[0][0])
def getBestDate(player_id, match_date):
""" Find most suitable player stats to use based
on date of match and return the corresponding row
id from the Player_Stats table. """
c.execute('SELECT id FROM Player_Stats WHERE player_api_id={}'.format(player_id))
ids = np.array([i[0] for i in c.fetchall()])
c.execute('SELECT date_stat FROM Player_Stats WHERE player_api_id={}'.format(player_id))
dates = [toDatetime(d[0]) for d in c.fetchall()]
dates_delta = np.array([abs(d - match_date) for d in dates])
return ids[dates_delta == dates_delta.min()][0]
def fill_empty_stats(stats, stat_names):
""" Input the incomplete EA player stats and corresponing
names, return the filled in stats list. Filling with
overall_rating or averaging otherwise (i.e. for goalies
where there is no overall_rating stat). """
if not np.sum([s == 0 or s == None for s in stats]):
return stats
stats_dict = {sn: s for (sn, s) in zip(stat_names, stats)}
try:
fill = stats_dict['overall_rating']
except:
fill = np.mean([s for s in stats if s != 0 and s != None])
filled_stats = []
for s in stats:
if s == None or s == 0:
filled_stats.append(fill)
else:
filled_stats.append(s)
return filled_stats
positions = ('G', 'D', 'M', 'F')
average_stats = {}
for position in positions:
if position == 'G':
stats = EA_stats['goalie']
else:
stats = EA_stats['player']
position_ids = player_api_id[player_pos == position]
average_stats[position] = np.zeros(len(stats.split(',')))
for player_id in position_ids:
best_date_id = getBestDate(player_id, match_date)
c.execute('SELECT {0:s} FROM Player_Stats WHERE id={1:d}'.format(stats, best_date_id))
query = np.array(c.fetchall()[0])
query = fill_empty_stats(query, stats.split(', '))
if sum([q == None or q == 0 for q in query]):
raise LookupError('Found null EA stats entry at stat_id={}'.format(best_date_id))
average_stats[position] += query
average_stats[position] /= len(position_ids)
try:
average_stats['G'] = np.array([average_stats['G'].mean()])
except:
pass
insert_value = np.mean([v[0] for v in average_stats.values() if not np.isnan(v)])
for (k, v) in average_stats.items():
if np.isnan(v[0]):
average_stats[k] = np.array([insert_value])
return average_stats
avg = getTeamScores(999, 'home', EA_stats, printout=True)
avg | code |
324947\cell_17 | [
"text_plain_output_1.png",
"text_plain_output_2.png"
] | from collections import Counter
import datetime as dt
import numpy as np
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n for (i, n) in zip(ids, names)}
id_league
ids = [i[0] for i in c.execute('SELECT id FROM Country').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM Country').fetchall()]
id_country = {i: n for (i, n) in zip(ids, names)}
c.execute('PRAGMA TABLE_INFO(Player_Stats)').fetchall()
cols = ', '.join(['home_player_Y' + str(i) for i in range(1, 12)])
c.execute('SELECT {0:s} FROM Match'.format(cols))
Y_array = c.fetchall()
Y = np.array([a for row in Y_array for a in row])
from collections import Counter
Counter(Y)
EA_stats = {'player': ', '.join(['overall_rating']), 'goalie': ', '.join(['gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning', 'gk_reflexes'])}
def getTeamScores(match_id, team, EA_stats, printout=False, group='forward_mid_defense_goalie'):
""" Return the cumulative average team scores for
a given EA Sports FIFA statistic. If particular EA
stats are not in the database that stat is taken as
the overall player rating. If any positional stat is
unavailable (i.e. no goalie information) that stat is
taken as the average of the others for that team.
team : str
'home' or 'away'
EA_stat : dict
Names of statistics to cumulate for goalie and players.
e.g. {'player': 'overall_rating, heading_accuracy',
'goalie': 'gk_diving, gk_handling'}
printout : boolean
Option to print out debug information,
defaults to False.
group : str
How to group scores:
'forward_mid_defense_goalie': output 4 values
'all': output 1 value (currently not implemented)
"""
if team == 'home':
player_cols = ', '.join(['home_player_' + str(i) for i in range(1, 12)])
player_Y_cols = np.array(['home_player_Y' + str(i) for i in range(1, 12)])
elif team == 'away':
player_cols = ', '.join(['away_player_' + str(i) for i in range(1, 12)])
player_Y_cols = np.array(['away_player_Y' + str(i) for i in range(1, 12)])
c.execute('SELECT {0:s} FROM Match WHERE id={1:d}'.format(player_cols, match_id))
player_api_id = np.array(c.fetchall()[0])
if False not in [p == 0 or p == None for p in player_api_id]:
return {'F': np.array([np.nan]), 'M': np.array([np.nan]), 'D': np.array([np.nan]), 'G': np.array([np.nan])}
empty_mask = player_api_id != np.array(None)
player_api_id = player_api_id[empty_mask]
player_Y_cols = ', '.join(player_Y_cols[empty_mask])
c.execute('SELECT {0:s} FROM Match WHERE id={1:d}'.format(player_Y_cols, match_id))
player_Y = c.fetchall()[0]
def givePosition(Y):
""" Input the Y position of the player (as opposed
to the lateral X position) and return the categorical
position. """
if Y == 1:
return 'G'
elif Y == 3:
return 'D'
elif Y == 5 or Y == 6 or Y == 7:
return 'M'
elif Y == 8 or Y == 9 or Y == 10 or (Y == 11):
return 'F'
else:
return 'NaN'
player_pos = np.array([givePosition(Y) for Y in player_Y])
def toDatetime(datetime):
""" Convert string date to datetime object. """
return dt.datetime.strptime(datetime, '%Y-%m-%d %H:%M:%S')
c.execute('SELECT date FROM Match WHERE id={}'.format(match_id))
match_date = toDatetime(c.fetchall()[0][0])
def getBestDate(player_id, match_date):
""" Find most suitable player stats to use based
on date of match and return the corresponding row
id from the Player_Stats table. """
c.execute('SELECT id FROM Player_Stats WHERE player_api_id={}'.format(player_id))
ids = np.array([i[0] for i in c.fetchall()])
c.execute('SELECT date_stat FROM Player_Stats WHERE player_api_id={}'.format(player_id))
dates = [toDatetime(d[0]) for d in c.fetchall()]
dates_delta = np.array([abs(d - match_date) for d in dates])
return ids[dates_delta == dates_delta.min()][0]
def fill_empty_stats(stats, stat_names):
""" Input the incomplete EA player stats and corresponing
names, return the filled in stats list. Filling with
overall_rating or averaging otherwise (i.e. for goalies
where there is no overall_rating stat). """
if not np.sum([s == 0 or s == None for s in stats]):
return stats
stats_dict = {sn: s for (sn, s) in zip(stat_names, stats)}
try:
fill = stats_dict['overall_rating']
except:
fill = np.mean([s for s in stats if s != 0 and s != None])
filled_stats = []
for s in stats:
if s == None or s == 0:
filled_stats.append(fill)
else:
filled_stats.append(s)
return filled_stats
positions = ('G', 'D', 'M', 'F')
average_stats = {}
for position in positions:
if position == 'G':
stats = EA_stats['goalie']
else:
stats = EA_stats['player']
position_ids = player_api_id[player_pos == position]
average_stats[position] = np.zeros(len(stats.split(',')))
for player_id in position_ids:
best_date_id = getBestDate(player_id, match_date)
c.execute('SELECT {0:s} FROM Player_Stats WHERE id={1:d}'.format(stats, best_date_id))
query = np.array(c.fetchall()[0])
query = fill_empty_stats(query, stats.split(', '))
if sum([q == None or q == 0 for q in query]):
raise LookupError('Found null EA stats entry at stat_id={}'.format(best_date_id))
average_stats[position] += query
average_stats[position] /= len(position_ids)
try:
average_stats['G'] = np.array([average_stats['G'].mean()])
except:
pass
insert_value = np.mean([v[0] for v in average_stats.values() if not np.isnan(v)])
for (k, v) in average_stats.items():
if np.isnan(v[0]):
average_stats[k] = np.array([insert_value])
return average_stats
avg = getTeamScores(999, 'home', EA_stats, printout=True)
avg
avg = getTeamScores(5, 'home', EA_stats, printout=True)
avg | code |
324947\cell_19 | [
"text_plain_output_1.png"
] | from collections import Counter
import datetime as dt
import numpy as np
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n for (i, n) in zip(ids, names)}
id_league
ids = [i[0] for i in c.execute('SELECT id FROM Country').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM Country').fetchall()]
id_country = {i: n for (i, n) in zip(ids, names)}
c.execute('PRAGMA TABLE_INFO(Player_Stats)').fetchall()
cols = ', '.join(['home_player_Y' + str(i) for i in range(1, 12)])
c.execute('SELECT {0:s} FROM Match'.format(cols))
Y_array = c.fetchall()
Y = np.array([a for row in Y_array for a in row])
from collections import Counter
Counter(Y)
EA_stats = {'player': ', '.join(['overall_rating']), 'goalie': ', '.join(['gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning', 'gk_reflexes'])}
def getTeamScores(match_id, team, EA_stats, printout=False, group='forward_mid_defense_goalie'):
""" Return the cumulative average team scores for
a given EA Sports FIFA statistic. If particular EA
stats are not in the database that stat is taken as
the overall player rating. If any positional stat is
unavailable (i.e. no goalie information) that stat is
taken as the average of the others for that team.
team : str
'home' or 'away'
EA_stat : dict
Names of statistics to cumulate for goalie and players.
e.g. {'player': 'overall_rating, heading_accuracy',
'goalie': 'gk_diving, gk_handling'}
printout : boolean
Option to print out debug information,
defaults to False.
group : str
How to group scores:
'forward_mid_defense_goalie': output 4 values
'all': output 1 value (currently not implemented)
"""
if team == 'home':
player_cols = ', '.join(['home_player_' + str(i) for i in range(1, 12)])
player_Y_cols = np.array(['home_player_Y' + str(i) for i in range(1, 12)])
elif team == 'away':
player_cols = ', '.join(['away_player_' + str(i) for i in range(1, 12)])
player_Y_cols = np.array(['away_player_Y' + str(i) for i in range(1, 12)])
c.execute('SELECT {0:s} FROM Match WHERE id={1:d}'.format(player_cols, match_id))
player_api_id = np.array(c.fetchall()[0])
if False not in [p == 0 or p == None for p in player_api_id]:
return {'F': np.array([np.nan]), 'M': np.array([np.nan]), 'D': np.array([np.nan]), 'G': np.array([np.nan])}
empty_mask = player_api_id != np.array(None)
player_api_id = player_api_id[empty_mask]
player_Y_cols = ', '.join(player_Y_cols[empty_mask])
c.execute('SELECT {0:s} FROM Match WHERE id={1:d}'.format(player_Y_cols, match_id))
player_Y = c.fetchall()[0]
def givePosition(Y):
""" Input the Y position of the player (as opposed
to the lateral X position) and return the categorical
position. """
if Y == 1:
return 'G'
elif Y == 3:
return 'D'
elif Y == 5 or Y == 6 or Y == 7:
return 'M'
elif Y == 8 or Y == 9 or Y == 10 or (Y == 11):
return 'F'
else:
return 'NaN'
player_pos = np.array([givePosition(Y) for Y in player_Y])
def toDatetime(datetime):
""" Convert string date to datetime object. """
return dt.datetime.strptime(datetime, '%Y-%m-%d %H:%M:%S')
c.execute('SELECT date FROM Match WHERE id={}'.format(match_id))
match_date = toDatetime(c.fetchall()[0][0])
def getBestDate(player_id, match_date):
""" Find most suitable player stats to use based
on date of match and return the corresponding row
id from the Player_Stats table. """
c.execute('SELECT id FROM Player_Stats WHERE player_api_id={}'.format(player_id))
ids = np.array([i[0] for i in c.fetchall()])
c.execute('SELECT date_stat FROM Player_Stats WHERE player_api_id={}'.format(player_id))
dates = [toDatetime(d[0]) for d in c.fetchall()]
dates_delta = np.array([abs(d - match_date) for d in dates])
return ids[dates_delta == dates_delta.min()][0]
def fill_empty_stats(stats, stat_names):
""" Input the incomplete EA player stats and corresponing
names, return the filled in stats list. Filling with
overall_rating or averaging otherwise (i.e. for goalies
where there is no overall_rating stat). """
if not np.sum([s == 0 or s == None for s in stats]):
return stats
stats_dict = {sn: s for (sn, s) in zip(stat_names, stats)}
try:
fill = stats_dict['overall_rating']
except:
fill = np.mean([s for s in stats if s != 0 and s != None])
filled_stats = []
for s in stats:
if s == None or s == 0:
filled_stats.append(fill)
else:
filled_stats.append(s)
return filled_stats
positions = ('G', 'D', 'M', 'F')
average_stats = {}
for position in positions:
if position == 'G':
stats = EA_stats['goalie']
else:
stats = EA_stats['player']
position_ids = player_api_id[player_pos == position]
average_stats[position] = np.zeros(len(stats.split(',')))
for player_id in position_ids:
best_date_id = getBestDate(player_id, match_date)
c.execute('SELECT {0:s} FROM Player_Stats WHERE id={1:d}'.format(stats, best_date_id))
query = np.array(c.fetchall()[0])
query = fill_empty_stats(query, stats.split(', '))
if sum([q == None or q == 0 for q in query]):
raise LookupError('Found null EA stats entry at stat_id={}'.format(best_date_id))
average_stats[position] += query
average_stats[position] /= len(position_ids)
try:
average_stats['G'] = np.array([average_stats['G'].mean()])
except:
pass
insert_value = np.mean([v[0] for v in average_stats.values() if not np.isnan(v)])
for (k, v) in average_stats.items():
if np.isnan(v[0]):
average_stats[k] = np.array([insert_value])
return average_stats
all_ids = c.execute('SELECT id FROM Match').fetchall()
all_ids = [i[0] for i in sorted(all_ids)]
(hF, hM, hD, hG) = ([], [], [], [])
(aF, aM, aD, aG) = ([], [], [], [])
for i in all_ids:
h_stats = getTeamScores(i, 'home', EA_stats, printout=False)
hF.append(h_stats['F'][0])
hM.append(h_stats['M'][0])
hD.append(h_stats['D'][0])
hG.append(h_stats['G'][0])
a_stats = getTeamScores(i, 'away', EA_stats, printout=False)
aF.append(a_stats['F'][0])
aM.append(a_stats['M'][0])
aD.append(a_stats['D'][0])
aG.append(a_stats['G'][0]) | code |
324947\cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
df = pd.read_sql(sql='SELECT {} FROM Match'.format('id, country_id, league_id, season, stage, ' + 'date, home_team_api_id, away_team_api_id, ' + 'home_team_goal, away_team_goal'), con=conn)
df.head() | code |
324947\cell_26 | [
"text_html_output_1.png"
] | from collections import Counter
import datetime as dt
import numpy as np
import pandas as pd
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n for (i, n) in zip(ids, names)}
id_league
ids = [i[0] for i in c.execute('SELECT id FROM Country').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM Country').fetchall()]
id_country = {i: n for (i, n) in zip(ids, names)}
c.execute('PRAGMA TABLE_INFO(Player_Stats)').fetchall()
cols = ', '.join(['home_player_Y' + str(i) for i in range(1, 12)])
c.execute('SELECT {0:s} FROM Match'.format(cols))
Y_array = c.fetchall()
Y = np.array([a for row in Y_array for a in row])
from collections import Counter
Counter(Y)
EA_stats = {'player': ', '.join(['overall_rating']), 'goalie': ', '.join(['gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning', 'gk_reflexes'])}
def getTeamScores(match_id, team, EA_stats, printout=False, group='forward_mid_defense_goalie'):
""" Return the cumulative average team scores for
a given EA Sports FIFA statistic. If particular EA
stats are not in the database that stat is taken as
the overall player rating. If any positional stat is
unavailable (i.e. no goalie information) that stat is
taken as the average of the others for that team.
team : str
'home' or 'away'
EA_stat : dict
Names of statistics to cumulate for goalie and players.
e.g. {'player': 'overall_rating, heading_accuracy',
'goalie': 'gk_diving, gk_handling'}
printout : boolean
Option to print out debug information,
defaults to False.
group : str
How to group scores:
'forward_mid_defense_goalie': output 4 values
'all': output 1 value (currently not implemented)
"""
if team == 'home':
player_cols = ', '.join(['home_player_' + str(i) for i in range(1, 12)])
player_Y_cols = np.array(['home_player_Y' + str(i) for i in range(1, 12)])
elif team == 'away':
player_cols = ', '.join(['away_player_' + str(i) for i in range(1, 12)])
player_Y_cols = np.array(['away_player_Y' + str(i) for i in range(1, 12)])
c.execute('SELECT {0:s} FROM Match WHERE id={1:d}'.format(player_cols, match_id))
player_api_id = np.array(c.fetchall()[0])
if False not in [p == 0 or p == None for p in player_api_id]:
return {'F': np.array([np.nan]), 'M': np.array([np.nan]), 'D': np.array([np.nan]), 'G': np.array([np.nan])}
empty_mask = player_api_id != np.array(None)
player_api_id = player_api_id[empty_mask]
player_Y_cols = ', '.join(player_Y_cols[empty_mask])
c.execute('SELECT {0:s} FROM Match WHERE id={1:d}'.format(player_Y_cols, match_id))
player_Y = c.fetchall()[0]
def givePosition(Y):
""" Input the Y position of the player (as opposed
to the lateral X position) and return the categorical
position. """
if Y == 1:
return 'G'
elif Y == 3:
return 'D'
elif Y == 5 or Y == 6 or Y == 7:
return 'M'
elif Y == 8 or Y == 9 or Y == 10 or (Y == 11):
return 'F'
else:
return 'NaN'
player_pos = np.array([givePosition(Y) for Y in player_Y])
def toDatetime(datetime):
""" Convert string date to datetime object. """
return dt.datetime.strptime(datetime, '%Y-%m-%d %H:%M:%S')
c.execute('SELECT date FROM Match WHERE id={}'.format(match_id))
match_date = toDatetime(c.fetchall()[0][0])
def getBestDate(player_id, match_date):
""" Find most suitable player stats to use based
on date of match and return the corresponding row
id from the Player_Stats table. """
c.execute('SELECT id FROM Player_Stats WHERE player_api_id={}'.format(player_id))
ids = np.array([i[0] for i in c.fetchall()])
c.execute('SELECT date_stat FROM Player_Stats WHERE player_api_id={}'.format(player_id))
dates = [toDatetime(d[0]) for d in c.fetchall()]
dates_delta = np.array([abs(d - match_date) for d in dates])
return ids[dates_delta == dates_delta.min()][0]
def fill_empty_stats(stats, stat_names):
""" Input the incomplete EA player stats and corresponing
names, return the filled in stats list. Filling with
overall_rating or averaging otherwise (i.e. for goalies
where there is no overall_rating stat). """
if not np.sum([s == 0 or s == None for s in stats]):
return stats
stats_dict = {sn: s for (sn, s) in zip(stat_names, stats)}
try:
fill = stats_dict['overall_rating']
except:
fill = np.mean([s for s in stats if s != 0 and s != None])
filled_stats = []
for s in stats:
if s == None or s == 0:
filled_stats.append(fill)
else:
filled_stats.append(s)
return filled_stats
positions = ('G', 'D', 'M', 'F')
average_stats = {}
for position in positions:
if position == 'G':
stats = EA_stats['goalie']
else:
stats = EA_stats['player']
position_ids = player_api_id[player_pos == position]
average_stats[position] = np.zeros(len(stats.split(',')))
for player_id in position_ids:
best_date_id = getBestDate(player_id, match_date)
c.execute('SELECT {0:s} FROM Player_Stats WHERE id={1:d}'.format(stats, best_date_id))
query = np.array(c.fetchall()[0])
query = fill_empty_stats(query, stats.split(', '))
if sum([q == None or q == 0 for q in query]):
raise LookupError('Found null EA stats entry at stat_id={}'.format(best_date_id))
average_stats[position] += query
average_stats[position] /= len(position_ids)
try:
average_stats['G'] = np.array([average_stats['G'].mean()])
except:
pass
insert_value = np.mean([v[0] for v in average_stats.values() if not np.isnan(v)])
for (k, v) in average_stats.items():
if np.isnan(v[0]):
average_stats[k] = np.array([insert_value])
return average_stats
df = pd.read_sql(sql='SELECT {} FROM Match'.format('id, country_id, league_id, season, stage, ' + 'date, home_team_api_id, away_team_api_id, ' + 'home_team_goal, away_team_goal'), con=conn)
df = df.dropna()
H = lambda x: x[0] > x[1]
D = lambda x: x[0] == x[1]
A = lambda x: x[0] < x[1]
(state, result) = ([], [])
for goals in df[['home_team_goal', 'away_team_goal']].values:
r = np.array([H(goals), D(goals), A(goals)])
state.append(r)
if (r == [1, 0, 0]).sum() == 3:
result.append(1)
elif (r == [0, 1, 0]).sum() == 3:
result.append(2)
elif (r == [0, 0, 1]).sum() == 3:
result.append(3)
df['game_state'] = state
df['game_result'] = result
df['date'] = pd.to_datetime(df['date'])
df['country'] = df['country_id'].map(id_country)
df['league'] = df['league_id'].map(id_league)
f = lambda x: np.mean(x)
df['home_mean_stats'] = list(map(f, df[['home_F_stats', 'home_M_stats', 'home_D_stats', 'home_G_stats']].values))
df['away_mean_stats'] = list(map(f, df[['away_F_stats', 'away_M_stats', 'away_D_stats', 'away_G_stats']].values))
df.dtypes | code |
324947\cell_7 | [
"image_output_1.png"
] | import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n for (i, n) in zip(ids, names)}
id_league | code |
324967\cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
324967\cell_4 | [
"text_plain_output_1.png"
] | import sqlite3
con = sqlite3.connect('../input/database.sqlite')
cursor = con.cursor()
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
print(cursor.fetchall()) | code |
324967\cell_7 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
cursor = con.cursor()
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
def load(what='NationalNames'):
assert what in ('NationalNames', 'StateNames')
cols = ['Name', 'Year', 'Gender', 'Count']
if what == 'StateNames':
cols.append('State')
df = pd.read_sql_query('SELECT {} from {}'.format(','.join(cols), what), con)
return df
df = load(what='NationalNames')
df.query('Name=="Alice"')[['Year', 'Count']].groupby('Year').sum().plot() | code |
324967\cell_9 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
cursor = con.cursor()
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
def load(what='NationalNames'):
assert what in ('NationalNames', 'StateNames')
cols = ['Name', 'Year', 'Gender', 'Count']
if what == 'StateNames':
cols.append('State')
df = pd.read_sql_query('SELECT {} from {}'.format(','.join(cols), what), con)
return df
df2 = load(what='StateNames')
tmp = df2.groupby(['Year', 'State']).agg({'Count': 'sum'}).reset_index()
largest_states = tmp.groupby('State').agg({'Count': 'sum'}).sort_values('Count', ascending=False).index[:5].tolist()
tmp.pivot(index='Year', columns='State', values='Count')[largest_states].plot() | code |
325017\cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
masterDF = pd.read_csv('../input/emails.csv')
messageList = masterDF['message'].tolist()
bodyList = []
for message in messageList:
firstSplit = message.split('X-FileName: ', 1)[1]
secondSplit = firstSplit.split('.')
if len(secondSplit) > 1:
secondSplit = secondSplit[1]
body = ''.join(secondSplit)[4:]
bodyList.append(body) | code |
325098\cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"text_plain_output_3.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
df = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
df.columns | code |
325098\cell_5 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
df = df[df['posteam'] == 'CHI']
df = df[df['DefensiveTeam'] == 'GB']
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
(X, test) = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
rf = RandomForestClassifier(n_estimators=1000)
rf.fit(X, y) | code |
325098\cell_7 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
df = df[df['posteam'] == 'CHI']
df = df[df['DefensiveTeam'] == 'GB']
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
(X, test) = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
rf = RandomForestClassifier(n_estimators=1000)
rf.fit(X, y)
test_y = test.pop('is_pass')
rf.score(test, test_y) | code |
325101\cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"text_plain_output_3.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
df = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
df.columns | code |
325101\cell_4 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
(X, test) = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
test_y = test.pop('is_pass')
rf = RandomForestClassifier(n_estimators=10)
gb = GradientBoostingClassifier(n_estimators=10)
sv = SVC()
rf.fit(X, y)
gb.fit(X, y)
sv.fit(X, y) | code |
325101\cell_5 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
(X, test) = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
test_y = test.pop('is_pass')
rf = RandomForestClassifier(n_estimators=10)
gb = GradientBoostingClassifier(n_estimators=10)
sv = SVC()
rf.fit(X, y)
gb.fit(X, y)
sv.fit(X, y)
rf.score(test, test_y) | code |
325101\cell_6 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
(X, test) = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
test_y = test.pop('is_pass')
rf = RandomForestClassifier(n_estimators=10)
gb = GradientBoostingClassifier(n_estimators=10)
sv = SVC()
rf.fit(X, y)
gb.fit(X, y)
sv.fit(X, y)
gb.score(test, test_y) | code |
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