path stringlengths 13 14 | screenshot_names listlengths 1 11 | code stringlengths 1 7.42k | cell_type stringclasses 1
value |
|---|---|---|---|
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) NumNonSelfV... | 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\nSELE... | 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) Num... | 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_... | 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_... | 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_... | 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_... | 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 repor... | 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(... | 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 repor... | 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(... | 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... | 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 repor... | 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(... | 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'... | 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'... | 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', 'shar... | 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 =... | 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', ... | 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(... | 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(... | 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(... | 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(... | 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(... | 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(... | 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... | 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.Cou... | 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.Cou... | 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.Cou... | 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.Cou... | 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]
el... | 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]
el... | 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 FR... | 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 FR... | 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 FR... | 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 FR... | 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 FR... | 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 FR... | 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 FR... | 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 FR... | 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... | 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.cou... | 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... | 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... | 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 TypeE... | 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(k... | 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... | 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... | 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_se... | 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... | 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... | 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.l... | 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... | 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.conn... | 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()
sqlS... | 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=... | 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(), c... | 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=... | 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=... | 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.va... | 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.va... | 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(), c... | 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=... | 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('SE... | 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, ... | 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 ... | 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 ... | 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 ... | 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(... | 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', ... | 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', ... | 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... | 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, GradientBoosti... | 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['PlayTy... | 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['PlayTy... | 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, GradientBoosti... | 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 = ['Pas... | 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 = ['Pas... | 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 = ['Pas... | code |
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