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stringlengths 13
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sequencelengths 1
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|---|---|---|---|
325101\cell_7 | [
"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)
sv.score(test, test_y) | code |
325602\cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"text_plain_output_2.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn import grid_search
from sklearn.preprocessing import LabelEncoder
df = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
df.columns | code |
325602\cell_5 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
"""
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', 'PosTeamScore', 'DefTeamScore', 'ydstogo', 'TimeSecs', 'ydsnet', 'is_pass', 'Drive']]
(X, test) = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
test_y = test.pop('is_pass')
parameters = {}
clf = RandomForestClassifier(n_jobs=-1, oob_score=True, n_estimators=100, min_samples_leaf=12, max_features=0.8)
clf.fit(X, y) | code |
325602\cell_6 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
"""
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', 'PosTeamScore', 'DefTeamScore', 'ydstogo', 'TimeSecs', 'ydsnet', 'is_pass', 'Drive']]
(X, test) = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
test_y = test.pop('is_pass')
parameters = {}
clf = RandomForestClassifier(n_jobs=-1, oob_score=True, n_estimators=100, min_samples_leaf=12, max_features=0.8)
clf.fit(X, y)
clf.score(test, test_y) | code |
325674\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'))
from matplotlib import pyplot as plt | code |
325674\cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
print(global_temperatures.info()) | code |
325674\cell_4 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
global_temperatures[global_temperatures.index.year > 2000]['LandAverageTemperature'].plot(figsize=(13, 7)) | code |
325674\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)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
global_temperatures.groupby(global_temperatures.index.year)['LandAverageTemperature'].mean().plot(figsize=(13, 7)) | code |
326100\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'))
from matplotlib import pyplot as plt
import seaborn as sbn | code |
326100\cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
print(global_temperatures.info()) | code |
326100\cell_4 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
global_temperatures[global_temperatures.index.year > 2000]['LandAverageTemperature'].plot(figsize=(13, 7)) | code |
326100\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)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
global_temperatures.groupby(global_temperatures.index.year)['LandAverageTemperature'].mean().plot(figsize=(13, 7)) | code |
326100\cell_8 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
global_temperatures.groupby(global_temperatures.index.year)['LandAverageTemperatureUncertainty'].mean().plot(figsize=(13, 7)) | code |
326306\cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
import seaborn
import matplotlib.pyplot as plot
seaborn.set(style='darkgrid', palette='husl') | code |
326306\cell_10 | [
"application_vnd.jupyter.stderr_output_1.png",
"application_vnd.jupyter.stderr_output_2.png"
] | import matplotlib.pyplot as plot
import pandas
import seaborn
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].groupby('char', as_index=False).first()
ding80.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'ding80time']
last70.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'last70time']
characters = pandas.merge(ding80[['char', 'race', 'charclass', 'guild', 'ding80time']], last70[['char', 'last70time']], on='char')
mean_leveling_time = characters['leveling_time'].mean()
std_leveling_time = characters['leveling_time'].std()
characters_no_slowpokes = characters[characters['leveling_time'] - mean_leveling_time <= 3 * std_leveling_time]
seaborn.boxplot(x='race', y='leveling_time', data=characters_no_slowpokes) | code |
326306\cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp'] | code |
326306\cell_3 | [
"application_vnd.jupyter.stderr_output_1.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime) | code |
326306\cell_4 | [
"application_vnd.jupyter.stderr_output_1.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].groupby('char', as_index=False).first()
ding80.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'ding80time']
last70.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'last70time']
characters = pandas.merge(ding80[['char', 'race', 'charclass', 'guild', 'ding80time']], last70[['char', 'last70time']], on='char') | code |
326306\cell_5 | [
"application_vnd.jupyter.stderr_output_1.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].groupby('char', as_index=False).first()
ding80.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'ding80time']
last70.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'last70time']
characters = pandas.merge(ding80[['char', 'race', 'charclass', 'guild', 'ding80time']], last70[['char', 'last70time']], on='char')
characters['leveling_time'] = characters['ding80time'] - characters['last70time'] | code |
326306\cell_6 | [
"application_vnd.jupyter.stderr_output_1.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].groupby('char', as_index=False).first()
ding80.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'ding80time']
last70.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'last70time']
characters = pandas.merge(ding80[['char', 'race', 'charclass', 'guild', 'ding80time']], last70[['char', 'last70time']], on='char')
mean_leveling_time = characters['leveling_time'].mean()
std_leveling_time = characters['leveling_time'].std()
characters_no_slowpokes = characters[characters['leveling_time'] - mean_leveling_time <= 3 * std_leveling_time] | code |
326306\cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].groupby('char', as_index=False).first()
ding80.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'ding80time']
last70.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'last70time']
characters = pandas.merge(ding80[['char', 'race', 'charclass', 'guild', 'ding80time']], last70[['char', 'last70time']], on='char')
characters[characters['leveling_time'].isin(characters['leveling_time'].nsmallest(10))].sort_values('leveling_time') | code |
326306\cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_5.png"
] | import pandas
import seaborn
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].groupby('char', as_index=False).first()
ding80.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'ding80time']
last70.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'last70time']
characters = pandas.merge(ding80[['char', 'race', 'charclass', 'guild', 'ding80time']], last70[['char', 'last70time']], on='char')
mean_leveling_time = characters['leveling_time'].mean()
std_leveling_time = characters['leveling_time'].std()
characters_no_slowpokes = characters[characters['leveling_time'] - mean_leveling_time <= 3 * std_leveling_time]
seaborn.boxplot(x='charclass', y='leveling_time', data=characters_no_slowpokes) | code |
326306\cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png"
] | import matplotlib.pyplot as plot
import pandas
import seaborn
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].groupby('char', as_index=False).first()
ding80.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'ding80time']
last70.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp', 'last70time']
characters = pandas.merge(ding80[['char', 'race', 'charclass', 'guild', 'ding80time']], last70[['char', 'last70time']], on='char')
mean_leveling_time = characters['leveling_time'].mean()
std_leveling_time = characters['leveling_time'].std()
characters_no_slowpokes = characters[characters['leveling_time'] - mean_leveling_time <= 3 * std_leveling_time]
plot.figure(figsize=(45, 10))
seaborn.boxplot(x='guild', y='leveling_time', data=characters_no_slowpokes) | code |
326551\cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
crashes = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
crashes.dtypes
print(crashes.describe()) | code |
326551\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 |
326551\cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
crashes = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
crashes.dtypes | code |
326551\cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
crashes = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
crashes.dtypes
crashes.head() | code |
326551\cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
crashes = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
crashes.dtypes
crashes['Date'][1].split('/') | code |
326551\cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
crashes = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
crashes.dtypes
set(crashes['Operator'].tolist()) | code |
327075\cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/Indicators.csv')
Indicator_array = df[['IndicatorName', 'IndicatorCode']].drop_duplicates().values
modified_indicators = []
unique_indicator_codes = []
for ele in Indicator_array:
indicator = ele[0]
indicator_code = ele[1].strip()
if indicator_code not in unique_indicator_codes:
new_indicator = re.sub('[,()]', '', indicator).lower()
new_indicator = re.sub('-', ' to ', new_indicator).lower()
modified_indicators.append([new_indicator, indicator_code])
unique_indicator_codes.append(indicator_code)
Indicators = pd.DataFrame(modified_indicators, columns=['IndicatorName', 'IndicatorCode'])
Indicators = Indicators.drop_duplicates()
key_word_dict = {}
key_word_dict['Demography'] = ['population', 'birth', 'death', 'fertility', 'mortality', 'expectancy']
key_word_dict['Food'] = ['food', 'grain', 'nutrition', 'calories']
key_word_dict['Trade'] = ['trade', 'import', 'export', 'good', 'shipping', 'shipment']
key_word_dict['Health'] = ['health', 'desease', 'hospital', 'mortality', 'doctor']
key_word_dict['Economy'] = ['income', 'gdp', 'gni', 'deficit', 'budget', 'market', 'stock', 'bond', 'infrastructure']
key_word_dict['Energy'] = ['fuel', 'energy', 'power', 'emission', 'electric', 'electricity']
key_word_dict['Education'] = ['education', 'literacy']
key_word_dict['Employment'] = ['employed', 'employment', 'umemployed', 'unemployment']
key_word_dict['Rural'] = ['rural', 'village']
key_word_dict['Urban'] = ['urban', 'city']
feature = 'Health'
for indicator_ele in Indicators.values:
for ele in key_word_dict[feature]:
word_list = indicator_ele[0].split()
if ele in word_list or ele + 's' in word_list:
print(indicator_ele)
break | code |
327075\cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/Indicators.csv')
Indicator_array = df[['IndicatorName', 'IndicatorCode']].drop_duplicates().values
modified_indicators = []
unique_indicator_codes = []
for ele in Indicator_array:
indicator = ele[0]
indicator_code = ele[1].strip()
if indicator_code not in unique_indicator_codes:
new_indicator = re.sub('[,()]', '', indicator).lower()
new_indicator = re.sub('-', ' to ', new_indicator).lower()
modified_indicators.append([new_indicator, indicator_code])
unique_indicator_codes.append(indicator_code)
Indicators = pd.DataFrame(modified_indicators, columns=['IndicatorName', 'IndicatorCode'])
Indicators = Indicators.drop_duplicates()
print(Indicators.shape) | code |
327240\cell_10 | [
"text_plain_output_1.png"
] | import matplotlib
import pandas as ps
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
matplotlib.rcParams['figure.figsize'] = (10, 5)
ops = fileR['Operator'].value_counts()[:20]
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
fileR['day'] = fileR['Date'].dt.day
sub_years = [1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010]
years_legend = list(string.ascii_letters[:len(sub_years)])
fileR['year_group'] = ''
for i in range(0, len(sub_years) - 1):
fileR.loc[(sub_years[i + 1] > fileR['year']) & (fileR['year'] >= sub_years[i]), ['year_group']] = years_legend[i]
matplotlib.rcParams['figure.figsize'] = (10, 5)
fileR[['Fatalities', 'year_group']].groupby('year_group').count().plot(kind='bar', fontsize=14, legend=True, color='g', title='Fatalities based on decades') | code |
327240\cell_12 | [
"image_output_1.png",
"text_plain_output_1.png"
] | from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
import pandas as ps
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
fileR['day'] = fileR['Date'].dt.day
sub_years = [1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010]
years_legend = list(string.ascii_letters[:len(sub_years)])
fileR['year_group'] = ''
for i in range(0, len(sub_years) - 1):
fileR.loc[(sub_years[i + 1] > fileR['year']) & (fileR['year'] >= sub_years[i]), ['year_group']] = years_legend[i]
labels = ['1900-1910', '1910-1920', '1920-1930', '1930-1940', '1940-1950', '1950-1960', '1960-1970', '1970-1980', '1980-1990', '1990-2000', '2000-2010']
sizes = fileR[['Fatalities', 'year_group']].groupby('year_group').sum()
explode = (0, 0, 0, 0, 0, 0, 0, 0.1, 0.1, 0, 0)
colors = cm.Set1(np.arange(20) / 30.0)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=45)
plt.axis('equal')
plt.show() | code |
327240\cell_17 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import pandas as ps
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
fileR['day'] = fileR['Date'].dt.day
sub_years = [1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010]
years_legend = list(string.ascii_letters[:len(sub_years)])
fileR['year_group'] = ''
for i in range(0, len(sub_years) - 1):
fileR.loc[(sub_years[i + 1] > fileR['year']) & (fileR['year'] >= sub_years[i]), ['year_group']] = years_legend[i]
subfile2 = fileR[['Aboard', 'Fatalities', 'year', 'Operator', 'Type']].groupby('Operator').sum()
subfile2['survived'] = subfile2['Aboard'] - subfile2['Fatalities']
subfile2['percentageSurvived'] = subfile2['survived'] / subfile2['Aboard']
subfile3 = subfile2[subfile2['year'] > max(fileR['year'])]
highSurvive = subfile3.sort_values(by='percentageSurvived', ascending=False)[:20]
highSurvive | code |
327240\cell_19 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import pandas as ps
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
fileR['day'] = fileR['Date'].dt.day
sub_years = [1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010]
years_legend = list(string.ascii_letters[:len(sub_years)])
fileR['year_group'] = ''
for i in range(0, len(sub_years) - 1):
fileR.loc[(sub_years[i + 1] > fileR['year']) & (fileR['year'] >= sub_years[i]), ['year_group']] = years_legend[i]
subfile2 = fileR[['Aboard', 'Fatalities', 'year', 'Operator', 'Type']].groupby('Operator').sum()
subfile2['survived'] = subfile2['Aboard'] - subfile2['Fatalities']
subfile2['percentageSurvived'] = subfile2['survived'] / subfile2['Aboard']
subfile3 = subfile2[subfile2['year'] > max(fileR['year'])]
highSurvive = subfile3.sort_values(by='percentageSurvived', ascending=False)[:20]
highSurvive
highSurvive['percentageSurvived'].plot(kind='bar', color='g', fontsize=14, title='Operators with high percentage of survivers') | code |
327240\cell_21 | [
"image_output_1.png"
] | import pandas as ps
import pylab
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
fileR['day'] = fileR['Date'].dt.day
sub_years = [1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010]
years_legend = list(string.ascii_letters[:len(sub_years)])
fileR['year_group'] = ''
for i in range(0, len(sub_years) - 1):
fileR.loc[(sub_years[i + 1] > fileR['year']) & (fileR['year'] >= sub_years[i]), ['year_group']] = years_legend[i]
subfile = fileR[['Aboard', 'Fatalities', 'year']].groupby('year').sum()
subfile['survived'] = subfile['Aboard'] - subfile['Fatalities']
pylab.plot(subfile['Aboard'], label='Aboard')
pylab.plot(subfile['Fatalities'], label='Fatalities')
pylab.plot(subfile['survived'], label='Survived')
pylab.legend(loc='upper left') | code |
327240\cell_25 | [
"text_html_output_1.png"
] | import pandas as ps
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
fileR['day'] = fileR['Date'].dt.day
sub_years = [1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010]
years_legend = list(string.ascii_letters[:len(sub_years)])
fileR['year_group'] = ''
for i in range(0, len(sub_years) - 1):
fileR.loc[(sub_years[i + 1] > fileR['year']) & (fileR['year'] >= sub_years[i]), ['year_group']] = years_legend[i]
countrySub = fileR.groupby('countries').sum()
dangerousCountries = countrySub.sort_values('Fatalities', ascending=False)
dangerousCountries['Fatalities'][:20].plot(kind='bar', color='g', fontsize=14, title='Highest fatalities based on the location') | code |
327240\cell_4 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import pandas as ps
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
print(fileR.head()) | code |
327240\cell_6 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import matplotlib
import pandas as ps
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
matplotlib.rcParams['figure.figsize'] = (10, 5)
ops = fileR['Operator'].value_counts()[:20]
ops.plot(kind='bar', legend='Operator', color='g', fontsize=10, title='Operators with Highest Crashes') | code |
327240\cell_7 | [
"image_output_1.png",
"text_plain_output_1.png"
] | import pandas as ps
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
types = fileR['Type'].value_counts()[:20]
types.plot(kind='bar', legend='Types', color='g', fontsize=10, title='Types with Highest Crashes') | code |
327702\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 |
327702\cell_3 | [
"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/pitching.csv')
df | code |
327702\cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | X | code |
327702\cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import svm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/pitching.csv')
df
df_sg = df[df.gs == df.g]
Y = df_sg.w / df_sg.gs
Y_class = np.floor(Y)
clf = svm.SVC()
clf.fit(X, Y_class) | code |
328194\cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')
def doRating(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['Rating'] = ts.rate(list(zip(dfResults['Rating'].T.values.tolist())), ranks=dfResults['R' + raceCol].T.values.tolist())
dfResults = doRating(16, dfResults)
dfResults['Rating'] = ts.Rating()
ts.rate([ts.Rating(), ts.Rating()], ranks=[1, 0]) | code |
328194\cell_12 | [
"text_plain_output_1.png"
] | r1 | code |
328194\cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')
def doRating(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['Rating'] = ts.rate(list(zip(dfResults['Rating'].T.values.tolist())), ranks=dfResults['R' + raceCol].T.values.tolist())
dfResults = doRating(16, dfResults) | code |
328194\cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')
def doRating(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['Rating'] = ts.rate(list(zip(dfResults['Rating'].T.values.tolist())), ranks=dfResults['R' + raceCol].T.values.tolist())
dfResults = doRating(16, dfResults)
dfResults['Rating'] | code |
328714\cell_1 | [
"application_vnd.jupyter.stderr_output_1.png",
"text_plain_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 1000]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_frame.sum())
ambiguity_data = ambiguity_measure(indexed_names.groupby(level=['Year', 'Name']))
yearly_ambiguity = ambiguity_data.groupby(level='Year') | code |
328714\cell_2 | [
"application_vnd.jupyter.stderr_output_10.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_8.png",
"text_plain_output_1.png",
"text_plain_output_11.png",
"text_plain_output_2.png",
"text_plain_output_3.png",
"text_plain_output_5.png",
"text_plain_output_7.png",
"text_plain_output_9.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 1000]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_frame.sum())
ambiguity_data = ambiguity_measure(indexed_names.groupby(level=['Year', 'Name']))
yearly_ambiguity = ambiguity_data.groupby(level='Year')
print('Average ambiguity: %s\n' % str(ambiguity_data.mean()))
print('Average by year: %s\n' % str(yearly_ambiguity.mean()))
print('Most ambiguous by year: %s' % str(yearly_ambiguity.idxmax().apply(lambda x: x[1]))) | code |
Subsets and Splits