path stringlengths 13 14 | screenshot_names listlengths 1 11 | code stringlengths 1 7.42k | cell_type stringclasses 1
value |
|---|---|---|---|
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 = ['Pas... | 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/nflplaybyplay20... | 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_pla... | 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_pla... | 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(figsi... | 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=... | 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].g... | 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].g... | 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].g... | 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].g... | 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['... | 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=... | 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]
i... | 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]
i... | 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... | 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
file... | 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,... | 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,... | 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, 193... | 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,... | 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.... | 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.... | 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.... | 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 ... | 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",
"t... | 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 ... | code |
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