path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72120119/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
333521/cell_13 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn import cross_validation
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import L... | code |
333521/cell_4 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"application_vnd.jupyter.stderr_o... | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(tita... | code |
333521/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
tita... | code |
333521/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic.describe() | code |
333521/cell_11 | [
"text_html_output_1.png"
] | from sklearn import cross_validation
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # ... | code |
333521/cell_1 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/... | code |
333521/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
... | from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import... | code |
333521/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_7.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(tita... | code |
333521/cell_10 | [
"text_html_output_1.png"
] | from sklearn import cross_validation
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import... | code |
333521/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import cross_validation
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import numpy as np # linear algebra
import operator
impor... | 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 |
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_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_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_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_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 |
106211998/cell_13 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_9 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_34 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_23 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_6 | [
"text_html_output_1.png"
] | import os
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
print('Number of files in train directory: ', len(train_files... | code |
106211998/cell_29 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_39 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_41 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_2 | [
"text_plain_output_1.png"
] | !pip install music21 | code |
106211998/cell_11 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_18 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_28 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_8 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_15 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_16 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_38 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_17 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_35 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_31 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_14 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_22 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_10 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
106211998/cell_12 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Chall... | code |
2024103/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average | code |
2024103/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll.head() | code |
2024103/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
poll.info() | code |
2024103/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_19 | [
"text_html_output_1.png"
] | import matplotlib as plt
import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
... | code |
2024103/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
sns.factorplot('Affiliation', data=poll, kind='count', legend=True, color='orange', size=6) | code |
2024103/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_28 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
sns.factorplot('Affiliation', data=poll, kind='count', legend=True, hue='Population', size=6... | code |
2024103/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
2024103/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
poll.head() | code |
2024103/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFram... | code |
122251391/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
dat... | code |
122251391/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
data.HomePlanet.unique() | code |
122251391/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.info() | code |
122251391/cell_34 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.... | code |
122251391/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
dat... | code |
122251391/cell_33 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.... | code |
122251391/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test.isnull().sum() | code |
122251391/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
dat... | code |
122251391/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.... | code |
122251391/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
dat... | code |
122251391/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
122251391/cell_32 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.... | code |
122251391/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
dat... | code |
122251391/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.head() | code |
122251391/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
dat... | code |
122251391/cell_35 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.... | code |
122251391/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
dat... | code |
122251391/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
dat... | code |
122251391/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum() | code |
122251391/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.... | code |
105210810/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2, 'CompanyName', CompanyName)
cars.drop(['CarName'], axis... | code |
105210810/cell_9 | [
"image_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns | code |
105210810/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2,... | code |
105210810/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2,... | code |
105210810/cell_6 | [
"image_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape | code |
105210810/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum() | code |
105210810/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2,... | code |
105210810/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.describe() | code |
105210810/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.info() | code |
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