path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
129022563/cell_46 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../in... | code |
129022563/cell_53 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
1007330/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
data.loc[data['avg_hour_project'] <= 749.333, 'avg_hour_project'] = 0
data.loc[(data['avg_hour_project'] > 749.333) & (data['avg_hour_project'] <= 1304.667), 'avg_hour_project'] = 1
data.loc... | code |
1007330/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
data.loc[data['avg_hour_project'] <= 749.333, 'avg_hour_project'] = 0
data.loc[(data['avg_hour_project'] > 749.333) & (data['avg_hour_project'] <= 1304.667), 'avg_hour_project'] = 1
data.loc... | code |
1007330/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
corr_left = pd.DataFrame(corr['left'].drop('left'))
corr_left.sort_values(by='left', ascending=False) | code |
1007330/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
data.loc[data['avg_hour_project'] <= 749.333, 'avg_hour_project'] = 0
data.loc[(data['avg_hour_project'] > 749.333) & (data['avg_hour_project'] <= 1304.667), 'avg_hour_project'] = 1
data.loc... | code |
1007330/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/HR_comma_sep.csv')
(data['sales'].unique(), data['salary'].unique()) | code |
1007330/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
corr_left = pd.DataFrame(corr['left'].drop('left'))
corr_left.sort_values(by='left', ascending=False)
data['avg_hour_project'] = data['average_montly_hours'] * 12 / data['number_project']
d... | code |
1007330/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
data.loc[data['avg_hour_project'] <= 749.333, 'avg_hour_project'] = 0
data.loc[(data['avg_hour_project'] > 749.333) & (data['avg_hour_project'] <= 1304.667), 'avg_hour_project'] = 1
data.loc... | code |
1007330/cell_18 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
data.loc[data['avg_hour_project'] <= 749.333, 'avg_hour_project'] = 0
data.loc[(data['avg_hour_project'] > 749.333) & (data['avg_hour_project'] <= 1304.667),... | code |
1007330/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
data.loc[data['avg_hour_project'] <= 749.333, 'avg_hour_project'] = 0
data.loc[(data['avg_hour_project'] > 749.333) & (data['avg_hour_project'] <= 1304.667), 'avg_hour_pr... | code |
1007330/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)
sns.plt.title('Heatmap of Correlation Matrix')
corr | code |
1007330/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
data.loc[data['avg_hour_project'] <= 749.333, 'avg_hour_project'] = 0
data.loc[(data['avg_hour_project'] > 749.333) & (data['avg_hour_project'] <= 1304.667), 'avg_hour_project'] = 1
data.loc... | code |
1007330/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/HR_comma_sep.csv')
data.head() | code |
1007330/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
corr_left = pd.DataFrame(corr['left'].drop('left'))
corr_left.sort_values(by='left', ascending=False)
data['avg_hour_project'] = data['average_montly_hours'] * 12 / data['number_project']
d... | code |
1007330/cell_22 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
data.loc[data['avg_hour_project'] <= 749.333, 'avg_hour_project'] = 0
data.loc[(data['avg_hour_project'] > 749.333) & (data['avg_hour_project'] <= 1304.667), 'avg_hour_pr... | code |
1007330/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/HR_comma_sep.csv')
corr = data.corr()
corr = corr
corr
data.loc[data['avg_hour_project'] <= 749.333, 'avg_hour_project'] = 0
data.loc[(data['avg_hour_project'] > 749.333) & (data['avg_hour_project'] <= 1304.667), 'avg_hour_pr... | code |
1007330/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/HR_comma_sep.csv')
data.info() | code |
89130759/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
89130759/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
89130759/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df_... | code |
89130759/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
89130759/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_train.head() | code |
89130759/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
89130759/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
89130759/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89130759/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
89130759/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
89130759/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import VarianceThreshold as VT
var_thres = VT(threshold=0)
var_thres.fit(X_train)
const_cols = [col for col in X_train.columns if col not in X_train.columns[var_thres.get_support()]]
print(const_cols) | code |
89130759/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
89130759/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_test.head() | code |
89130759/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
89130759/cell_14 | [
"text_html_output_1.png"
] | from sklearn.feature_selection import VarianceThreshold as VT
var_thres = VT(threshold=0)
var_thres.fit(X_train) | code |
89130759/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
89130759/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')
df_test = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')
df_merge = pd.concat([df_test.assign(ind='test'), df_train.assign(ind='train')])
df... | code |
105174887/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda... | code |
105174887/cell_13 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda... | code |
105174887/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda.isnull().sum()
train_eda.shape | code |
105174887/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.head() | code |
105174887/cell_23 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda... | code |
105174887/cell_20 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda... | code |
105174887/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train['Embarked'] | code |
105174887/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test = test.drop(['Pclass', 'Name', 'Cabin'], axis='columns')
test.head() | code |
105174887/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda.isnull().sum()
train_eda.shape
train_eda.columns | code |
105174887/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda... | code |
105174887/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105174887/cell_32 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test = test.drop(['Pclass', 'Name', 'Cabin'], axis='columns')
test.isnull().sum()
test.loc[test.Sex == 'female', 'Sex'] = 1
test.loc[test.Sex == 'm... | code |
105174887/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda.isnull().sum() | code |
105174887/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda... | code |
105174887/cell_16 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda... | code |
105174887/cell_31 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test = test.drop(['Pclass', 'Name', 'Cabin'], axis='columns')
test.isnull().sum()
test.loc[test.Sex == 'female', 'Sex'] = 1
test.loc[test.Sex == 'm... | code |
105174887/cell_14 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda... | code |
105174887/cell_22 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda... | code |
105174887/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda.isnull().sum()
train_eda.shape
train_eda.describe() | code |
105174887/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test = test.drop(['Pclass', 'Name', 'Cabin'], axis='columns')
test.isnull().sum() | code |
105174887/cell_12 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train_eda = train.drop(['PassengerId', 'Name', 'Cabin', 'Ticket'], axis='columns')
train_eda... | code |
105174887/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.info() | code |
90157584/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
train.head() | code |
90157584/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.heatmap(test.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
90157584/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
train = train.fillna(train['Emb... | code |
90157584/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.head() | code |
90157584/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
sns.heatmap(test.isnull(), ytic... | code |
90157584/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
def impute_age(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):
if Pclass == 1:
... | code |
90157584/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
test.drop('Cabin', axis=1, inplace=True)
test.head() | code |
90157584/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
train = train.fillna(train['Emb... | code |
90157584/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
train = train.fillna(train['Emb... | code |
90157584/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
train = train.fillna(train['Emb... | code |
90157584/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
90157584/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
90157584/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
sns.heatmap(test.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
90157584/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
def impute_age(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):
if Pclass == 1:
... | code |
90157584/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis'... | code |
90157584/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
sns.countplot(x='Survived', hue='Pclass', data=train, palette='rainbow') | code |
90157584/cell_37 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
train = train.fillna(train['Emb... | code |
90157584/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
plt.figure(figsize=(12, 7))
sns.boxplot(x='Pclass', y='Age', data=train, palette='winter') | code |
90157584/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.head() | code |
90157584/cell_36 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
train = train.fillna(train['Emb... | code |
32067553/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] =... | code |
32067553/cell_23 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] =... | code |
32067553/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
data_flight = pd.read_csv('/kaggle/input/covid19/covid19_flight_countries_mod.csv')
data_daily_tested = pd.read_csv('... | code |
32067553/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] =... | code |
32067553/cell_11 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.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)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] =... | code |
32067553/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
data_flight = pd.read_csv('/kaggle/input/covid19/covid19_flight_countries_mod.csv')
data_daily_tested = pd.read_csv('... | code |
32067553/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 |
32067553/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
confirmed_total_date_Italy = data_train[data_train['Country_Region'] == 'Italy'].grou... | code |
32067553/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
data_flight = pd.read_csv('/kaggle/input/covid19/covid19_flight_countries_mod.csv')
data_daily_tested = pd.read_csv('... | code |
32067553/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
data_flight = pd.read_csv('/kaggle/input/covid19/covid19_flight_countries_mod.csv')
data_flight.head() | code |
32067553/cell_16 | [
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
data_flight = pd.read_csv('/kaggle/input/covid19/covid19_flight_countries_mod.csv')
data_flight.plot(x='Date', figsiz... | code |
32067553/cell_38 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
confirmed_total_... | code |
32067553/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] =... | code |
32067553/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] =... | code |
32067553/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
confirmed_total_... | code |
32067553/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '/kaggle/input/covid19-global-forecasting-week-4/train.csv'
data_train = pd.read_csv(data_path)
display(data_train.head())
display(data_train.describe())
display(data_train.info()) | code |
32067553/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import sys
class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
self.weights_... | code |
32068790/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train =... | code |
32068790/cell_9 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train =... | code |
32068790/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train = pd.read_csv('../inp... | code |
32068790/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train = pd.read_csv('../inp... | code |
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