path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90103033/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape
df.groupby('left').mean()
df_new = df[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']]
dummy_salary = pd.get_dummies(df_new... | code |
90103033/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape
df.groupby('left').mean()
df_new = df[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']]
df_new.head() | code |
90103033/cell_22 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
model.predict(X_test) | code |
90103033/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape
df.groupby('left').mean()
pd.crosstab(df.salary, df.left).plot(kind='bar') | code |
90103033/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape
df.groupby('left').mean()
pd.crosstab(df.Department, df.left).plot(kind='bar') | code |
90103033/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape | code |
16162987/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
f = open('../input/glove840b300dtxt/glove.840B.300d.txt', encoding='utf-8')
embeddings_index = {}
for line in f:
values = line.split()
word = ''.join(values[:-300])
coefs = np.asarray(values[-300:], dtype='float32')
embeddings_index[word] = coefs
f.close()
print('Found {} word vector... | code |
18159032/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew # To compute statistic metrics
from subprocess import check_output
import matplotlib.pyplot as plt # Graphs
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
... | code |
18159032/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew # To compute statistic metrics
from subprocess import check_output
import matplotlib.pyplot as plt # Graphs
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as p... | code |
18159032/cell_4 | [
"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 seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
import matplotlib.pyplot as plt
import seaborn as sns
color = sn... | code |
18159032/cell_2 | [
"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 seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
import matplotlib.pyplot as plt
import seaborn as sns
color = sn... | code |
18159032/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt # Graphs
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
import matplotlib.pypl... | code |
18159032/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt # Graphs
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
import matplotlib.pypl... | code |
18159032/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt # Graphs
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
import matplotlib.pypl... | code |
18159032/cell_3 | [
"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 seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
import matplotlib.pyplot as plt
import seaborn as sns
color = sn... | code |
18159032/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew # To compute statistic metrics
from subprocess import check_output
import matplotlib.pyplot as plt # Graphs
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
... | code |
18159032/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt # Graphs
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
import matplotlib.pypl... | code |
18159032/cell_5 | [
"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 seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
import matplotlib.pyplot as plt
import seaborn as sns
color = sn... | code |
32063106/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique()
... | code |
32063106/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique() | code |
32063106/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.head() | code |
32063106/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape | code |
32063106/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique()
... | code |
32063106/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique()
... | code |
32063106/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique()
... | code |
32063106/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique()
... | code |
32063106/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique()
df.region.value_counts... | code |
32063106/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique()
... | code |
32063106/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.head() | code |
32063106/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique()
... | code |
32063106/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique()
df.tail(20) | code |
32063106/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape
df.region.unique()
... | code |
32063106/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.drop(columns='year', inplace=True)
df[['Year', 'Month', 'day']] = df.Date.str.split('-', expand=True)
df.drop(columns='Date', inplace=True)
df.drop_duplicates(inplace=True)
df.shape | code |
32063106/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/avocado-prices/avocado.csv')
df.shape
df.info() | code |
2040421/cell_4 | [
"text_html_output_1.png"
] | import datetime
import numpy as np
import pandas as pd
def LeaveOneOut(data1, data2, groupcolumns, columnName, useLOO=False, cut=1, addNoise=False):
features = list([])
for a in groupcolumns:
features.append(a)
if columnName is not None:
features.append(columnName)
grpCount = data1.gr... | code |
2040421/cell_7 | [
"text_plain_output_1.png"
] | import datetime
import numpy as np
import pandas as pd
def LeaveOneOut(data1, data2, groupcolumns, columnName, useLOO=False, cut=1, addNoise=False):
features = list([])
for a in groupcolumns:
features.append(a)
if columnName is not None:
features.append(columnName)
grpCount = data1.gr... | code |
2040421/cell_8 | [
"text_plain_output_1.png"
] | import datetime
import numpy as np
import pandas as pd
def LeaveOneOut(data1, data2, groupcolumns, columnName, useLOO=False, cut=1, addNoise=False):
features = list([])
for a in groupcolumns:
features.append(a)
if columnName is not None:
features.append(columnName)
grpCount = data1.gr... | code |
2040421/cell_5 | [
"text_plain_output_1.png"
] | import datetime
import numpy as np
import pandas as pd
def LeaveOneOut(data1, data2, groupcolumns, columnName, useLOO=False, cut=1, addNoise=False):
features = list([])
for a in groupcolumns:
features.append(a)
if columnName is not None:
features.append(columnName)
grpCount = data1.gr... | code |
50210004/cell_9 | [
"text_plain_output_1.png"
] | import math
import math
import math
def sigmoid(x):
value = math.exp(x) / (1 + math.exp(x))
return value
sigmoid(-3) | code |
50210004/cell_4 | [
"text_plain_output_1.png"
] | import math
import math
import math
def sigmoid(x):
value = math.exp(x) / (1 + math.exp(x))
return value
sigmoid(10) | code |
50210004/cell_6 | [
"text_plain_output_1.png"
] | import math
import math
import math
def sigmoid(x):
value = math.exp(x) / (1 + math.exp(x))
return value
sigmoid(0.75) | code |
50210004/cell_7 | [
"text_plain_output_1.png"
] | import math
import math
import math
def sigmoid(x):
value = math.exp(x) / (1 + math.exp(x))
return value
sigmoid(20) | code |
50210004/cell_8 | [
"text_plain_output_1.png"
] | import math
import math
import math
def sigmoid(x):
value = math.exp(x) / (1 + math.exp(x))
return value
sigmoid(-1) | code |
50210004/cell_10 | [
"text_plain_output_1.png"
] | import math
import math
import math
def sigmoid(x):
value = math.exp(x) / (1 + math.exp(x))
return value
import math
def binary_sigmoid(x):
value = math.exp(x) / (1 + math.exp(x))
if value >= 0.5:
value = 1
else:
value = 0
return value
val_list = [1.1, 2.3, 3.5, 4.8, 5.6, -1.... | code |
50210004/cell_5 | [
"text_plain_output_1.png"
] | import math
import math
import math
def sigmoid(x):
value = math.exp(x) / (1 + math.exp(x))
return value
sigmoid(3) | code |
90147270/cell_4 | [
"image_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)
df = pd.read_csv('../input/ex-1-dataset-iris/iris.csv')
df.shape
df.loc[df['variety'] == 'Setosa']
df_Setosa = df.loc[df['variety'] == '... | code |
90147270/cell_6 | [
"text_plain_output_1.png",
"image_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 seaborn as sns
df = pd.read_csv('../input/ex-1-dataset-iris/iris.csv')
df.shape
df.loc[df['variety'] == 'Setosa']
df_Setosa = df... | code |
90147270/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 |
90147270/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ex-1-dataset-iris/iris.csv')
df.head(150)
df.shape | code |
90147270/cell_5 | [
"image_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 seaborn as sns
df = pd.read_csv('../input/ex-1-dataset-iris/iris.csv')
df.shape
df.loc[df['variety'] == 'Setosa']
df_Setosa = df... | code |
90140128/cell_9 | [
"text_plain_output_2.png",
"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
df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
def detect_NaNs(df_temp: pd.DataFrame, name='', silent: bool=False, plot: bool=True):
"""... | code |
90140128/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
import typing
df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
def detect_NaNs(df_temp: pd.DataFrame, name='', silent: bool=False, plot: bool... | code |
90140128/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
df.head() | code |
104129202/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pd.options.display.min_rows = 100
pd.options.display.max_rows = 100
plt.style.use('seaborn-whitegrid')
plt.rc('figure', autolayout... | code |
104129202/cell_3 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pd.options.display.min_rows = 100
pd.options.display.max_rows = 100
plt.style.use('seaborn-whitegrid')
plt.rc('figure', autolayout=True, titlesize=18, titleweight='bold')
pl... | code |
33120194/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
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)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/... | code |
33120194/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house-prices-advanced-regressio... | code |
33120194/cell_9 | [
"application_vnd.jupyter.stderr_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)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house-prices-advanced-regressio... | code |
33120194/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house... | code |
33120194/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
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 seaborn as sns
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test =... | code |
33120194/cell_26 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house... | code |
33120194/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
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 seaborn as sns
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test =... | code |
33120194/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house-prices-advanced-regressio... | code |
33120194/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 |
33120194/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house-prices-advanced-regressio... | code |
33120194/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house-prices-advanced-regressio... | code |
33120194/cell_51 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
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 seaborn as sns
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test =... | code |
33120194/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house... | code |
33120194/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.r... | code |
33120194/cell_47 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
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 seaborn as sns
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test =... | code |
33120194/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house-prices-advanced-regressio... | code |
33120194/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.r... | code |
33120194/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
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)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/... | code |
33120194/cell_31 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house... | code |
33120194/cell_46 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
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 seaborn as sns
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test =... | code |
33120194/cell_22 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = pd.read_csv('../input/house... | code |
33120194/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
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)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
test = pd.read_csv('../input/... | code |
32066191/cell_9 | [
"image_output_1.png"
] | junk = ['Id', 'Date', 'Province_State']
train.drop(junk, axis=1, inplace=True)
train.head() | code |
32066191/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | train.tail() | code |
32066191/cell_19 | [
"text_plain_output_1.png"
] | from statsmodels.tsa.statespace.sarimax import SARIMAX
import math
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
c = list()
for i, x in enumerate(train['Province_State']):
if x is not np.nan:
c.append(x + ' - ' + train['Country_Region'][i])
... | code |
32066191/cell_18 | [
"text_html_output_1.png"
] | from statsmodels.tsa.statespace.sarimax import SARIMAX
import math
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
c = list()
for i, x in enumerate(train['Province_State']):
if x is not np.nan:
c.append(x + ' - ' + train['Country_Region'][i])
... | code |
32066191/cell_3 | [
"text_plain_output_1.png"
] | train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv') | code |
32066191/cell_10 | [
"text_plain_output_1.png"
] | junk = ['Id', 'Date', 'Province_State']
train.drop(junk, axis=1, inplace=True)
end = 84
country_list = train['Country_Region'][0::end]
print(len(country_list))
print(country_list) | code |
32066191/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
c = list()
for i, x in enumerate(train['Province_State']):
if x is not np.nan:
c.append(x + ' - ' + train['Country_Region'][i])
else:
c.append(train['Country_Region'][i])
print(len(c)) | code |
89134183/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.preprocessing import image
from pathlib import Path
import numpy as np # linear algebra
import tensorflow as tf
img_root_path = Path('../input/chest-xray-masks-and-labels/Lung Segmentation/CXR_png/')
mask_root_path = Path('../input/chest-xray-masks-and-labels/Lung Segmentation/masks')
test_root_path = Pa... | code |
89134183/cell_9 | [
"text_plain_output_35.png",
"text_plain_output_43.png",
"text_plain_output_37.png",
"text_plain_output_5.png",
"text_plain_output_48.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_44.png",
"text_plain_output_40.png",
"text_plain_output... | from keras.preprocessing import image
from pathlib import Path
import numpy as np # linear algebra
img_root_path = Path('../input/chest-xray-masks-and-labels/Lung Segmentation/CXR_png/')
mask_root_path = Path('../input/chest-xray-masks-and-labels/Lung Segmentation/masks')
test_root_path = Path('../input/chest-xray-m... | code |
89134183/cell_25 | [
"text_plain_output_1.png"
] | from IPython.display import clear_output
from keras.layers import (Activation, Input, MaxPooling2D, BatchNormalization,
from keras.models import Model
from keras.preprocessing import image
from pathlib import Path
from tensorflow.keras.utils import plot_model
import keras
import matplotlib.pyplot as plt
import ... | code |
89134183/cell_6 | [
"image_output_1.png"
] | from keras.preprocessing import image
from pathlib import Path
import matplotlib.pyplot as plt
img_root_path = Path('../input/chest-xray-masks-and-labels/Lung Segmentation/CXR_png/')
mask_root_path = Path('../input/chest-xray-masks-and-labels/Lung Segmentation/masks')
test_root_path = Path('../input/chest-xray-masks... | code |
89134183/cell_1 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"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 |
89134183/cell_16 | [
"text_plain_output_1.png"
] | (X_train.shape, y_train.shape, X_test.shape, y_test.shape) | code |
89134183/cell_24 | [
"image_output_1.png"
] | from IPython.display import clear_output
from keras.layers import (Activation, Input, MaxPooling2D, BatchNormalization,
from keras.models import Model
from keras.preprocessing import image
from pathlib import Path
import keras
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import tensorflow... | code |
89134183/cell_27 | [
"text_plain_output_1.png"
] | from IPython.display import clear_output
from keras.layers import (Activation, Input, MaxPooling2D, BatchNormalization,
from keras.models import Model
from keras.preprocessing import image
from pathlib import Path
from tensorflow.keras.optimizers import Adam
import keras
import keras.backend as K
import matplot... | code |
89134183/cell_12 | [
"image_output_1.png"
] | from keras.preprocessing import image
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import tensorflow as tf
img_root_path = Path('../input/chest-xray-masks-and-labels/Lung Segmentation/CXR_png/')
mask_root_path = Path('../input/chest-xray-masks-and-labels/Lung Segment... | code |
89134183/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing import image
from pathlib import Path
import matplotlib.pyplot as plt
img_root_path = Path('../input/chest-xray-masks-and-labels/Lung Segmentation/CXR_png/')
mask_root_path = Path('../input/chest-xray-masks-and-labels/Lung Segmentation/masks')
test_root_path = Path('../input/chest-xray-masks... | code |
121154806/cell_21 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
import segmentation_models as sm
import tensorflow as tf
sm.set_framework('tf.keras')
sm.framework()
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif'... | code |
121154806/cell_9 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import gif2numpy
import matplotlib.pyplot as plt
plt.imshow(gif2numpy.convert(masks_drive_test[0])[0][0]) | code |
121154806/cell_23 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
import segmentation_models as sm
import tensorflow as tf
sm.set_framework('tf.keras')
sm.framework()
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif'... | code |
121154806/cell_26 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
import segmentation_models as sm
import tensorflow as tf
sm.set_framework('tf.keras')
sm.framework()
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif'... | code |
121154806/cell_2 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import segmentation_models as sm
sm.set_framework('tf.keras')
sm.framework() | code |
121154806/cell_11 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif', 'gif', 'ppm')
def Data_sorting(input_data, target_data, exts):
images = sorted([os.path.join(inpu... | code |
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