path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32062628/cell_11 | [
"text_html_output_1.png"
] | from IPython.core.display import display, HTML
from copy import deepcopy
from spacy.matcher import Matcher
from spacy.matcher import PhraseMatcher
import gc
import json
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import spacy... | code |
32062628/cell_8 | [
"text_html_output_1.png"
] | from IPython.core.display import display, HTML
from copy import deepcopy
from spacy.matcher import Matcher
from spacy.matcher import PhraseMatcher
import gc
import json
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import spacy
import time
SAMPLE_SIZE_BI... | code |
1010130/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import random
import tensorflow as tf
import tensorflow as tf
features = ['Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female', 'Age', 'SibSp', 'Parch', 'Fare']
d... | code |
1010130/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import tensorflow as tf
features = ['Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female', 'Age', 'SibSp', 'Parch', 'Fare']
df = pd.read_csv('../input/train.csv')
t = pd.DataFrame({'Validation': list(map(lambda x: random.random() < 0.3, ... | code |
1010130/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import tensorflow as tf
import random
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1010130/cell_7 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import numpy as np
import pandas as pd
import tensorflow as tf
import random
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1010130/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import random
import tensorflow as tf
import tensorflow as tf
features = ['Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female', 'Age', 'SibSp', 'Parch', 'Fare']
d... | code |
1010130/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
features = ['Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female', 'Age', 'SibSp', 'Parch', 'Fare']
df = pd.read_csv('../input/train.csv')
t = pd.DataFrame({'Validation': list(map(lambda x: random.random() < 0.3, range(891)))})
x_preproce... | code |
129029982/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/suicide-attempts-in-shandong-china/SuicideChina.csv')
data.info() | code |
129029982/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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 |
129029982/cell_7 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.p... | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/suicide-attempts-in-shandong-china/SuicideChina.csv')
data = data.drop(columns=['Unnamed: 0', 'Person_ID'])
data.head() | code |
129029982/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/suicide-attempts-in-shandong-china/SuicideChina.csv')
data = data.drop(columns=['Unnamed: 0', 'Person_ID'])
data.Age.describe() | code |
129029982/cell_10 | [
"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)
data = pd.read_csv('/kaggle/input/suicide-attempts-in-shandong-china/SuicideChina.csv')
data = data.drop(columns=['Unnamed: 0', 'Person_ID'])
def bar_plot(variable):
"""
input: variable ex: "Sex"
outpu... | code |
129029982/cell_12 | [
"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)
data = pd.read_csv('/kaggle/input/suicide-attempts-in-shandong-china/SuicideChina.csv')
data = data.drop(columns=['Unnamed: 0', 'Person_ID'])
def bar_plot(variable):
"""
input: variable ex: "Sex"
outpu... | code |
129029982/cell_5 | [
"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/suicide-attempts-in-shandong-china/SuicideChina.csv')
data.head() | code |
2005813/cell_9 | [
"text_html_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
from tqdm import tqdm
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = datagen.flow_from_directory('../input/train/', batch... | code |
2005813/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
import matplotlib.pyplot as plt
import numpy as np
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = datagen.flow_from_directory('../input/train/', batch_size=1, class_mode='categorical')
x, y = train_generator.next()
p... | code |
2005813/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !ls ../input/train/ | code |
2005813/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
from glob import glob
from tqdm import tqdm
import matplotlib.pyplot as plt
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.applications.vgg16 import VGG16
from keras.applications.inception_v3 import InceptionV3
from keras.callbacks ... | code |
2005813/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | def get_model():
input_img = Input((256, 256, 3))
X = BatchNormalization()(input_img)
X = Convolution2D(16, (3, 3), activation='relu')(X)
X = BatchNormalization()(X)
X = Convolution2D(16, (3, 3), activation='relu')(X)
X = MaxPooling2D()(X)
X = Convolution2D(32, (3, 3), activation='relu')(X)
... | code |
2005813/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
from tqdm import tqdm
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = datagen.flow_from_directory('.... | code |
2005813/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = datagen.flow_from_directory('../input/train/', batch_size=1, class_mode='categorical') | code |
2005813/cell_10 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
from tqdm import tqdm
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = datagen.f... | code |
2005813/cell_12 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
from tqdm import tqdm
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = datagen.f... | code |
2005813/cell_5 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
from tqdm import tqdm
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = datagen.flow_from_directory('../input/train/', batch_size=1, class_mode='... | code |
333447/cell_9 | [
"text_plain_output_1.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 numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd... | code |
333447/cell_4 | [
"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['Age'] = titanic['Age'].fillna(tita... | code |
333447/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_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 |
333447/cell_2 | [
"text_html_output_1.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.describe() | code |
333447/cell_1 | [
"text_html_output_1.png",
"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
print(check_output(['ls', '../input']).decode('utf8'))
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/... | code |
333447/cell_7 | [
"text_plain_output_1.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 |
333447/cell_3 | [
"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
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(tita... | code |
333447/cell_5 | [
"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 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... | code |
18157867/cell_13 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t2.shape | code |
18157867/cell_9 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t3 = torch.tensor([[5.0, 6], [7, 8], [9, 10]])
t3 | code |
18157867/cell_4 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1 | code |
18157867/cell_23 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t3 = torch.tensor([[5.0, 6], [7, 8], [9, 10]])
t3
t4 = torch.tensor([[[11, 12, 13], [13, 14, 15]], [[15, 16, 17], [17, 18, 19.0]]])
t4
x = torch.tensor(3.0)
w = torch.tensor(4.0, requires_grad=True)
b = torch.tensor(5.0, requires_grad=True... | code |
18157867/cell_30 | [
"text_plain_output_1.png"
] | import numpy as np
import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t3 = torch.tensor([[5.0, 6], [7, 8], [9, 10]])
t3
t4 = torch.tensor([[[11, 12, 13], [13, 14, 15]], [[15, 16, 17], [17, 18, 19.0]]])
t4
x = torch.tensor(3.0)
w = torch.tensor(4.0, requires_grad=True)
b = torch.tensor(5.0... | code |
18157867/cell_6 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1
t1.dtype | code |
18157867/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t3 = torch.tensor([[5.0, 6], [7, 8], [9, 10]])
t3
t4 = torch.tensor([[[11, 12, 13], [13, 14, 15]], [[15, 16, 17], [17, 18, 19.0]]])
t4
x = torch.tensor(3.0)
w = torch.tensor(4.0, requires_grad=True)
b = torch.tensor(5.0... | code |
18157867/cell_19 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t3 = torch.tensor([[5.0, 6], [7, 8], [9, 10]])
t3
t4 = torch.tensor([[[11, 12, 13], [13, 14, 15]], [[15, 16, 17], [17, 18, 19.0]]])
t4
x = torch.tensor(3.0)
w = torch.tensor(4.0, requires_grad=True)
b = torch.tensor(5.0, requires_grad=True... | code |
18157867/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np
import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t3 = torch.tensor([[5.0, 6], [7, 8], [9, 10]])
t3
t4 = torch.tensor([[[11, 12, 13], [13, 14, 15]], [[15, 16, 17], [17, 18, 19.0]]])
t4
x = torch.tensor(3.0)
w = torch.tensor(4.0, requires_grad=True)
b = torch.tensor(5.0... | code |
18157867/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t3 = torch.tensor([[5.0, 6], [7, 8], [9, 10]])
t3
t4 = torch.tensor([[[11, 12, 13], [13, 14, 15]], [[15, 16, 17], [17, 18, 19.0]]])
t4
x = torch.tensor(3.0)
w = torch.tensor(4.0, requires_grad=True)
b = torch.tensor(5.0... | code |
18157867/cell_8 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2 | code |
18157867/cell_15 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t3 = torch.tensor([[5.0, 6], [7, 8], [9, 10]])
t3
t4 = torch.tensor([[[11, 12, 13], [13, 14, 15]], [[15, 16, 17], [17, 18, 19.0]]])
t4
t4.shape | code |
18157867/cell_35 | [
"text_plain_output_1.png"
] | import jovian
import jovian
jovian.commit() | code |
18157867/cell_14 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t3 = torch.tensor([[5.0, 6], [7, 8], [9, 10]])
t3
t3.shape | code |
18157867/cell_10 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1
t2 = torch.tensor([1.0, 2, 3, 4])
t2
t3 = torch.tensor([[5.0, 6], [7, 8], [9, 10]])
t3
t4 = torch.tensor([[[11, 12, 13], [13, 14, 15]], [[15, 16, 17], [17, 18, 19.0]]])
t4 | code |
18157867/cell_12 | [
"text_plain_output_1.png"
] | import torch
t1 = torch.tensor(4.0)
t1
t1.dtype
t1.shape | code |
34144897/cell_4 | [
"text_plain_output_1.png"
] | from torchvision import datasets, models, transforms
from torchvision.datasets import ImageFolder
import torch
from torchvision import datasets, models, transforms
import urllib
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.autograd import Variable
from collections import Ordered... | code |
73060659/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
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
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
#... | code |
73060659/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
wine[wine.columns[:11]].describe() | code |
73060659/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
from sklearn.preprocessi... | code |
73060659/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.head() | code |
73060659/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
sns.countplot(wine['quality']) | code |
73060659/cell_26 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, precision_score, recall_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessi... | code |
73060659/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
#getting the histograms of the data
fig = plt.figure(figsize=(30, 20))
plt.su... | code |
73060659/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
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
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
#getting the histograms of the data
fig = plt.figure(f... | code |
73060659/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 |
73060659/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns | code |
73060659/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
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
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
#getting the histograms of the data
fig = plt.figure(f... | code |
73060659/cell_8 | [
"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)
import seaborn as sns
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
sns.pairplot(wine) | code |
73060659/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
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
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
#getting the histograms of the data
fig = plt.figure(f... | code |
73060659/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
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
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
#getting the histograms of the data
fig = plt.figure(f... | code |
73060659/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
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
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
#getting the histograms of the data
fig = plt.figure(f... | code |
73060659/cell_22 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
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
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
#... | code |
73060659/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
fig = plt.figure(figsize=(30, 20))
plt.suptitle('Histograms of the respective... | code |
73060659/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, precision_score, recall_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessi... | code |
73060659/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine.columns
#getting the histograms of the data
fig = plt.figure(figsize=(30, 20))
plt.su... | code |
73060659/cell_5 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
from sklearn.preprocessing import LabelEncoder
bins = (2, 6.5, 8)
group_names = ['bad', 'good']
wine['quality'] =... | code |
130002580/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import tensorflow as tf
training_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
training_outputs = np.array([[0], [1], [1], [1]], dtype=np.float32)
i = tf.keras.Input(2)
x = tf.keras.layers.Dense(8, activation='relu')(i)
x = tf.keras.layers.Dense(16, activation='relu')(x)
x ... | code |
130002580/cell_2 | [
"text_plain_output_1.png"
] | def or_gate(a, b):
return a or b
print('A\tB\tOutput')
print('-' * 25)
for a in [False, True]:
for b in [False, True]:
output = or_gate(a, b)
print(f'{a}\t{b}\t{output}') | code |
130002580/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
training_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
training_outputs = np.array([[0], [1], [1], [1]], dtype=np.float32)
i = tf.keras.Input(2)
x = tf.keras.layers.Dense(8, activation='relu')(i)
x = tf.keras.layers.D... | code |
130002580/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt | code |
130002580/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import tensorflow as tf
training_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
training_outputs = np.array([[0], [1], [1], [1]], dtype=np.float32)
i = tf.keras.Input(2)
x = tf.keras.layers.Dense(8, activation='relu')(i)
x = tf.keras.layers.Dense(16, activation='relu')(x)
x ... | code |
130002580/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import tensorflow as tf
training_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
training_outputs = np.array([[0], [1], [1], [1]], dtype=np.float32)
i = tf.keras.Input(2)
x = tf.keras.layers.Dense(8, activation='relu')(i)
x = tf.keras.layers.Dense(16, activation='relu')(x)
x ... | code |
130002580/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
training_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
training_outputs = np.array([[0], [1], [1], [1]], dtype=np.float32)
i = tf.keras.Input(2)
x = tf.keras.layers.Dense(8, activation='relu')(i)
x = tf.keras.layers.D... | code |
130002580/cell_5 | [
"text_plain_output_1.png"
] | import tensorflow as tf
i = tf.keras.Input(2)
x = tf.keras.layers.Dense(8, activation='relu')(i)
x = tf.keras.layers.Dense(16, activation='relu')(x)
x = tf.keras.layers.Dense(32, activation='tanh')(x)
x = tf.keras.layers.Dense(8, activation='tanh')(x)
x = tf.keras.layers.Dense(1, activation='sigmoid')(x)
model = tf.ke... | code |
329567/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
def cleanResults(raceName,raceColumns,dfResultsTemp,dfResults):
for raceCol in raceColumns:
dfResultsTemp.index = dfResultsTemp.index.str.replace(r"(\w)([A-Z])", r"\1 \2")
dfResultsTemp.index = d... | code |
329567/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | dfRatings.index = dfRatings['mu_minus_3sigma'].rank(ascending=False)
dfRatings.sort('mu_minus_3sigma', ascending=False) | code |
34140243/cell_25 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import requests #library used to download web pages.
G = 6.674 * 10 ** (-11)
M_e = 5.97 * 10 ** 24
R_e = 6.37 * 10 ** 6
def escape_velocity():
pass
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.get(URL)
page.status_... | code |
34140243/cell_33 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests #library used to download web pages.
G = 6.674 * 10 ** (-11)
M_e = 5.97 * 10 ** 24
R_e = 6.37 * 10 ** 6
def escape_velocity():
pass
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.g... | code |
34140243/cell_29 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests #library used to download web pages.
G = 6.674 * 10 ** (-11)
M_e = 5.97 * 10 ** 24
R_e = 6.37 * 10 ** 6
def escape_velocity():
pass
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.g... | code |
34140243/cell_11 | [
"text_plain_output_1.png"
] | import requests #library used to download web pages.
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.get(URL)
page.status_code | code |
34140243/cell_19 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import requests #library used to download web pages.
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.get(URL)
page.status_code
HTMLstr = page.text
soup = BeautifulSoup(HTMLstr, 'html.parser')
soup.title
soup.a
all_lin... | code |
34140243/cell_16 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import requests #library used to download web pages.
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.get(URL)
page.status_code
HTMLstr = page.text
soup = BeautifulSoup(HTMLstr, 'html.parser')
soup.title | code |
34140243/cell_17 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import requests #library used to download web pages.
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.get(URL)
page.status_code
HTMLstr = page.text
soup = BeautifulSoup(HTMLstr, 'html.parser')
soup.title
soup.a | code |
34140243/cell_35 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests #library used to download web pages.
G = 6.674 * 10 ** (-11)
M_e = 5.97 * 10 ** 24
R_e = 6.37 * 10 ** 6
def escape_velocity():
pass
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.g... | code |
34140243/cell_31 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests #library used to download web pages.
G = 6.674 * 10 ** (-11)
M_e = 5.97 * 10 ** 24
R_e = 6.37 * 10 ** 6
def escape_velocity():
pass
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.g... | code |
34140243/cell_10 | [
"text_plain_output_1.png"
] | import requests #library used to download web pages.
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.get(URL)
type(page) | code |
34140243/cell_37 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests #library used to download web pages.
G = 6.674 * 10 ** (-11)
M_e = 5.97 * 10 ** 24
R_e = 6.37 * 10 ** 6
def escape_velocity():
pass
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.g... | code |
34140243/cell_12 | [
"text_plain_output_1.png"
] | import requests #library used to download web pages.
import requests
URL = 'https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html'
page = requests.get(URL)
page.status_code
HTMLstr = page.text
print(HTMLstr[:1000]) | code |
34140243/cell_5 | [
"text_html_output_1.png"
] | G = 6.674 * 10 ** (-11)
M_e = 5.97 * 10 ** 24
R_e = 6.37 * 10 ** 6
def escape_velocity():
pass
escape_velocity() | code |
121148449/cell_13 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Flatten, Dense
from keras.models import Sequential
from keras.utils import normalize
from keras.utils import to_categorical
(x_train.shape, y_train.shape)
(x_test.shape, y_test.shape)
x_train = normalize(x_train, axis=1)
x_test = normalize(x_test... | code |
121148449/cell_4 | [
"text_plain_output_1.png"
] | from PIL import Image
import cv2
import numpy as np
import os
image_dir = 'datasets/'
no_tumour = os.listdir(image_dir + 'no/')
yes_tumour = os.listdir(image_dir + 'yes/')
dataset = []
label = []
for i, image_name in enumerate(no_tumour):
if image_name.split('.')[1] == 'jpg':
image = cv2.imread(image_d... | code |
121148449/cell_7 | [
"text_plain_output_1.png"
] | (x_train.shape, y_train.shape) | code |
121148449/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | (x_test.shape, y_test.shape) | code |
90108519/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
print('Cases of nonconformity by gender: {}'.format(sum(df['total'] - df['... | code |
90108519/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
df.head() | code |
90108519/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df[... | code |
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