path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73099078/cell_10 | [
"text_html_output_1.png"
] | y_train[:5] | code |
73099078/cell_5 | [
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
messages = pd.read_csv('../input/spam-or-ham/spam.csv', usecols=['v1', 'v2'], encoding='ISO-8859-1')
messages = messages.rename(columns={'v1': 'label', 'v2': 'message'})
y = list(messages['label'])
y[:5]
y = list(pd.get_dummies(y, drop_first=True)['spam'])
y[:5] | code |
128039963/cell_42 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_1... | from albumentations.pytorch import ToTensorV2
from pycocotools.coco import COCO
from torch.utils.data import DataLoader, sampler, random_split, Dataset
from torchvision import datasets, models
from torchvision.utils import draw_bounding_boxes
import albumentations as A # our data augmentation library
import copy... | code |
128039963/cell_13 | [
"text_plain_output_1.png"
] | # our dataset is in cocoformat, we will need pypcoco tools
!pip install pycocotools
from pycocotools.coco import COCO | code |
128039963/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from albumentations.pytorch import ToTensorV2
from pycocotools.coco import COCO
from torchvision import datasets, models
from torchvision.utils import draw_bounding_boxes
import albumentations as A # our data augmentation library
import copy
import cv2
import matplotlib.pyplot as plt
import os
import torch
i... | code |
128039963/cell_23 | [
"text_plain_output_1.png"
] | from albumentations.pytorch import ToTensorV2
import albumentations as A # our data augmentation library
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV... | code |
128039963/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from albumentations.pytorch import ToTensorV2
from pycocotools.coco import COCO
from torchvision import datasets, models
import albumentations as A # our data augmentation library
import copy
import cv2
import os
import torch
import torchvision
def get_transforms(train=False):
if train:
transform ... | code |
128039963/cell_26 | [
"text_plain_output_1.png"
] | from albumentations.pytorch import ToTensorV2
import albumentations as A # our data augmentation library
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV... | code |
128039963/cell_11 | [
"text_plain_output_1.png"
] | import torch
import torchvision
print(torch.__version__)
print(torchvision.__version__) | code |
128039963/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from albumentations.pytorch import ToTensorV2
from pycocotools.coco import COCO
from torchvision import datasets, models
import albumentations as A # our data augmentation library
import copy
import cv2
import os
import torch
import torchvision
def get_transforms(train=False):
if train:
transform ... | code |
128039963/cell_46 | [
"text_plain_output_1.png"
] | from albumentations.pytorch import ToTensorV2
import albumentations as A # our data augmentation library
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV... | code |
128039963/cell_22 | [
"text_plain_output_1.png"
] | from albumentations.pytorch import ToTensorV2
from pycocotools.coco import COCO
from torchvision import datasets, models
import albumentations as A # our data augmentation library
import copy
import cv2
import os
import torch
import torchvision
def get_transforms(train=False):
if train:
transform ... | code |
88093804/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import scanpy as sc
import scipy
import seaborn as sns
import time
import time
import time
adata = sc.datasets.krumsiek11()
adata.var.index
adata = sc.datasets.pbmc3k_processed()
adata.var.index
import time
for dataset i... | code |
88093804/cell_6 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import scanpy as sc
adata = sc.datasets.krumsiek11()
print(adata)
adata.var.index | code |
88093804/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | !pip install scanpy | code |
88093804/cell_7 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"application_vnd.jupyter.stderr_output_8.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_10.png",
"text_plain_output_3.png",
... | !pip install openpyxl
# Requires: !pip install openpyxl
adata = getattr(sc.datasets, "moignard15")()
print( adata )
adata.var.index | code |
88093804/cell_8 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import scanpy as sc
adata = sc.datasets.krumsiek11()
adata.var.index
adata = sc.datasets.pbmc3k_processed()
print(adata)
adata.var.index | code |
88093804/cell_15 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import scanpy as sc
import scipy
import seaborn as sns
import time
import time
import time
adata = sc.datasets.krumsiek11()
adata.var.index
adata = sc.datasets.pbmc3k_processed()
adata.var.index
import time
for dataset i... | code |
88093804/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install openpyxl | code |
88093804/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import scanpy as sc
import time
import time
adata = sc.datasets.krumsiek11()
adata.var.index
adata = sc.datasets.pbmc3k_processed()
adata.var.index
import time
for dataset in ['moignard15', 'pbmc3k', 'pbmc3k_processed', 'pbmc68k_reduced', 'paul15', 'krumsiek11']:
print(dataset)
t0 = tim... | code |
74044395/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
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 statsmodels.formula.api as smf
mpg_df = pd.read_csv('/kaggle/input/autompg-... | code |
74044395/cell_13 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
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
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
... | code |
74044395/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsep... | code |
74044395/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
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
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
... | code |
74044395/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns | code |
74044395/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
mpg_df.describe().transpose() | code |
74044395/cell_11 | [
"text_html_output_1.png"
] | 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
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.... | code |
74044395/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
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
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
... | code |
74044395/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 |
74044395/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsep... | code |
74044395/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsep... | code |
74044395/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
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
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
... | code |
74044395/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
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
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
... | code |
74044395/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
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
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
... | code |
74044395/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import math
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
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mp... | code |
74044395/cell_14 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
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
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
... | code |
74044395/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
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 statsmodels.formula.api as smf
mpg_df = pd.read_csv('/kaggle/input/autompg-... | code |
74044395/cell_10 | [
"text_plain_output_1.png"
] | 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
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.... | code |
129009262/cell_9 | [
"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
FOLDS = 5
STRA... | code |
129009262/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import clear_output
!pip3 install -U lazypredict
!pip3 install -U pandas #Upgrading pandas
clear_output() | code |
129009262/cell_7 | [
"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
FOLDS = 5
STRA... | code |
128048432/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.st... | code |
128048432/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.info() | code |
128048432/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.st... | code |
128048432/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.isna().sum()
df.describe() | code |
128048432/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.st... | code |
128048432/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.st... | code |
128048432/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape | code |
128048432/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.st... | code |
128048432/cell_3 | [
"text_plain_output_1.png"
] | import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25) | code |
128048432/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.st... | code |
128048432/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.isna().sum() | code |
128048432/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df | code |
105196046/cell_4 | [
"text_plain_output_1.png"
] | a = 4
b = 89
c = 47
a = 4
b = -89
c = 47
if a > 0 and b > 0 and (c > 0):
print('positive')
else:
print('negative') | code |
105196046/cell_6 | [
"text_plain_output_1.png"
] | a = 4
b = 89
c = 47
a = 4
b = -89
c = 47
a = 4
b = 89
c = 47
if a > 0 or b < 0 or c == 0:
print('positive')
else:
print('negative') | code |
105196046/cell_8 | [
"text_plain_output_1.png"
] | t = 8
v = 876
not t < v | code |
105196046/cell_3 | [
"text_plain_output_1.png"
] | a = 4
b = 89
c = 47
if a > 0 and b > 0 and (c > 0):
print('positive')
else:
print('negative') | code |
2004795/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
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 warnings
import nu... | code |
2004795/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
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 warnings
import nu... | code |
2004795/cell_3 | [
"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 warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
train = pd.rea... | code |
2004795/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
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 warnings
import nu... | code |
2004795/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
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 warnings
import nu... | code |
74063412/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import re
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
suv = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/surv.csv')
vic = pd.read_csv('/kaggle/input/titanicscraper/src/... | code |
74063412/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
suv = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/surv.csv')
vic = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/tita... | code |
74063412/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
suv = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/surv.csv')
vic = pd.read_csv('/kaggle/input/titanicscraper/src/... | code |
74063412/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import re
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
suv = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/surv.csv')
vic = pd.read_csv('/kaggle/input... | code |
74063412/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
print(f'{train.shape}_train, {test.shape}_test') | code |
74063412/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
suv = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/surv.csv')
vic = pd.read_csv('/kaggle/input/titanicscraper/src/... | code |
1008146/cell_2 | [
"text_plain_output_1.png"
] | from collections import defaultdict
import csv
import re
import re
import csv
import operator
from collections import defaultdict
stop_words = set(['a', "a's", 'able', 'about', 'above', 'according', 'accordingly', 'across', 'actually', 'after', 'actual', 'afterwards', 'again', 'against', "ain't", 'all', 'allow', 'al... | code |
1008146/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
90130653/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ran... | code |
90130653/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ran... | code |
90130653/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 |
90130653/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ran... | code |
90130653/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ran... | code |
90130653/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ran... | code |
90130653/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ran... | code |
90130653/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ran... | code |
90130653/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ran... | code |
89135256/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-persona... | code |
89135256/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-persona... | code |
89135256/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-persona... | code |
89135256/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 |
89135256/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-persona... | code |
89135256/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-persona... | code |
89135256/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-persona... | code |
1004737/cell_6 | [
"text_plain_output_1.png"
] | from csv import DictReader
from keras import layers
from keras import models
import numpy as np # linear algebra
import random
people = list(DictReader(open('../input/train.csv')))
people
def letter_for_cabin(cabin):
return cabin[0] if len(cabin) else ''
cabin_letters = list(set([letter_for_cabin(p['Cabin']) ... | code |
1004737/cell_2 | [
"text_plain_output_1.png"
] | from csv import DictReader
people = list(DictReader(open('../input/train.csv')))
people | code |
1004737/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import keras
from keras import models
from keras import layers
from csv import DictReader
import random
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1004737/cell_7 | [
"text_plain_output_1.png"
] | from csv import DictReader
from keras import layers
from keras import models
import numpy as np # linear algebra
import random
people = list(DictReader(open('../input/train.csv')))
people
def letter_for_cabin(cabin):
return cabin[0] if len(cabin) else ''
cabin_letters = list(set([letter_for_cabin(p['Cabin']) ... | code |
1004737/cell_3 | [
"text_plain_output_1.png"
] | from csv import DictReader
people = list(DictReader(open('../input/train.csv')))
people
def letter_for_cabin(cabin):
return cabin[0] if len(cabin) else ''
cabin_letters = list(set([letter_for_cabin(p['Cabin']) for p in people]))
print(cabin_letters) | code |
130017710/cell_4 | [
"text_plain_output_1.png"
] | import os
data_dir = '/kaggle/input/audio-mnist/data'
paths = []
labels = []
t = 0
for dirname, _, filenames in os.walk(data_dir):
if t < 20:
t += 1
for filename in filenames:
if filename[-4:] == '.wav':
paths += [os.path.join(dirname, filename)]
labels +... | code |
130017710/cell_6 | [
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_16.... | import cv2
import librosa
import matplotlib.pyplot as plt
import numpy as np
import os
data_dir = '/kaggle/input/audio-mnist/data'
paths = []
labels = []
t = 0
for dirname, _, filenames in os.walk(data_dir):
if t < 20:
t += 1
for filename in filenames:
if filename[-4:] == '.wav':
... | code |
17136778/cell_21 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
class CustomImageList(ImageList):
def open(self, fn):
img = fn.reshape(28, 28)
img = np.stack((img,) * 3, axis=-1)
return Image(p... | code |
17136778/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normali... | code |
17136778/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
class CustomImageList(ImageList):
def open(self, fn):
img = fn.reshape(28, 28)
img = np.stack((img,) * 3, axis=-1)
return Image(p... | code |
17136778/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
class CustomImageList(ImageList):
def open(self, fn):
img = fn.reshape(28, 28)
img = np.stack((img,) * 3, axis=-1)
return Image(p... | code |
17136778/cell_33 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
class CustomImageList(ImageList):
def open(self, fn):
img = fn.reshape(28, 28)
img = np.stack((img,) * 3, axis=-1)
return Image(p... | code |
17136778/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
class CustomImageList(ImageList):
def open(self, fn):
img = fn.reshape(28, 28)
img = np.stack((img,) * 3, axis=-1)
return Image(p... | code |
17136778/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
from fastai.vision import *
from fastai.metrics import *
import os
path = '../input'
print(os.listdir(path)) | code |
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