path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32062669/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
test_df.head(5) | code |
32062669/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.appl... | code |
32062669/cell_15 | [
"text_html_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
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 torch
gpu_status = torch.cuda.is_available()
train_df = pd.rea... | code |
32062669/cell_16 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
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 torch
gpu_status = torch.cuda.is_available()
train_df = pd.rea... | code |
32062669/cell_31 | [
"text_plain_output_1.png"
] | from IPython.display import FileLink
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import datetime
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 torch
im... | code |
32062669/cell_24 | [
"text_html_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch.nn as nn
import torch.nn.functional as F
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../inp... | code |
32062669/cell_14 | [
"text_html_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
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 torch
gpu_status = torch.cuda.is_available()
train_df = pd.rea... | code |
32062669/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)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_... | code |
32062669/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
test_df.head(5) | code |
32062669/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
train_df.describe(include='all') | code |
122256053/cell_4 | [
"text_plain_output_1.png"
] | from io import open
import glob
import os
import string
import unicodedata
from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os
def findFiles(path):
return glob.glob(path)
print(findFiles('/kaggle/input/classifying-names-data-from-pytorch-tutorial/names/*.... | code |
122256053/cell_6 | [
"text_plain_output_1.png"
] | from io import open
import glob
import os
import string
import unicodedata
from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os
def findFiles(path):
return glob.glob(path)
import unicodedata
import string
all_letters = string.ascii_letters + " .,;'"
n_lett... | code |
122256053/cell_1 | [
"text_plain_output_1.png"
] | !pip install git+https://github.com/Ilykuleshov/pytorch-toolz.git | code |
122256053/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from io import open
import glob
import os
import string
import torch
import unicodedata
from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os
def findFiles(path):
return glob.glob(path)
import unicodedata
import string
all_letters = string.ascii_letters + ... | code |
122256053/cell_10 | [
"text_plain_output_1.png"
] | from io import open
from pytorch_toolz.functools import Reduce
from pytorch_toolz.functools import Sequential
from pytorch_toolz.operator import Apply
import glob
import os
import string
import torch
import torch.nn as nn
import unicodedata
from __future__ import unicode_literals, print_function, division
... | code |
18105807/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survi... | code |
18105807/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import os
print(os.listdir('../input'))
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.head() | code |
18105807/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survi... | code |
18105807/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Su... | code |
106210695/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';... | code |
106210695/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';... | code |
106210695/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';... | code |
106210695/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';... | code |
106210695/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';... | code |
106210695/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
d... | code |
106210695/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
d... | code |
106210695/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';... | code |
106210695/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';... | code |
106210695/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';... | code |
106210695/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
d... | code |
106210695/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';... | code |
106210695/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
d... | code |
106210695/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
f... | code |
106196764/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from torchvision import datasets, transforms
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import time
transform_train = transforms.Compose([transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor(), transforms.Normalize((0.485, 0.456,... | code |
128040664/cell_21 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_13 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
print(classific... | code |
128040664/cell_9 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_25 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_4 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_34 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_23 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_33 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_20 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_6 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test1.head() | code |
128040664/cell_11 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
from sklearn.metrics import classification_report
print(classification_report(test0['label'], test0['prediction'])) | code |
128040664/cell_19 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test0.head() | code |
128040664/cell_7 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_18 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_32 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_8 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_15 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_16 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_38 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test2.head() | code |
128040664/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_35 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_31 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_24 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_14 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_22 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_27 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_37 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
128040664/cell_12 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
print(classification_report(test1['label'], test1['prediction'])) | code |
128040664/cell_5 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.cs... | code |
128040664/cell_36 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read... | code |
1009148/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | skip_border = 50
skip_middle = 3
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 5))
ax1.imshow(montage2d(filter_fossil_data[skip_border:-skip_border:skip_middle]), **im_args)
ax1.set_title('Axial Slices')
ax1.axis('off')
ax2.imshow(montage2d(filter_fossil_data.transpose(1, 2, 0)[skip_border:-skip_border:skip_mi... | code |
1009148/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.imshow(montage2d(fossil_data), cmap='bone')
ax1.set_title('Axial Slices')
_ = ax2.hist(fossil_data.ravel(), 20)
ax2.set_title('Overall Histogram') | code |
1009148/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
test_slice = fossil_data[int(fossil_data.shape[0] / 2)]
ax1.imshow(test_slice, cmap='bone')
ax1.set_title('Axial Slices')
_ = ax2.imshow(test_slice > 70)
ax2.set_title('Thresheld Slice') | code |
1009148/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.ndimage.filters import median_filter
filter_fossil_data = median_filter(fossil_data, (3, 3, 3))
slice_idx = int(fossil_data.shape[0] / 2)
test_slice = fossil_data[slice_idx]
test_filt_slice = filter_fossil_data[slice_idx]
im_args = dict(cmap='bone', vmin=50, vmax=70)
fig, (ax1, ax2) = plt.subplots(1, 2, figs... | code |
1009148/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from scipy.ndimage import zoom
from skimage import measure
import matplotlib.pyplot as plt
import numpy as np # linear algebra
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from skimage import measure
def show_3d_mesh(p, threshold):
verts, faces... | code |
1009148/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage.io import imread
fossil_path = '../input/Gut-PhilElvCropped.tif'
fossil_data = imread(fossil_path)
print('Loading Fossil Data sized {}'.format(fossil_data.shape)) | code |
1009148/cell_10 | [
"text_plain_output_1.png"
] | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
thresh_fossil_data = filter_fossil_data > 70
thresh_slice = thresh_fossil_data[slice_idx]
ax1.imshow(test_filt_slice, cmap='bone')
ax1.set_title('Filtered Slices')
_ = ax2.imshow(thresh_slice)
ax2.set_title('Slice with Threshold') | code |
1009148/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage.morphology import binary_closing, ball
closed_fossil_data = binary_closing(thresh_fossil_data, ball(5))
close_slice = closed_fossil_data[slice_idx]
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.imshow(test_filt_slice, cmap='bone')
ax1.set_title('Filtered Slices')
_ = ax2.imshow(close_slice)
ax2... | code |
33098668/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Dat... | code |
33098668/cell_9 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Dat... | code |
33098668/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
print('train :', '\n', train.head(3), '\n', '... | code |
33098668/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
print('Sample having 0 sales :', train[train[... | code |
33098668/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv') | code |
33098668/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Dat... | code |
33098668/cell_1 | [
"text_plain_output_1.png"
] | from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import cufflinks as cf
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook... | code |
33098668/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
print('New ... | code |
33098668/cell_8 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train.info... | code |
33098668/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
print('training dataset shape', train.shape)
... | code |
33098668/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/st... | code |
33098668/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Dat... | code |
33098668/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Dat... | code |
33098668/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train.head() | code |
2001733/cell_9 | [
"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)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
zoos = museums['Revenue'][museums['Museum Type'] == 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
other ... | code |
2001733/cell_4 | [
"text_plain_output_1.png"
] | from scipy.stats import probplot
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pylab
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
probplot(museums['Revenue'], dist='norm', plot=pylab) | code |
2001733/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0] | code |
2001733/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
zoos = museums['Revenue'][museums['Museum Type'] == 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
other = museums['Revenue'][museums['Mus... | code |
2001733/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from scipy.stats import ttest_ind
from scipy.stats import probplot
import matplotlib.pyplot as plt
import pylab
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2001733/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import ttest_ind
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
zoos = museums['Revenue'][museums['Museum Type'] == 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
othe... | code |
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