path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
33098715/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)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xtick... | code |
33098715/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xtick... | code |
33098715/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
print(f'This dataset has {data.shape[0]} rows')
print(f'This dataset has {data.shape[1]} columns') | code |
34134065/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_48 | [
"text_plain_output_1.png"
] | pip install google | code |
34134065/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_49 | [
"text_plain_output_1.png"
] | from googlesearch import search
try:
from googlesearch import search
except ImportError:
print('Error/Not found')
query = 'COVID 19 population studies'
for j in search(query, tld='co.in', num=10, stop=10, pause=2):
print(j) | code |
34134065/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_51 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from googlesearch import search
from googlesearch import search
try:
from googlesearch import search
except ImportError:
print('Error/Not found')
query2 = 'COVID 19 resources failure'
for j2 in search(query2, tld='co.in', num=10, stop=10, pause=2):
print(j2) | code |
34134065/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
tablesTable = t[['Question', 'Table Format']]
tablesTable | code |
34134065/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_47 | [
"text_html_output_1.png"
] | pip install beautifulsoup4 | code |
34134065/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.head(3) | code |
34134065/cell_12 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
34134065/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TABLEFORMAT = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/list_of_tables_and_table_formats.csv')
t = TABLEFORMAT[0:5]
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
journals = df1[['title', 'abstract', 'publish_... | code |
2002739/cell_13 | [
"image_output_1.png"
] | from pandas.plotting import autocorrelation_plot, lag_plot
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
cityTable = pd.read_csv('../input/city_attributes.csv')
temperatureDF = pd.read_csv('../input/temperature.csv', index_col=0)
temperatureDF.index =... | code |
2002739/cell_4 | [
"image_output_1.png"
] | import pandas as pd
cityTable = pd.read_csv('../input/city_attributes.csv')
temperatureDF = pd.read_csv('../input/temperature.csv', index_col=0)
temperatureDF.index = pd.to_datetime(temperatureDF.index)
cityTable | code |
2002739/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
cityTable = pd.read_csv('../input/city_attributes.csv')
temperatureDF = pd.read_csv('../input/temperature.csv', index_col=0)
temperatureDF.index = pd.to_datetime(temperatureDF.index)
cityTable
citiesToShow = ['Los Angeles', 'Chicago', 'Montreal', 'Houston']
t0 = te... | code |
2002739/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas.plotting import autocorrelation_plot, lag_plot
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
cityTable = pd.read_csv('../input/city_attributes.csv')
temperatureDF = pd.read_csv('../input/temperature.csv', index_col=0)
temperatureDF.index =... | code |
2002739/cell_15 | [
"image_output_1.png"
] | from pandas.plotting import autocorrelation_plot, lag_plot
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
cityTable = pd.read_csv('../input/city_attributes.csv')
temperatureDF = pd.read_csv('../input/temperature.csv', index_col=0)
temperatureDF.index =... | code |
2002739/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas.plotting import autocorrelation_plot, lag_plot
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
cityTable = pd.read_csv('../input/city_attributes.csv')
temperatureDF = pd.read_csv('../input/temperature.csv', index_col=0)
temperatureDF.index =... | code |
2002739/cell_10 | [
"text_html_output_1.png"
] | from pandas.plotting import autocorrelation_plot, lag_plot
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
cityTable = pd.read_csv('../input/city_attributes.csv')
temperatureDF = pd.read_csv('../input/temperature.csv', index_col=0)
temperatureDF.index =... | code |
88100476/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import ImageDataGenerator
val_batch = 10
train_batch = 32
train_datagen = ImageDataGenerator(rescale=1.0 / 255, shear_range=0.4, zoom_range=0.3, validation_split=0.3, horizontal_flip=True)
train_generator = train_datagen.flow_from_... | code |
88100476/cell_31 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
test_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_generator = test_datagen.flow_from_directory('/kaggle/input/dogsvscatsmytestdata/training_set/', target_size=(130, 130), batch_size=32, class_mode='binary', color_mode='rgb') | code |
88099900/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('/kaggle/input/titanic/train.csv')
raw_test = pd.read_csv('/kaggle/input/titanic/test.csv')
train_copy = raw_data.copy()
train_copy.set_index('PassengerId', inplace=True, drop=True)
test_copy = raw_test.... | code |
88099900/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('/kaggle/input/titanic/train.csv')
raw_test = pd.read_csv('/kaggle/input/titanic/test.csv')
train_copy = raw_data.copy()
train_copy.set_index('PassengerId', inplace=True, drop=True)
test_copy = raw_test.... | code |
88099900/cell_19 | [
"text_html_output_1.png"
] | list(prefixes) | code |
88099900/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 |
88099900/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('/kaggle/input/titanic/train.csv')
raw_test = pd.read_csv('/kaggle/input/titanic/test.csv')
train_copy = raw_data.copy()
train_copy.set_index('PassengerId', inplace=True, drop=True)
test_copy = raw_test.... | code |
88099900/cell_8 | [
"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)
raw_data = pd.read_csv('/kaggle/input/titanic/train.csv')
raw_test = pd.read_csv('/kaggle/input/titanic/test.csv')
train_copy = raw_data.copy()
train_copy.set_index('PassengerId', inplace=True, drop=True)
test_copy = raw_test.... | code |
88099900/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('/kaggle/input/titanic/train.csv')
raw_test = pd.read_csv('/kaggle/input/titanic/test.csv')
train_copy = raw_data.copy()
train_copy.set_index('PassengerId', inplace=True, drop=True)
test_copy = raw_test.... | code |
88099900/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('/kaggle/input/titanic/train.csv')
raw_test = pd.read_csv('/kaggle/input/titanic/test.csv')
train_copy = raw_data.copy()
train_copy.set_index('PassengerId', inplace=True, drop=True)
test_copy = raw_test.... | code |
88099900/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('/kaggle/input/titanic/train.csv')
raw_test = pd.read_csv('/kaggle/input/titanic/test.csv')
raw_data.head(10) | code |
130002559/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from PIL import Image
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, ConfusionMatrixDisplay, confusion_matrix, roc_curve, auc
from torch import nn
from torch.optim import Adam
from torch.utils.data import Dataset, DataLoader
from torchvision import trans... | code |
130002559/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import torch
import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from torch.optim import Adam
import glob
from tqdm.notebook import tqdm
from PIL import Image
im... | code |
130002559/cell_8 | [
"text_plain_output_100.png",
"text_plain_output_84.png",
"text_plain_output_56.png",
"text_plain_output_35.png",
"text_plain_output_98.png",
"text_plain_output_43.png",
"text_plain_output_78.png",
"text_plain_output_37.png",
"text_plain_output_90.png",
"text_plain_output_79.png",
"text_plain_out... | from PIL import Image
from torch import nn
from torch.optim import Adam
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import os
import pandas as pd
import timm
import torch
import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
import t... | code |
130002559/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, ConfusionMatrixDisplay, confusion_matrix, roc_curve, auc
from sklearn.metrics import confusion_matrix, roc_curve, auc
from torch import nn
from torch.optim import Adam
from torch.utils.... | code |
2003266/cell_42 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
2003266/cell_25 | [
"text_html_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
2003266/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
train.fillna('nan')
test = pd.read_csv('../input/test.csv')
test.fillna('nan')
test.head() | code |
2003266/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
train.fillna('nan')
test = pd.read_csv('../input/test.csv')
test.fillna('nan')
submission = pd.read_csv('../input/sample_submission.csv')
submission.head() | code |
2003266/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
2003266/cell_40 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import OrderedDict
from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessin... | code |
2003266/cell_29 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
2003266/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
2003266/cell_26 | [
"text_plain_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
2003266/cell_48 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
2003266/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
from itertools import chain
from nltk.tokenize import wordpunct_tokenize
from keras.preprocessing import text, sequence
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Seq... | code |
2003266/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | !fasttext print-word-vectors embedding-model.bin < fasttext-words.txt > fasttext-vectors.txt | code |
2003266/cell_52 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import OrderedDict
from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessin... | code |
2003266/cell_45 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
2003266/cell_49 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import OrderedDict
from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessin... | code |
2003266/cell_51 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
2003266/cell_28 | [
"text_plain_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
2003266/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
train.fillna('nan')
train.head() | code |
2003266/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import OrderedDict
from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessin... | code |
2003266/cell_46 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import OrderedDict
from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessin... | code |
2003266/cell_24 | [
"text_html_output_1.png"
] | from itertools import chain
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.tokenize import wordpunct_tokenize
from sklearn.model_selecti... | code |
2003266/cell_37 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import OrderedDict
from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessin... | code |
2003266/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
train.fillna('nan')
test = pd.read_csv('../input/test.csv')
test.fillna('nan')
submission = pd.read_csv('../input/sample_submission.csv')
submission.head() | code |
2003266/cell_36 | [
"text_html_output_1.png"
] | from itertools import chain
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Dropout, LSTM, Bidirectional, GlobalMaxPool1D, InputLayer, BatchNormalization, Activation
from keras.models import Sequential
from keras.preprocessing import text, sequence
from nltk.to... | code |
90123657/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['adr'].idxmax()
ind
name = df.iloc[ind]['name']
amount = df.iloc[ind]['adr']
np.around(df['adr'].mean(), 2)
np.around((df['stays_in_week_nights'] + df['stays_in_w... | code |
90123657/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['adr'].idxmax()
ind
name = df.iloc[ind]['name']
amount = df.iloc[ind]['adr']
print(f'name: {name}\namount = {amount}') | code |
90123657/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['adr'].idxmax()
ind
name = df.iloc[ind]['name']
amount = df.iloc[ind]['adr']
df['last name'] = df['name'].apply(lambda x: x[x.index(' ') + 1:])
idx = (df['children'] + df['babies']).... | code |
90123657/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape | code |
90123657/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['adr'].idxmax()
ind
name = df.iloc[ind]['name']
amount = df.iloc[ind]['adr']
idx = (df['children'] + df['babies']).idxmax()
maximum = (df['children'] + df['babies']).iloc[idx]
maximu... | code |
90123657/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
df['country'].value_counts()[:5] | code |
90123657/cell_29 | [
"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
sns.set_theme(style='whitegrid')
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['a... | code |
90123657/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.head() | code |
90123657/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['adr'].idxmax()
ind
name = df.iloc[ind]['name']
amount = df.iloc[ind]['adr']
np.around(df['adr'].mean(), 2) | code |
90123657/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['adr'].idxmax()
ind
name = df.iloc[ind]['name']
amount = df.iloc[ind]['adr']
df['last name'].value_counts()[:5] | code |
90123657/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['adr'].idxmax()
ind | code |
90123657/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['adr'].idxmax()
ind
name = df.iloc[ind]['name']
amount = df.iloc[ind]['adr']
df.head() | code |
90123657/cell_28 | [
"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
sns.set_theme(style='whitegrid')
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['a... | code |
90123657/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['adr'].idxmax()
ind
name = df.iloc[ind]['name']
amount = df.iloc[ind]['adr']
df[df['total_of_special_requests'] == 5][['name', 'email']] | code |
90123657/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.info() | code |
90123657/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['adr'].idxmax()
ind
name = df.iloc[ind]['name']
amount = df.iloc[ind]['adr']
idx = (df['children'] + df['babies']).idxmax()
maximum = (df['children'] + df['babies']).iloc[idx]
for i ... | code |
90123657/cell_27 | [
"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
sns.set_theme(style='whitegrid')
df = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
df.shape
df = df.drop('company', axis=1)
ind = df['a... | code |
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