path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72073997/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.... | code |
72073997/cell_38 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-da... | code |
72073997/cell_47 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read... | code |
72073997/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.... | code |
72073997/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/... | code |
72073997/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-... | code |
72073997/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/... | code |
72073997/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
print('Training Data Shape: ', train.shape)
print('Testin... | code |
72073997/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
t... | code |
72073997/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-da... | code |
72073997/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.info() | code |
32069487/cell_4 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
import missingno as msno
msno.matrix(p) | code |
32069487/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)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.head() | code |
32069487/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 |
32069487/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
import missingno as msno
msno.matrix(p)
msno.matrix(p... | code |
32069487/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p.head() | code |
32069487/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isn... | code |
32069487/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any() | code |
32069487/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p['deaths'... | code |
32069487/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p.head() | code |
32069487/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p.drop(p.columns[[4, 30, 31, 32, 33, 34, 35]], axis=1, inplace=True)
p.head() | code |
32069487/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p.info() | code |
49126654/cell_11 | [
"text_html_output_1.png",
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
games = pd.read_csv('../input/nfl-big-data-bowl-2021/games.csv')
players = pd.read_csv('../input/nfl-big-data-bowl-2021/players.csv')
plays = pd.read_csv('../input/nfl-big-data-bowl-2021/pla... | code |
49126654/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
games = pd.read_csv('../input/nfl-big-data-bowl-2021/games.csv')
players = pd.read_csv('../input/nfl-big-data-bowl-2021/players.csv')
plays = pd.read_csv('../input/nfl-big-data-bowl-2021/plays.csv')
week_1 = pd.read_csv('../input/nfl-big-data-bowl-... | code |
16144430/cell_21 | [
"image_output_1.png"
] | from PIL import Image
from PIL import Image
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
import torch
import torchvision
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/... | code |
16144430/cell_30 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import random
import torch
transform_ship = transforms.Compose... | code |
16144430/cell_33 | [
"image_output_1.png"
] | from PIL import Image
from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
import torch
im... | code |
16144430/cell_44 | [
"text_plain_output_1.png"
] | from torchvision import models
model_ft = models.resnet50(pretrained=True)
model_ft.fc.in_features | code |
16144430/cell_40 | [
"text_plain_output_1.png"
] | from torchvision import models
model_ft = models.resnet50(pretrained=True)
print('Number of trainable parameters: ', sum((p.numel() for p in model_ft.parameters() if p.requires_grad))) | code |
16144430/cell_39 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from torchvision import models
model_ft = models.resnet50(pretrained=True)
print(model_ft) | code |
16144430/cell_41 | [
"text_plain_output_1.png"
] | from torchvision import models
model_ft = models.resnet50(pretrained=True)
for name, child in model_ft.named_children():
print(name) | code |
16144430/cell_49 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
from torchvision import models
import numpy as np
import os
import pandas as pd
import random
import torch
imp... | code |
16144430/cell_38 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import random
import torch
transform_ship = transforms.Compose... | code |
16144430/cell_47 | [
"text_plain_output_1.png"
] | from torchvision import models
import torch.nn as nn
model_ft = models.resnet50(pretrained=True)
model_ft.fc.in_features
from collections import OrderedDict
fc = nn.Sequential(nn.Linear(model_ft.fc.in_features, 720), nn.ReLU(), nn.Dropout(0.5), nn.Linear(720, 256), nn.ReLU(), nn.Dropout(0.4), nn.Linear(256, 64), nn... | code |
16144430/cell_43 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import random
import torch
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(see... | code |
16144430/cell_31 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import random
import torch
transform_ship = transforms.Compose... | code |
16144430/cell_24 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import random
import torch
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(see... | code |
16144430/cell_22 | [
"image_output_1.png"
] | from PIL import Image
from PIL import Image
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
import torch
import torchvision
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/... | code |
16144430/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
import random
import torch
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.c... | code |
16144430/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
import random
import torch
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.c... | code |
128000719/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
test = a[11]
test | code |
128000719/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[5] | code |
128000719/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[3] | code |
128000719/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
LSG = a[6]
LSG['Team'] = 'LSG'
LSG.rename(columns={'2022 Squad LSG': 'Players'}, inplace=True)
LSG | code |
128000719/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
RR = a[9]
RR['Team'] = 'RR'
RR.rename(columns={'2022 Squad RR': 'Players'}, inplace=True)
RR | code |
128000719/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[8] | code |
128000719/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[9] | code |
128000719/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[0] | code |
128000719/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[11] | code |
128000719/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
test = a[11]
test.rename(columns={'Base Price IN ₹ (CR.)': 'Base Price'}, inplace=True)
test.rename(columns={'Player': 'Players'}, inplace=True)
test.drop('Base Price IN $ (000)', axis=1, inplace=True)
test = t... | code |
128000719/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR | code |
128000719/cell_50 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[1] | code |
128000719/cell_45 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
test = a[11]
test.rename(columns={'Base Price IN ₹ (CR.)': 'Base Price'}, inplace=True)
test.rename(columns={'Player': 'Players'}, inplace=True)
test.drop('Base Price IN $ (000)', axis=1, inplace=True)
test | code |
128000719/cell_49 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
MI = a[7]
MI['Team'] = 'MI'
MI.rename(columns={'2022 Squad MI': 'Players'}, inplace=True)
MI | code |
128000719/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT | code |
128000719/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC | code |
128000719/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_3 | [
"text_html_output_1.png"
] | !pip install openpyxl | code |
128000719/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[4] | code |
128000719/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
RCB = a[8]
RCB['Team'] = 'RCB'
RCB.rename(columns={'2022 Squad RCB': 'Players'}, inplace=True)
RCB | code |
128000719/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[6] | code |
128000719/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
PBK = a[5]
PBK['Team'] = 'PBK'
PBK.rename(columns={'2022 Squad PBKS': 'Players'}, inplace=True)
PBK | code |
128000719/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK | code |
128000719/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[7] | code |
128000719/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
SRH = a[10]
SRH['Team'] = 'SRH'
SRH.rename(columns={'2022 Squad SRH': 'Players'}, inplace=True)
SRH | code |
128000719/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(... | code |
128000719/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a | code |
128000719/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[10] | code |
17132420/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 numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_mod... | code |
17132420/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_mod... | code |
17132420/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_mod... | code |
17132420/cell_33 | [
"image_output_1.png"
] | from scipy import stats
from sklearn import metrics
from sklearn.ensemble import GradientBoostingRegressor
import numpy as np
max_r2 = 0
for i in np.linspace(0.1, 1, 50):
gbr = GradientBoostingRegressor(learning_rate=i)
gbr.fit(x_train, y_train)
y_pred = gbr.predict(x_test)
print('For learning rate ... | code |
17132420/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition im... | code |
17132420/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition im... | code |
17132420/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegress... | code |
17132420/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition im... | code |
17132420/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition im... | code |
17132420/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition im... | code |
17132420/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition im... | code |
130008558/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.inde... | code |
130008558/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
... | code |
130008558/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
... | code |
130008558/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
... | code |
130008558/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.inde... | code |
130008558/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda ... | code |
130008558/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
... | code |
130008558/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
print(df.head())
df.plot() | code |
130008558/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
... | code |
130008558/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from matplotlib import pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import GRU, Dense
from keras.layers import LSTM
from keras import callbacks
from keras import optimizers
import pandas as pd
import tensorflow as tf
import numpy as np | code |
130008558/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
... | code |
130008558/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.