path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
34150026/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submiss... | code |
73070475/cell_4 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
plat1 = cv2.imread('/kaggle/input/plat-nomer/4.jpg', 50)
edges = cv2.Canny(plat1, 100, 200)
(plt.subplot(121), plt.imshow(plat1, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edg... | code |
73070475/cell_6 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
plat1 = cv2.imread('/kaggle/input/plat-nomer/4.jpg', 50)
edges = cv2.Canny(plat1, 100, 200)
(plt.subplot(121), plt.imshow(plat1, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edg... | code |
73070475/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73070475/cell_7 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
plat1 = cv2.imread('/kaggle/input/plat-nomer/4.jpg', 50)
edges = cv2.Canny(plat1, 100, 200)
(plt.subplot(121), plt.imshow(plat1, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edg... | code |
73070475/cell_5 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
plat1 = cv2.imread('/kaggle/input/plat-nomer/4.jpg', 50)
edges = cv2.Canny(plat1, 100, 200)
(plt.subplot(121), plt.imshow(plat1, cmap='gray'))
(plt.title('Gambar Asli'), plt.xticks([]), plt.yticks([]))
(plt.subplot(122), plt.imshow(edges, cmap='gray'))
(plt.title('Gambar Edg... | code |
325101/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pas... | code |
325101/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pas... | code |
325101/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoosti... | code |
325101/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pas... | code |
325101/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
from sklearn.svm import SVC
"""
Boiler-Plate/Feature-Engineering to get frame into a testable format
"""
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pas... | code |
106214685/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
... | code |
106214685/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
dat... | code |
106214685/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS'] | code |
106214685/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
... | code |
106214685/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
... | code |
106214685/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns | code |
106214685/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
dat... | code |
106214685/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 |
106214685/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique() | code |
106214685/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)
import seaborn as sns
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
... | code |
106214685/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean() | code |
106214685/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
dat... | code |
106214685/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
... | code |
106214685/cell_3 | [
"text_plain_output_1.png"
] | import os
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.filterwarnings('ignore')
def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn
import pandas as pd
import datetime
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as m... | code |
106214685/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
dat... | code |
106214685/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.columns
data.Date.unique()
data.groupby('Itemname').mean()
data[data.Itemname == '12 COLOURED PARTY BALLOONS']
data.isna().sum()
dat... | code |
106214685/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx')
data.head() | code |
32068762/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (... | code |
32068762/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (... | code |
32068762/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (... | code |
32068762/cell_2 | [
"text_plain_output_1.png"
] | from datetime import datetime
from datetime import datetime
time_format = '%d%b%Y %H:%M'
datetime.now().strftime(time_format) | code |
32068762/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (... | code |
32068762/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (... | code |
32068762/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (... | code |
32068762/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (... | code |
32068762/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
np.random.seed(2019)
os.environ['PYTHONHASHSEED'] = '2019'
plt.style.use('seaborn-ticks')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['font.size'] = 11.0
plt.rcParams['figure.figsize'] = (... | code |
17108052/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim impo... | code |
17108052/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim impo... | code |
17108052/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim impo... | code |
17108052/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as tran... | code |
17108052/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as opti... | code |
17108052/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as tran... | code |
17108052/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim impo... | code |
17108052/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as tran... | code |
17108052/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvisio... | code |
17108052/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as tran... | code |
17108052/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as tran... | code |
17108052/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim impo... | code |
17108052/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as tran... | code |
17108052/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim impo... | code |
73063106/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday',... | code |
73063106/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday',... | code |
73063106/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories... | code |
73063106/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape | code |
73063106/cell_34 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as ... | code |
73063106/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount... | code |
73063106/cell_33 | [
"image_output_1.png"
] | from kneed import KneeLocator
from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
i... | code |
73063106/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum() | code |
73063106/cell_29 | [
"text_html_output_1.png"
] | pip install kneed | code |
73063106/cell_39 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories... | code |
73063106/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import... | code |
73063106/cell_41 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories... | code |
73063106/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday',... | code |
73063106/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 |
73063106/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.info() | code |
73063106/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday',... | code |
73063106/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data proce... | code |
73063106/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday',... | code |
73063106/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday',... | code |
73063106/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from kneed import KneeLocator
from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
i... | code |
73063106/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday',... | code |
73063106/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday',... | code |
73063106/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns
df.isnull().sum()
df.columns = ['customer', 'order', 'total_items', 'discount_percent', 'weekday',... | code |
73063106/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories_partials_2017.csv')
df.shape
df.columns | code |
73063106/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/ulabox-orders-with-categories-partials-2017/ulabox_orders_with_categories... | code |
34133142/cell_20 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.tokenize import RegexpTokenizer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
fro... | code |
34133142/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline impo... | code |
34133142/cell_7 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.tokenize import RegexpTokenizer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnbdm/train.csv')
test = pd.read_csv('/kaggle/inp... | code |
34133142/cell_3 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnbdm/train.csv')
test = pd.read_csv('/kaggle/input/airbnbdm/test.csv')
submission = pd.DataFrame()
submission['Id'] = test['id'].copy()
df.columns | code |
33111475/cell_13 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33111475/cell_9 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33111475/cell_6 | [
"text_plain_output_1.png"
] | from datetime import date
d1 = date(2014, 8, 29)
d2 = date(2016, 3, 23)
delta = d2 - d1
print(delta) | code |
33111475/cell_11 | [
"text_html_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33111475/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 |
33111475/cell_7 | [
"image_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33111475/cell_8 | [
"image_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33111475/cell_15 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33111475/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
df.head() | code |
33111475/cell_14 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33111475/cell_10 | [
"text_html_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33111475/cell_12 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33111475/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
from datetime import date
import datetime as dt
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_n... | code |
49115994/cell_9 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Amiata.csv')
Lake_Bilancino = pd.read... | code |
49115994/cell_7 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_9.png"
] | import pandas as pd
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Amiata.csv')
Lake_Bilancino = pd.read... | code |
49115994/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-predi... | code |
49115994/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-predi... | code |
49115994/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Amiata.csv')
Lake_Bilancino = pd.read... | code |
49115994/cell_5 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_9.png"
] | import pandas as pd
Aquifer_Doganella = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Doganella.csv')
Aquifer_Auser = pd.read_csv('/kaggle/input/acea-water-prediction/Aquifer_Auser.csv')
Water_Spring_Amiata = pd.read_csv('/kaggle/input/acea-water-prediction/Water_Spring_Amiata.csv')
Lake_Bilancino = pd.read... | code |
89130056/cell_13 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
import re
import zipfile
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors impor... | code |
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