path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
104114403/cell_53 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve, roc_auc_score, auc
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.sha... | code |
104114403/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape | code |
104114403/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first create a correlation matrix and store... | code |
104114403/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
pima['Outcome'].value_counts() | code |
104114403/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.head(3) | code |
130027731/cell_42 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape'... | code |
130027731/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)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
f... | code |
130027731/cell_25 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotSha... | code |
130027731/cell_57 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch
import torch.nn as nn
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubCl... | code |
130027731/cell_34 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/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)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_30 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotSha... | code |
130027731/cell_33 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_44 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearB... | code |
130027731/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape'... | code |
130027731/cell_55 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch
import torch.nn as nn
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubCl... | code |
130027731/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)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_40 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotSha... | code |
130027731/cell_39 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_48 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'Lot... | code |
130027731/cell_61 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'S... | code |
130027731/cell_60 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'S... | code |
130027731/cell_50 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'Lot... | code |
130027731/cell_52 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/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 |
130027731/cell_7 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_45 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearB... | code |
130027731/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)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotSha... | code |
130027731/cell_59 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_28 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotSha... | code |
130027731/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)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/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)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_47 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'Lot... | code |
130027731/cell_43 | [
"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 torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']... | code |
130027731/cell_31 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF... | code |
130027731/cell_27 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_37 | [
"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/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
d... | code |
130027731/cell_12 | [
"text_plain_output_1.png"
] | import datetime
import datetime
datetime.datetime.now().year | code |
130027731/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('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape | code |
17115578/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels[... | code |
17115578/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_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.shape
df_levels['Index'].nunique() | code |
17115578/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Dat... | code |
17115578/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels.head(2) | code |
17115578/cell_23 | [
"text_html_output_1.png"
] | df_rain.describe() | code |
17115578/cell_20 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_... | code |
17115578/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.head(2) | code |
17115578/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17115578/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
"\nplt.subplot(411)\nplt.plot(df_rain['POONDI'])\nplt.xlabel('Poondi')\nplt.tight_layout()\nplt.subplot(412)\nplt.plot(df_levels['CHOLAVARAM'])\nplt.xlabel('CHOLAVARAM')\nplt.tight_layout()\nplt.subplot(413)\nplt.plot(df_levels['REDHILLS'])\nplt.xlabel('REDHILLS')\nplt.tight_layout()\npl... | code |
17115578/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels[... | code |
17115578/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.head(2) | code |
17115578/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_... | code |
17115578/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.tail(2) | code |
17115578/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_... | code |
17115578/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_... | code |
17115578/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_... | code |
17115578/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels[... | code |
17115578/cell_22 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_... | code |
17115578/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)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.s... | code |
17115578/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels[... | code |
17115578/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels.describe() | code |
73078726/cell_13 | [
"image_output_1.png"
] | from kaggle_datasets import KaggleDatasets
from tensorflow.keras import layers
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPU... | code |
73078726/cell_2 | [
"image_output_1.png"
] | import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Device:', tpu.master())
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
except:
strategy = tf.distri... | code |
73078726/cell_11 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.... | code |
73078726/cell_15 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_syst... | code |
73078726/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
plt.figure(figsize=(16, 7))
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
ax1 = plt.subplot(1, 2, 1)
ax1.plot(epochs, acc, 'r')
ax1.plot(epochs, val_acc, 'b... | code |
17141482/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pylab
import seaborn as sns
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv'... | code |
17141482/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
def Income_bracket_binarization(feat_val):
if... | code |
17141482/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
dataset.head() | code |
17141482/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.python.framework import ops
import sklearn
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pylab
from sklearn.preprocessing import LabelEncoder
from sklearn.base import BaseEstim... | code |
17141482/cell_7 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
def Income_bracket_binarization(feat_val):
if... | code |
17141482/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
test_dataset.head() | code |
17141482/cell_12 | [
"text_html_output_1.png"
] | from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pylab
import seaborn as sns
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.cs... | code |
17141482/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
test_dataset.head() | code |
34140277/cell_21 | [
"text_html_output_1.png"
] | """
cols = ['pCut::Motor_Torque',
'pCut::CTRL_Position_controller::Lag_error',
'pCut::CTRL_Position_controller::Actual_position',
'pCut::CTRL_Position_controller::Actual_speed',
'pSvolFilm::CTRL_Position_controller::Actual_position',
'pSvolFilm::CTRL_Position_controller::Actual_speed'... | code |
34140277/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
... | code |
34140277/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minut... | code |
34140277/cell_4 | [
"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"
] | """
if not os.path.exists('/kaggle/working/compiled_df'):
os.makedirs('/kaggle/working/compiled_df')
#Saves dataframe to a csv file, removes a index
df.to_csv('/kaggle/working/compiled_df/Combined.csv', index=False)
""" | code |
34140277/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/... | code |
34140277/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
... | code |
34140277/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
... | code |
34140277/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)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
df.head(10) | code |
34140277/cell_2 | [
"text_html_output_1.png"
] | import os
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 |
34140277/cell_1 | [
"text_plain_output_1.png"
] | """
import os
import glob
import pandas as pd
#os.chdir("/mydir")
files = [i for i in glob.glob('/kaggle/input/one-year-industrial-component-degradation/*.{}'.format('csv'))]
files
extension = 'csv'
all_filenames = [i for i in glob.glob('/kaggle/input/one-year-industrial-component-degradation/*[mode1].{}'.format(extens... | code |
34140277/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
... | code |
34140277/cell_3 | [
"text_plain_output_1.png"
] | """
filenames = os.listdir('/kaggle/input/one-year-industrial-component-degradation/')
filenames = [i.strip(".csv") for i in filenames]
filenames.sort()
filenames.remove('oneyeardata')
parsed_filenames = []
for name in filenames:
temp = name.split("T")
month, date = temp[0].split("-")
rhs = temp[1].split("_... | code |
34140277/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/... | code |
34140277/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
... | code |
34140277/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
... | code |
34140277/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
... | code |
34140277/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
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
df.info() | code |
89129165/cell_34 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud,ImageColorGenerator,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set... | code |
89129165/cell_23 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud,ImageColorGenerator,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set... | code |
89129165/cell_30 | [
"image_output_1.png"
] | from wordcloud import WordCloud,ImageColorGenerator,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set... | code |
89129165/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_6 | [
"image_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89129165/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
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