path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88099842/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import wfdb
data = '../input/mit-bih-arrhythmia-database/'
patients = ['100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '118', '119', '121', '122', '123', '124', '200', '201', '202', '... | code |
88099842/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import wfdb
data = '../input/mit-bih-arrhythmia-database/'
patients = ['100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '118', '119', '121', '122', '123', '124', '200', '201', '202', '... | code |
88099842/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
88099842/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import wfdb
data = '../input/mit-bih-arrhythmia-database/'
patients = ['100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '118', '119', '121', '122', '123', '124', '200', '201', '202', '... | code |
88099842/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import wfdb
data = '../input/mit-bih-arrhythmia-database/'
patients = ['100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '118', '119', '121', '122', '123', '124', '200', '201', '202', '... | code |
88099842/cell_3 | [
"text_html_output_1.png"
] | !pip install wfdb | code |
88099842/cell_5 | [
"text_plain_output_1.png"
] | pip install matplotlib==3.1.3 | code |
1003427/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | train_data = pd.read_csv('/Users/apple/Desktop/Data science/datasets/train.csv')
test_data = pd.read_csv('/Users/apple/Desktop/Data science/datasets/test.csv')
gender_submission = pd.read_csv('/Users/apple/Desktop/Data science/datasets/gender_submission.csv') | code |
1003427/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | train_data = pd.read_csv('/Users/apple/Desktop/Data science/datasets/train.csv')
test_data = pd.read_csv('/Users/apple/Desktop/Data science/datasets/test.csv')
gender_submission = pd.read_csv('/Users/apple/Desktop/Data science/datasets/gender_submission.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'],... | code |
122265203/cell_21 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train... | code |
122265203/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citati... | code |
122265203/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number ... | code |
122265203/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train... | code |
122265203/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names']) | code |
122265203/cell_23 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train... | code |
122265203/cell_20 | [
"text_html_output_1.png"
] | from tensorflow import keras
import tensorflow as tf
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
mod... | code |
122265203/cell_6 | [
"text_html_output_1.png"
] | import cv2
import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number ... | code |
122265203/cell_26 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train... | code |
122265203/cell_11 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citati... | code |
122265203/cell_19 | [
"text_html_output_1.png"
] | from tensorflow import keras
import tensorflow as tf
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
mod... | code |
122265203/cell_7 | [
"text_html_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
... | code |
122265203/cell_28 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train... | code |
122265203/cell_8 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
... | code |
122265203/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = ... | code |
122265203/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train... | code |
122265203/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train... | code |
122265203/cell_10 | [
"text_html_output_1.png"
] | import cv2
import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number ... | code |
122265203/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train... | code |
122265203/cell_12 | [
"text_html_output_1.png"
] | import cv2
import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number ... | code |
122265203/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files | code |
89142662/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_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum()
data = data.drop_duplicates()
data.duplicated().sum()
data.i... | code |
89142662/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum()
data.head(20) | code |
89142662/cell_4 | [
"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_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data['T (degC)'].plot(figsize=(15, 10)) | code |
89142662/cell_6 | [
"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_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum() | code |
89142662/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_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum()
data = data.drop_duplicates()
data.duplicated().sum() | code |
89142662/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 |
89142662/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)
data = pd.read_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum() | code |
89142662/cell_8 | [
"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_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum() | code |
89142662/cell_15 | [
"text_plain_output_1.png"
] | (len(data_hourly), data_hourly.isna().sum()) | code |
89142662/cell_3 | [
"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_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data | code |
89142662/cell_14 | [
"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_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum()
data = data.drop_duplicates()
data.duplicated().sum()
data.i... | code |
89142662/cell_12 | [
"text_plain_output_1.png",
"image_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_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum()
data = data.drop_duplicates()
data.duplicated().sum()
data.h... | code |
89142662/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_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data.info() | code |
90154574/cell_13 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
... | code |
90154574/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
plt.figure(figsize=(15, 10))
sns.heatmap(corr, vmax=0.8, annot=True, fmt='.2f')
plt.show() | code |
90154574/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housedata/output.csv')
df.head() | code |
90154574/cell_20 | [
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
... | code |
90154574/cell_26 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
re... | code |
90154574/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/housedata/output.csv')
df.hist(figsize=(20, 20))
plt.show() | code |
90154574/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
df.isnull().sum() | code |
90154574/cell_24 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
re... | code |
90154574/cell_14 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
... | code |
90154574/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
... | code |
90154574/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housedata/output.csv')
df.describe() | code |
18116806/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
scipy.stats.chisquare(... | code |
18116806/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.figure(figsize=(20, 10))
sns.countplot(x='a... | code |
18116806/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.describe(hosp.age) | code |
18116806/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes | code |
18116806/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
scipy.stats.chisquare(... | code |
18116806/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
hosp.head(5) | code |
18116806/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.info() | code |
18116806/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy as scipy
from scipy import stats
import os
print(os.listdir('../input')) | code |
18116806/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
hosp['AdmitDiagnosis'].unique().shape | code |
18116806/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
scipy.stats.chisquare(... | code |
18116806/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
hosp['age'].unique().shape | code |
18116806/cell_16 | [
"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 scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
scipy.stats.chisquare(... | code |
18116806/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.describe() | code |
18116806/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
plt.figure(figsize=(20... | code |
18116806/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age) | code |
18116806/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum() | code |
18116806/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape | code |
105210311/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.du... | code |
105210311/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls | code |
105210311/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.du... | code |
105210311/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.du... | code |
105210311/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.head() | code |
105210311/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.du... | code |
105210311/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.du... | code |
105210311/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
passengers.describe() | code |
105210311/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.du... | code |
105210311/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.info() | code |
105210311/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.du... | code |
105210311/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape | code |
105210311/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
da... | code |
105210311/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
da... | code |
105210311/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.du... | code |
105210311/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum() | code |
105210311/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
pl... | code |
105210311/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns | code |
72081346/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing impor... | code |
72081346/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test = pd.read_csv('../input/30-days-of-ml/train.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.shape | code |
72081346/cell_11 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days... | code |
72081346/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 |
72081346/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test = pd.read_csv('../input/30-days-of-ml/train.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submissi... | code |
72081346/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test = pd.read_csv('../input/30-days-of-ml/train.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train | code |
104123689/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
"\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n" | code |
104123689/cell_7 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import json
import matplotlib.pylab as plt
import rasterio
image = '/kaggle/input/hubmap-organ-segmentation/train_images/15329.tiff'
tiff = rasterio.open(image)
img = tiff.read()
boundry = '/kaggle/input/hubmap-organ-segmentation/train_annotations/15329.json'
with open(boundry) as json_file:
data = json.load(js... | code |
104123689/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import rasterio
image = '/kaggle/input/hubmap-organ-segmentation/train_images/15329.tiff'
tiff = rasterio.open(image)
img = tiff.read() | code |
128048094/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
impor... | code |
128048094/cell_33 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data = data.dropna()
data_encoded = pd.get_dummies(data, columns=['Property_Area'])
from sklearn.model_selection import trai... | code |
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