path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
129024934/cell_24 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_22 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_53 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_27 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_37 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(d)
ser1 = pd.Series([1, 2, 3, 4... | code |
129024934/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels) | code |
33095866/cell_13 | [
"text_html_output_2.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px # plotly express
lockdown_df = pd.read_csv(files['countryLockdowndates.csv'])
lockdown_df['LockDown Date'] = pd.to_datetime(lockdown_df['Date'], format='%d/%m/%Y')
lockdown_df.sort_v... | code |
33095866/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px # plotly express
lockdown_df = pd.read_csv(files['countryLockdowndates.csv'])
lockdown_df['LockDown Date'] = pd.to_datetime(lockdown_df['Date'], format='%d/%m/%Y')
lockdown_df.sort_values('LockDown Date', inplace=True)
... | code |
33095866/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lockdown_df = pd.read_csv(files['countryLockdowndates.csv'])
lockdown_df['LockDown Date'] = pd.to_datetime(lockdown_df['Date'], format='%d/%m/%Y')
lockdown_df.sort_values('LockDown Date', inplace=True)
df = pd.read_csv(files['time_series_covid_19_c... | code |
33095866/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import plotly.express as px
import pycountry
from geopy.geocoders import Nominatim
import os
file_input = ['/kaggle/input', '../../../datasets/extracts/']
files = {}
for dirname, _, filenames in os.walk(file_input[0]):
for filename in filenames:
files[filename] = os.pa... | code |
33095866/cell_8 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px # plotly express
lockdown_df = pd.read_csv(files['countryLockdowndates.csv'])
lockdown_df['LockDown Date'] = pd.to_datetime(lockdown_df['Date'], format='%d/%m/%Y')
lockdown_df.sort_values('LockDown Date', inplace=True)
... | code |
33095866/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px # plotly express
lockdown_df = pd.read_csv(files['countryLockdowndates.csv'])
lockdown_df['LockDown Date'] = pd.to_datetime(lockdown_df['Date'], format='%d/%m/%Y')
lockdown_df.sort_values('LockDown Date', inplace=True)
... | code |
50227879/cell_4 | [
"text_plain_output_1.png"
] | from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data() | code |
50227879/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
x = x_train[1]
plt.imshow(x, cmap='gray') | code |
50227879/cell_11 | [
"text_plain_output_1.png"
] | import keras
img_cols, img_rows = (28, 28)
input_shape = (img_cols, img_rows, 1)
batch_size = 128
num_classes = 10
epochs = 12
x_train = x_train.reshape(x_train.shape[0], img_cols, img_rows, 1)
x_test = x_test.reshape(x_test.shape[0], img_cols, img_rows, 1)
y_train = keras.utils.to_categorical(y_train, num_classes)
y_... | code |
50227879/cell_19 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPool2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
import keras
img_cols, img_rows = (28, 28)
input_shape = (img_cols, img_rows, 1)
batch_size = 128
num_classes = 10
epochs = 12
x_train = x_train.reshape(x_train.shape[0], img_cols, img_row... | code |
50227879/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 |
50227879/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
x = x_train[1]
x.shape | code |
50227879/cell_18 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPool2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
import keras
img_cols, img_rows = (28, 28)
input_shape = (img_cols, img_rows, 1)
batch_size = 128
num_classes = 10
epochs = 12
x_train = x_train.reshape(x_train.shape[0], img_cols, img_row... | code |
50227879/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | print('Train size : \n')
print(x_train.shape)
print(y_train.shape)
print('\n Test size : \n')
print(x_test.shape)
print(y_test.shape) | code |
50227879/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPool2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
import keras
img_cols, img_rows = (28, 28)
input_shape = (img_cols, img_rows, 1)
batch_size = 128
num_classes = 10
epochs = 12
x_train = x_train.reshape(x_train.shape[0], img_cols, img_row... | code |
33095778/cell_4 | [
"image_output_1.png"
] | import yfinance
raw_data = yfinance.download(tickers='^GSPC ^FTSE ^N225 ^GDAXI', start='1994-01-07', end='2019-09-01', interval='1d', group_by='ticker', auto_adjust=True, treads=True) | code |
33095778/cell_34 | [
"image_output_1.png"
] | from statsmodels.tsa.arima_model import ARIMA
import matplotlib.pyplot as plt
model_ar = ARIMA(df.ftse, order=(1, 0, 0))
results_ar = model_ar.fit()
start_date = '2014-07-16'
end_date = '2015-01-01'
model_ret_ar = ARIMA(df.ret_ftse[1:], order=(5, 0, 0))
results_ret_ar = model_ret_ar.fit()
df_pred_ret_ar = results_... | code |
33095778/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
start_date = '2014-07-16'
end_date = '2015-01-01'
df_pred.predictions[start_date:end_date].plot(figsize=(20, 5), color='red')
df_test.ftse[start_date:end_date].plot(color='blue')
plt.title('Predictions v/s Actuals', size=24)
plt.legend()
plt.show() | code |
33095778/cell_41 | [
"image_output_1.png"
] | from statsmodels.tsa.arima_model import ARIMA
import matplotlib.pyplot as plt
model_ar = ARIMA(df.ftse, order=(1, 0, 0))
results_ar = model_ar.fit()
start_date = '2014-07-16'
end_date = '2015-01-01'
model_ret_ar = ARIMA(df.ret_ftse[1:], order=(5, 0, 0))
results_ret_ar = model_ret_ar.fit()
df_pred_ret_ar = results_... | code |
33095778/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import scipy
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn
import statsmodels.graphics.tsaplots as sgt
import statsmodels.tsa.stattools as sts
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.statespace.sarimax ... | code |
33095778/cell_28 | [
"image_output_1.png"
] | from statsmodels.tsa.arima_model import ARIMA
import matplotlib.pyplot as plt
model_ar = ARIMA(df.ftse, order=(1, 0, 0))
results_ar = model_ar.fit()
start_date = '2014-07-16'
end_date = '2015-01-01'
model_ret_ar = ARIMA(df.ret_ftse[1:], order=(5, 0, 0))
results_ret_ar = model_ret_ar.fit()
df_pred_ret_ar = results_... | code |
33095778/cell_16 | [
"text_plain_output_1.png"
] | from statsmodels.tsa.arima_model import ARIMA
model_ar = ARIMA(df.ftse, order=(1, 0, 0))
results_ar = model_ar.fit()
df.tail() | code |
33095778/cell_38 | [
"image_output_1.png"
] | from statsmodels.tsa.arima_model import ARIMA
import matplotlib.pyplot as plt
model_ar = ARIMA(df.ftse, order=(1, 0, 0))
results_ar = model_ar.fit()
start_date = '2014-07-16'
end_date = '2015-01-01'
model_ret_ar = ARIMA(df.ret_ftse[1:], order=(5, 0, 0))
results_ret_ar = model_ret_ar.fit()
df_pred_ret_ar = results_... | code |
33095778/cell_43 | [
"image_output_1.png"
] | from statsmodels.tsa.arima_model import ARIMA
import matplotlib.pyplot as plt
model_ar = ARIMA(df.ftse, order=(1, 0, 0))
results_ar = model_ar.fit()
start_date = '2014-07-16'
end_date = '2015-01-01'
model_ret_ar = ARIMA(df.ret_ftse[1:], order=(5, 0, 0))
results_ret_ar = model_ret_ar.fit()
df_pred_ret_ar = results_... | code |
33095778/cell_36 | [
"image_output_1.png"
] | from statsmodels.tsa.arima_model import ARIMA
import matplotlib.pyplot as plt
model_ar = ARIMA(df.ftse, order=(1, 0, 0))
results_ar = model_ar.fit()
start_date = '2014-07-16'
end_date = '2015-01-01'
model_ret_ar = ARIMA(df.ret_ftse[1:], order=(5, 0, 0))
results_ret_ar = model_ret_ar.fit()
df_pred_ret_ar = results_... | code |
128029153/cell_13 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image, ImageDraw
from pycocotools.coco import COCO
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
import os
import os
import torch
import torch
import torchvision
import torchvi... | code |
128029153/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from pycocotools.coco import COCO
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
import os
import os
import torch
import torch
import torchvision
import torchvision
import torchvision.datasets ... | code |
128029153/cell_4 | [
"image_output_1.png"
] | import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
path2data = '/kaggle/input/levi9-hack9-2023/train'
path2json = '/kag... | code |
128029153/cell_6 | [
"text_plain_output_1.png"
] | import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
path2data = '/kaggle/input/levi9-hack9-2023/train'
path2json = '/kag... | code |
128029153/cell_2 | [
"text_plain_output_1.png"
] | !pip install pycocotools | code |
128029153/cell_11 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image, ImageDraw
from pycocotools.coco import COCO
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
import os
import os
import torch
import torch
import torchvision
import torchvi... | code |
128029153/cell_14 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image, ImageDraw
from pycocotools.coco import COCO
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
import os
import os
import torch
import torch
import torchvision
import torchvi... | code |
128029153/cell_10 | [
"text_plain_output_1.png"
] | from PIL import Image
from pycocotools.coco import COCO
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
import os
import os
import torch
import torch
import torchvision
import torchvision
import torchvision.datasets ... | code |
128029153/cell_12 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image, ImageDraw
from pycocotools.coco import COCO
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
import os
import os
import torch
import torch
import torchvision
import torchvi... | code |
128029153/cell_5 | [
"text_plain_output_1.png"
] | import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
path2data = '/kaggle/input/levi9-hack9-2023/train'
path2json = '/kag... | code |
16121288/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
reg... | code |
16121288/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/world-happiness-report-2019.csv')
df.isnull().sum()
corrmat=df.corr()
fig=plt.figure(figsize=(12,9))
sns.heatmap(corrmat,vmax=.8, square= True,annot=True)
plt.show... | code |
16121288/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 = pd.read_csv('../input/world-happiness-report-2019.csv')
df.describe(include='all') | code |
16121288/cell_20 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/world-happiness-report-2019.csv')
df.isnull().sum()
corrmat=d... | code |
16121288/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/world-happiness-report-2019.csv')
df.isnull().sum()
sns.pairplot(data=df) | code |
16121288/cell_11 | [
"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 seaborn as sns
df = pd.read_csv('../input/world-happiness-report-2019.csv')
df.isnull().sum()
corrmat=df.corr()
fig=plt.figure(figsize=(12,9))
sns.heatmap(corrmat,vmax=.8, square= True,annot=True)
plt.show... | code |
16121288/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
16121288/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
regressor.fit(X_train, y_train) | code |
16121288/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/world-happiness-report-2019.csv')
df.isnull().sum()
corrmat = df.corr()
fig = plt.figure(figsize=(12, 9))
sns.heatmap(corrmat, vmax=0.8, square=True, annot=True)
p... | code |
16121288/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-happiness-report-2019.csv')
df.head() | code |
16121288/cell_22 | [
"image_output_1.png"
] | from sklearn import metrics
from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = r... | code |
16121288/cell_10 | [
"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 seaborn as sns
df = pd.read_csv('../input/world-happiness-report-2019.csv')
df.isnull().sum()
corrmat=df.corr()
fig=plt.figure(figsize=(12,9))
sns.heatmap(corrmat,vmax=.8, square= True,annot=True)
plt.show... | code |
16121288/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/world-happiness-report-2019.csv')
df.isnull().sum()
corrmat=df.corr()
fig=plt.figure(figsize=(12,9))
sns.heatmap(corrmat,vmax=.8, square= True,annot=True)
plt.show... | code |
16121288/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('../input/world-happiness-report-2019.csv')
df.isnull().sum() | code |
18127116/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import DataFrame
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'ho... | code |
18127116/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from pandas import DataFrame
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0,... | code |
18127116/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas import DataFrame
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'horse_performance': [4, 4, 5, 5], 'avg_performance... | code |
18127116/cell_6 | [
"text_plain_output_1.png"
] | from pandas import DataFrame
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'horse_performance': [4, 4, 5, 5], 'avg_performance... | code |
18127116/cell_19 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from pandas import DataFrame
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'ho... | code |
18127116/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18127116/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from pandas import DataFrame
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'horse_performance': [4, 4, 5, 5], 'avg_performance... | code |
18127116/cell_18 | [
"image_output_1.png"
] | from pandas import DataFrame
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'ho... | code |
18127116/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from pandas import DataFrame
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'horse_performance': [4, 4, 5, 5], 'avg_performance... | code |
18127116/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import DataFrame
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, ... | code |
18127116/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import DataFrame
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'ho... | code |
18127116/cell_3 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from pandas import DataFrame
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'horse_performance': [4, 4, 5, 5], 'avg_performance... | code |
18127116/cell_17 | [
"image_output_1.png"
] | from pandas import DataFrame
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date'... | code |
18127116/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import DataFrame
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'ho... | code |
18127116/cell_10 | [
"text_html_output_1.png"
] | from pandas import DataFrame
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0,... | code |
18127116/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import DataFrame
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'ho... | code |
18127116/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from pandas import DataFrame
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'horse_performance': [4, 4, 5, 5], 'avg_performance... | code |
16147946/cell_3 | [
"text_plain_output_1.png"
] | !ls ../input | code |
16147946/cell_10 | [
"text_plain_output_1.png"
] | from pathlib import Path
import pandas as pd
import pandas as pd
from pathlib import Path
input_root_path = Path('../input')
sub = pd.read_csv(input_root_path.joinpath('sample_submission.csv'))
all_zeros = sub.copy()
all_zeros['y'] = 0
all_zeros.to_csv('baseline_probe_0.0.csv', index=False)
P_bp = -59.2822
idx_1_re... | code |
16147946/cell_12 | [
"text_plain_output_1.png"
] | from pathlib import Path
import pandas as pd
import pandas as pd
from pathlib import Path
input_root_path = Path('../input')
sub = pd.read_csv(input_root_path.joinpath('sample_submission.csv'))
all_zeros = sub.copy()
all_zeros['y'] = 0
all_zeros.to_csv('baseline_probe_0.0.csv', index=False)
P_bp = -59.2822
idx_1_re... | code |
128018068/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
d = pd.read_excel('/kaggle/input/rice-dataset-commeo-and-osmancik/Rice_Dataset_Commeo_and_Osmanc... | code |
128018068/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sb
d = pd.read_excel('/kaggle/input/rice-dataset-commeo-and-osmancik/Rice_Dataset_Commeo_and_Osmancik/Rice_Cammeo_Osmancik.xlsx')
d
sb.pairplot(d) | code |
128018068/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sb
d = pd.read_excel('/kaggle/input/rice-dataset-commeo-and-osmancik/Rice_Dataset_Commeo_and_Osmancik/Rice_Cammeo_Osmancik.xlsx')
d
sb.pairplot(d, hue='Class') | code |
128018068/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
d = pd.read_excel('/kaggle/input/rice-dataset-commeo-and-osmancik/Rice_Dataset_Commeo_and_Osmancik/Rice_Cammeo_Osmancik.xlsx')
d | code |
128018068/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
d = pd.read_excel('/kaggle/input/rice-dataset-commeo-and-osmancik/Rice_Dataset_Commeo_and_Osmancik/Rice_Cammeo_Osmancik.xlsx')
d
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
x_t... | code |
128018068/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
d = pd.read_excel('/kaggle/input/rice-dataset-commeo-and-osmancik/Rice_Dataset_Commeo_and_Osmancik/Rice_Cammeo_Osmancik.xlsx')
d
... | code |
33111788/cell_13 | [
"text_html_output_1.png"
] | from efficientnet_pytorch import EfficientNet
from pathlib import Path
from sklearn.metrics import accuracy_score, confusion_matrix, log_loss, roc_auc_score
from tqdm import tqdm
import numpy as np
import os
import pandas as pd
path_working_dir = Path().resolve()
path_input_nih = (path_working_dir / f'../input/d... | code |
33111788/cell_9 | [
"text_html_output_1.png"
] | from efficientnet_pytorch import EfficientNet
from pathlib import Path
from tqdm import tqdm
import numpy as np
import os
import pandas as pd
path_working_dir = Path().resolve()
path_input_nih = (path_working_dir / f'../input/data').resolve()
path_input_alias = (path_working_dir / f'./alias').resolve()
path_input... | code |
33111788/cell_11 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_4.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_13.png",
... | from efficientnet_pytorch import EfficientNet
from pathlib import Path
from sklearn.metrics import accuracy_score, confusion_matrix, log_loss, roc_auc_score
from tqdm import tqdm
import numpy as np
import os
import pandas as pd
path_working_dir = Path().resolve()
path_input_nih = (path_working_dir / f'../input/d... | code |
33111788/cell_1 | [
"text_plain_output_1.png"
] | !pip install efficientnet_pytorch | code |
33111788/cell_7 | [
"text_html_output_1.png"
] | from pathlib import Path
from tqdm import tqdm
import pandas as pd
path_working_dir = Path().resolve()
path_input_nih = (path_working_dir / f'../input/data').resolve()
path_input_alias = (path_working_dir / f'./alias').resolve()
path_input_model = (path_working_dir / f'../input/nih-chest-xrays-trained-models').resol... | code |
33111788/cell_15 | [
"text_html_output_1.png"
] | from efficientnet_pytorch import EfficientNet
from pathlib import Path
from sklearn.metrics import accuracy_score, confusion_matrix, log_loss, roc_auc_score
from tqdm import tqdm
import numpy as np
import os
import pandas as pd
path_working_dir = Path().resolve()
path_input_nih = (path_working_dir / f'../input/d... | code |
33111788/cell_16 | [
"text_html_output_1.png"
] | from efficientnet_pytorch import EfficientNet
from pathlib import Path
from sklearn.metrics import accuracy_score, confusion_matrix, log_loss, roc_auc_score
from tqdm import tqdm
import numpy as np
import os
import pandas as pd
path_working_dir = Path().resolve()
path_input_nih = (path_working_dir / f'../input/d... | code |
33111788/cell_14 | [
"text_html_output_1.png"
] | from efficientnet_pytorch import EfficientNet
from pathlib import Path
from sklearn.metrics import accuracy_score, confusion_matrix, log_loss, roc_auc_score
from tqdm import tqdm
import numpy as np
import os
import pandas as pd
path_working_dir = Path().resolve()
path_input_nih = (path_working_dir / f'../input/d... | code |
33111788/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from efficientnet_pytorch import EfficientNet
from pathlib import Path
from tqdm import tqdm
import numpy as np
import os
import pandas as pd
path_working_dir = Path().resolve()
path_input_nih = (path_working_dir / f'../input/data').resolve()
path_input_alias = (path_working_dir / f'./alias').resolve()
path_input... | code |
33111788/cell_12 | [
"text_html_output_1.png"
] | from efficientnet_pytorch import EfficientNet
from pathlib import Path
from sklearn.metrics import accuracy_score, confusion_matrix, log_loss, roc_auc_score
from tqdm import tqdm
import numpy as np
import os
import pandas as pd
path_working_dir = Path().resolve()
path_input_nih = (path_working_dir / f'../input/d... | code |
89135215/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = pd.read_csv('../input/h... | code |
89135215/cell_25 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = ... | code |
89135215/cell_23 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = ... | code |
89135215/cell_20 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = ... | code |
89135215/cell_2 | [
"image_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 |
89135215/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = pd.read_csv('../input/h... | code |
89135215/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = house_train.select_dtypes(exclude=['object'])
test = hous... | code |
89135215/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train = house_train.select_dtypes(exclude=['object'])
test = hous... | code |
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