path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122252043/cell_8 | [
"text_plain_output_1.png"
] | 41215 | code |
122252043/cell_80 | [
"text_plain_output_1.png"
] | a = 12670
b = 12.344
print(f'a={a:>07,d},b={b:>+6.0f}') | code |
122252043/cell_15 | [
"text_plain_output_1.png"
] | 73 | code |
122252043/cell_16 | [
"text_plain_output_1.png"
] | 217366 | code |
122252043/cell_38 | [
"text_plain_output_1.png"
] | x = 5
x += 2
x = 12
y = 8
x ^ y | code |
122252043/cell_47 | [
"text_plain_output_1.png"
] | True + (not 'piggy') | code |
122252043/cell_66 | [
"text_plain_output_1.png"
] | int('0b1001001', base=2) | code |
122252043/cell_35 | [
"text_plain_output_1.png"
] | x = 5
x += 2
x = 12
y = 8
x & y | code |
122252043/cell_77 | [
"text_plain_output_1.png"
] | a = 12670
b = 12.344
print(f'a={a:>7d},b={b:>6.2f}') | code |
122252043/cell_43 | [
"text_plain_output_1.png"
] | x = 12
y = 8
y << 2 | code |
122252043/cell_24 | [
"text_plain_output_1.png"
] | 128 % 39 | code |
122252043/cell_14 | [
"text_plain_output_1.png"
] | 202 | code |
122252043/cell_22 | [
"text_plain_output_1.png"
] | 2 ** 120 | code |
122252043/cell_53 | [
"text_plain_output_1.png"
] | eval('66+18') | code |
122252043/cell_10 | [
"text_plain_output_1.png"
] | 11325731 | code |
122252043/cell_37 | [
"text_plain_output_1.png"
] | x = 5
x += 2
x = 12
y = 8
~x | code |
122252043/cell_12 | [
"text_plain_output_1.png"
] | 492 | code |
122252043/cell_71 | [
"text_plain_output_1.png"
] | print("'Holliday'") | code |
122252043/cell_5 | [
"text_plain_output_1.png"
] | type(6000.0) | code |
122252043/cell_36 | [
"text_plain_output_1.png"
] | x = 5
x += 2
x = 12
y = 8
x | y | code |
327702/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import svm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/pitching.csv')
df
df_sg = df[df.gs == df.g]
Y = df_sg.w / df_sg.gs
Y_class = np.floor(Y)
clf = svm.SVC()
clf.fit(X, Y_class) | code |
327702/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
327702/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | X | code |
327702/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/pitching.csv')
df | code |
32071198/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from functools import partial
from joblib import Parallel, delayed
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import VotingRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics import confusion_matrix, cohen_kappa_score
from... | code |
32071198/cell_6 | [
"text_plain_output_1.png"
] | from functools import partial
from joblib import Parallel, delayed
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.metrics import cohen_kappa_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder, FunctionTra... | code |
32071198/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, FunctionTransformer
from joblib import Parallel, delayed
import multiprocessing
... | code |
129032013/cell_4 | [
"image_output_1.png"
] | import json
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from skl... | code |
129032013/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
dir = '/kaggle/input/the-movies-dataset/'
ratings = pd.read_csv(dir + 'ratings_small... | code |
129032013/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
dir = '/kaggle/input/the-movies-dataset/'
ratings = pd.read_csv(dir + 'ratings_small... | code |
129032013/cell_7 | [
"image_output_1.png"
] | import json
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from s... | code |
129032013/cell_8 | [
"image_output_1.png"
] | import json
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from s... | code |
129032013/cell_5 | [
"image_output_1.png"
] | import json
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from s... | code |
49126539/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.shape
null_in_train_csv = df.isnull().sum()
null_in_train_csv = null_in_train_csv[nu... | code |
49126539/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.shape
null_in_train_csv = df.isnull().sum()
null_in_train_csv = null_in_train_csv[nu... | code |
49126539/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.shape
null_in_train_csv = df.isnull().sum()
null_in_train_csv = null_in_train_csv[nu... | code |
49126539/cell_2 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.info()
df.shape | code |
49126539/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.shape
null_in_train_csv = df.isnull().sum()
null_in_train_csv = null_in_train_csv[nu... | code |
49126539/cell_1 | [
"text_plain_output_1.png"
] | 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))
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import stats
from scipy.stats impo... | code |
49126539/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.shape
null_in_train_csv = df.isnull().sum()
null_in_train_csv = null_in_train_csv[nu... | code |
49126539/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.shape
null_in_train_csv = df.isnull().sum()
null_in_train_csv = null_in_train_csv[nu... | code |
49126539/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)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.shape
null_in_train_csv = df.isnull().sum()
null_in_train_csv = null_in_train_csv[null_in_train_csv > 0]
null_in_train_csv.sort_values(inpla... | code |
49126539/cell_10 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.shape
null_in_train_csv = df.isnull().sum()
null_in_train_csv = null_in_train_csv[nu... | code |
333996/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
fm = pd.read_csv('../input/ForumMessages.csv')
fm.info() | code |
333996/cell_7 | [
"image_output_1.png"
] | from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import warnings
import warnings
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
fm = pd.read_csv('../input/ForumMessages.csv')
impor... | code |
333996/cell_5 | [
"image_output_1.png"
] | from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import warnings
import warnings
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
fm = pd.read_csv('../input/ForumMessages.csv')
impor... | code |
105194092/cell_21 | [
"text_plain_output_1.png"
] | import gc
import numpy as np
import pandas as pd
import pickle
def correlation_score(y_true, y_pred):
"""Scores the predictions according to the competition rules.
It is assumed that the predictions are not constant.
Returns the average of each sample's Pearson correlation coefficient"""
if type(y_... | code |
105194092/cell_13 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | eval_ids = pd.read_parquet('../input/multimodal-single-cell-as-sparse-matrix/evaluation.parquet')
eval_ids.cell_id = eval_ids.cell_id.astype(pd.CategoricalDtype())
eval_ids.gene_id = eval_ids.gene_id.astype(pd.CategoricalDtype()) | code |
105194092/cell_29 | [
"text_plain_output_1.png"
] | import gc
import numpy as np
import pandas as pd
import pickle
def correlation_score(y_true, y_pred):
"""Scores the predictions according to the competition rules.
It is assumed that the predictions are not constant.
Returns the average of each sample's Pearson correlation coefficient"""
if type(y_... | code |
105194092/cell_11 | [
"text_plain_output_1.png"
] | import gc
import numpy as np
import pandas as pd
import pickle
def correlation_score(y_true, y_pred):
"""Scores the predictions according to the competition rules.
It is assumed that the predictions are not constant.
Returns the average of each sample's Pearson correlation coefficient"""
if type(y_... | code |
105194092/cell_7 | [
"text_plain_output_1.png"
] | multi_test_x = scipy.sparse.load_npz('../input/multimodal-single-cell-as-sparse-matrix/test_multi_inputs_values.sparse.npz')
multi_test_x = pca.transform(multi_test_x) | code |
105194092/cell_32 | [
"text_plain_output_1.png"
] | !head submission.csv | code |
105194092/cell_28 | [
"text_plain_output_1.png"
] | import gc
import numpy as np
import pandas as pd
import pickle
def correlation_score(y_true, y_pred):
"""Scores the predictions according to the competition rules.
It is assumed that the predictions are not constant.
Returns the average of each sample's Pearson correlation coefficient"""
if type(y_... | code |
105194092/cell_8 | [
"text_plain_output_1.png"
] | import gc
n = 1
test_len = multi_test_x.shape[0]
d = test_len // n
x = []
for i in range(n):
x.append(multi_test_x[i * d:i * d + d])
del multi_test_x
gc.collect() | code |
105194092/cell_16 | [
"text_plain_output_1.png"
] | y_columns = np.load('../input/multimodal-single-cell-as-sparse-matrix/train_multi_targets_idxcol.npz', allow_pickle=True)['columns']
test_index = np.load('../input/multimodal-single-cell-as-sparse-matrix/test_multi_inputs_idxcol.npz', allow_pickle=True)['index'] | code |
105194092/cell_31 | [
"text_plain_output_1.png"
] | !head submission.csv | code |
105194092/cell_14 | [
"text_plain_output_1.png"
] | import gc
import numpy as np
import pandas as pd
import pickle
def correlation_score(y_true, y_pred):
"""Scores the predictions according to the competition rules.
It is assumed that the predictions are not constant.
Returns the average of each sample's Pearson correlation coefficient"""
if type(y_... | code |
105194092/cell_22 | [
"text_plain_output_1.png"
] | import gc
import numpy as np
import pandas as pd
import pickle
def correlation_score(y_true, y_pred):
"""Scores the predictions according to the competition rules.
It is assumed that the predictions are not constant.
Returns the average of each sample's Pearson correlation coefficient"""
if type(y_... | code |
105194092/cell_10 | [
"text_plain_output_1.png"
] | import gc
import numpy as np
import pandas as pd
import pickle
def correlation_score(y_true, y_pred):
"""Scores the predictions according to the competition rules.
It is assumed that the predictions are not constant.
Returns the average of each sample's Pearson correlation coefficient"""
if type(y_... | code |
105194092/cell_27 | [
"text_plain_output_1.png"
] | import gc
import numpy as np
import pandas as pd
import pickle
def correlation_score(y_true, y_pred):
"""Scores the predictions according to the competition rules.
It is assumed that the predictions are not constant.
Returns the average of each sample's Pearson correlation coefficient"""
if type(y_... | code |
105212890/cell_4 | [
"text_plain_output_1.png"
] | n = 3
my_list = [2, 3, 4, 5, 6, 7, 8]
my_new_list = my_list[:n + 1]
print(my_new_list) | code |
105212890/cell_6 | [
"text_plain_output_1.png"
] | n = 3
my_list = [2, 3, 4, 5, 6, 7, 8]
my_new_list = my_list[:n + 1]
my_list = ['haha', 'one', 'two', 'serious']
first_item = my_list[0]
last_item = my_list[-1]
my_result = first_item == last_item
print(my_result) | code |
105212890/cell_2 | [
"text_plain_output_1.png"
] | number_1 = 60
number_2 = 50
my_product = number_1 * number_2
if my_product > 1000:
print('Product:', my_product)
else:
my_sum = number_1 + number_2
print('Sum:', my_sum) | code |
105212890/cell_8 | [
"text_plain_output_1.png"
] | sampleDict = {'class': {'student': {'name': 'Mike', 'marks': {'physics': 70, 'history': 80}}}}
my_print = sampleDict['class']['student']['marks']['history']
print(my_print) | code |
105212890/cell_10 | [
"text_plain_output_1.png"
] | sample_dict = {'emp1': {'name': 'Javi', 'salary': 7500}, 'emp2': {'name': 'Laura', 'salary': 8000}, 'emp3': {'name': 'Dimitris', 'salary': 500}}
sample_dict['emp2']['salary'] = 0
print(sample_dict) | code |
105212890/cell_12 | [
"text_plain_output_1.png"
] | set1 = {20, 50, 4, 88, 15, 3}
set2 = {20, 40, 50, 15}
update = set1.intersection(set2)
set1 = update
print(set1) | code |
89142931/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from torch import nn
from torchvision.datasets import ImageFolder
from torchvision.models import resnet18
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from tqdm.auto import tqdm
import numpy as np
import sys
import torch
from torchvision.datasets import ImageFolder
from torchvision.tr... | code |
89142931/cell_4 | [
"text_plain_output_56.png",
"text_plain_output_35.png",
"text_plain_output_43.png",
"text_plain_output_37.png",
"text_plain_output_5.png",
"text_plain_output_48.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_44.png",
"text_plain_output... | from torch import nn
from torchvision.models import resnet18
from torchvision.models import resnet18
model = resnet18(pretrained=True)
for param in model.parameters():
param.requires_grad = False
model.fc = nn.Linear(512, 6) | code |
89142931/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from torch import nn
from torchvision.datasets import ImageFolder
from torchvision.models import resnet18
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from tqdm.auto import tqdm
import matplotlib.pyplot as plt
import numpy as np
import sys
import torch
from torchvision.datasets impor... | code |
72086322/cell_21 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df_gene = pd.read_csv('../input/colorectal-cancer-patients/crc_ge.txt', sep='\t')
df_gene = df_gene.transpose()
col_names = df_gene.iloc[0].tolist()
df_gene.columns = col_names
df_gene = df_gene.drop(axis=0, index='ID_R... | code |
72086322/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df = df.drop('Unnamed: 9', axis=1)
df = df.drop('Unnamed: 10', axis=1)
df = df.drop('Unnamed: 11', axis=1)
df = df.drop(index=62, axis=0)
df
features_num = ['Age (in years)', 'DFS (in mon... | code |
72086322/cell_25 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df = df.drop('Unnamed: 9', axis=1)
df = df.drop('Unnamed: 10', axis=1)
df = df.drop('Unnamed: 11', axis=1)
df = df.drop(index=62, axis=0)
df
features_num = ['Age (i... | code |
72086322/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df = df.drop('Unnamed: 9', axis=1)
df = df.drop('Unnamed: 10', axis=1)
df = df.drop('Unnamed: 11', axis=1)
df = df.drop(index=62, axis=0)
df | code |
72086322/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df = df.drop('Unnamed: 9', axis=1)
df = df.drop('Unnamed: 10', axis=1)
df = df.drop('Unnamed: 11', axis=1)
df = df.drop(index=62, axis=0)
df
features_num = ['Age (i... | code |
72086322/cell_18 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df_gene = pd.read_csv('../input/colorectal-cancer-patients/crc_ge.txt', sep='\t')
df_gene = df_gene.transpose()
df_gene.head() | code |
72086322/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df = df.drop('Unnamed: 9', axis=1)
df = df.drop('Unnamed: 10', axis=1)
df = df.drop('Unnamed: 11', axis=1)
df = df.drop(index=62, axis=0)
df
features_num = ['Age (i... | code |
72086322/cell_8 | [
"image_output_11.png",
"text_plain_output_35.png",
"image_output_24.png",
"text_plain_output_37.png",
"image_output_25.png",
"text_plain_output_5.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"image_output_17.png",
"image_output_30.png",
"text_plain_output_9.png",
"image_output... | import pandas as pd
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df = df.drop('Unnamed: 9', axis=1)
df = df.drop('Unnamed: 10', axis=1)
df = df.drop('Unnamed: 11', axis=1)
df = df.drop(index=62, axis=0)
df
features_num = ['Age (in years)', 'DFS (in months)']
df[features_num].describe... | code |
72086322/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df_gene = pd.read_csv('../input/colorectal-cancer-patients/crc_ge.txt', sep='\t')
df_gene.head() | code |
72086322/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df | code |
72086322/cell_24 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df_gene = pd.read_csv('../input/colorectal-cancer-patients/crc_ge.txt', sep='\t')
df_gene = df_gene.transpose()
col_names = df_gene.iloc[0].tolist()
df_gene.columns = col_names
df_gene = df_gene.drop(axis=0, index='ID_R... | code |
72086322/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df = df.drop('Unnamed: 9', axis=1)
df = df.drop('Unnamed: 10', axis=1)
df = df.drop('Unnamed: 11', axis=1)
df = df.drop(index=62, axis=0)
df
features_num = ['Age (i... | code |
72086322/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df = df.drop('Unnamed: 9', axis=1)
df = df.drop('Unnamed: 10', axis=1)
df = df.drop('Unnamed: 11', axis=1)
df = df.drop(index=62, axis=0)
df
features_num = ['Age (i... | code |
72086322/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/colorectal-cancer-patients/crc.txt', sep='\t')
df
df = df.drop('Unnamed: 9', axis=1)
df = df.drop('Unnamed: 10', axis=1)
df = df.drop('Unnamed: 11', axis=1)
df = df.drop(index=62, axis=0)
df
df.info() | code |
16146582/cell_6 | [
"image_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
import numpy as np
import pandas as pd
train_csv_df = pd.read_csv('../input/train.csv')
test_csv_df = pd.read_csv('../input/test.csv')
y_train = np.array(train_csv_df['label'])
X_train = np.array(train_csv_df.drop('label', 1))
X_test = np.array(test... | code |
16146582/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from keras.models import Sequential
from keras.layers import Dense | code |
16146582/cell_3 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
train_csv_df = pd.read_csv('../input/train.csv')
test_csv_df = pd.read_csv('../input/test.csv')
y_train = np.array(train_csv_df['label'])
X_train = np.array(train_csv_df.drop('label', 1))
X_test = np.array(test_csv_df)
count = 0
for index in range(0, 6):
plt.subplot(2, 3, c... | code |
16146582/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
import numpy as np
import pandas as pd
train_csv_df = pd.read_csv('../input/train.csv')
test_csv_df = pd.read_csv('../input/test.csv')
y_train = np.array(train_csv_df['label'])
X_train = np.array(train_csv_df.drop('label', 1))
X_test = np.array(test... | code |
90102656/cell_7 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
face_model = cv2.CascadeClassifier('../input/haarcascades/haarcascade_frontalface_default.xml')
img = cv2.imread('../input/face-mask-detection/images/maksssksksss244.png')
img = cv2.cvtColor(img, cv2.IMREAD_GRAYSCALE)
faces = face_model.detectMultiScale(img, scaleFactor=1.1, minNeighbors=4)
out_img = cv2.c... | code |
90102656/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
train_dir = '../input/face-mask-12k-images-dataset/Face Mask Dataset/Train'
test_dir = '../input/face-mask-12k-images-dataset/Face Mask Dataset/Test'
val_dir = '../input/face-mask-12k-images-dataset/Face Mask Dataset/Validation'
train_datagen = ImageDataGenerat... | code |
90102656/cell_3 | [
"text_plain_output_1.png"
] | import os
print(os.listdir('../input'))
print(os.listdir('../working')) | code |
90102656/cell_17 | [
"image_output_1.png"
] | from keras import Sequential
from keras.applications.vgg19 import VGG19
from keras.layers import Flatten, Dense
vgg19 = VGG19(weights='imagenet', include_top=False, input_shape=(128, 128, 3))
for layer in vgg19.layers:
layer.trainable = False
model = Sequential()
model.add(vgg19)
model.add(Flatten())
model.add(D... | code |
90102656/cell_10 | [
"text_plain_output_1.png"
] | from scipy.spatial import distance
import cv2
face_model = cv2.CascadeClassifier('../input/haarcascades/haarcascade_frontalface_default.xml')
img = cv2.imread('../input/face-mask-detection/images/maksssksksss244.png')
img = cv2.cvtColor(img, cv2.IMREAD_GRAYSCALE)
faces = face_model.detectMultiScale(img, scaleFactor=... | code |
89127402/cell_21 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_13 | [
"text_html_output_1.png"
] | import json
import json
import plotly.graph_objs as go
import urllib.request
def read_geojson(url):
with urllib.request.urlopen(url) as url:
jdata = json.loads(url.read().decode())
return jdata
ireland_url = 'https://gist.githubusercontent.com/pnewall/9a122c05ba2865c3a58f15008548fbbd/raw/5bb4f84d918b8... | code |
89127402/cell_25 | [
"text_html_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_23 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_20 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_26 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
89127402/cell_2 | [
"text_plain_output_1.png"
] | !pip install pmdarima | code |
89127402/cell_19 | [
"text_html_output_1.png"
] | from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import pandas as pd
from dateutil.relativedelta import relativedelta
# rolling averages and std
def rolling_stat(timeseries, window_size):
# Determing rolling statistics
rolmean =... | code |
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