path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90124932/cell_4 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_img_csv = '../input/mura-dataset/MURA-v1.1/train_image_paths.csv'
train_images_paths = pd.read_csv(os.path.join(train_img_csv),... | code |
90124932/cell_6 | [
"text_plain_output_1.png"
] | from keras.models import Sequential,Model,load_model,Input
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import pandas as pd
import os
from glob import g... | code |
90124932/cell_1 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import pandas as pd
import os
from glob import glob
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
from keras.models import Model
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
fr... | code |
90124932/cell_7 | [
"text_plain_output_1.png"
] | from keras.models import Sequential,Model,load_model,Input
from keras_preprocessing.image import ImageDataGenerator
import math
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
im... | code |
90124932/cell_8 | [
"text_plain_output_1.png"
] | from keras.models import Sequential,Model,load_model,Input
from keras_preprocessing.image import ImageDataGenerator
import math
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # showing and rendering figures
import os
import os
import pandas as pd
import pandas a... | code |
90124932/cell_14 | [
"text_plain_output_1.png"
] | from keras.models import Sequential,Model,load_model,Input
from keras_preprocessing.image import ImageDataGenerator
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorfl... | code |
90124932/cell_12 | [
"text_plain_output_1.png"
] | from keras.models import Sequential,Model,load_model,Input
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import pandas as pd
import os
from glob import g... | code |
90124932/cell_5 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from keras_preprocessing.image import ImageDataGenerator
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_img_csv = '../input/mura-dataset/MURA-v1.1/train_image_paths.csv'
tra... | code |
72068023/cell_42 | [
"text_plain_output_1.png"
] | from numba import njit, jit, cuda, guvectorize
from scipy.signal import butter, filtfilt, sosfiltfilt
from scipy.signal import cwt, ricker
from scipy.signal import spectrogram
import glob
import librosa
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import torch
import torc... | code |
72068023/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from numba import njit, jit, cuda, guvectorize
from scipy.signal import butter, filtfilt, sosfiltfilt
from scipy.signal import spectrogram
import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import torch
import glob
import pathlib
head = pathlib.Path('../input/g2net-gr... | code |
72068023/cell_25 | [
"image_output_1.png"
] | t.min() | code |
72068023/cell_4 | [
"text_plain_output_1.png"
] | import fastai
import fastai
import torch
fastai.__version__ | code |
72068023/cell_33 | [
"text_plain_output_1.png"
] | import gc
import gc
gc.collect() | code |
72068023/cell_40 | [
"text_plain_output_1.png"
] | from numba import njit, jit, cuda, guvectorize
from scipy.signal import butter, filtfilt, sosfiltfilt
from scipy.signal import cwt, ricker
from scipy.signal import spectrogram
import glob
import librosa
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import torch
import torc... | code |
72068023/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.signal import butter, filtfilt, sosfiltfilt
import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import torch
import glob
import pathlib
head = pathlib.Path('../input/g2net-gravitational-wave-detection')
train_files = sorted(glob.glob('../input/g2net-gravitatio... | code |
72068023/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.signal import butter, filtfilt, sosfiltfilt
import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import torch
import glob
import pathlib
head = pathlib.Path('../input/g2net-gravitational-wave-detection')
train_files = sorted(glob.glob('../input/g2net-gravitatio... | code |
72068023/cell_24 | [
"image_output_1.png"
] | from numba import njit, jit, cuda, guvectorize
from scipy.signal import butter, filtfilt, sosfiltfilt
from scipy.signal import spectrogram
import glob
import librosa
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import torch
import glob
import pathlib
head = pathlib.Path('.... | code |
72068023/cell_10 | [
"text_plain_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import glob
import pathlib
head = pathlib.Path('../input/g2net-gravitational-wave-detection')
train_files = sorted(glob.glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*.npy'))
wave = np.load(train_files[0]... | code |
72068023/cell_37 | [
"text_plain_output_1.png"
] | from numba import njit, jit, cuda, guvectorize
from scipy.signal import butter, filtfilt, sosfiltfilt
from scipy.signal import cwt, ricker
from scipy.signal import spectrogram
import glob
import librosa
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import torch
import torc... | code |
72068023/cell_36 | [
"text_plain_output_1.png"
] | import glob
import numpy as np # linear algebra
import pathlib
import glob
import pathlib
head = pathlib.Path('../input/g2net-gravitational-wave-detection')
train_files = sorted(glob.glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*.npy'))
wave = np.load(train_files[0])
wave.shape | code |
90123330/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
Nomes = 'Masculino Feminino'.split()
Med = [1500, 1500]
import matplotlib.pyplot as plt
Nomes = 'Masculino Feminino'.split()
Med = [1500, 1500]
plt.pie(Med, labels=Nomes)
plt.show() | code |
90123330/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
Nomes = 'Masculino Feminino'.split()
Med = [1500, 1500]
plt.pie(Med, labels=Nomes)
plt.show() | code |
74054195/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
model_lr_smt = LogisticRegression(solver='liblinear')
model_lr_smt.fit(X_train, y_train) | code |
74054195/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)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
fraud.Amount.describe() | code |
74054195/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
legit_sample = legit.sample(n=492)
new_df = pd.concat([legit_sa... | code |
74054195/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
import numpy as np
import numpy as np # linear algebra
import seaborn as sns
model_lr = LogisticRegression(max_iter=120, random_state=0, n_jobs=20, ... | code |
74054195/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
model_lr = LogisticRegression(max_iter=120, random_state=0, n_jobs=20, solver='liblinear')
model_lr.fit(X_train, y_train)
trn_lr_pred = model_lr.pred... | code |
74054195/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
legit_sample = legit.sample(n=492)
new_df = pd.concat([legit_sa... | code |
74054195/cell_6 | [
"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)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.info() | code |
74054195/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
i... | code |
74054195/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
pipeline = Pipeline([('model', LogisticRegression(solver='liblinear'))])
pipeline.fit(X_oversampled, y_oversampled) | code |
74054195/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
model_lr = LogisticRegression(max_iter=120, random_state=0, n_jobs=20, solver='liblinear')
model_lr.fit(X_train, y_train) | code |
74054195/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)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
print(legit.shape)
print(fraud.shape) | code |
74054195/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
legit_sample = legit.sample(n=492)
new_df = pd.concat([legit_sa... | code |
74054195/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 |
74054195/cell_7 | [
"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)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum() | code |
74054195/cell_45 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
i... | code |
74054195/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
legit_sample = legit.sample(n=492)
new_df = pd.concat([legit_sa... | code |
74054195/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
model_lr = LogisticRegression(max_iter=120, random_state=0, n_jobs=20, solver='liblinear')
model_lr.fit(X_train, y_train)
trn_lr_pred = model_lr.pred... | code |
74054195/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
model_lr = LogisticRegression(max_iter=120, random_state=0, n_jobs=20, solver='liblinear')
model_lr.fit(X_train, y_train)
trn_lr_pred = model_lr.pred... | code |
74054195/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
df['Class'].value_counts() | code |
74054195/cell_47 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
model_lr = LogisticRegression(max_iter=120, random_state=0, n_jobs=20, solver='liblinear')
model_lr.fit(X_train, y_train)
trn_lr_pred = model_lr.pred... | code |
74054195/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
legit_sample = legit.sample(n=492)
new_df = pd.concat([legit_sa... | code |
74054195/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
df.groupby('Class').mean()
df.shape | code |
74054195/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
model_lr = LogisticRegression(max_iter=120, random_state=0, n_jobs=20, solver='liblinear')
model_lr.fit(X_train, y_train)
trn_lr_pred = model_lr.pred... | code |
74054195/cell_31 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
import matplotlib.pyplot as plt
model_lr = LogisticRegression(max_iter=120, random_state=0, n_jobs=20, solver='liblinear')
model_lr.fit(X_train, y_tr... | code |
74054195/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
import matplotlib.pyplot as plt
model_lr = LogisticRegression(max_iter=120, random_state=0, n_jobs=20, solver='liblinear')
model_lr.fit(X_train, y_tr... | code |
74054195/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)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
df.groupby('Class').mean() | code |
74054195/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
legit_sample = legit.sample(n=492)
new_df = pd.concat([legit_sa... | code |
74054195/cell_27 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, plot_roc_curve, classification_report
model_lr = LogisticRegression(max_iter=120, random_state=0, n_jobs=20, solver='liblinear')
model_lr.fit(X_train, y_train)
trn_lr_pred = model_lr.pred... | code |
74054195/cell_37 | [
"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)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
df.groupby('Class').mean()
df.shape
X1 = df.drop(columns='Clas... | code |
74054195/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.isnull().sum()
legit = df[df.Class == 0]
fraud = df[df.Class == 1]
legit.Amount.describe() | code |
74054195/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)
main_df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df = main_df.copy()
df.head() | code |
106202730/cell_4 | [
"image_output_1.png"
] | import igraph as ig
import igraph as ig
import igraph as ig
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import igraph as ig
import matplotlib.pyplot as plt
g = ig.Graph.Famous('Zachary')
# Compute cliques
cliques = g.cliques(4, 4)
# Plot each clique highlight... | code |
106202730/cell_2 | [
"image_output_1.png"
] | import igraph as ig
import matplotlib.pyplot as plt
import igraph as ig
import matplotlib.pyplot as plt
g = ig.Graph.Famous('Zachary')
cliques = g.cliques(4, 4)
fig, axs = plt.subplots(3, 4, figsize=(20, 10))
axs = axs.ravel()
for clique, ax in zip(cliques, axs):
ig.plot(ig.VertexCover(g, [clique]), mark_groups=T... | code |
106202730/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 |
106202730/cell_8 | [
"image_output_1.png"
] | import igraph as ig
import igraph as ig
import igraph as ig
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import igraph as ig
import matplotlib.pyplot as plt
g = ig.Graph.Famous('Zachary')
# Compute cliques
cliques = g.cliques(4, 4)
# Plot each clique highlight... | code |
106202730/cell_3 | [
"image_output_1.png"
] | import igraph as ig
import igraph as ig
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import igraph as ig
import matplotlib.pyplot as plt
g = ig.Graph.Famous('Zachary')
# Compute cliques
cliques = g.cliques(4, 4)
# Plot each clique highlighted in a separate axes
fig, axs = plt.subplots(3, 4, fi... | code |
106202730/cell_5 | [
"text_plain_output_1.png"
] | import igraph as ig
import igraph as ig
import igraph as ig
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import igraph as ig
import matplotlib.pyplot as plt
g = ig.Graph.Famous('Zachary')
# Compute cliques
cliques = g.cliques(4, 4)
# Plot each clique highlight... | code |
50211763/cell_9 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo,Selamat Datang'
sapa = 'Hallo, Selamat Datang'
print(sapa[1:20]) | code |
50211763/cell_4 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
print(sapa[0]) | code |
50211763/cell_20 | [
"text_plain_output_1.png"
] | string = 'Qbert'
add_string = '!!!'
a = string.replace('b', '*')
print(a + str(add_string)) | code |
50211763/cell_6 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
print(sapa[-3:]) | code |
50211763/cell_2 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
print('Jumlah total karakter dalam string :', len(sapa)) | code |
50211763/cell_11 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo,Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
pr... | code |
50211763/cell_7 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
print(sapa[::-1]) | code |
50211763/cell_18 | [
"text_plain_output_1.png"
] | def vowelcheck():
l = input('Enter a word : ')
if 'a' in l:
return 'Your word contains a vowel'
if 'e' in l:
return 'Your word contains a vowel'
if 'i' in l:
return 'Your word contains a vowel'
if 'o' in l:
return 'Your word contains a vowel'
if 'u' in l:
... | code |
50211763/cell_8 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo,Selamat Datang'
print(sapa[7]) | code |
50211763/cell_16 | [
"text_plain_output_1.png"
] | str1 = input('Please Enter your Own String : ')
total = 1
for i in range(len(str1)):
if str1[i] == ' ' or str1 == '\n' or str1 == '\t':
total = total + 1
class py_solution:
def is_valid_parenthese(self, str1):
stack, pchar = ([], {'(': ')', '{': '}', '[': ']'})
for parenthese in str1:
... | code |
50211763/cell_3 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
print(sapa * 10) | code |
50211763/cell_14 | [
"text_plain_output_1.png"
] | str1 = input('Please Enter your Own String : ')
total = 1
for i in range(len(str1)):
if str1[i] == ' ' or str1 == '\n' or str1 == '\t':
total = total + 1
print('Total Number of Words in this String = ', total) | code |
50211763/cell_10 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo,Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
print(sapa.upper()) | code |
50211763/cell_12 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo,Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
s... | code |
50211763/cell_5 | [
"text_plain_output_1.png"
] | sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
sapa = 'Hallo, Selamat Datang'
print(sapa[:3]) | code |
105193863/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers, models, Input
from tensorflow.keras import layers,models
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from tensorflow.keras.models import Model
import matplotlib.pyplot as... | code |
105193863/cell_9 | [
"image_output_1.png"
] | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
import numpy as np
import matplotlib.pyplot as plt
import os, PIL, pathlib
data_dir = '../input/kafeidou/49-data'
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*.png')))
batch_size = 32
img_he... | code |
105193863/cell_23 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers, models, Input
from tensorflow.keras import layers,models
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from tensorflow.keras.models import Model
import matplotlib.pyplot as... | code |
105193863/cell_19 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers,models
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
import numpy as np
import matplotlib.pyplot as plt
import os, PIL, pathlib... | code |
105193863/cell_7 | [
"text_plain_output_1.png"
] | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
import numpy as np
import matplotlib.pyplot as plt
import os, PIL, pathlib
data_dir = '../input/kafeidou/49-data'
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*.png')))
batch_size = 32
img_he... | code |
105193863/cell_18 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras import layers,models
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
import numpy as np
import matplotlib.pyplot as plt
import os, PIL, pathlib... | code |
105193863/cell_8 | [
"text_plain_output_1.png"
] | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
import numpy as np
import matplotlib.pyplot as plt
import os, PIL, pathlib
data_dir = '../input/kafeidou/49-data'
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*.png')))
batch_size = 32
img_he... | code |
105193863/cell_16 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
import numpy as np
import matplotlib.pyplot as plt
import os, PIL, pathlib
data_dir = '../input/kafeidou/49-data'
data... | code |
105193863/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from IPython.display import clear_output
!pip install -q tensorflow==2.4.1
clear_output()
import tensorflow as tf
gpus = tf.config.list_physical_devices("GPU")
if gpus:
tf.config.experimental.set_memory_growth(gpus[0], True) #设置GPU显存用量按需使用
tf.config.set_visible_devices([gpus[0]],"GPU") | code |
105193863/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from tensorflow.keras import layers, models, Input
from tensorflow.keras import layers,models
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from tensorflow.keras.models import Model
import matplotlib.pyplot as... | code |
105193863/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
import numpy as np
import matplotlib.pyplot as plt
import os, PIL, pathlib
data_dir = '../input/kafeidou/49-data'
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/... | code |
105193863/cell_5 | [
"image_output_1.png"
] | from tensorflow import keras
from tensorflow.keras import layers, models
import numpy as np
import matplotlib.pyplot as plt
import os, PIL, pathlib
data_dir = '../input/kafeidou/49-data'
data_dir = pathlib.Path(data_dir)
image_count = len(list(data_dir.glob('*/*.png')))
print('图片总数为：', image_count) | code |
88075725/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
omicron_data = pd.read_csv('/kaggle/input/omicron-covid19-variant-daily-cases/covid-variants.csv')
omicron_data = pd.DataFrame(omicron_data)
omicron_data.loc[(omicron_data['location'] == 'Thailand') & (omicron_data['variant'] == 'Omicron')] | code |
88075725/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
omicron_data = pd.read_csv('/kaggle/input/omicron-covid19-variant-daily-cases/covid-variants.csv')
omicron_data = pd.DataFrame(omicron_data)
omicron_data.loc[(omicron_data['location'] == 'Thailand') & (omicron_data['variant'] == 'Omicron')]
omic... | code |
88075725/cell_3 | [
"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 |
88075725/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
omicron_data = pd.read_csv('/kaggle/input/omicron-covid19-variant-daily-cases/covid-variants.csv')
omicron_data = pd.DataFrame(omicron_data)
omicron_data.head() | code |
33096616/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import os
base = '/kaggle/input/alaska2-image-steganalysis/'
id = '{:05d}'.format(20)
cover_path = os.path.join(base, 'Cover', id + '.jpg')
img = plt.imread(cover_path)
from PIL import Image
def genData(data):
newd = []
for i in data:
newd.append... | code |
33096616/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
base = '/kaggle/input/alaska2-image-steganalysis/'
id = '{:05d}'.format(20)
cover_path = os.path.join(base, 'Cover', id + '.jpg')
img = plt.imread(cover_path)
cover_path | code |
33096616/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
base = '/kaggle/input/alaska2-image-steganalysis/'
id = '{:05d}'.format(20)
cover_path = os.path.join(base, 'Cover', id + '.jpg')
img = plt.imread(cover_path)
plt.imshow(img) | code |
90123428/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.dtypes
train_data.isnull().sum()
test_data.isnull().sum()
train_data.fillna(train_data.Age.mean(), inplace=True)
t... | code |
90123428/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.dtypes
train_data.isnull().sum()
test_data.isnull().sum()
train_data.fillna(train_data.Age.mean(), inplace=True)
t... | code |
90123428/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data.isnull().sum() | code |
90123428/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data.head() | code |
90123428/cell_23 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
Ks = 100
mean_acc = np.zeros(Ks - 1)
std_acc = np.zeros(Ks - 1)
for n in range(1, Ks):
neigh = KNeighborsClassifier(n_neighbors=n).fit(x_train, y_train)
yhat = neigh.predict(x_test)
mean_acc... | code |
90123428/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
parameters = {'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random'], 'max_depth': [2 * n for n in range(1, 10)], 'max_features': ['auto', 'sqrt'], 'min_sampl... | code |
90123428/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data['Age'].describe() | code |
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