path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
104126055/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import GaussianNB
classifier3 = GaussianNB()
classifier3.fit(X_train, Y_train)
Y_pred3 = classifier3.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_pred3) * 100, '%') | code |
104126055/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
classifier2 = KNeighborsClassifier(n_neighbors=3, metric='minkowski', p=1)
classifier2.fit(X_train, Y_train)
Y_pred2 = classifier2.predict(X_test)
print('Accuracy = ', accur... | code |
104126055/cell_33 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
from sklearn.metrics import accuracy_score
from catboost import CatBoostClassifier
classifier_cb = CatBoostClassifier()
classifier_cb.fit(X_train, Y_train)
Y_predcb = classifier_cb.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_predcb) * 100, '%') | code |
104126055/cell_29 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
classifier4 = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier4.fit(X_train, Y_train)
Y_pred4 = classifi... | code |
104126055/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset['Cholesterol'].unique() | code |
104126055/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 |
104126055/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset.info() | code |
104126055/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)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset.describe() | code |
104126055/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
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)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset... | code |
104126055/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from xgboost import XGBClassifier
from xgboost import XGBClassifier
classifier_xg = XGBClassifier()
classifier_xg.fit(X_train, Y_train)
Y_predxg = classifier_xg.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_predxg) * 100, '%') | code |
104126055/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset['BP'].unique() | code |
104126055/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
classifier4 = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier4.fit(X_train, Y_train)
Y_pred4 = classifier4.predict(X_test)
print('Accuracy = ', accuracy_sco... | code |
104126055/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset['Drug'].unique() | code |
104126055/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)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset.head() | code |
32073217/cell_6 | [
"image_output_2.png",
"image_output_1.png"
] | from fipy import Variable, FaceVariable, CellVariable, Grid1D, ExplicitDiffusionTerm, TransientTerm, DiffusionTerm, Viewer
from fipy.tools import numerix
nx = 50
Lx = 1.0
dx = Lx / nx
mesh = Grid1D(Lx=Lx, dx=dx)
x = mesh.cellCenters[0]
T = CellVariable(name='solution variable', mesh=mesh, value=0.0)
T.setValue(0)
a =... | code |
32073217/cell_1 | [
"text_plain_output_1.png"
] | !pip install ht;
!pip install future;
!pip install fipy
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from fipy import Variable, FaceVariable, CellVariable, Grid1D, ExplicitDiffusionTerm, TransientTerm, DiffusionTerm, Viewer
from fipy.tools import numerix
fro... | code |
32073217/cell_10 | [
"image_output_2.png",
"image_output_1.png"
] | from fipy import Variable, FaceVariable, CellVariable, Grid1D, ExplicitDiffusionTerm, TransientTerm, DiffusionTerm, Viewer
from fipy.tools import numerix
nx = 50
Lx = 1.0
dx = Lx / nx
mesh = Grid1D(Lx=Lx, dx=dx)
x = mesh.cellCenters[0]
T = CellVariable(name='solution variable', mesh=mesh, value=0.0)
T.setValue(0)
a =... | code |
106207999/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_... | code |
106207999/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_... | code |
106207999/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index... | code |
106207999/cell_33 | [
"text_plain_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree... | code |
106207999/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
no_stroke = df['stroke'].value_counts()[0] / len(df['stroke']) * 100
had_stroke = df['stroke'].value_count... | code |
106207999/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df.info() | code |
106207999/cell_29 | [
"image_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_leve... | code |
106207999/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index... | code |
106207999/cell_41 | [
"text_plain_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifi... | code |
106207999/cell_2 | [
"image_output_1.png"
] | import os
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106207999/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index... | code |
106207999/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.info() | code |
106207999/cell_45 | [
"text_plain_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross... | code |
106207999/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_leve... | code |
106207999/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.describe(include='all') | code |
106207999/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df.head() | code |
106207999/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum() | code |
106207999/cell_35 | [
"text_html_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from... | code |
106207999/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifi... | code |
106207999/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index... | code |
106207999/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
print(df['stroke'].value_counts())
no_stroke = df['stroke'].value_counts()[0] / len(df['stroke']) * 100
ha... | code |
106207999/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_... | code |
106207999/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_... | code |
106207999/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_... | code |
106207999/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df.describe(include='all') | code |
106207999/cell_36 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from... | code |
34133512/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import torch
if torch.cuda.is_available():
device = torch.device('cuda')
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device('cpu') | code |
34133512/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.multiclass import OneVsRestClassifier
import pandas as pd
try:
df_train = pd.read... | code |
34133512/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm.notebook import tqdm_notebook
import pandas as pd
import numpy as np
import random
import torch
import os
from sklearn.metrics import f1_score
from sklearn.multiclass import OneVsRestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.linear_model i... | code |
34133512/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from spacy import displacy
from spacy.util import compounding, minibatch
import pandas as pd
import random
import re
import spacy
try:
df_train = pd.read_csv('data/train.csv')
d... | code |
34133512/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from spacy.util import compounding, minibatch
import pandas as pd
import random
import re
import spacy
try:
df_train = pd.read_csv('data/train.csv')
df_test = pd.read_csv('data/t... | code |
34133512/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from spacy.util import compounding, minibatch
import pandas as pd
import random
import re
import spacy
try:
df_train = pd.read_csv('data/train.csv')
df_test = pd.read_csv('data/t... | code |
73069445/cell_2 | [
"text_plain_output_1.png"
] | import wandb
import wandb
print(wandb.__version__) | code |
73069445/cell_1 | [
"text_plain_output_1.png"
] | !pip install wandb --upgrade | code |
73069445/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from kaggle_secrets import UserSecretsClient
import wandb
import wandb
try:
from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()
secret_value_0 = user_secrets.get_secret('wandb_api')
wandb.login(key=secret_value_0)
except:
print('Go to Add-ons -> Secrets and provide you... | code |
122251563/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympi... | code |
122251563/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')... | code |
122251563/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
df = pd.merge(df1, df2, left_on='NOC', right_on='NOC')
df = df.query('... | code |
122251563/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
df = pd.merge(df1, df2, left_on='NOC', right_on='NOC')
df = df.query('... | code |
122251563/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input... | code |
122251563/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
df = pd.merge(df1, df2, left_on='NOC', right_on='NOC')
df = df.query('... | code |
18120119/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist'] | code |
18120119/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
with open(example2_path, 'w') as file2:
file2.write('This is line A')
with open(example2_path, 'w') as file2:
file2.write('This is line A\n')
file2.write('This is line B\n')
file2.write('This is line C\n') | code |
18120119/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close() | code |
18120119/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | !wget -O ../data/Example1.txt https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/example1.txt | code |
18120119/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
df.head() | code |
18120119/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
with open(example2_path, 'w') as file2:
file2.write('This is line A')
with open(example2_path, 'w') as file2:
file2.write('This is line A\n')
file2.write('This is line B\n')
file2.write('This is line C\n')
lines = ['This is line D\n', 'This is line E\n', 'This i... | code |
18120119/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url) | code |
18120119/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
... | code |
18120119/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
with open(example2_path, 'w') as file2:
file2.write('This is line A')
with open(example2_path, 'r') as file2:
print(file2.read()) | code |
18120119/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
print(f'file1 object = {file1}')
print(f'Type of file1 object = {type(file1)}') | code |
18120119/cell_40 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.head() | code |
18120119/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path) | code |
18120119/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
example3_path = '../data/Example3.txt'
with open(example2_path, 'r') as readfile:
with open(example3_path, 'w') as writefile:
for line in readfile:
writefile.write(line) | code |
18120119/cell_48 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
... | code |
18120119/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0] | code |
18120119/cell_54 | [
"application_vnd.jupyter.stderr_output_1.png"
] | with open('../data/new_songs.csv', 'r') as songsfile:
print(songsfile.read()) | code |
18120119/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
with open(example2_path, 'w') as file2:
file2.write('This is line A') | code |
18120119/cell_50 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
... | code |
18120119/cell_52 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
... | code |
18120119/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name | code |
18120119/cell_45 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
... | code |
18120119/cell_49 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
... | code |
18120119/cell_51 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
... | code |
18120119/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example3_path = '../data/Example3.txt'
testfile.name | code |
18120119/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode | code |
18120119/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close()
file1.closed
with open(example1_path, 'r') as file1:
file_contents = file1.read()
with open(example1_path, 'r') as file1:
file_contents = file1.readlines()
with open(example1_path, 'r') as file1:
... | code |
18120119/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close()
file1.closed
with open(example1_path, 'r') as file1:
file_contents = file1.read()
with open(example1_path, 'r') as file1:
file_contents = file1.readlines()
with open(example1_path, 'r') as file1:
... | code |
18120119/cell_47 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
... | code |
18120119/cell_3 | [
"text_plain_output_1.png"
] | # Check current working dirctory
!pwd | code |
18120119/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist'] | code |
18120119/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
with open(example2_path, 'w') as file2:
file2.write('This is line A')
with open(example2_path, 'w') as file2:
file2.write('This is line A\n')
file2.write('This is line B\n')
file2.write('This is line C\n')
lines = ['This is line D\n', 'This is line E\n', 'This i... | code |
18120119/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close()
file1.closed
with open(example1_path, 'r') as file1:
file_contents = file1.read()
with open(example1_path, 'r') as file1:
file_contents = file1.readlines()
print(f'file_contents \n{file_content... | code |
18120119/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | lines = ['This is line D\n', 'This is line E\n', 'This is line F\n']
lines | code |
18120119/cell_53 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
... | code |
18120119/cell_10 | [
"text_plain_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close()
file1.closed | code |
18120119/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example3_path = '../data/Example3.txt'
with open(example3_path, 'r') as testfile:
print(testfile.read()) | code |
18120119/cell_37 | [
"application_vnd.jupyter.stderr_output_1.png"
] | !wget -O ./data/TopSellingAlbums.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv | code |
18120119/cell_12 | [
"text_plain_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close()
file1.closed
with open(example1_path, 'r') as file1:
file_contents = file1.read()
print(f'file_contents \n{file_contents}')
print(file1.closed)
print(f'file_contents \n{file_contents}') | code |
128043237/cell_9 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
from torch.utils.data import random_split, DataLoader, Dataset
from torchvision.io import read_image, ImageReadMode
import matplotlib.pyplot as plt
import os
import torchvision.transforms.functional as TF
BATCH_SIZE = 16
IMAGE_SIZE = (256, 256)
IN_CHANNELS = 3
LEARNING_RATE = 0.0001
IMAGES_D... | code |
128043237/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from torch import nn
from torch.utils.data import random_split, DataLoader, Dataset
from torchvision.io import read_image, ImageReadMode
import matplotlib.pyplot as plt
import os
import pytorch_lightning as pl
import torch
import torchvision.transforms as T
import torchvision.transforms.f... | code |
128043237/cell_22 | [
"text_plain_output_1.png"
] | from PIL import Image
from torch.utils.data import random_split, DataLoader, Dataset
from torchvision.io import read_image, ImageReadMode
import matplotlib.pyplot as plt
import os
import torchvision.transforms.functional as TF
BATCH_SIZE = 16
IMAGE_SIZE = (256, 256)
IN_CHANNELS = 3
LEARNING_RATE = 0.0001
IMAGES_D... | code |
128043237/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import os
BATCH_SIZE = 16
IMAGE_SIZE = (256, 256)
IN_CHANNELS = 3
LEARNING_RATE = 0.0001
IMAGES_DIR = '/kaggle/input/danish-golf-courses-orthophotos/1. orthophotos/'
SEGMASKS_DIR = '/kaggle/input/danish-golf-courses-orthophotos/2. segmentation masks/'
LABELMASKS_... | code |
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