path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
33118743/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from tqdm import tqdm
import json
import numpy as np # linear algebra
import os
train_path = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluation_path = '/kaggle/input/abstraction-and-reasoning-challenge/evaluation/'
test_path = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
same_sha... | code |
33118743/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import json
import numpy as np # linear algebra
import os
train_path = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluation_path = '/kaggle/input/abstraction-and-reasoning-challenge/evaluation/'
test_path = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
same_sha... | code |
33118743/cell_3 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import json
import numpy as np # linear algebra
import os
train_path = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluation_path = '/kaggle/input/abstraction-and-reasoning-challenge/evaluation/'
test_path = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
same_sha... | code |
33118743/cell_10 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import json
import numpy as np # linear algebra
import os
train_path = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluation_path = '/kaggle/input/abstraction-and-reasoning-challenge/evaluation/'
test_path = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
same_sha... | code |
88095138/cell_9 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.utils import shuffle
import pandas as pd
test = pd.read_csv('../input/tabular-playground-series-feb-2022/test.csv')
train = pd.read_csv('../input/tabular-playground-series-feb-2022/train.csv')
from sklearn.u... | code |
88095138/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pyradox_tabular.data import DataLoader
from pyradox_tabular.data_config import DataConfig
from pyradox_tabular.model_config import TabTransformerConfig
from pyradox_tabular.nn import TabTransformer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.pre... | code |
88095138/cell_23 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from pyradox_tabular.data import DataLoader
from pyradox_tabular.data_config import DataConfig
from pyradox_tabular.model_config import TabTransformerConfig
from pyradox_tabular.nn import TabTransformer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.pre... | code |
88095138/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pyradox_tabular.data import DataLoader
from pyradox_tabular.data_config import DataConfig
from pyradox_tabular.model_config import TabTransformerConfig
from pyradox_tabular.nn import TabTransformer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.pre... | code |
88095138/cell_1 | [
"text_plain_output_1.png"
] | !pip install pyradox-tabular -q
import pandas as pd
import numpy as np
import sklearn
from pyradox_tabular.data import DataLoader
from pyradox_tabular.data_config import DataConfig
from pyradox_tabular.model_config import TabTransformerConfig
from pyradox_tabular.nn import TabTransformer | code |
88095138/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import RobustScaler
from sklearn.utils import shuffle
import pandas as pd
test = pd.read_csv('../input/tabular-playground-series-feb-2022/test.csv')
train = pd.read_csv('../input/tabular-playgr... | code |
88095138/cell_16 | [
"text_plain_output_1.png"
] | from pyradox_tabular.data import DataLoader
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import RobustScaler
from sklearn.utils import shuffle
import pandas as pd
test = pd.read_csv('../input/tabular-playground-series-feb-2022/test.csv')... | code |
88095138/cell_24 | [
"text_plain_output_1.png"
] | from pyradox_tabular.data import DataLoader
from pyradox_tabular.data_config import DataConfig
from pyradox_tabular.model_config import TabTransformerConfig
from pyradox_tabular.nn import TabTransformer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.pre... | code |
106198657/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-dataset/diabetes.csv')
data
X = pd.DataFrame(data, columns=['Pregnancies', 'Glucose', 'BloodPressure', 'Skinthickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'])
y = data.Outco... | code |
106198657/cell_13 | [
"image_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-dataset/diabetes.csv')
data
X = pd.DataFrame(data, columns=['Pregnancies'... | code |
106198657/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
logreg = LogisticRegression(solver='liblinear')
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
y_pred_proba = logreg.predict_proba(X_test)[:,... | code |
106198657/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-dataset/diabetes.csv')
data
X = pd.DataFrame(data, columns=['Pregnancies', 'Glucose', 'BloodPressure', 'Skinthickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'])
y = data.Outco... | code |
106198657/cell_26 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-dataset/diabetes.csv')
data
X = pd.DataFrame(data, columns=['Pregnancies', 'Glucose', 'BloodPressure', 'Skinthickness', 'Insulin', 'BMI... | code |
106198657/cell_11 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
logreg = LogisticRegression(solver='liblinear')
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
y_pred_proba = logreg.predict_proba(X_test)[:,... | code |
106198657/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-dataset/diabetes.csv')
data
X = pd.DataFrame(data, columns=['Pregnancies', 'Glucose', 'BloodPressure', 'Skinthickness', 'Insulin', 'BMI... | code |
106198657/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 |
106198657/cell_7 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression(solver='liblinear')
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)
print('Accuracy:', metrics.accuracy_score(y_test, y_pred)) | code |
106198657/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-datas... | code |
106198657/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
logreg = LogisticRegression(solver='liblinear')
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
plt.plot(fpr, tpr, label='data 1')
plt.legend(l... | code |
106198657/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-dataset/diabetes.csv')
data
X = pd.DataFrame(data, columns=['Pregnancies'... | code |
106198657/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-datas... | code |
106198657/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-dataset/diabetes.csv')
data | code |
106198657/cell_17 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-datas... | code |
106198657/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-dataset/diabetes.csv')
data
X = pd.DataFrame(data, columns=['Pregnancies', 'Glucose', 'BloodPressure', 'Skinthickness', 'Insulin', 'BMI... | code |
106198657/cell_14 | [
"image_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes-dataset/diabetes.csv')
data
X = pd.DataFrame(data, columns=['Pregnancies'... | code |
106198657/cell_10 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
logreg = LogisticRegression(solver='liblinear')
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
y_pred_proba = logreg.predict_proba(X_test)[:,... | code |
106198657/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
logreg = LogisticRegression(solver='liblinear')
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
y_pred_proba = logreg.predict_proba(X_test)[:,... | code |
1008193/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
trn = pd.read_json(open('../input/train.json', 'r'))
tst = pd.read_json(open('../input/test.json', 'r'))
trn.head() | code |
1008193/cell_1 | [
"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 |
1008193/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
trn = pd.read_json(open('../input/train.json', 'r'))
tst = pd.read_json(open('../input/test.json', 'r'))
print('Train set: ', trn.shape)
print('Test set: ', tst.shape) | code |
17111364/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdat... | code |
17111364/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped... | code |
18155947/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
def one_hot_encoder(df, nan_as_category=True):
original_columns = list(df.columns)
categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
df = pd.get_dummies(df, columns=categorical_columns, dummy_na=nan_as_category)
new_columns = [c for c... | code |
18155947/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
def one_hot_encoder(df, nan_as_category=True):
original_columns = list(df.columns)
categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
df = pd.get_dummies(df, columns=categorical_columns, dummy_na=nan_as_category)
new_columns = [c for c... | code |
18155947/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
def one_hot_encoder(df, nan_as_category=True):
original_columns = list(df.columns)
categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
df = pd.get_dummies(df, columns=categorical_columns, dummy_na=nan_as_category)
new_columns = [c for c... | code |
18155947/cell_12 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
def one_hot_encoder(df, nan_as_category=True):
original_columns = list(df.columns)
categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
df = pd.get_dummies(df, columns=categorical_columns, dummy_na=nan_as_category)
new_columns = [c for c... | code |
128009699/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
file_path = '/kaggle/input/twitter-suicidal-data/twitter-suicidal_data.txt'
d = {'tweet': [], 'intention': []}
column_names = []
file = open(file_path)
for f in file:
content = f.split(',')
if conten... | code |
128009699/cell_4 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from datasets import load_dataset
dataset_hugging = load_dataset('dannyvas23/notas_suicidios') | code |
128009699/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
file_path = '/kaggle/input/twitter-suicidal-data/twitter-suicidal_data.txt'
d = {'tweet': [], 'intention': []}
column_names = []
file = open(file_path)
for f in file:
content = f.split(',')
if conten... | code |
2038426/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.Id, test.columns[test.isnull().any()].tolist())))... | code |
2038426/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist()))) | code |
2038426/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.I... | code |
2038426/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.Id, test.columns[test.isnull().any()].tolist())))... | code |
2038426/cell_41 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.Id, test.columns[test.isnull().any()].tolist())))... | code |
2038426/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.Id, test.columns[test.isnull().any()].tolist()))) | code |
2038426/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.Id, test.columns[test.isnull().any()].tolist())))... | code |
2038426/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns))) | code |
2038426/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.I... | code |
2038426/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.Id, test.columns[test.isnull().any()].tolist())))... | code |
2038426/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.Id, test.columns[test.isnull().any()].tolist())))... | code |
2038426/cell_43 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.Id, test.columns[test.isnull().any()].tolist())))... | code |
2038426/cell_37 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
np.array(list(zip(train.Id, train.columns[train.isnull().any()].tolist())))
np.array(list(zip(train.Id, test.columns[test.isnull().any()].tolist())))... | code |
2038426/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
72086968/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/nlp-getting-started/train.csv')
test_df = pd.read_csv('../input/nlp-getting-started/test.csv')
train_df.head(5) | code |
72086968/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/nlp-getting-started/train.csv')
test_df = pd.read_csv('../input/nlp-getting-started/test.csv')
sub_df = pd.read_csv('../input/nlp-getting-started/sample_submission.csv')
sub_df.head() | code |
72086968/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import feature_extraction
from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import cross_val_score
import pandas as pd
train_df = pd.read_csv('../input/nlp-getting-started/train.csv')
test_df = pd.read_csv('../input/nlp-getting-started/test.csv')
count_vectorizer = feature_... | code |
72086968/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/nlp-getting-started/train.csv')
test_df = pd.read_csv('../input/nlp-getting-started/test.csv')
train_df[train_df['target'] == 1].text.values[1] | code |
72086968/cell_11 | [
"text_html_output_1.png"
] | from sklearn import feature_extraction
import pandas as pd
train_df = pd.read_csv('../input/nlp-getting-started/train.csv')
test_df = pd.read_csv('../input/nlp-getting-started/test.csv')
count_vectorizer = feature_extraction.text.CountVectorizer()
example_train_vectors = count_vectorizer.fit_transform(train_df['text... | code |
72086968/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/nlp-getting-started/train.csv')
test_df = pd.read_csv('../input/nlp-getting-started/test.csv')
train_df[train_df['target'] == 0].text.values[1] | code |
72086968/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import feature_extraction
from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import cross_val_score
import pandas as pd
train_df = pd.read_csv('../input/nlp-getting-started/train.csv')
test_df = pd.read_csv('../input/nlp-getting-started/test.csv')
count_vectorizer = feature_... | code |
72086968/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score
help(cross_val_score) | code |
90155596/cell_5 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
path = '../input/fzxdata2/imgs/longan2berry/TestB/3.jpg'
def img_read(path):
"""读取一张图片"""
img = cv2.imread(path)
img = img[..., ::-1]
img = cv2.resize(img, (256, 256), interpolation=cv2.INTER_LINEAR)
return im... | code |
129026803/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample_submission_data = pd.read_csv('/kaggle/... | code |
129026803/cell_9 | [
"application_vnd.jupyter.stderr_output_766.png",
"application_vnd.jupyter.stderr_output_116.png",
"application_vnd.jupyter.stderr_output_74.png",
"application_vnd.jupyter.stderr_output_268.png",
"application_vnd.jupyter.stderr_output_145.png",
"application_vnd.jupyter.stderr_output_362.png",
"applicatio... | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample_submission_data = pd.read_csv('/kaggle/... | code |
129026803/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample_submission_data = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
t... | code |
129026803/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample_submission_data = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
t... | code |
129026803/cell_2 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns | code |
129026803/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/... | code |
129026803/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 |
129026803/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample_submission_data = pd.read_csv('/kaggle/input/playground-series... | code |
129026803/cell_17 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/... | code |
129026803/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample_submission_data = pd.read_csv('/kaggle/... | code |
129026803/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split, GridSearchCV
import matplotlib.pyp... | code |
129026803/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample_submission_data = pd.read_csv('/kaggle/... | code |
104117646/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a
a | code |
104117646/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a
b = np.array([1, 2, 3, 4.5])
b
c = np.arange(10)
c
d = np.arange(10, 20)
d
e = np.arange(10, 20, 2)
e | code |
104117646/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a
b = np.array([1, 2, 3, 4.5])
b
c = np.arange(10)
c
d = np.arange(10, 20)
d
e = np.arange(10, 20, 2)
e
f = np.linspace(1, 10, 10)
f | code |
104117646/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a
b = np.array([1, 2, 3, 4.5])
b
c = np.arange(10)
c | code |
104117646/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a
b = np.array([1, 2, 3, 4.5])
b
c = np.arange(10)
c
d = np.arange(10, 20)
d | code |
104117646/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a
a.ndim
a.shape | code |
104117646/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a | code |
104117646/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a
a.ndim | code |
104117646/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a
b = np.array([1, 2, 3, 4.5])
b | code |
17115081/cell_21 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.info() | code |
17115081/cell_34 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().sum()
test.isnull().sum()
train_df = train.drop(['Cabin', 'Ticket'], axis=1)
test_df = test.drop(['Cabin'... | code |
17115081/cell_23 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().sum() | code |
17115081/cell_30 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().sum()
test.isnull().sum()
train_df = train.drop(['Cabin', 'Ticket'], axis=1)
test_df = test.drop(['Cabin'... | code |
17115081/cell_33 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().sum()
test.isnull().sum()
train_df = train.drop(['Cabin', 'Ticket'], axis=1)
test_df = test.drop(['Cabin'... | code |
17115081/cell_20 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.info() | code |
17115081/cell_29 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().sum()
test.isnull().sum()
train_df = train.drop(['Cabin', 'Ticket'], axis=1)
test_df = test.drop(['Cabin'... | code |
17115081/cell_26 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().sum()
train.describe(include='all') | code |
17115081/cell_18 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head(10) | code |
17115081/cell_24 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.isnull().sum() | code |
17115081/cell_27 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
import os
os.listdir('../input')
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.isnull().sum()
test.describe(include='all') | code |
18124779/cell_9 | [
"image_output_1.png"
] | from pandas import DataFrame
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0,... | code |
18124779/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import DataFrame
import pandas as pd
from pandas import DataFrame
performance = {'id': [1, 2, 3, 4], 'date': ['19/12/2018', '20/12/2018', '21/12/2018', '22/12/2018'], 'time': [45, 50, 90, 50], 'km': [6.0, 5.5, 6.0, 4.0], 'rider_performance': [3, 4, 4, 4], 'horse_performance': [4, 4, 5, 5], 'avg_performance... | code |
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