path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18153807/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
object_list = train.select_dtypes(include=['object']).columns
display(train[object_list].sample(10).T)
for f in object_list:
print('Unique in column ', f, ' is -... | code |
18153807/cell_6 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
object_list = train.select_dtypes(include=['object']).columns
float_list = train.select_dtypes(include=['float64']).c... | code |
18153807/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import warnings
warnings.filterwarnings('ignore') | code |
18153807/cell_8 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
object_list = train.select_dtypes(include=['object']).columns
float_list = train.select_dtypes(include=['float64']).c... | code |
130011087/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
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df = coffee_code_df.dropna(how='any')
sns.scatterplot(data=cof... | code |
130011087/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df = coffee_code_df.dropna(how='any')
sns.countplot(data=coffe... | code |
130011087/cell_19 | [
"image_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
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df = coffee_code_df.dropna(how='any')
plt.xticks(rotation=45)
... | code |
130011087/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.... | code |
130011087/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df.describe()
coffee_code_df.info() | code |
130011087/cell_15 | [
"image_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
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df = coffee_code_df.dropna(how='any')
sns.countplot(data=coffe... | code |
130011087/cell_17 | [
"image_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
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df = coffee_code_df.dropna(how='any')
plt.xticks(rotation=45)
... | code |
130011087/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df.head(5) | code |
18142262/cell_4 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import re
import nltk
import spacy
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
df['text_lower'] = df['text'].str.lower()
df.head() | code |
18142262/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import re
import nltk
import spacy
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
full_df.head() | code |
33120214/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 |
33120214/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from datetime import timedelta
from matplotlib.dates import WeekdayLocator, DateFormatter
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
covid19 = pd.read_csv('/kaggle/input/hospital-resources-during-covid19-pandemic/Hospitalization_all_lo... | code |
34117774/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
img_rows, img_co... | code |
34117774/cell_26 | [
"text_plain_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
labels = ['zero'... | code |
34117774/cell_11 | [
"text_plain_output_1.png"
] | import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
y_train = tf.keras.utils.to_categorical(y_tra... | code |
34117774/cell_19 | [
"text_plain_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
img_rows, img_co... | code |
34117774/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
labels = ['zero'... | code |
34117774/cell_28 | [
"image_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import matplotlib.pyplot as plt
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config... | code |
34117774/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | print('X_train set shape of {}'.format(X_train.shape))
print('X_test set shape of {}'.format(X_test.shape))
print('y_train set shape of {}'.format(y_train.shape))
print('y_test set shape of {}'.format(y_test.shape)) | code |
34117774/cell_14 | [
"text_plain_output_1.png"
] | import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape((-1, img_rows, img_... | code |
34117774/cell_10 | [
"text_plain_output_1.png"
] | img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape((-1, img_rows, img_cols, channels))
X_test = X_test.reshape((-1, img_rows, img_cols, channels))
print('X_train set shape of {}'.format(X_train.shape))
print('X_test set shape of {}'.forma... | code |
34117774/cell_27 | [
"text_plain_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import matplotlib.pyplot as plt
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config... | code |
121148913/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/behaviors.tsv', sep='\t', names=['impressionId', 'userId', 'timestamp', 'click_history', 'impressions'])
print(f'The dataset consist of {len(raw_behaviour)} number of inte... | code |
121148913/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from collections import Counter
from torch.utils.data import Dataset, DataLoader
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
raw_behaviour = pd.read_cs... | code |
121148913/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/behaviors.tsv', sep='\t', names=['impressionId', 'userId', 'timestamp', 'click_history', 'impressions'])
news = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_tra... | code |
121148913/cell_15 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/behaviors.tsv', sep='\t', names=['impressionId', 'userId', 'timestamp', 'click_history', 'impressions'])
news = pd.read_csv('/kaggle/... | code |
121148913/cell_22 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-datase... | code |
121148913/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/behaviors.tsv', sep='\t', names=['impressionId', 'userId', 'timestamp', 'click_history', 'impressions'])
news = pd.read_csv('/kaggle/... | code |
49124155/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
target = train['Survived']
m = pd.DataFrame(test['PassengerId'])
print('Shape of train:', train.shape)
print('Shape of test:', test.shape) | code |
49124155/cell_2 | [
"text_plain_output_1.png"
] | test | code |
49124155/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 |
49124155/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
target = train['Survived']
m = pd.DataFrame(test['PassengerId'])
import seaborn as sns
import matplotlib.pyplot as plt
dataset = pd.concat(... | code |
49124155/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
target = train['Survived']
m = pd.DataFrame(test['PassengerId'])
import seaborn as... | code |
49124155/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
target = train['Survived']
m = pd.DataFrame(test['PassengerId'])
import seaborn as sns
import matplotlib.pyplot as plt
dataset = pd.concat(... | code |
72082831/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
useful_features = [c for c in df_train.co... | code |
72082831/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-... | code |
72082831/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
sample_submission.head() | code |
90133716/cell_13 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_... | code |
90133716/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], nor... | code |
90133716/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
colormap = plt.cm.get_cmap('Greens')
fig, ax = plt.subplots(figsize=(12, 3))
plot = ax.pcolor(sales_salary.T, cmap=colormap, edgecolor='black')
ax.set_xlabel('sales')
ax.set_xticks(np.arange(len(sales_salary.index.values)) + 0.5)
ax.set_xticklabels(sales_salary.index.v... | code |
90133716/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = ... | code |
90133716/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
df.head() | code |
90133716/cell_11 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], n... | code |
90133716/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sal... | code |
90133716/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as p... | code |
90133716/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = ... | code |
90133716/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd
import pandas as pd... | code |
90133716/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], n... | code |
90133716/cell_12 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sale... | code |
90133716/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = ... | code |
33100747/cell_6 | [
"image_output_1.png"
] | from dateutil.relativedelta import relativedelta
from keras.layers.core import Dense
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing... | code |
33100747/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/hourly-energy-consumption/AEP_hourly.csv')
print(data.columns)
print(data.head) | code |
33100747/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 |
33100747/cell_7 | [
"image_output_1.png"
] | from dateutil.relativedelta import relativedelta
from keras.layers.core import Dense
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import datetime
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd ... | code |
33100747/cell_3 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from sklearn.preprocessing import MinMaxScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/hourly-energy-consumption/AEP_hourly.csv')
from sklearn.preprocessing import M... | code |
33100747/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from dateutil.relativedelta import relativedelta
from keras.layers.core import Dense
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)... | code |
2013148/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tflearn
df = pd.read_csv('../input/train.csv')
X = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
X = X[columns]
for i in columns:
X = X[~X[i].isnull(... | code |
2013148/cell_11 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import Imputer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tflearn
df = pd.read_csv('../input/train.csv')
X = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
X = X[columns... | code |
2013148/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import tflearn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2013148/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import Imputer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tflearn
df = pd.read_csv('../input/train.csv')
X = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
X = X[columns... | code |
2013148/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import Imputer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tflearn
df = pd.read_csv('../input/train.csv')
X = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
X = X[columns... | code |
2013148/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import Imputer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tflearn
df = pd.read_csv('../input/train.csv')
X = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
X = X[columns... | code |
130020137/cell_13 | [
"text_plain_output_1.png"
] | from collections import deque
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
print('Jumlah Antrian : ', antrian)
antrian.append(6)
print('Nasabah ke ', 6)
print('Jumlah Antrian :', antrian)
antrian.append(7)
print('Nasabah ke ', 7)
print('Jumlah Antrian :', antrian)
out = antrian.popleft()
print('Nasab... | code |
130020137/cell_4 | [
"text_plain_output_1.png"
] | batubata = [1, 2, 3, 4, 5]
print(batubata)
batubata.append(6)
print('Batu Bata yang ditambah menjadi', 6)
print('Batu Bata yang diangkut', batubata)
batubata.append(7)
print('Batu Bata yang ditambah menjadi', 7)
print('Batu Bata yang diangkut', batubata)
batubatalelah = batubata.pop()
print('Batu bata yang dikeluarkan ... | code |
130020137/cell_6 | [
"text_plain_output_1.png"
] | sepedamotor = [1, 2, 3]
print('Jumlah Sepeda Motor :', sepedamotor)
sepedamotor.append(4)
print('Penambahan Sepeda motor menjadi', 4)
print('Jumlah Sepeda Motor : ', sepedamotor)
sepedamotor.pop()
print('Pengambilan Sepeda Motor', sepedamotor)
print('Jumlah Sepeda Motor : ', sepedamotor)
sepedamotor.pop()
print('Pengam... | code |
130020137/cell_2 | [
"text_plain_output_1.png"
] | buku = [1, 2, 3, 4, 5, 6]
print('Jumlah Buku Awal:', buku)
buku.append(7)
print('Penambahan Buku', 7)
print('Jumlah Buku : ', buku)
buku.append(8)
print('Penambahan Buku', 8)
print('Jumlah Buku : ', buku)
buku.pop()
print('Pengambilan Buku oleh Pelanggan', buku)
print('Jumlah Buku : ', buku) | code |
130020137/cell_19 | [
"text_plain_output_1.png"
] | from collections import deque
from collections import deque
from collections import deque
from collections import deque
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
antrian.append(6)
antrian.append(7)
out = antrian.popleft()
out = antrian.popleft()
out = antrian.popleft()
antrian.append(8)
from c... | code |
130020137/cell_8 | [
"text_plain_output_1.png"
] | baju = [1, 2, 3, 4, 5]
print('jumlah baju awal:', baju)
baju.append(6)
print('penambahan baju', 6)
print('jumlah baju : ', baju)
baju.pop()
print('pengambilan baju oleh sibapak', baju)
print('jumlah baju : ', baju) | code |
130020137/cell_15 | [
"text_plain_output_1.png"
] | from collections import deque
from collections import deque
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
antrian.append(6)
antrian.append(7)
out = antrian.popleft()
out = antrian.popleft()
out = antrian.popleft()
antrian.append(8)
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
npm =... | code |
130020137/cell_17 | [
"text_plain_output_1.png"
] | from collections import deque
from collections import deque
from collections import deque
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
antrian.append(6)
antrian.append(7)
out = antrian.popleft()
out = antrian.popleft()
out = antrian.popleft()
antrian.append(8)
from collections import deque
antrian... | code |
130020137/cell_10 | [
"text_plain_output_1.png"
] | baju = [1, 2, 3, 4, 5]
baju.append(6)
baju.pop()
baju = [5, 6, 7, 8, 9, 10]
print('jumlah baju awal:', baju)
lipatan = [5, 6, 7, 8, 9]
print('jumlah baju yang sudah dilipat:', lipatan)
lipatan.pop(4)
print('pengambilan baju oleh siadik : ', lipatan)
lipatan.append(10)
print('Akhir jumlah baju : ', lipatan) | code |
105204964/cell_21 | [
"image_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-r... | code |
105204964/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(da... | code |
105204964/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
data.isna().sum().sum()
data.isna().sum().sum() | code |
105204964/cell_4 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(da... | code |
105204964/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-r... | code |
105204964/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
data.head() | code |
105204964/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(da... | code |
105204964/cell_19 | [
"text_html_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-r... | code |
105204964/cell_18 | [
"text_html_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-r... | code |
105204964/cell_16 | [
"text_plain_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-se... | code |
105204964/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
data.describe() | code |
105204964/cell_17 | [
"text_html_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-r... | code |
105204964/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(da... | code |
105204964/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(da... | code |
105204964/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
data.isna().sum().sum() | code |
90155584/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
df_bank = marketing.groupby(['y']).apply(lambda x: x.sample(n=199, random_state=123))
df_bank.drop(columns='y', axis=1, inplace=True)
df_bank.reset_index(inplace=True)
df_bank.drop(colum... | code |
90155584/cell_9 | [
"text_plain_output_1.png"
] | N = 45211
e = 0.05
n = N / (1 + N * e ** 2)
n | code |
90155584/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5) | code |
90155584/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
marketing.describe() | code |
90155584/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 8))
sns.histplot(data=marketing, x='y', ax=ax1)
sns.boxplot(data=marketing, x='y'... | code |
90155584/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import scipy.stats as st
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
df_bank = marketing.groupby(['y']).apply(lambda x: x.sample(n=199, random_state=123))
df_bank.drop(columns='y', axis=1, inplace=True)
df_bank.reset_index(inplac... | code |
90155584/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
#Visualize the data
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,8))
sns.histplot(data=marketing, x='y', ax... | code |
90155584/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
df_bank = marketing.groupby(['y']).apply(lambda x: x.sample(n=199, random_state=123))
df_bank.drop(columns='y', axis=1, inplace=True)
df_bank.reset_index(inplace=True)
df_bank.drop(colum... | code |
32067430/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
team_stats = pd.read_csv('/kaggle/input/college-basketball-dataset/cbb.csv')
team_stats.head(5) | code |
32067430/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
team_stats = pd.read_csv('/kaggle/input/college-basketball-dataset/cbb.csv')
avg_off = team_stats['ADJOE'].mean()
avg_def = team_stats['ADJDE'].mean()
team_stats[team_stats['POSTSEASON'] == 'Champions']['ADJOE'].mean() - avg_off | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.