path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122255862/cell_17 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/cell_35 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import plotly.offline as offline
import seaborn as sns
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic_gender = titanic['Sex... | code |
122255862/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic['avg_fare_class'] = titanic.groupby('Pclass')['... | code |
122255862/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1) | code |
122255862/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic_gender = titanic['Sex'].value_counts(normalize=True)
wp = {'linewidth': 1, 'edgecolor'... | code |
122255862/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic_gender = titanic['Sex'].value_counts(normalize=True)
wp = {'linewidth': 1, 'edgecolor': 'black'}
plt.pie(tita... | code |
122255862/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class | code |
122255862/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
print(f'The table above contains: \nrows: {titanic.shape[0]} \ncolumns: {titanic.shape[1]}') | code |
73090244/cell_13 | [
"text_html_output_1.png"
] | code | |
73090244/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import cudf
PATH = '/kaggle/input/optiver-realized-volatility-prediction'
def load_data(mode, path='/kaggle/input/optiver-realized-volatility-prediction'):
file_name = f'{path}/{mode}.csv'
return cudf.read_csv(file_name)
dev_df = load_data('train', path=PATH)
SCALE = 100
dev_df['target'] = SCALE * dev_df['tar... | code |
73090244/cell_6 | [
"text_plain_output_1.png"
] | import cudf
import glob
PATH = '/kaggle/input/optiver-realized-volatility-prediction'
def load_data(mode, path='/kaggle/input/optiver-realized-volatility-prediction'):
file_name = f'{path}/{mode}.csv'
return cudf.read_csv(file_name)
dev_df = load_data('train', path=PATH)
order_book_training = glob.glob(f'{PA... | code |
73090244/cell_2 | [
"text_plain_output_1.png"
] | import cupy as cp
import cudf
import cuml
import glob
from tqdm import tqdm
import lightgbm as lgb
import numpy as np
from sklearn.model_selection import KFold
import matplotlib.pyplot as plt | code |
73090244/cell_8 | [
"text_plain_output_1.png"
] | code | |
73090244/cell_16 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import cu_utils.transform as cutran
import cudf
import cupy as cp
import glob
PATH = '/kaggle/input/optiver-realized-volatility-prediction'
def load_data(mode, path='/kaggle/input/optiver-realized-volatility-prediction'):
file_name = f'{path}/{mode}.csv'
return cudf.read_csv(file_name... | code |
73090244/cell_3 | [
"text_plain_output_1.png"
] | import cudf
PATH = '/kaggle/input/optiver-realized-volatility-prediction'
def load_data(mode, path='/kaggle/input/optiver-realized-volatility-prediction'):
file_name = f'{path}/{mode}.csv'
return cudf.read_csv(file_name)
dev_df = load_data('train', path=PATH)
dev_df.head() | code |
73090244/cell_5 | [
"text_plain_output_1.png"
] | import cudf
import glob
PATH = '/kaggle/input/optiver-realized-volatility-prediction'
def load_data(mode, path='/kaggle/input/optiver-realized-volatility-prediction'):
file_name = f'{path}/{mode}.csv'
return cudf.read_csv(file_name)
dev_df = load_data('train', path=PATH)
order_book_training = glob.glob(f'{PA... | code |
73099200/cell_21 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()... | code |
73099200/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()
data.shape
data.isnull().sum()
data.fillna(data.mean())
data.select_dtypes(include='int')
num_data = data.select_dtypes(... | code |
73099200/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()
data.shape
data.isnull().sum()
data.fillna(data.mean())
data.select_dtypes(include='int') | code |
73099200/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns | code |
73099200/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing impor... | code |
73099200/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()... | code |
73099200/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold
from sklearn.model_selection import KFold
kf = KFold(n_splits=3)
for i in kf.split([0, 1, 2, 3, 4, 5, 6, 7, 8]):
print(i) | code |
73099200/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder... | code |
73099200/cell_20 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()... | code |
73099200/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()
data.shape | code |
73099200/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Te... | code |
73099200/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.head() | code |
73099200/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()
data.shape
data.isnull().sum()
data.fillna(data.mean())
data.select_dtypes(include='int')
num_data = data.select_dtypes(... | code |
73099200/cell_19 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()... | code |
73099200/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 |
73099200/cell_7 | [
"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/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()
data.shape
data.isnull().sum() | code |
73099200/cell_18 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()... | code |
73099200/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV fil... | code |
73099200/cell_8 | [
"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/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()
data.shape
data.isnull().sum()
data.fillna(data.mean()) | code |
73099200/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()... | code |
73099200/cell_3 | [
"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/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data | code |
73099200/cell_17 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()... | code |
73099200/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read... | code |
73099200/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()
data.shape
data.isnull().sum()
data.fillna(data.mean())
data.select_dtype... | code |
73099200/cell_22 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()... | code |
73099200/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()
data.shape
data.isnull().sum()
data.fillna(data.mean())
data.select_dtypes(include='int')
num_data = data.select_dtypes(... | code |
73099200/cell_27 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder,LabelEncoder, OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/telco-customer-churn/WA_Fn-UseC_-T... | code |
73099200/cell_12 | [
"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/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull()
data.shape
data.isnull().sum()
data.fillna(data.mean())
data.select_dtypes(include='int')
num_data = data.select_dtypes(... | code |
73099200/cell_5 | [
"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/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')
data.columns
data.isnull() | code |
329077/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import math
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 10]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_fr... | code |
329077/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import math
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 10]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_fr... | code |
329077/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import math
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 10]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_fr... | code |
329077/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import math
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 10]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_fr... | code |
106198852/cell_4 | [
"text_plain_output_1.png"
] | !pip install transformers
from transformers import BertForQuestionAnswering, AutoTokenizer
modelname = 'deepset/bert-base-cased-squad2'
model = BertForQuestionAnswering.from_pretrained(modelname)
tokenizer = AutoTokenizer.from_pretrained(modelname) | code |
106198852/cell_7 | [
"text_plain_output_1.png"
] | from transformers import pipeline
context = 'The Intergovernmental Panel on Climate Change (IPCC) is a scientifie intergovernmental body under the auspicesof the United Notio ns, set up at the request of member governments. It was first established in 1988 by two UnitedNations organizations, the World Me teorological ... | code |
106198852/cell_8 | [
"text_plain_output_1.png"
] | from transformers import pipeline
context = 'The Intergovernmental Panel on Climate Change (IPCC) is a scientifie intergovernmental body under the auspicesof the United Notio ns, set up at the request of member governments. It was first established in 1988 by two UnitedNations organizations, the World Me teorological ... | code |
106198852/cell_5 | [
"text_plain_output_1.png"
] | questions = ['what orpanization is the IPCC a part of?', 'What UN organizations established the IPCC?', 'What does the UN want to stabilize?']
tokenizer.encode(questions[0], truncation=True, padding=True) | code |
130024391/cell_21 | [
"text_plain_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_directory = '/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/'
defog_meta = pd.read_csv(data_directory + 'defog_metadata.csv')
tdcsfog_meta = pd.read_csv(data_direct... | code |
130024391/cell_13 | [
"text_html_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_directory = '/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/'
defog_meta = pd.read_csv(data_directory + 'defog_metadata.csv')
tdcsfog_meta = pd.read_csv(data_directory + 'tdcsfog_metadata.csv')
def prepare_fog_table(df_... | code |
130024391/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_directory = '/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/'
defog_meta = pd.read_csv(data_directory + 'defog_metadata.csv')
tdcsfog_meta = pd.read_csv(data_directory + 'tdcsfog_metadata.csv')
# Creat function to display main info ... | code |
130024391/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_directory = '/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/'
defog_meta = pd.read_csv(data_directory + 'defog_metadata.csv')
tdcsfog_meta = pd.read_csv(data_directory + 'tdcsfog_metadata.csv')
tdcsfog_meta.head() | code |
130024391/cell_19 | [
"text_html_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_directory = '/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/'
defog_meta = pd.read_csv(data_directory + 'defog_metadata.csv')
tdcsfog_meta = pd.read_csv(data_directory + 'tdcsfog_metadata.csv')
def prepare_fog_table(df_... | code |
130024391/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130024391/cell_17 | [
"text_html_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_directory = '/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/'
defog_meta = pd.read_csv(data_directory + 'defog_metadata.csv')
tdcsfog_meta = pd.read_csv(data_directory + 'tdcsfog_metadata.csv')
def prepare_fog_table(df_... | code |
130024391/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_directory = '/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/'
defog_meta = pd.read_csv(data_directory + 'defog_metadata.csv')
tdcsfog_meta = pd.read_csv(data_directory + 'tdcsfog_metadata.csv')
defog_meta.head() | code |
16124614/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import tensorflow as tf
print('Version: {}'.format(tf.VERSION)) | code |
16124614/cell_6 | [
"text_plain_output_1.png"
] | import pathlib
main_path = pathlib.Path('../input/oct2017/OCT2017 ')
train_path = main_path / 'train'
test_path = main_path / 'test'
val_path = main_path / 'val'
train_path | code |
16124614/cell_29 | [
"text_plain_output_1.png"
] | print('Model Accuracy on Test Data: {:.1f}%'.format(test_acc * 100)) | code |
16124614/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pathlib
import random
import tensorflow as tf
AUTOTUNE = tf.data.experimental.AUTOTUNE
main_path = pathlib.Path('../input/oct2017/OCT2017 ')
train_path = main_path / 'train'
test_path = main_path / 'test'
val_path = main_path / 'val'
train_path
import random
train_image_path... | code |
16124614/cell_28 | [
"text_plain_output_1.png"
] | from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import os
import os
import pathlib
import random
import tensorflow as tf
AUTOTUNE = tf.data.experimental.AUTOTUNE
main_path = pathlib.Path('../input/oct2017/OCT2017 ')
train_path = main_path / 'train'
test_path = main_path / '... | code |
16124614/cell_8 | [
"text_plain_output_1.png"
] | import pathlib
import random
main_path = pathlib.Path('../input/oct2017/OCT2017 ')
train_path = main_path / 'train'
test_path = main_path / 'test'
val_path = main_path / 'val'
train_path
import random
train_image_paths = [str(path) for path in list(train_path.glob('*/*.jpeg'))]
random.shuffle(train_image_paths)
test... | code |
16124614/cell_24 | [
"text_plain_output_1.png"
] | from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import pathlib
import random
import tensorflow as tf
AUTOTUNE = tf.data.experimental.AUTOTUNE
main_path = pathlib.Path('../input/oct2017/OCT2017 ')
train_path = main_path / 'train'
test_path = main_path / 'test'
val_path = main_... | code |
16124614/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pathlib
import random
import tensorflow as tf
AUTOTUNE = tf.data.experimental.AUTOTUNE
main_path = pathlib.Path('../input/oct2017/OCT2017 ')
train_path = main_path / 'train'
test_path = main_path / 'test'
val_path = main_path / 'val'
train_path
import random
train_image_path... | code |
16124614/cell_10 | [
"text_plain_output_1.png"
] | import pathlib
import random
main_path = pathlib.Path('../input/oct2017/OCT2017 ')
train_path = main_path / 'train'
test_path = main_path / 'test'
val_path = main_path / 'val'
train_path
import random
train_image_paths = [str(path) for path in list(train_path.glob('*/*.jpeg'))]
random.shuffle(train_image_paths)
test... | code |
16124614/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import os
import os
import pathlib
import random
import tensorflow as tf
AUTOTUNE = tf.data.experimental.AUTOTUNE
main_path = pathlib.Path('../input/oct2017/OCT2017 ')
train_path = main_path / 'train'
test_path = main_path / '... | code |
16124614/cell_12 | [
"text_plain_output_1.png"
] | import pathlib
import random
main_path = pathlib.Path('../input/oct2017/OCT2017 ')
train_path = main_path / 'train'
test_path = main_path / 'test'
val_path = main_path / 'val'
train_path
import random
train_image_paths = [str(path) for path in list(train_path.glob('*/*.jpeg'))]
random.shuffle(train_image_paths)
test... | code |
122255004/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | def thing1():
thing = input('put something: ')
a = 0
for x in list(thing):
if x == 'a':
a += 1
print('total characters:', len(thing), "\nnumber of a's:", a)
thing() | code |
72115124/cell_4 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import Callback
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV f... | code |
72115124/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import Callback
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from... | code |
72115124/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import Callback
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
impor... | code |
89132235/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
plt.figure(figsize=(10, 10))
for i in range(25):
plt.subplot(5, 5, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i]) | code |
89132235/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras import datasets, layers, models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2... | code |
89132235/cell_7 | [
"text_plain_output_1.png"
] | from tensorflow.keras import datasets, layers, models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2... | code |
89132235/cell_3 | [
"text_plain_output_1.png"
] | from tensorflow.keras import datasets, layers, models
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() | code |
129014806/cell_4 | [
"image_output_11.png",
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_4.png",
"image_output_14.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_html_output_2.png",
"image_output_13.png",
"text_html_output_5.png",
"image_output_5.png",
"text_... | from solarcurtailment import curtailment_calculation
file_path = '/kaggle/input/solarunsw/Data'
for i in [1, 11, 14, 4, 5, 9]:
sample_number = i
print('Analyzing sample number {}'.format(i))
data_file = '/data_sample_{}.csv'.format(sample_number)
ghi_file = '/ghi_sample_{}.csv'.format(sample_number)
... | code |
129014806/cell_2 | [
"text_plain_output_1.png"
] | ! pip install solarcurtailment | code |
129014806/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from solarcurtailment import curtailment_calculation | code |
2041009/cell_4 | [
"text_html_output_1.png"
] | import datetime
import numpy as np
import pandas as pd
def LeaveOneOut(data1, data2, groupcolumns, columnName, useLOO=False, cut=1, addNoise=False):
features = list([])
for a in groupcolumns:
features.append(a)
if columnName is not None:
features.append(columnName)
grpCount = data1.gr... | code |
2041009/cell_7 | [
"text_plain_output_1.png"
] | import datetime
import numpy as np
import pandas as pd
def LeaveOneOut(data1, data2, groupcolumns, columnName, useLOO=False, cut=1, addNoise=False):
features = list([])
for a in groupcolumns:
features.append(a)
if columnName is not None:
features.append(columnName)
grpCount = data1.gr... | code |
2041009/cell_8 | [
"text_plain_output_1.png"
] | import datetime
import numpy as np
import pandas as pd
def LeaveOneOut(data1, data2, groupcolumns, columnName, useLOO=False, cut=1, addNoise=False):
features = list([])
for a in groupcolumns:
features.append(a)
if columnName is not None:
features.append(columnName)
grpCount = data1.gr... | code |
2041009/cell_5 | [
"text_plain_output_1.png"
] | import datetime
import numpy as np
import pandas as pd
def LeaveOneOut(data1, data2, groupcolumns, columnName, useLOO=False, cut=1, addNoise=False):
features = list([])
for a in groupcolumns:
features.append(a)
if columnName is not None:
features.append(columnName)
grpCount = data1.gr... | code |
32065505/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import KeyedVectors
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
import nltk
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
FILE_PATH = '../input/fasttext-wikinews/wiki-news-300d-1M.vec'
key... | code |
32065505/cell_8 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
FILE_PATH = '../input/fasttext-wikinews/wiki-news-300d-1M.vec'
keyed_vec = KeyedVectors.load_word2vec_format(FILE_PATH)
for word in ['hello', '!', '2', 'Turing', 'foobarz', 'hi!']:
print(word, 'is in the vocabulary:', word in keyed_vec.vocab) | code |
32065505/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_sample = pd.read_csv('../input/quora-insincere-questions-classification/train.csv', nrows=6000)
train_sample = data_sample[:5000]
test_sample = data_sample[5000:]
train_sample.head() | code |
32065505/cell_3 | [
"text_html_output_1.png"
] | import os
print(os.listdir('../input')) | code |
32065505/cell_17 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
import nltk
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
FILE_PATH = '../input/fasttext-wikinews/wiki-news-300d-1M.vec'
keyed_vec = KeyedVectors.load_word2vec_format(FILE_PATH)
word_vec = keyed_vec.get_vector('foo... | code |
32065505/cell_10 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
FILE_PATH = '../input/fasttext-wikinews/wiki-news-300d-1M.vec'
keyed_vec = KeyedVectors.load_word2vec_format(FILE_PATH)
word_vec = keyed_vec.get_vector('foobar')
print(word_vec.shape)
print(word_vec[:25]) | code |
32065505/cell_12 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
FILE_PATH = '../input/fasttext-wikinews/wiki-news-300d-1M.vec'
keyed_vec = KeyedVectors.load_word2vec_format(FILE_PATH)
word_vec = keyed_vec.get_vector('foobar')
keras_embedding = keyed_vec.get_keras_embedding()
keras_embedding.get_config() | code |
32065505/cell_5 | [
"text_plain_output_1.png"
] | FILE_PATH = '../input/fasttext-wikinews/wiki-news-300d-1M.vec'
with open(FILE_PATH) as f:
for _ in range(5):
print(f.readline()[:80]) | code |
72089413/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import json_lines
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
print(len(data0[0])) | code |
72089413/cell_34 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm import tqdm
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
import random
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
... | code |
72089413/cell_23 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
import random
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
... | code |
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