path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2025748/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 |
2025748/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
from sklearn.model_selection import train_test_split
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
print(len(trai... | code |
2025748/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing['income_cat'].where(housing['inc... | code |
2025748/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing... | code |
2025748/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing.info() | code |
2025748/cell_14 | [
"image_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing... | code |
2025748/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing... | code |
2025748/cell_12 | [
"text_plain_output_1.png"
] | from pandas.tools.plotting import scatter_matrix
from sklearn.model_selection import StratifiedShuffleSplit
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat']... | code |
2025748/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing.describe() | code |
327301/cell_6 | [
"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)
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df['Age'].groupby(titanic_df['Survived']).mean() | code |
327301/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
327301/cell_3 | [
"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)
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
print('Number of records in titanic_df= {}'.format(len(titanic_df))... | code |
327301/cell_5 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
pd.crosstab(index=titanic_df['Survived'], columns=titanic_df['Sex'... | code |
34118808/cell_9 | [
"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/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T
train_df.head().T
train_df = train_df.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test_df = ... | code |
34118808/cell_4 | [
"image_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/titanic/train.csv')
train_df.head().T
train_df.head().T | code |
34118808/cell_6 | [
"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/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T
test_df.describe(include='all') | code |
34118808/cell_2 | [
"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/titanic/train.csv')
train_df.head().T | code |
34118808/cell_19 | [
"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/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T
train_df.head().T
train_df = train_df.dr... | code |
34118808/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, Lin... | code |
34118808/cell_18 | [
"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/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T
train_df.head().T
train_df = train_df.dr... | code |
34118808/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_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/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T
train_df.head().T
train_df = train_df.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test_df = ... | code |
34118808/cell_15 | [
"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/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T
train_df.head().T
train_df = train_df.dr... | code |
34118808/cell_16 | [
"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/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T
train_df.head().T
train_df = train_df.dr... | code |
34118808/cell_3 | [
"text_html_output_1.png",
"image_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/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T | code |
34118808/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/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T
train_df.head().T
train_df = train_df.dr... | code |
34118808/cell_10 | [
"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/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T
train_df.head().T
train_df = train_df.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test_df = ... | code |
34118808/cell_12 | [
"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)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
train_df.head().T
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_df.head().T
train_df.head().T
train_df = train_df.drop(['PassengerId', 'Nam... | code |
34118808/cell_5 | [
"text_plain_output_1.png",
"image_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/titanic/train.csv')
train_df.head().T
train_df.head().T
train_df.describe(include='all') | code |
18103228/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survi... | code |
18103228/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import os
print(os.listdir('../input'))
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv') | code |
18103228/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survi... | code |
90119831/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_c... | code |
90119831/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
90119831/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
90119831/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['tim... | code |
90119831/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
90119831/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
90119831/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
90119831/cell_27 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from sklearn.preprocessing import LabelEncoder
import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id... | code |
90119831/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
333806/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='... | code |
333806/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy_space_split = [str(i).split()... | code |
333806/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='... | code |
333806/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy_space_split = [str(i).split() for i in fanboy_data['tweets']]
fanboy_ha... | code |
333806/cell_3 | [
"image_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
from matplotlib import *
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
333806/cell_14 | [
"text_plain_output_1.png"
] | import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy_space_split = [str(i).split() for i in fanboy_da... | code |
333806/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys() | code |
72063178/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_district.isnull().sum()
df_district.columns
df_district.shape
df_district.dtypes
df_district = df_district.drop_duplicates()
plt... | code |
72063178/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_district.isnull().sum()
df_district.columns
df_district.shape
df_district.dtypes
df_district = df_district.drop_duplicates()
plt... | code |
72063178/cell_23 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_district.isnull().sum()
df_district.columns
df_district.shape
df_district.dtypes
df_district = df_district.drop_duplicates()
plt... | code |
72063178/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_district.isnull().sum()
df_district.columns
df_district.shape
df_district.dtypes
df_district = df_district.drop_duplicates()
plt... | code |
72063178/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_district.head(5) | code |
72063178/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_district.isnull().sum()
df_district.columns
df_district.shape
df_district.dtypes | code |
72063178/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_district.isnull().sum()
df_district.columns
df_district.shape
df_district.dtypes
df_district = df_district.drop_duplicates()
plt... | code |
72063178/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_district.isnull().sum()
df_district.columns
df_district.shape | code |
72063178/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_district.isnull().sum() | code |
72063178/cell_27 | [
"image_output_1.png"
] | import pandas as pd
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_products = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
df_products.head(5) | code |
72063178/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df_district = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
df_district.isnull().sum()
df_district.columns | code |
129006184/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import warnings
import torch
import matplotlib.pyplot as plt
import numpy as np
import torchvision
from torchvision import datasets, transforms
import torch.nn as nn
import torch.optim as optim
import seaborn as sns
import warnings
warnings.filterwarnings('ignore') | code |
129006184/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import mlflow
get_ipython().system_raw('mlflow ui --port 5000 &')
mlflow.pytorch.autolog() | code |
129006184/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_1.png"
] | from pyngrok import ngrok
from pyngrok import ngrok
from getpass import getpass
ngrok.kill()
NGROK_AUTH_TOKEN = '2Padn9VzXvPy7nJXe6eAUTR3Dbd_6cXCwQeLNLwZDCWL5ypKs'
ngrok.set_auth_token(NGROK_AUTH_TOKEN)
ngrok_tunnel = ngrok.connect(addr='5000', proto='http', bind_tls=True)
print('MLflow Tracking UI:', ngrok_tunnel.pub... | code |
129006184/cell_3 | [
"text_plain_output_1.png"
] | # Install the requiered packages to run MLFlow
!pip install mlflow --quiet
!pip install pyngrok --quiet | code |
90116628/cell_3 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import requests
webpage_response = requests.get('https://bank.gov.ua/ua/markets/exchangerates')
webpage = webpage_response.content
soup = BeautifulSoup(webpage, 'html.parser')
soup.table | code |
90116628/cell_5 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests
webpage_response = requests.get('https://bank.gov.ua/ua/markets/exchangerates')
webpage = webpage_response.content
soup = BeautifulSoup(webpage, 'html.parser')
soup.table
k = soup.find_all(attrs={'data-label': 'Офіційний курс'})
t1 = []
for i in k:
... | code |
2037081/cell_9 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def sigmoid(z):
return 1.0 / (1 + np.exp(-z))
def z(theta, x):
assert thet... | code |
2037081/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
def sigmoid(z):
return 1.0 / (1 + np.exp(-z))
def z(theta, x):
assert theta.shape[1] == 1
assert theta.shape[0] == x.shape[1]
return np.dot(x, theta)
a = np.array([[1, 2], [3, 4]])
b = np.array([[4, 1], [2, 2]])
print('a.T*b is:', np.dot(a.T, b)... | code |
2037081/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'))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split | code |
121149831/cell_9 | [
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.datasets import fetch_openml
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.inspection import permutation_importance
from sklearn.model_selection import train_test_split
from sklearn.pipeline impo... | code |
121149831/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.datasets import fetch_openml
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OrdinalE... | code |
121149831/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.datasets import fetch_openml
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OrdinalE... | code |
121149831/cell_8 | [
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.datasets import fetch_openml
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OrdinalE... | code |
121149831/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.datasets import fetch_openml
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OrdinalE... | code |
34144733/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | !pip install hyperopt
!pip install geffnet | code |
34144733/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 |
34144733/cell_10 | [
"text_plain_output_1.png"
] | from PIL import ImageOps, ImageEnhance
from abc import ABC, abstractmethod
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
import geffnet
import numpy as np
import numpy as np # linear algebra
import pickle
import torch
import torch.nn as nn
import torch.nn as nn
import torch.utils.data as Data
import ... | code |
34121284/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import requests
import json
import pandas as pd
import time
import plotly
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89135088/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
89135088/cell_9 | [
"image_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
89135088/cell_4 | [
"text_plain_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
89135088/cell_6 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
per... | code |
89135088/cell_11 | [
"text_plain_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
89135088/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from collections import Counter
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89135088/cell_7 | [
"text_plain_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
89135088/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
89135088/cell_15 | [
"text_plain_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
89135088/cell_16 | [
"text_plain_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
89135088/cell_3 | [
"text_plain_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
89135088/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import seaborn as sns
median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.c... | code |
89135088/cell_14 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import seaborn as sns
median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.c... | code |
89135088/cell_10 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import seaborn as sns
median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.c... | code |
89135088/cell_12 | [
"text_plain_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
89135088/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | median_house = pd.read_csv('../input/fatal-police-shootings-in-the-us/MedianHouseholdIncome2015.csv', encoding='windows-1252')
percent_over = pd.read_csv('../input/fatal-police-shootings-in-the-us/PercentOver25CompletedHighSchool.csv', encoding='windows-1252')
percentage_people = pd.read_csv('../input/fatal-police-shoo... | code |
17104067/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumen... | code |
17104067/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
print(f'There are {nRow} rows and {nCol} columns') | code |
17104067/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
#partido_valor['sgpartido'] = ... | code |
17104067/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
cota_total = pd.DataFrame(cota.groupby(['sgpartido'])['vlrdocumento'].sum().sort_values(ascending=False))
... | code |
17104067/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumen... | code |
17104067/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumen... | code |
17104067/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
#partido_valor['sgpartido'] = cota['sgpartido']
#partido_valor['vlrdocumen... | code |
17104067/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
cota.head(10) | code |
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