path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17136778/cell_11 | [
"image_output_1.png"
] | test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normali... | code |
17136778/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normali... | code |
17136778/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normali... | code |
17136778/cell_14 | [
"text_html_output_1.png"
] | test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normali... | code |
17136778/cell_10 | [
"text_plain_output_1.png"
] | test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normali... | code |
17136778/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normali... | code |
89130914/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df_1 = df.set_index('Date')
df_1.sample(5)
df_1['Rolling 7: 7Days Rolling'] = df_1.High.rolling(7).mean()
df_1['Rolling 30: 30Days Rolling'] = df_1.High.r... | code |
89130914/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df_1 = df.set_index('Date')
df_1.sample(5)
df_1['Rolling 7: 7Days Rolling'] = df_1.High.rolling(7).mean()
df_1['Rolling 30: 30Days Rolling'] = df_1.High.r... | code |
89130914/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df.info() | code |
89130914/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df.info() | code |
89130914/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df_1 = df.set_index('Date')
df_1.sample(5) | code |
89130914/cell_54 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df_1 = df.set_index('Date')
df_1.sample(5)
df_1['Rolling 7: 7Days Rolling'] = df_1.High.rolling(7).mean()
df_1['Rolling 30: 30Days Rolling'] = df_1.High.r... | code |
89130914/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df_1 = df.set_index('Date')
df_1.sample(5)
df_1['Rolling 7: 7Days Rolling'] = df_1.High.rolling(7).mean()
df_1['Rolling 30: 30Days Rolling'] = df_1.High.r... | code |
89130914/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df_1 = df.set_index('Date')
df_1.sample(5)
df_1['Rolling 7: 7Days Rolling'] = df_1.High.rolling(7).mean()
df_1['Rolling 30: 30Days Rolling'] = df_1.High.r... | code |
89130914/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df_1 = df.set_index('Date')
df_1.sample(5)
df_1['Rolling 7: 7Days Rolling'] = df_1.High.rolling(7).mean()
df_1['Rolling 30: 30Days Rolling'] = df_1.High.r... | code |
89130914/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df.tail(2) | code |
89130914/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df_1 = df.set_index('Date')
df_1.sample(5)
df_1['Close'].plot(figsize=(20, 5), color='g')
plt.title('AIRTEL Stock Price - 5Y', fontsize=20) | code |
89130914/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df_1 = df.set_index('Date')
df_1.sample(5)
df_1.plot() | code |
89130914/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.head(2) | code |
89130914/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5) | code |
89130914/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns | code |
89130914/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape
df.columns
df.sample(5)
df_1 = df.set_index('Date')
df_1.sample(5)
df_1['Rolling 7: 7Days Rolling'] = df_1.High.rolling(7).mean()
df_1['Rolling 30: 30Days Rolling'] = df_1.High.r... | code |
89130914/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bharti-airtel-stock-proce/BHARTIARTL.NS.csv')
df.shape | code |
74056813/cell_6 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
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 matplotlib.pyplot as plt
import seaborn as sns
sns.set
data = pd.read_csv('../input/boston-housing-dataset/HousingData.csv')
... | code |
74056813/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set
data = pd.read_csv('../input/boston-housing-dataset/HousingData.csv')
data = data.dropna()
data | code |
74056813/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set
data = pd.read_csv(... | code |
74056813/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
... | code |
74056813/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set
data = pd.read_csv('../input/boston-housing-dataset/HousingData.csv')
data = data.dropna()
data
X = data.drop('MEDV', axis=1).values
Y = data['MEDV'].values
X | code |
74056813/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set
data = pd.read_csv('../input/boston-housing-dataset/HousingData.csv')
data = data.dropna()
data
X = data.drop('MEDV', axis=1).values
Y = dat... | code |
128041288/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
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
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.de... | code |
128041288/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.describe().T
df.skew() | code |
128041288/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum() | code |
128041288/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.describe().T | code |
128041288/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.describe().T
df.skew()
df.corr | code |
128041288/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 |
128041288/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.describe().T
df.hist(figsize=(16, 10), color='green') | code |
128041288/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
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
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.de... | code |
128041288/cell_8 | [
"text_plain_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
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.describe().T
fig, axis = plt.subplots(nrows=1, ncols=3, figsize=(... | code |
128041288/cell_15 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
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
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.de... | code |
128041288/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df | code |
128041288/cell_17 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
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
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.de... | code |
128041288/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
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
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.de... | code |
128041288/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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.describe().T
fig, axis= plt.subplots(nrows=1, ncols=3, figsize= (... | code |
128041288/cell_12 | [
"text_plain_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
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.describe().T
fig, axis= plt.subplots(nrows=1, ncols=3, figsize= (... | code |
128041288/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)
df = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
df
df.isnull().sum()
df.info() | code |
32068524/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
cols_with_missing | code |
32068524/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv', index_col='PassengerId')
test_data.head() | code |
32068524/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestClassifie... | code |
32068524/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv', index_col='PassengerId')
cols_with_missing = [col for col in train_data.columns if train_data[col].isnu... | code |
32068524/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv', index_col='PassengerId')
cols_with_missing = [col for col in train_data.columns if train_data[col].isnu... | code |
32068524/cell_44 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection ... | code |
32068524/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv', index_col='PassengerId')
test_cols_with_missing = [col for col in test_data.columns if test_data[col].i... | code |
32068524/cell_40 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import O... | code |
32068524/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv', index_col='PassengerId')
cols_with_missing = [col for col in train_data.columns if train_data[col].isnu... | code |
32068524/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv', index_col='PassengerId')
test_cols_with_missing = [col for col in test_data.columns if test_data[col].i... | code |
32068524/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
train_data.head() | code |
32068524/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
train_data.describe() | code |
32068524/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
cols_with_missing
cat = train_data.dtypes == 'object'
object_cols = lis... | code |
32068524/cell_38 | [
"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 OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col=... | code |
32068524/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv', index_col='PassengerId')
cols_with_missing = [col for ... | code |
32068524/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
cols_with_missing
cat = train_data.dtypes == 'object'
object_cols = lis... | code |
32068524/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv', index_col='PassengerId')
cols_with_missing = [col for col in train_data.columns if train_data[col].isnu... | code |
32068524/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv', index_col='PassengerId')
test_data.describe() | code |
32068524/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv', index_col='PassengerId')
test_data = ... | code |
90128354/cell_4 | [
"text_html_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, sep=' ')
train.columns = ['patient id', 'filename', 'class', 'data source']
print('Training data sha... | code |
90128354/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, sep=' ')
train.columns = ['patient id', 'fil... | code |
90128354/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, s... | code |
90128354/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, sep=' ')
train.columns = ['patient id', 'fil... | code |
90128354/cell_12 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, s... | code |
90128354/cell_5 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, sep=' ')
train.columns = ['patient id', 'filename', 'class', 'data source']
print('Classes:\n', tra... | code |
32071200/cell_21 | [
"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')
team_stats.groupby('YEAR').size()
team_stats.groupby('TEAM').size()[team_stats.groupby('TEAM').size() == 1]
team_stats['ADJOE'].idxmax()
team_stats.loc[1]['POSTSEASON'] | code |
32071200/cell_4 | [
"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')
team_stats.head(5) | code |
32071200/cell_11 | [
"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.groupby('YEAR').size()
team_stats.groupby('TEAM').size()[team_stats.groupby('TEAM').size() == 1] | code |
32071200/cell_18 | [
"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')
team_stats.groupby('YEAR').size()
team_stats.groupby('TEAM').size()[team_stats.groupby('TEAM').size() == 1]
avg_off = team_stats['ADJOE'].mean()
avg_def = team_stats['A... | code |
32071200/cell_8 | [
"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')
team_stats.groupby('YEAR').size() | code |
32071200/cell_17 | [
"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')
team_stats.groupby('YEAR').size()
team_stats.groupby('TEAM').size()[team_stats.groupby('TEAM').size() == 1]
avg_off = team_stats['ADJOE'].mean()
avg_def = team_stats['A... | code |
72074805/cell_13 | [
"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)
train_df = pd.read_csv('/kaggle/input/bike-sharing-demand/train.csv')
test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
train_df = train_df[np.abs(train_df['count'] - train_df['count'].mean()... | code |
72074805/cell_9 | [
"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/bike-sharing-demand/train.csv')
test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
train_df.info() | code |
72074805/cell_4 | [
"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/bike-sharing-demand/train.csv')
test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
submission_df = pd.read_csv('/kaggle/input/bike-sharing-demand/sampleSubmission.csv')
submission_df.head() | code |
72074805/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/bike-sharing-demand/train.csv')
test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
print(train_df.isnull().sum())
print('*' * 50)
print(test_df.isnull().sum()) | code |
72074805/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/bike-sharing-demand/train.csv')
train_df.head() | code |
72074805/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
train_df = pd.read_csv('/kaggle/input/bike-sharing-demand/train.csv')
test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
import matplotlib.pyplot as plt
import seaborn as sn... | code |
72074805/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 |
72074805/cell_7 | [
"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/bike-sharing-demand/train.csv')
test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
train_df.describe() | code |
72074805/cell_15 | [
"text_plain_output_1.png"
] | 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)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/bike-sharing-demand/train.csv')
test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
import matplotl... | code |
72074805/cell_3 | [
"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/bike-sharing-demand/train.csv')
test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
test_df.head() | code |
72074805/cell_12 | [
"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)
train_df = pd.read_csv('/kaggle/input/bike-sharing-demand/train.csv')
test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
print('shape with outliers: ', train_df.shape)
train_df = train_df[np.a... | code |
72074805/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/bike-sharing-demand/train.csv')
test_df = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
submission_df = pd.read_csv('/kaggle/input/bike-sharing-demand/sampleSubmission.csv')
submission_df['count'... | code |
32068402/cell_42 | [
"text_plain_output_1.png"
] | from matplotlib import pylab
from sklearn.manifold import TSNE
from sklearn.preprocessing import normalize
import numpy as np
import os
fasttext_model_dir = '../input/fasttext-no-subwords-trigrams'
num_points = 400
first_line = True
index_to_word = []
with open(os.path.join(fasttext_model_dir, 'word-vectors-100d.... | code |
32068402/cell_56 | [
"text_plain_output_1.png"
] | from gensim.models.phrases import Phraser
from pprint import pprint
from sklearn.preprocessing import normalize
import gensim.models.keyedvectors as word2vec
import numpy as np
import os
import pandas as pd
sentences_df = pd.read_csv('../input/covid19sentencesmetadata/sentences_with_metadata.csv')
bigram_model ... | code |
32068402/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
sentences_df = pd.read_csv('../input/covid19sentencesmetadata/sentences_with_metadata.csv')
sentences_df.head() | code |
32068402/cell_33 | [
"text_plain_output_1.png"
] | from gensim.models.phrases import Phraser
from typing import List
import contractions
import ftfy
import re
import spacy
import string
import spacy
import scispacy
nlp = spacy.load('../input/scispacymodels/en_core_sci_sm/en_core_sci_sm-0.2.4')
nlp.max_length = 2000000
import re
CURRENCIES = {'$': 'USD', 'zł': '... | code |
32068402/cell_65 | [
"text_plain_output_1.png"
] | bart_summarizer = BartSummarizer() | code |
32068402/cell_48 | [
"text_plain_output_1.png"
] | from pprint import pprint
from sklearn.preprocessing import normalize
import gensim.models.keyedvectors as word2vec
import numpy as np
import os
fasttext_model_dir = '../input/fasttext-no-subwords-trigrams'
num_points = 400
first_line = True
index_to_word = []
with open(os.path.join(fasttext_model_dir, 'word-vect... | code |
32068402/cell_73 | [
"text_plain_output_1.png"
] | from IPython.display import display, HTML
from datetime import datetime
from gensim.models.phrases import Phraser
from pprint import pprint
from sklearn.preprocessing import normalize
from transformers import BartTokenizer, BartForConditionalGeneration
from typing import List
import contractions
import ftfy
im... | code |
32068402/cell_61 | [
"text_plain_output_1.png"
] | import json
task_id = 2
import json
with open(f'../input/covid19seedsentences/{task_id}.json') as in_fp:
seed_sentences_json = json.load(in_fp)
print(seed_sentences_json['taskName']) | code |
32068402/cell_11 | [
"text_plain_output_1.png"
] | # Install scispacy package
!pip install scispacy | code |
32068402/cell_19 | [
"text_plain_output_1.png"
] | from typing import List
import contractions
import ftfy
import re
import spacy
import string
import spacy
import scispacy
nlp = spacy.load('../input/scispacymodels/en_core_sci_sm/en_core_sci_sm-0.2.4')
nlp.max_length = 2000000
import re
CURRENCIES = {'$': 'USD', 'zł': 'PLN', '£': 'GBP', '¥': 'JPY', '฿': 'THB', '... | code |
32068402/cell_50 | [
"text_plain_output_1.png"
] | [(0, '0.079"•" + 0.019"blood" + 0.015"associated" + 0.013"cells" + 0.012"ace2" + 0.012"protein" + 0.011"important" + 0.011"levels" + 0.010"diseases" + 0.010"cell"'), (1, '0.110"who" + 0.088"it" + 0.056"response" + 0.043"could" + 0.036"under" + 0.035"available" + 0.032"major" + 0.032"as" + 0.030"without" + 0.024"muscle"... | code |
32068402/cell_68 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import json
task_id = 2
import json
with open(f'../input/covid19seedsentences/{task_id}.json') as in_fp:
seed_sentences_json = json.load(in_fp)
bart_summarizer = BartSummarizer()
with open(f'../input/covid19seedsentences/{task_id}_relevant_sentences.json') as in_fp:
relevant_sentences_json = json.load(in_fp... | code |
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