path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17145266/cell_18 | [
"text_plain_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data = src.label_from_df(cols=2).databunch(bs=48)
data.vocab.itos[:10]
data.train_ds[0][0]
data.train_ds[0][0].data[:10] | code |
17145266/cell_32 | [
"text_html_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_28 | [
"text_html_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_8 | [
"text_plain_output_1.png"
] | (df_train.shape, df_valid.shape) | code |
17145266/cell_15 | [
"text_html_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data = src.label_from_df(cols=2).databunch(bs=48)
data.show_batch() | code |
17145266/cell_16 | [
"text_plain_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data = src.label_from_df(cols=2).databunch(bs=48)
data.vocab.itos[:10] | code |
17145266/cell_17 | [
"text_plain_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data = src.label_from_df(cols=2).databunch(bs=48)
data.vocab.itos[:10]
data.train_ds[0][0] | code |
17145266/cell_35 | [
"text_plain_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_24 | [
"text_plain_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_27 | [
"text_plain_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_12 | [
"text_plain_output_1.png"
] | path = Path('../input/')
path.ls() | code |
17145266/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_json('../input/Sarcasm_Headlines_Dataset_v2.json', lines=True)
df.shape | code |
128044967/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import ultralytics
ultralytics.checks() | code |
128044967/cell_1 | [
"text_plain_output_1.png"
] | import cv2
import os
import shutil
import warnings # попытка поймать сообщения: libpng warning: iCCP: known incorrect sRGB profile
import numpy as np
import pandas as pd
import cv2
from PIL import Image
import warnings
warnings.filterwarnings('error')
import os
import shutil
for dirname, _, filenames in os.walk('/k... | code |
128044967/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from ultralytics import YOLO
from ultralytics import YOLO
model = YOLO('yolov8n-cls.pt')
model.train(data='/kaggle/working/fruit-and-vegetable-image-recognition/', epochs=3) | code |
16133438/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum()
df[df.duplicated()]
df = df.drop_duplicates()
df.shape
df.corr()
df.corr()['count']
df.windspeed.median()
x = df.drop(... | code |
16133438/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum()
df[df.duplicated()]
df = df.drop_duplicates()
df.shape
df.windspeed.plot(kind='box') | code |
16133438/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum() | code |
16133438/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape | code |
16133438/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_x, train_y)
predict_train = model.predict(train_x)
predict_test = model.predict(test_x)
r2_train ... | code |
16133438/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_x, train_y) | code |
16133438/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum()
df[df.duplicated()]
df = df.drop_duplicates()
df.shape
df.corr()
df.corr()['count']
df.windspeed.median()
x = df.drop(... | code |
16133438/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns | code |
16133438/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_x, train_y)
predict_train = model.predict(train_x)
predict_test = model.predict(test_x)
train_MAE... | code |
16133438/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum()
df[df.duplicated()]
df = df.drop_duplicates()
df.shape
df.corr()
df.corr()['count']
df.windspeed.median() | code |
16133438/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16133438/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.info() | code |
16133438/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
plt.figure(figsize=(10, 3))
corr = df.corr()
sns.heatmap(corr, annot=True) | code |
16133438/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score
import numpy as np # linear algebra
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_x, train_y)
predict_train = model.predict(train_x)
predict_te... | code |
16133438/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum() | code |
16133438/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum()
df[df.duplicated()]
df = df.drop_duplicates()
df.shape
df.registered.plot(kind='box') | code |
16133438/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum()
df[df.duplicated()]
df = df.drop_duplicates()
df.shape
df.corr() | code |
16133438/cell_3 | [
"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)
df = pd.read_csv('../input/bike_share.csv')
df.head() | code |
16133438/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum()
df[df.duplicated()]
df = df.drop_duplicates()
df.shape
df.corr()
df.corr()['count'] | code |
16133438/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum()
df[df.duplicated()]
df = df.drop_duplicates()
df.shape
df.casual.plot(kind='box') | code |
16133438/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum()
df[df.duplicated()] | code |
16133438/cell_27 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_x, train_y)
predict_train = model.predict(train_x)
predict_test = model.predict(test_x)
train_MSE... | code |
16133438/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T
df.columns
df.isna().sum()
df.duplicated().sum()
df[df.duplicated()]
df = df.drop_duplicates()
df.shape | code |
16133438/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bike_share.csv')
df.shape
df.describe().T | code |
2026131/cell_13 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = train.select_dtypes(include=[np.number])
trainCat... | code |
2026131/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.columns[test.isnull().any()].tolist() | code |
2026131/cell_57 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnu... | code |
2026131/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnu... | code |
2026131/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = t... | code |
2026131/cell_55 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = t... | code |
2026131/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = t... | code |
2026131/cell_41 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = train.select_dtypes(include=[np.number])
trainCat... | code |
2026131/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = train.select_dtypes(include=[np.number])
trainCat... | code |
2026131/cell_50 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = t... | code |
2026131/cell_7 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist() | code |
2026131/cell_45 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = train.select_dtypes(include=[np.number])
trainCat... | code |
2026131/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = train.select_dtypes(include=[np.number])
trainCat... | code |
2026131/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
2026131/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = train.select_dtypes(include=[np.number])
trainCat... | code |
2026131/cell_43 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = train.select_dtypes(include=[np.number])
trainCat... | code |
2026131/cell_37 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns)))
train.columns[train.isnull().any()].tolist()
test.columns[test.isnull().any()].tolist()
trainNum = train.select_dtypes(include=[np.number])
trainCat... | code |
2026131/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
np.array(list(zip(train.Id, train.columns))) | code |
16111049/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/'
df_train = pd.read_csv(f'{path}train.csv', index_col='PassengerId')
df_test = pd.read_csv(f'{path}test.csv', index_col='PassengerId')
target = df_train['Survived']
target.columns = ['Survived']
df_train = df_train.drop(labels='Su... | code |
16111049/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/'
df_train = pd.read_csv(f'{path}train.csv', index_col='PassengerId')
df_test = pd.read_csv(f'{path}test.csv', index_col='PassengerId')
target = df_train['Survived']
target.columns = ['Survived']
df_train = df_train.drop(labels='Su... | code |
16111049/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/'
df_train = pd.read_csv(f'{path}train.csv', index_col='PassengerId')
df_test = pd.read_csv(f'{path}test.csv', index_col='PassengerId')
target = df_train['Survived']
target.columns = ['Survived']
df_train = df_train.drop(labels='Su... | code |
16111049/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
from torch import optim
import sklearn
import os
print(os.listdir('../input')) | code |
16111049/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/'
df_train = pd.read_csv(f'{path}train.csv', index_col='PassengerId')
df_test = pd.read_csv(f'{path}test.csv', index_col='PassengerId')
target = df_train['Survived']
target.columns = ['Survived']
df_train = df_train.drop(labels='Su... | code |
16111049/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/'
df_train = pd.read_csv(f'{path}train.csv', index_col='PassengerId')
df_test = pd.read_csv(f'{path}test.csv', index_col='PassengerId')
target = df_train['Survived']
target.columns = ['Survived']
df_train = df_train.drop(labels='Su... | code |
105216451/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum()
data.columns
distplot_df=data.loc[:,['Age', 'Flight Distance',
'Departure Delay', 'Arrival ... | code |
105216451/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum()
data.columns
distplot_df=data.loc[:,['Age', 'Flight Distance',
'Departure Delay', 'Arrival ... | code |
105216451/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum() | code |
105216451/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum()
data.columns
distplot_df=data.loc[:,['Age', 'Flight Distance',
'Departure Delay', 'Arrival ... | code |
105216451/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.info() | code |
105216451/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum()
data.columns | code |
105216451/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum()
data.columns
distplot_df=data.loc[:,['Age', 'Flight Distance',
'Departure Delay', 'Arrival ... | code |
105216451/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
plt.figure(figsize=(14, 10))
sns.heatmap(data.isnull()) | code |
105216451/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum()
data.columns
distplot_df=data.loc[:,['Age', 'Flight Distance',
'Departure Delay', 'Arrival ... | code |
105216451/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.describe() | code |
105216451/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum()
data.columns
distplot_df=data.loc[:,['Age', 'Flight Distance',
'Departure Delay', 'Arrival ... | code |
105216451/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum()
data.columns
distplot_df=data.loc[:,['Age', 'Flight Distance',
'Departure Delay', 'Arrival ... | code |
105216451/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum()
data.columns
distplot_df=data.loc[:,['Age', 'Flight Distance',
'Departure Delay', 'Arrival ... | code |
105216451/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum() | code |
105216451/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.isnull().sum()
data.duplicated().sum()
data.columns
distplot_df = data.loc[:, ['Age', 'Flight Distance', 'Departure Delay', 'Arrival Dela... | code |
105216451/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data.head(15) | code |
2032126/cell_9 | [
"text_plain_output_1.png"
] | from mlxtend.classifier import StackingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.linear_model import Logis... | code |
2032126/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df2.head() | code |
2032126/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
train = df1.drop(['Ticket', 'Fare', 'Embarked', 'Cabin', 'Name'], axis=1)
test = df2.drop(['Ticket', 'Fare', 'Embarked', 'Cabin', 'Name'], axis=1)
X = train.drop(['Survived'], axis=1)
y = pd.DataFra... | code |
2032126/cell_7 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
train = df1.drop(['Ticket', 'Fare', 'Embarked', 'Cabin', 'Name'], axis=1)
test = df2.drop(['Ticket', 'Fare', 'Embarked', 'Cabin', 'Name'], axis=1)
X = train.drop(['Survived'], axis=1)
y = pd.DataFra... | code |
2032126/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1.head() | code |
2032126/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
train = df1.drop(['Ticket', 'Fare', 'Embarked', 'Cabin', 'Name'], axis=1)
test = df2.drop(['Ticket', 'Fare', 'Embarked', 'Cabin', 'Name'], axis=1)
PassengerId = test['PassengerId']
type(PassengerId) | code |
2032126/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
train = df1.drop(['Ticket', 'Fare', 'Embarked', 'Cabin', 'Name'], axis=1)
test = df2.drop(['Ticket', 'Fare', 'Embarked', 'Cabin', 'Name'], axis=1)
train.head() | code |
32062432/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32069396/cell_13 | [
"text_html_output_1.png"
] | from PIL import Image
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from torch import optim
from torch.optim import lr_scheduler
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, models, transforms
from torchvision import transforms, ut... | code |
32069396/cell_4 | [
"image_output_1.png"
] | from tqdm import tqdm
import cv2
import numpy as np
import pandas as pd
test = pd.read_csv('/kaggle/input/lego-dataset/Test.csv')
train = pd.read_csv('/kaggle/input/lego-dataset/Train.csv')
train_img = []
for img_name in tqdm(train['name']):
image_path = '/kaggle/input/lego-dataset/train/' + str(img_name)
... | code |
32069396/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
test = pd.read_csv('/kaggle/input/lego-dataset/Test.csv')
train = pd.read_csv('/kaggle/input/lego-dataset/Train.csv')
train_img = []
for img_name in tqdm(train['name']):
image_path = '/kaggle/input/lego-dat... | code |
32069396/cell_19 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from torch import optim
from torch.autograd import Variable
from torch.optim import lr_scheduler
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, models, transforms
f... | code |
32069396/cell_7 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from tqdm import tqdm
import cv2
import numpy as np
import pandas as pd
test = pd.read_csv('/kaggle/input/lego-dataset/Test.csv')
train = pd.read_csv('/kaggle/input/lego-dataset/Train.csv')
train_img = []
for img_name in tqdm(train['name']):
image_path = '/... | code |
32069396/cell_18 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from torch import optim
from torch.autograd import Variable
from torch.optim import lr_scheduler
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, models, transforms
f... | code |
32069396/cell_15 | [
"image_output_1.png"
] | from PIL import Image
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import numbers
import numpy as np
import pandas as pd
test = pd.read_csv('/kaggle/input/lego-dataset/Test.csv')
train = pd.read_csv('/kaggle/input/lego-dataset/Train.csv')
train_img = []
for img_name in tqdm(train['name']):
... | code |
32069396/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
test = pd.read_csv('/kaggle/input/lego-dataset/Test.csv')
train = pd.read_csv('/kaggle/input/lego-dataset/Train.csv')
train.head() | code |
32069396/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from tqdm import tqdm
import cv2
import numbers
import numpy as np
import pandas as pd
test = pd.read_csv('/kaggle/input/lego-dataset/Test.csv')
train = pd.read_csv('/kaggle/input/lego-dataset/Train.csv')
train_img = []
for img_name in tqdm(train['name']):
image_path = '/kaggle/input/le... | code |
106198328/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data['Date'] = pd.to_datetime(stock_data['Date'])
ibov_visu = pd.read_csv('../input/ibovespa-index/ibovespa_info.csv') | code |
106198328/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.isna().mean() | code |
106198328/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.isna().mean()
round(stock_data.isna().mean().sum(), 2)
stock_data.isna().sum().sum() | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.