path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2033003/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
import seaborn as sns
plt.close()
from mpl_toolkits.mplot3d import Axes3D
im... | code |
2033003/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
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
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
import seaborn as sn... | code |
2033003/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
haberman = pd.read_csv('../input/haberman.csv')
print(haberman.shape) | code |
2033003/cell_14 | [
"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
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
import seaborn as sns
plt.close()
sns.pairplot(haberman, hue='status')
plt.show() | code |
2033003/cell_10 | [
"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)
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
haberman['status'].value_counts() | code |
2033003/cell_12 | [
"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)
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
haberman.plot(kind='scatter', x='age', y='axil_nodes')
plt.show() | code |
2033003/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
haberman = pd.read_csv('../input/haberman.csv')
haberman.head(5) | code |
72084578/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import pandas as pd
import random
import tensorflow as tf
import tensorflow_hub as hub
train_dir = '../input/yoga-poses-dataset/DATA... | code |
72084578/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_dir = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_dir = '../input/yoga-poses-dataset/DATASET/TEST'
from tensorflow.keras.preprocessing.image import ImageDataGenerator
IMAGE_SHAPE = (224, 224)
BATCH_SIZE = 32
train_datagen = ImageDataGenera... | code |
72084578/cell_15 | [
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import pandas as pd
import random
import tensorflow as tf
import tensorflow_hub as hub
train_dir = '../input/yoga-poses-dataset/DATA... | code |
72084578/cell_14 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
import tensorflow_hub as hub
train_dir = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_dir = '../input/yoga-poses-dataset/DATASET/TEST'
from tensorflow.keras.preprocessing.image import... | code |
72084578/cell_12 | [
"image_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
import tensorflow_hub as hub
train_dir = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_dir = '../input/yoga-poses-dataset/DATASET/TEST'
from tensorflow.keras.preprocessing.image import... | code |
72084578/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import random
train_dir = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_dir = '../input/yoga-poses-dataset/DATASET/TEST'
def plot_yoga_images(train_dir):
for i, col in enumerate(os.listdir(train_dir)):
image = random.choice(o... | code |
128037854/cell_4 | [
"text_plain_output_1.png"
] | # 开始训练模型前50轮
!python /kaggle/working/yolov5-6-1ming/train.py --img 544 --batch 16 --epochs 50 --data /kaggle/working/widerpersonming/WiderPerson/person.yaml --cfg /kaggle/working/widerpersonming/yolov5s_SE.yaml | code |
128037854/cell_6 | [
"text_plain_output_1.png"
] | import datetime
import os
import os
import os
import zipfile
import os
import zipfile
import datetime
def file2zip(packagePath, zipPath):
"""
:param packagePath: 文件夹路径
:param zipPath: 压缩包路径
:return:
"""
zip = zipfile.ZipFile(zipPath, 'w', zipfile.ZIP_DEFLATED)
for path, dirNames, fileNames in o... | code |
128037854/cell_2 | [
"text_plain_output_1.png"
] | import os
filepath = '/kaggle/working/widerpersonming/WiderPerson/person.yaml'
datas = []
datas.append('train: /kaggle/working/widerpersonming/WiderPerson/train/images')
datas.append('\n')
datas.append('val: /kaggle/working/widerpersonming/WiderPerson/val/images')
datas.append('\n')
datas.append('nc: 1')
datas.append('... | code |
128037854/cell_1 | [
"text_plain_output_1.png"
] | import shutil
import shutil
shutil.copytree('../input/yolov5-6-1ming-with-se', './yolov5-6-1ming')
shutil.copytree('../input/widerpersonming-only-pedestrians', './widerpersonming') | code |
128037854/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from IPython.display import FileLink
FileLink('yolov5s_SE-50-544-simplified-one-layer-only-pedestrians.zip') | code |
128037854/cell_3 | [
"text_plain_output_1.png"
] | import os
filepath = '/kaggle/working/widerpersonming/WiderPerson/person.yaml'
datas = []
datas.append('train: /kaggle/working/widerpersonming/WiderPerson/train/images')
datas.append('\n')
datas.append('val: /kaggle/working/widerpersonming/WiderPerson/val/images')
datas.append('\n')
datas.append('nc: 1')
datas.append('... | code |
128025412/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
display(df_iris.head(3))
display(df_iris.tail(3))
display(df_iris.describe()) | code |
128025412/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size() | code |
128025412/cell_26 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
... | code |
128025412/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
from pandas.plotting import andrews_curves
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
from sklearn.metrics import RocCurveDisplay, classification_report
fr... | code |
128025412/cell_11 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | from pandas.plotting import andrews_curves
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv')... | code |
128025412/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import RocCurveDisplay, classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import pandas as pd
df_ir... | code |
128025412/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
... | code |
128025412/cell_15 | [
"text_html_output_4.png",
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_ir... | code |
128025412/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas.plotting import andrews_curves
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import RocCurveDisplay, classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder... | code |
128025412/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas.plotting import andrews_curves
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import matplotlib.pyplot as plt
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Spe... | code |
128025412/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import RocCurveDisplay, classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import pandas as pd
df_ir... | code |
128025412/cell_12 | [
"text_plain_output_1.png"
] | from pandas.plotting import andrews_curves
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv')... | code |
50242767/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.linear_mo... | code |
50242767/cell_9 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegressio... | code |
50242767/cell_25 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
data[data['Age'] == 30] | code |
50242767/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv') | code |
50242767/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.linear_model import LinearReg... | code |
50242767/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialF... | code |
50242767/cell_15 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.linear_model import LinearReg... | code |
50242767/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
data.head(4) | code |
50242767/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.linear_mo... | code |
50242767/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
sns.scatterplot(x='Age', y='Height', data=data) | code |
88090787/cell_42 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape) | code |
88090787/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.describe() | code |
88090787/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.info() | code |
88090787/cell_4 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns | code |
88090787/cell_56 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
... | code |
88090787/cell_33 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
g = sns.FacetGrid(data=train, col='Survived')
g = g.map(plt.hist, 'Age', bins=25)
grid = sns.FacetGrid(data=train, col='Survived... | code |
88090787/cell_44 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
pd.crosstab(train['T... | code |
88090787/cell_55 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
... | code |
88090787/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.head() | code |
88090787/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train[['Parch', 'Survived']].groupby('Parch', as_index=False).mean().sort_values(by='Parch', ascending=True) | code |
88090787/cell_39 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
g = sns.FacetGrid(data=train, col='Survived')
g = g.map(plt.hist, 'Age', bins=25)
grid = sns.FacetGrid(data=train, col='Survived... | code |
88090787/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train[['Sex', 'Survived']].groupby('Sex', as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
88090787/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]... | code |
88090787/cell_54 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
... | code |
88090787/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.info() | code |
88090787/cell_50 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
g = sns.FacetGrid(data=train, col='Survived')
g = g.map(plt.hist, 'Age', bins=25)
gr... | code |
88090787/cell_52 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
... | code |
88090787/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]... | code |
88090787/cell_49 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]... | code |
88090787/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.describe(include=['O']) | code |
88090787/cell_51 | [
"text_html_output_1.png"
] | import numpy as np
ages = np.zeros((2, 3))
ages | code |
88090787/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train[['SibSp', 'Survived']].groupby('SibSp', as_index=False).mean().sort_values(by='SibSp', ascending=True) | code |
88090787/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]... | code |
88090787/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape | code |
88090787/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
train['Title'] = train['Name'].str.extract('([A-Za-z]+)\\.')
tes... | code |
88090787/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
g = sns.FacetGrid(data=train, col='Survived')
g = g.map(plt.hist, 'Age', bins=25) | code |
88090787/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
88090787/cell_53 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
... | code |
88090787/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
g = sns.FacetGrid(data=train, col='Survived')
g = g.map(plt.hist, 'Age', bins=25)
grid = sns.FacetGrid(data=train, col='Survived... | code |
88090787/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.head() | code |
1006521/cell_4 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
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)
import plotly.graph_objs as go
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
imp... | code |
1006521/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
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)
import plotly.graph_objs as go
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
imp... | code |
1006521/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 |
1006521/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
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)
import plotly.graph_objs as go
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
imp... | code |
72065241/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sweetviz
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
report = sweetviz.analyze([train_data, 'train'], 'target') | code |
72065241/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 |
72065241/cell_3 | [
"text_html_output_1.png"
] | !pip install sweetviz | code |
72065241/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sweetviz
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
report = sweetviz.analyze([train_data, 'train'], 'target')
my_report = sweetviz.compare([train_data, 'train']... | code |
72065241/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sweetviz
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
report = sweetviz.analyze([train_data, 'train'], 'target')
report.show_notebook() | code |
130003745/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_27.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_24.png",
"text_plain_output_2... | from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import fbeta_score,accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors... | code |
130003745/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import fbeta_score,accuracy_score
from time import time
import numpy as np # linear algebra
from sklearn.metrics import fbeta_score, accuracy_score
from time import time
def train_predict(learner, sample_size, X_train, y_train, X_test, y_test):
"""
inputs:
- learner: the learning ... | code |
130003745/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 |
327044/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csva') | code |
327044/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 |
34120494/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
diabetes_data = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
diabetes_data[:60]
diabetes_data.shape
diabetes_data.isna().sum()
diabetes_data.dtypes | code |
34120494/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.svm import LinearSVC
svc_model = LinearSVC(max_iter=10000)
svc_model.fit(X_train, y_train) | code |
34120494/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
diabetes_data = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
diabetes_data.head() | code |
34120494/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
import numpy as np
svc_model = LinearSVC(max_iter=10000)
svc_model.fit(X_train, y_train)
svc_score = svc_model.score(X_test, y_test)
if svc_score < 0.6:
print(f'SVC Model Score is Less : {svc_score}'.format())
else:
random... | code |
34120494/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
diabetes_data = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
diabetes_data[:60]
diabetes_data.shape | code |
104119796/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/destiny-2-guns/guns.csv')
features = ['Element ', 'Rarity']
fig, ax = plt.subplots(1, len(features), figsize=(16, 5), sharex=False)
for cnt, feature in enumerate(['Element ', 'Rarity']):
df.groupby(['weapon_type', feature]).size(... | code |
104119796/cell_1 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import warnings
sns.set_style('darkgrid')
warnings.filterwarnings('ignore')
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
... | code |
104119796/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import warnings
sns.set_style('darkgrid')
warnings.filterwarnings('ignore')
df = pd.read_csv('/kaggle/input/des... | code |
88103172/cell_4 | [
"text_plain_output_1.png"
] | from aitextgen import aitextgen
from aitextgen.TokenDataset import TokenDataset
from aitextgen.tokenizers import train_tokenizer
from aitextgen.utils import GPTNeoConfigCPU
file_name = '/kaggle/input/annomi/dataset.txt'
train_tokenizer(file_name)
tokenizer_file = 'aitextgen.tokenizer.json'
config = GPTNeoConfigCPU... | code |
88103172/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from aitextgen import aitextgen
from aitextgen.TokenDataset import TokenDataset
from aitextgen.tokenizers import train_tokenizer
from aitextgen.utils import GPTNeoConfigCPU
file_name = '/kaggle/input/annomi/dataset.txt'
train_tokenizer(file_name)
tokenizer_file = 'aitextgen.tokenizer.json'
config = GPTNeoConfigCPU... | code |
88103172/cell_1 | [
"text_plain_output_1.png"
] | ! pip install aitextgen | code |
88103172/cell_7 | [
"text_plain_output_1.png"
] | from aitextgen import aitextgen
ai2 = aitextgen(model_folder='./trained_model', tokenizer_file='aitextgen.tokenizer.json')
ai2.generate(10, prompt='<START>\nTHERAPIST:\nHi, Emily.\nCLIENT:\nHi. I am feeling low and have been drinking alcohol every day. What do you think that is happening\n') | code |
50240545/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/catch-me-if-you-can/train_sessions.csv', index_col='session_id')
test_df = pd.read_csv('../input/catch-me-if-you-can/test_sessions.csv', index_col='session_id')
features = pd.DataFrame()
ti... | code |
50240545/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/catch-me-if-you-can/train_sessions.csv', index_col='session_id')
test_df = pd.read_csv('../input/catch-me-if-you-can/test_sessions.csv', index_col='session_id')
train_df.head() | code |
50240545/cell_5 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/catch-me-if-you-can/train_sessions.csv', index_col='session_id')
test_df = pd.read_csv('../input/catch-me-if-you-can/test_sessions.csv', index_col='session_id')
features = pd.DataFrame()
timepoints = train_df[['time%s' % i for i in range(1, 11)]... | code |
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