path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90122454/cell_33 | [
"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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt()
data_train.Age.skew()
sns.displot(data_train.SibSp, kde=Tr... | code |
90122454/cell_29 | [
"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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt()
data_train.Age.skew() | code |
90122454/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
sns.displot(data_train.Pclass, kde=False) | code |
90122454/cell_18 | [
"text_plain_output_1.png",
"image_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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Pclass.describe() | code |
90122454/cell_32 | [
"text_plain_output_1.png",
"image_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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt()
data_train.Age.skew()
data_train.SibSp.describe() | code |
90122454/cell_28 | [
"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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt() | code |
90122454/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
sns.displot(data_train.Survived, kde=False) | code |
90122454/cell_16 | [
"text_plain_output_1.png",
"image_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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
plt.figure(figsize=(24, 4))
sns.barplot(x=data_train.index[0:200], y=data_train.Su... | code |
90122454/cell_35 | [
"text_plain_output_1.png",
"image_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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt()
data_train.Age.skew()
data_train.Parch.describe() | code |
90122454/cell_24 | [
"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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Sex.describe() | code |
90122454/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Survived.describe() | code |
90122454/cell_22 | [
"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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train['Name'].value_counts() | code |
90122454/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.head() | code |
90122454/cell_27 | [
"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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.describe() | code |
90122454/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.PassengerId.describe() | code |
90122454/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import norm, expon
from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, StandardScaler, minmax_scale
from... | code |
90137202/cell_42 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_13 | [
"text_html_output_1.png"
] | 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 seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_te... | code |
90137202/cell_9 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegressio... | code |
90137202/cell_30 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_33 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_44 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_40 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_39 | [
"image_output_1.png"
] | 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 seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_te... | code |
90137202/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_48 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_41 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/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 |
90137202/cell_7 | [
"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 seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegressio... | code |
90137202/cell_51 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_8 | [
"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 seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegressio... | code |
90137202/cell_15 | [
"text_plain_output_1.png"
] | 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 seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_te... | code |
90137202/cell_38 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_47 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_17 | [
"text_plain_output_1.png"
] | 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 seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_te... | code |
90137202/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_43 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_46 | [
"image_output_1.png"
] | 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 seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_te... | code |
90137202/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_14 | [
"text_plain_output_1.png"
] | 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 seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_te... | code |
90137202/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_53 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90137202/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegressio... | code |
90137202/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
73081309/cell_4 | [
"image_output_1.png"
] | import histomicstk as htk
import matplotlib.pyplot as plt
input_image_file = ('https://data.kitware.com/api/v1/file/'
'576ad39b8d777f1ecd6702f2/download') # Easy1.png
im_input = skimage.io.imread(input_image_file)[:, :, :3]
plt.imshow(im_input)
_ = plt.title('Input Image', fontsize=16)
ref_ima... | code |
73081309/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import histomicstk as htk
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
input_image_file = ('https://data.kitware.com/api/v1/file/'
'576ad39b8d777f1ecd6702f2/download') # Easy1.png
im_input = skimage.io.imread(input_image_file)[:, ... | code |
73081309/cell_1 | [
"text_plain_output_1.png"
] | !pip install histomicstk --find-links https://girder.github.io/large_image_wheels | code |
73081309/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
input_image_file = 'https://data.kitware.com/api/v1/file/576ad39b8d777f1ecd6702f2/download'
im_input = skimage.io.imread(input_image_file)[:, :, :3]
plt.imshow(im_input)
_ = plt.title('Input Image', fontsize=16) | code |
73081309/cell_5 | [
"image_output_1.png"
] | import histomicstk as htk
import matplotlib.pyplot as plt
import numpy as np
input_image_file = ('https://data.kitware.com/api/v1/file/'
'576ad39b8d777f1ecd6702f2/download') # Easy1.png
im_input = skimage.io.imread(input_image_file)[:, :, :3]
plt.imshow(im_input)
_ = plt.title('Input Image', f... | code |
1005122/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/HR_comma_sep.csv')
data.head() | code |
72101196/cell_21 | [
"text_html_output_1.png"
] | from matplotlib.lines import Line2D
import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula ... | code |
72101196/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.co... | code |
72101196/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.columns.values[1:])
formula | code |
72101196/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.co... | code |
72101196/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.head() | code |
72101196/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.co... | code |
72101196/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.co... | code |
72101196/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.co... | code |
72101196/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.co... | code |
72101196/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.co... | code |
106209898/cell_4 | [
"text_plain_output_1.png"
] | def add(a, b):
pass
def add(a, b):
return a + b
addition = add(2, 10)
addition = addition + 10
print(addition) | code |
106209898/cell_6 | [
"text_plain_output_1.png"
] | def add(a, b):
pass
def add(a, b):
return a + b
def add(a, b):
add = a + b
sub = a - b
div = a / b
mul = a * b
return (add, sub, div, mul)
a, b, c, d = add(2, 10)
print(a, b, c, d) | code |
106209898/cell_2 | [
"text_plain_output_1.png"
] | def add(a, b):
pass
add(2, 10) | code |
2001740/cell_4 | [
"text_plain_output_1.png"
] | from datetime import datetime
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.read_csv('../input/air_st... | code |
2001740/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import plotly.plotly as py
import numpy as np
import pandas as pd
from subprocess import check_output
pd_air_reserve = pd.read_csv('../input/air_re... | code |
2001740/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.read_csv('../input/air_store_info.csv')
pd_air_visit_dat... | code |
2001740/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.r... | code |
2001740/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from datetime import datetime
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.read_csv('../input/air_st... | code |
2001740/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.read_csv('../input/air_st... | code |
1006177/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names() | code |
1006177/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']]
data_y = data_train['Survived']
age_mean = data_x['Age'].dropna().me... | code |
1006177/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex', 'Si... | code |
1006177/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex', 'Si... | code |
1006177/cell_16 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv'... | code |
1006177/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.info() | code |
1006177/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']]
data_y = data_train['Survived']
age_mean = data_x['Age'].dropna().median()
fare_mean = data_x['Fare'].dropna().median()
da... | code |
17101114/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain) | code |
17101114/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain)
joblib_file = 'joblib_RL_Model.pkl'
joblib.dump(LR_Model, joblib_file) | code |
17101114/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.externals import joblib | code |
17101114/cell_29 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
import pickle
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain)
Pkl_Filename = 'Pickle_RL_Model.pkl'
with open(Pkl_Filename, 'wb') as file:
pick... | code |
17101114/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pickle
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain)
Pkl_Filename = 'Pickle_RL_Model.pkl'
with open(Pkl_Filename, 'wb') as file:
pickle.dump(LR_Model, file)
with open(Pkl... | code |
17101114/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pickle
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain)
Pkl_Filename = 'Pickle_RL_Model.pkl'
with open(Pkl_Filename, 'wb') as file:
pickle.dump(LR_Model, file)
with open(Pkl... | code |
17101114/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain)
joblib_file = 'joblib_RL_Model.pkl'
joblib.dump(LR_Model, joblib_file)
joblib_LR_model = joblib.lo... | code |
129022282/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
df_train['Status'].value_counts() | code |
129022282/cell_25 | [
"text_html_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
smt = SMOTE(random_state=0)
xtrain_res, ytrain_res = smt.fit_resample(xtrain, ytrain)
rf = RandomForestClassifier(max_depth=3, random_state=0)
rf.fit(xtrain_res, ytrain_res) | code |
129022282/cell_23 | [
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
smt = SMOTE(random_state=0)
xtrain_res, ytrain_res = smt.fit_resample(xtrain, ytrain)
print(f'Distribuition BEFORE balancing:\n{ytrain.value_counts()}')
print()
print(f'Distribuition AFTER balancing:\n{ytrain_res.value_counts()}') | code |
129022282/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_test.head() | code |
129022282/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
smt = SMOTE(random_state=0)
xtrain_res, ytrain_res = smt.fit_resample(xtrain, ytrain)
rf = RandomForestClassifier(max_depth=3, random_state=0)
rf.fit(xtrain_res, ytrain_res)
ypred... | code |
129022282/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
columns_list... | code |
129022282/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.info() | code |
129022282/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
columns_list... | code |
129022282/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum() | code |
129022282/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
columns_list = list(df_train.column... | code |
129022282/cell_31 | [
"text_html_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = ... | code |
129022282/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
columns_list = list(df_train.columns)
plt.figure(figsize=(12, 20))
for i in range(l... | code |
129022282/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum() | code |
129022282/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.head() | code |
72106185/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
train
train.drop('id', axis=1, inplace=True)
na_count = pd.Series([train[col].isna().sum() for col in train.co... | code |
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