path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72107386/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
print('The training set is {}.'.format(train.shape))
print('The test set is {}.'.format(test.shape)) | code |
17141799/cell_21 | [
"text_html_output_1.png"
] | from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder
from tensorflow.python.framework import ops
import math
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_... | code |
17141799/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"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 pylab
import seaborn as sns
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv'... | code |
17141799/cell_4 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
def Income_bracket_binarization(feat_val):
if... | code |
17141799/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
dataset.head() | code |
17141799/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.python.framework import ops
import sklearn
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pylab
from sklearn.preprocessing import LabelEncoder
from sklearn.base import BaseEstim... | code |
17141799/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
def Income_bracket_binarization(feat_val):
if... | code |
17141799/cell_15 | [
"text_html_output_1.png"
] | from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pylab
import seaborn as sns
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.cs... | code |
17141799/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
test_dataset.head() | code |
17141799/cell_12 | [
"text_html_output_1.png"
] | from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pylab
import seaborn as sns
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.cs... | code |
17141799/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
test_dataset.head() | code |
16145643/cell_21 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16145643/cell_25 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16145643/cell_20 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16145643/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import missingno
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShe... | code |
16145643/cell_11 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16145643/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import In... | code |
16145643/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16145643/cell_38 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=100)
clf.fit(x_train, y_train)
clf.score(x_train, y_train) | code |
16145643/cell_3 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16145643/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
pd.set_option('display.max_columns', 1000)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShel... | code |
16145643/cell_22 | [
"text_plain_output_1.png"
] | sortlabels = defaultdict()
['No', 'Gd', 'Mn', 'Av', 'none']
['1.No', '4.Gd', '2.Mn', '3.Av', '0.none']
['Typ', 'Min1', 'Maj1', 'Min2', 'Mod', 'Maj2', 'Sev']
['none', 'TA', 'Gd', 'Fa', 'Ex', 'Po']
['0.none', '3.TA', '4.Gd', '2.Fa', '5.Ex', '1.Po']
['Y', 'N', 'P']
['3.Y', '1.N', '2.P']
['none', 'MnPrv', 'GdWo', 'GdPrv', ... | code |
17115300/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import model_selection
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
url = '../input/d... | code |
17115300/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
url = '../input/data.csv'
df = pd.read_csv(url, index_col=0)
df.head(3) | code |
17115300/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17115300/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import model_selection
from sklearn.ensemble import BaggingClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
url = '../input/data.csv'
df = pd.read_csv(url, index_col=0)
df.r... | code |
88099839/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
88099839/cell_57 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
88099839/cell_56 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
88099839/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
88099839/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
ax.set_title('Correlatio... | code |
88099839/cell_30 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
88099839/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
88099839/cell_55 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
boxplot_data = data.drop(['Overall rank', 'Country or region', 'Score'], axis=1)
props = dict(boxes='darkblue', whiskers='black', medians='red', caps='black')
boxplot_data.plot.box(color=props, patch_ar... | code |
88099839/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
histogram_data = data[['Score', 'GDP per capita']]
histogram_data.head(3) | code |
88099839/cell_41 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
histogram_data = data[['Score', 'GDP per capita']]
histogram_data.plot.hist(bins=40, color=['darkblue', 'darkgreen'], figsize=(10, 4), edgecolor='black', lw=1) | code |
88099839/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/world-happiness/2019.csv')
data.head() | code |
88099839/cell_50 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
88099839/cell_45 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
88099839/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
fig, ax = plt.subplots(figsize=(6, 6))
cp = ax.matshow(correlation_matrix_data)
ax.set_title('Correlation Matrix Plot')
for (i, j), z in np.ndenumerat... | code |
88099839/cell_59 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
88099839/cell_43 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
88099839/cell_53 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
boxplot_data = data.drop(['Overall rank', 'Country or region', 'Score'], axis=1)
boxplot_data.head(3) | code |
88099839/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/world-happiness/2019.csv')
correlation_matrix_data = data.corr()
# Correlation Matrix function in Matplotlib 👩🚒
fig, ax= plt.subplots(figsize=(6,6))
cp=ax.matshow(correlation_matrix_data)
a... | code |
129028814/cell_4 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/covid19-dataset/Covid Data.csv')
df.loc[df.DATE_DIED != '9999-99-99', 'DATE_DIED'] = 1
df.loc[df.DATE_DIED == '9999-99-99', 'DATE_DIED'] =... | code |
129028814/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/covid19-dataset/Covid Data.csv')
df.loc[df.DATE_DIED != '9999-99-99', 'DATE_DIED'] = 1
df.loc[df.DATE_DIED == '9999-99-99', 'DATE_DIED'] = 2
columns = ['USMER',... | code |
129028814/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/covid19-dataset/Covid Data.csv')
df.head() | code |
129028814/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 |
129028814/cell_8 | [
"image_output_4.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/covid19-dataset/Covid Data.csv')
df.loc[df.DATE_DIED != '9999-99-99', 'DATE_DIED'] = 1
df.loc[df.DATE_DIED == '9999-99-99', 'DATE_DIED'] =... | code |
129028814/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/covid19-dataset/Covid Data.csv')
df.loc[df.DATE_DIED != '9999-99-99', 'DATE_DIED'] = 1
df.loc[df.DATE_DIED == '9999-99-99', 'DATE_DIED'] = 2
df.tail() | code |
129028814/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/covid19-dataset/Covid Data.csv')
df.loc[df.DATE_DIED != '9999-99-99', 'DATE_DIED'] = 1
df.loc[df.DATE_DIED == '9999-99-99', 'DATE_DIED'] = 2
columns = ['USMER',... | code |
90129087/cell_21 | [
"text_plain_output_1.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
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
train['Age'].hist(bins=30, color='orange', alpha=0.7) | code |
90129087/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
fig, ax = plt.subplots(1,2,figsize=(12,6)... | code |
90129087/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull() | code |
90129087/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
fig, ax = plt.subplots(1,2,figsize=(12,6)... | code |
90129087/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
fig, ax = plt.subplots(1,2,figsize=(12,6)... | code |
90129087/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
fig, ax = plt.subplots(1,2,figsize=(12,6)... | code |
90129087/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.head(3) | code |
90129087/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
fig, ax = plt.subplots(1, 2, figsize=(12,... | code |
90129087/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
fig, ax = plt.subplots(1,2,figsize=(12,6)... | code |
90129087/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 |
90129087/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns | code |
90129087/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
fig, ax = plt.subplots(1,2,figsize=(12,6)... | code |
90129087/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
fig, ax = plt.subplots(1,2,figsize=(12,6)... | code |
90129087/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
fig, ax = plt.subplots(1,2,figsize=(12,6)... | code |
90129087/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
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.isnull()
sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='OrRd_r') | code |
90138335/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from kaggle_datasets import KaggleDatasets
import matplotlib.pyplot as plt
import numpy as np
import re
import tensorflow as tf
HEIGHT = 256
WIDTH = 256
BATCH_SIZE = 1
CHANNELS = 3
LAMBDA = 10
EPOCHS = 250
GCS_PATH = KaggleDatasets().get_gcs_path('gan-getting-started')
try:
tpu = tf.distribute.cluster_resolve... | code |
90138335/cell_20 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from kaggle_datasets import KaggleDatasets
import matplotlib.pyplot as plt
import numpy as np
import re
import tensorflow as tf
HEIGHT = 256
WIDTH = 256
BATCH_SIZE = 1
CHANNELS = 3
LAMBDA = 10
EPOCHS = 250
GCS_PATH = KaggleDatasets().get_gcs_path('gan-getting-started')
try:
tpu = tf.distribute.cluster_resolve... | code |
90138335/cell_6 | [
"image_output_1.png"
] | import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print(f'Running on TPU {tpu.master()}')
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimenta... | code |
90138335/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
import numpy as np
import re
import tensorflow as tf
HEIGHT = 256
WIDTH = 256
BATCH_SIZE = 1
CHANNELS = 3
LAMBDA = 10
EPOCHS = 250
GCS_PATH = KaggleDatasets().get_gcs_path('gan-getting-started')
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except Val... | code |
90138335/cell_16 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
from tensorflow.keras import layers, Model, losses, optimizers
import numpy as np
import re
import tensorflow as tf
HEIGHT = 256
WIDTH = 256
BATCH_SIZE = 1
CHANNELS = 3
LAMBDA = 10
EPOCHS = 250
GCS_PATH = KaggleDatasets().get_gcs_path('gan-getting-started')
try:
tpu ... | code |
122255609/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_4 | [
"text_html_output_1.png"
] | db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials | code |
122255609/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_2 | [
"text_html_output_1.png"
] | !pip install psycopg2-binary | code |
122255609/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
122255609/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
db = 'new_db'
user = 'postgres'
passwd = '1234'
port = 5432
host = 'ec2-44-201-58-213.compute-1.amazonaws.com'
credentials = 'postgresql://{}:{}@{}:{}/{}'.format(user, passwd, host, port, db)
credentials
def schemaGen(dataframe, schemaName):
localSchema = pd.io.sql.get_schema(dataframe, schema... | code |
73080198/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
cutoff = 5
print(f"{(train['target'] < cutoff).sum() / len(train) * 100:.3f}% of the target values are less than {cutoff}") | code |
73080198/cell_9 | [
"image_output_1.png"
] | from scipy import stats
from scipy import stats
import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
from scipy import stats
stats.mstats.skew(train['target']).data
from scipy import stats
sta... | code |
73080198/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
predictions_base = pd.read_csv('/kaggle/input/submissionstevenferrercsv/submissionStevenFerrer.csv', low_memory=False)
predictions_base.head(... | code |
73080198/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
fig, ax = plt.subplots(figsize=(12, 6))
bars = ax.hist(train["target"],
b... | code |
73080198/cell_20 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
cuts = [i / 1000 for i in range(6940, 10400)]
train['target'].value_counts(bins=cuts)
cuts = [i / 1000 for i in range(8050, 8150)]
train['tar... | code |
73080198/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
predictions_base = pd.read_csv('/kaggle/input/submissionstevenferrercsv/submissionStevenFerrer.csv', low_memory=False)
predictions_base.head(... | code |
73080198/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
fig, ax = plt.subplots(figsize=(12, 6))
bars = ax.hist(train["target"],
b... | code |
73080198/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import random
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
import os
for dirname, _, filen... | code |
73080198/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
fig, ax = plt.subplots(figsize=(12, 6))
bars = ax.hist(train["target"],
bins=40,
... | code |
73080198/cell_8 | [
"text_plain_output_1.png"
] | from scipy import stats
import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
from scipy import stats
stats.mstats.skew(train['target']).data | code |
73080198/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
fig, ax = plt.subplots(figsize=(12, 6))
bars = ax.hist(train["target"],
bins=40,
... | code |
73080198/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
cuts = [i / 1000 for i in range(6940, 10400)]
train['target'].value_counts(bins=cuts) | code |
73080198/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
predictions_base = pd.read_csv('/kaggle/input/submissionstevenferrercsv/submissionStevenF... | code |
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