path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
1005893/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"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'))
import csv
import tflearn
import tensorflow as tf
from keras.utils.np_utils import to_categorical | code |
1005893/cell_5 | [
"text_plain_output_1.png"
] | from keras.utils.np_utils import to_categorical
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tflearn
df_trn = pd.read_csv('../input/train.csv')
df_tst = pd.read_csv('../input/test.csv')
x_trn = df_trn.ix[:, 1:].values
y_trn = df_trn.ix[:, 0].values
y_trn_cat ... | code |
326660/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
train_types = {'Agencia_ID': np.uint16, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
test_types = {'Agencia_ID': np.uint16, 'Ruta_SAK': np.uint16, 'Cliente_I... | code |
326660/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
train_types = {'Agencia_ID': np.uint16, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
test_types = {'Agencia_ID': np.uint16, 'Ruta_SAK'... | code |
326660/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
train_types = {'Agencia_ID': np.uint16, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
test_types = {'Agencia_ID': np.uint16, 'Ruta_SAK'... | code |
18141740/cell_4 | [
"image_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
df_source = pd.read_csv('../input/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, inplace=True)
df_source_time = df_source.copy()
df_source_time['rep_time'] = df_source_time['... | code |
18141740/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
from pandas import DataFrame
import datetime as dt
import matplotlib as mpl
import matplotlib.pyplot as plt
import os
import lowess as lo
print(os.listdir('../input')) | code |
18141740/cell_3 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | import datetime as dt
import pandas as pd
df_source = pd.read_csv('../input/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, inplace=True)
df_source_time = df_source.copy()
df_source_time['rep_time'] = df_source_time['rep_date'].apply(lambda x: dt.dat... | code |
18141740/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from pandas import DataFrame
import datetime as dt
import lowess as lo
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_source = pd.read_csv('../input/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, inplace=True)
df_sourc... | code |
73081315/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30days-folds/train_folds.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.head() | code |
73081315/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30days-folds/train_folds.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
sum(train.isnull().sum())
y = train['target']
features = train.drop(['target'], axis=1)
features.head() | code |
73081315/cell_7 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"te... | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd
import time
train = pd.read_csv('../input/30days-folds/train_folds.csv', index_col=0)
test = pd.rea... | code |
73081315/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30days-folds/train_folds.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
sum(train.isnull().sum()) | code |
72099958/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, figsize=(10,10))
ax=ax.ravel()
for index, col in enumera... | code |
72099958/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
df['site'] = df['site'].apply(lambda x: str(x))
df.info() | code |
72099958/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
df.info() | code |
72099958/cell_23 | [
"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
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, ... | code |
72099958/cell_20 | [
"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
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, ... | code |
72099958/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
for col in cat:
print(f'In {col}: {df[col].unique()}') | code |
72099958/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 |
72099958/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig, ax = plt.subplots(3, figsize=(10, 10))
ax = ax.ravel()
for index, col in en... | code |
72099958/cell_18 | [
"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
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, ... | code |
72099958/cell_15 | [
"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
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, ... | code |
72099958/cell_16 | [
"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
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, ... | code |
72099958/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/openintro-possum/possum.csv')
df | code |
72099958/cell_14 | [
"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
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, ... | code |
72099958/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, figsize=(10,10))
ax=ax.ravel()
for index, col in enumera... | code |
17137459/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 6))
sns.... | code |
17137459/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 6))
sns.... | code |
17137459/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
df1.head(10) | code |
17137459/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 6))
sns.... | code |
17137459/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 6))
sns.... | code |
73068056/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
df_train.head() | code |
73068056/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
df_test = pd.read_c... | code |
73068056/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import OrdinalEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squa... | code |
73068056/cell_27 | [
"text_html_output_1.png"
] | s = X_train.dtypes == 'object'
object_cols = list(s[s].index)
print('Categorical variables:')
print(object_cols) | code |
34139290/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2... | code |
34139290/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns | code |
34139290/cell_25 | [
"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
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2... | code |
34139290/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.head() | code |
34139290/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2... | code |
34139290/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
print(df.dtypes) | code |
34139290/cell_18 | [
"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
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2... | code |
34139290/cell_16 | [
"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
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2... | code |
34139290/cell_22 | [
"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
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2... | code |
34139290/cell_12 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2] for i in df['publication_date']]
df['decade'] = [int(i... | code |
34139290/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.describe(include='all') | code |
18116881/cell_63 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set.info() | code |
18116881/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
def missing_zero_values_table(df):
zero_val = (df == 0.0).astype(int).sum(axis=0)
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mz_table = pd.concat([zero_val, mis_val, mis_val... | code |
18116881/cell_57 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_56 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
def missing_zero_values_table(df):
zero_val = (df == 0.0).astype(int).sum(axis=0)
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mz_table = pd.concat([zero_val, mis_val, mis_val... | code |
18116881/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set['Embarked'][61] = 'S'
train_set['Embarked'][829] = 'S' | code |
18116881/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set['Name'] | code |
18116881/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_65 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_48 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
def extract_title(name):
return name.split(',')[1].split()[0].strip()
train_set['Title'] = train_set['Name'].apply(extract_title) | code |
18116881/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_54 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set['Title'].value_counts() | code |
18116881/cell_67 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_60 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set.head() | code |
18116881/cell_49 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set['Title'].value_counts() | code |
18116881/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set[train_set['Age'].isna()]['Sex'].value_counts() | code |
18116881/cell_58 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set.head() | code |
18116881/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set[(train_set['Pclass'] == 1) & (train_set['Embarked'] == 'Q')]['Sex'].value_counts() | code |
18116881/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set.describe() | code |
18116881/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_38 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_66 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set.head() | code |
18116881/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
test_set[test_set['Fare'].isna()] | code |
18116881/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_53 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
def refine_title(title):
if title in ['Mr.', 'Sir.', 'Major.', 'Dr.', 'Capt.']:
return 'mr'
elif title == 'Master.':
return 'master'
elif title in ['Miss.', 'Ms.']:
return 'miss'
elif ti... | code |
18116881/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set[train_set['Embarked'].isna()] | code |
18116881/cell_37 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, b... | code |
18116881/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=No... | code |
18116881/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv') | code |
128014857/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug | code |
128014857/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in ... | code |
128014857/cell_44 | [
"text_html_output_1.png"
] | from sklearn import linear_model, naive_bayes, neighbors, svm
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from typing import Tuple
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.... | code |
128014857/cell_39 | [
"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
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in ... | code |
128014857/cell_2 | [
"text_plain_output_1.png",
"image_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 |
128014857/cell_19 | [
"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 matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in ... | code |
128014857/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
df_drug['Age'].describe() | code |
128014857/cell_18 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.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
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in ... | code |
128014857/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
for name_col in name_cols:
print('\n')
plt.figure(... | code |
128014857/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in ... | code |
128014857/cell_24 | [
"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
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in ... | code |
128014857/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in ... | code |
128014857/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in ... | code |
128014857/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in ... | code |
128014857/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
df_drug.info() | code |
128014857/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.base import BaseEstimator, TransformerMixin
from typing import Tuple
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
def find_boxplot_boundaries(
col: pd.Series, Na_to_K_coeff: float = 1.5
) -... | code |
49129249/cell_21 | [
"text_plain_output_1.png"
] | from imutils import paths
from tqdm import tqdm
import torchvision.transforms as transforms
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
def create_img_df(dir):
img_list = list(paths.list_images(dir))
data... | code |
49129249/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from imutils import paths
from tqdm import tqdm
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
def create_img_df(dir):
img_list = list(paths.list_images(dir))
data = pd.DataFrame(columns=['File_name', 'Target... | code |
49129249/cell_23 | [
"text_plain_output_1.png"
] | from PIL import Image
from imutils import paths
from torch.utils.data import Dataset, random_split, DataLoader
from tqdm import tqdm
import torchvision.transforms as transforms
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superh... | code |
49129249/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from torch.utils.data import Dataset, random_split, DataLoader
batch_size = 50
input_size = 129 * 129
output_size = 12
train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=2, pin_memory=True)
val_dl = DataLoader(val_ds, batch_size * 2, num_workers=2, pin_memory=True)
for a, b in val_dl:
print(a.... | code |
49129249/cell_20 | [
"text_html_output_1.png"
] | from imutils import paths
from tqdm import tqdm
import torchvision.transforms as transforms
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
def create_img_df(dir):
img_list = list(paths.list_images(dir))
data... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.