path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18161218/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import pandas_profiling as pp
import os
print(os.listdir('../input')) | code |
18161218/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas_profiling as pp
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
profile = pp.ProfileReport(data)
profile.to_file('HousingSales.html')
pp.P... | code |
18161218/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
missing_list = data.columns[data.isna().any()].tolist()
data.columns... | code |
18161218/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
missing_list = data.columns[data.isna().any()].tolist()
data.columns... | code |
18161218/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape | code |
18161218/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
missing_list = data.columns[data.isna().any()].tolist()
data.columns... | code |
18161218/cell_14 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
missing_list = data.columns[data.isna().any()].tolist()
data.columns... | code |
18161218/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv', dtype={'YearBuilt': 'str', 'YrSold': 'str', 'GarageYrBlt': 'str', 'YearRemodAdd': 'str'})
data.shape
missing_list = data.columns[data.isna().any()].tolist()
data.columns[data.isna().any()].tolist()
data.sh... | code |
2021553/cell_13 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.... | code |
2021553/cell_9 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=T... | code |
2021553/cell_11 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.... | code |
2021553/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2021553/cell_7 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=T... | code |
2021553/cell_16 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.... | code |
2021553/cell_17 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.... | code |
2021553/cell_5 | [
"text_html_output_1.png"
] | with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
len(CR) | code |
50235721/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data['deadline'] = pd.to_datetime(data['deadline'])
dat... | code |
50235721/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data['deadline'] = pd.to_datetime(data['deadline'])
dat... | code |
50235721/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data['deadline'] = pd.to_datetime(data['deadline'])
dat... | code |
50235721/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data.info() | code |
50235721/cell_23 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data['deadline'] = pd.to_datetime(data['deadline'])
dat... | code |
50235721/cell_20 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data['deadline'] = pd.to_datetime(data['deadline'])
dat... | code |
50235721/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data.isnull().sum() | code |
50235721/cell_19 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data['deadline'] = pd.to_datetime(data['deadline'])
dat... | code |
50235721/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data.isnull().sum()
data[data['usd pledged'].isnull()]... | code |
50235721/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data['deadline'] = pd.to_datetime(data['deadline'])
dat... | code |
50235721/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import squarify
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data[... | code |
50235721/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5) | code |
50235721/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data['deadline'] = pd.to_datetime(data['deadline'])
dat... | code |
50235721/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data['deadline'] = pd.... | code |
50235721/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:,}'.format
data = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', index_col='ID')
data.sample(5)
data['deadline'] = pd.to_datetime(data['deadline'])
dat... | code |
17111721/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Arguments:
df: dataframe
Ou... | code |
17111721/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by co... | code |
17111721/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Arguments:
df: dataframe
Ou... | code |
17111721/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
print('Training data shape: ', app_train.shape)
app_train.head() | code |
17111721/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Arguments:
df: dataframe
Ou... | code |
17111721/cell_39 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculat... | code |
17111721/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Arguments:
df: dataframe
Ou... | code |
17111721/cell_48 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
... | code |
17111721/cell_41 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculat... | code |
17111721/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_train['TARGET'].value_counts() | code |
17111721/cell_19 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Arguments:
df: dataframe
Ou... | code |
17111721/cell_50 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
... | code |
17111721/cell_49 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
... | code |
17111721/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_train.dtypes.value_counts() | code |
17111721/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Arguments:
df: dataframe
Ou... | code |
17111721/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
print('Testing data shape:', app_test.shape)
app_test.head() | code |
17111721/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Arguments:
df: dataframe
Ou... | code |
17111721/cell_35 | [
"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)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Argume... | code |
17111721/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Arguments:
df: dataframe
Ou... | code |
17111721/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Arguments:
df: dataframe
Ou... | code |
17111721/cell_46 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculat... | code |
17111721/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_test = pd.read_csv('../input/application_test.csv')
def missing_values_table(df):
"""
Function to calculate missing values by column
Arguments:
df: dataframe
Ou... | code |
17111721/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
app_train = pd.read_csv('../input/application_train.csv')
app_train['TARGET'].astype(int).plot.hist() | code |
17111721/cell_5 | [
"image_output_1.png"
] | import os
print(os.listdir('../input/')) | code |
90108087/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
train_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/test.csv')
sample_sub = pd.read_csv('/kag... | code |
90108087/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
train_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/test.csv')
sample_sub = pd.read_csv('/kag... | code |
90108087/cell_23 | [
"text_plain_output_1.png"
] | """
train_df[train_df["number_na10_away_previous_games"]>0]
list_na_columns = []
for index, row in train_df[train_df["number_na10_away_previous_games"]>0].iterrows():
historical_columns_template_home = [x for x in historical_columns_template if x.startswith("away")]
for column in historical_columns_template_hom... | code |
90108087/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
train_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/test.csv')
sample_sub = pd.read_csv('/kag... | code |
90108087/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
train_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/test.csv')
sample_sub = pd.read_csv('/kag... | code |
90108087/cell_19 | [
"text_html_output_1.png"
] | """
train_df[train_df["number_na10_home_previous_games"]>0]
list_na_columns = []
for index, row in train_df[train_df["number_na10_home_previous_games"]>0].iterrows():
historical_columns_template_home = [x for x in historical_columns_template if x.startswith("home")]
for column in historical_columns_template_hom... | code |
90108087/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 |
90108087/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
train_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/test.csv')
sample_sub = pd.read_csv('/kag... | code |
90108087/cell_15 | [
"text_plain_output_1.png"
] | """
def count_not_na(row, home):
number_previous_games = row[f"number_{home}_previous_games"]
if number_previous_games!=10:
number_previous_games+=1
else:
return 0
count_notna = 0
historical_columns_template_home = [x for x in historical_columns_template if x.startswith(home)]
fo... | code |
90108087/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
train_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/football-match-probability-prediction/test.csv')
sample_sub = pd.read_csv('/kag... | code |
32068788/cell_6 | [
"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 pandas as pd
import numpy as np
data = pd.read_csv('../input/digit-recognizer/train.csv')
X = data.drop('label', axis=1).values
s = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
... | code |
32068788/cell_2 | [
"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 pandas as pd
import numpy as np
data = pd.read_csv('../input/digit-recognizer/train.csv')
X = data.drop('label', axis=1).values
s = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
... | code |
32068788/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 |
32068788/cell_7 | [
"text_plain_output_1.png"
] | (106862.31418188661 - 101041.82401718189) / 101041.82401718189 | code |
32068788/cell_3 | [
"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 time
import pandas as pd
import numpy as np
data = pd.read_csv('../input/digit-recognizer/train.csv')
X = data.drop('label', axis=1).values
s = [0, 1, 2, 3, 4, 5,... | code |
32068788/cell_5 | [
"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 time
import pandas as pd
import numpy as np
data = pd.read_csv('../input/digit-recognizer/train.csv')
X = data.drop('label', axis=1).values
s = [0, 1, 2, 3, 4, 5,... | code |
88101762/cell_13 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import load_img,img_to_array
import numpy as np
import os
import tensorflow as tf
stored_data_path = '../input/magnetictiledefect'
CLASS_NAME = ['Blowhole', 'Free', 'Break', ... | code |
88101762/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import load_img,img_to_array
import numpy as np
import os
stored_data_path = '../input/magnetictiledefect'
CLASS_NAME = ['Blowhole', 'Free', 'Break', 'Fray', 'Uneven', 'Crack'... | code |
88101762/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import load_img,img_to_array
import numpy as np
import os
stored_data_path = '../input/magnetictiledefect'
CLASS_NAME = ['Blowhole', 'Free', 'Break', 'Fray', 'Uneven', 'Crack'... | code |
88101762/cell_20 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from sklearn.metrics import classification_report
from tensorflow.keras.layers import Conv2D, MaxPooling2D,GlobalAveragePooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow... | code |
88101762/cell_2 | [
"text_plain_output_1.png"
] | import os
print(os.listdir('../input/magneticTileDefect')) | code |
88101762/cell_11 | [
"text_plain_output_1.png"
] | print('Shape X_train_raw', X_train_raw.shape)
print('Shape y_train', y_train.shape)
print('Shape X_test_raw', X_test_raw.shape)
print('Shape y_test', y_test.shape) | code |
88101762/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import load_img,img_to_array
import numpy as np
import os
stored_data_path = '../input/magnetictiledefect'
CLASS_NAME = ['Blowhole', 'Free', 'Break', 'Fray', 'Uneven', 'Crack'... | code |
88101762/cell_18 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import array_to_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from sklearn.metrics import classification_report
from tensorflow.keras.layers import Conv2D, MaxPooling2D,GlobalAveragePooling2D
from tensorflow.keras.layers import ... | code |
88101762/cell_8 | [
"image_output_1.png"
] | from keras.preprocessing.image import array_to_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import load_img,img_to_array
import matplotlib.pyplot as plt
import numpy as np
import os
stored_data_path = '../input/mag... | code |
88101762/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from sklearn.metrics import classification_report
from tensorflow.keras.layers import Conv2D, MaxPooling2D,GlobalAveragePooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow... | code |
88101762/cell_14 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import load_img,img_to_array
import numpy as np
import os
import tensorflow as tf
stored_data_path = '../input/magnetictiledefect'
CLASS_NAME = ['Blowhole', 'Free', 'Break', ... | code |
2003917/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
test_corpus = np.load('../input/preprocessing/test_corp.npy')
train_corpus = np.load('../input/preprocessing/train_corp.npy')
glove_table = pd.read_csv('../input/preprocessing/filled_glove_table.csv', index_col=0)
glove_table.describe() | code |
2003917/cell_7 | [
"text_plain_output_1.png"
] | from hmmlearn import hmm
import numpy as np
import pandas as pd
import warnings
test_corpus = np.load('../input/preprocessing/test_corp.npy')
train_corpus = np.load('../input/preprocessing/train_corp.npy')
glove_table = pd.read_csv('../input/preprocessing/filled_glove_table.csv', index_col=0)
glove_table.loc[['man... | code |
2003917/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
test_corpus = np.load('../input/preprocessing/test_corp.npy')
train_corpus = np.load('../input/preprocessing/train_corp.npy')
glove_table = pd.read_csv('../input/preprocessing/filled_glove_table.csv', index_col=0)
glove_table.loc[['man', 'woman', 'man']].as_matrix().shape | code |
74069086/cell_6 | [
"text_plain_output_1.png"
] | start_page = 'https://www.checkthepolice.org/database'
trio.run(main, start_page) | code |
74069086/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | pip uninstall -y typing # trouble for gazpacho
pip install asks trio gazpacho | code |
74069086/cell_7 | [
"text_plain_output_1.png"
] | ! zip -r dept_contracts.zip /home/contracts/*.pdf | code |
74069086/cell_8 | [
"text_plain_output_1.png"
] | ! ls -U /home/contracts | head -10 | code |
73083545/cell_20 | [
"text_plain_output_1.png"
] | from functools import partial
from scipy.stats.mstats import winsorize
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
from tabular import TabularTransformer, DataGenerator
from tabular import gelu, Mish, mish
from tensorflow.keras.callbacks import EarlyStopping,... | code |
73083545/cell_6 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
from functools import partial
import matplotlib.pyplot as plt
from scipy.stats.mstats import winsorize
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import OrdinalEncoder
from sklearn.decomposition imp... | code |
130003860/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum()
data.dtypes[data.dtypes == 'object']
data.dtypes[data.dtypes == 'object'].isnull()
data.drop('Id', axis=1, inplace=True)
data.columns | code |
130003860/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum()
data.dtypes[data.dtypes == 'object']
data.dtypes[data.dtypes == 'object'].isnull()
data.drop('Id', axis=1, inplace=True)
data['EJ'].nunique() | code |
130003860/cell_4 | [
"text_plain_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore') | code |
130003860/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum()
data.dtypes[data.dtypes == 'object']
data.dtypes[data.dtypes == 'object'].isnull()
data.drop('Id', axis=1, inplace=True)
data.columns
data[['BQ', 'DU', 'EL', 'FC', 'FL', 'FS', 'GL... | code |
130003860/cell_33 | [
"text_plain_output_1.png"
] | (x_train.shape, y_train.shape, x_test.shape, y_test.shape) | code |
130003860/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.head() | code |
130003860/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
from sklearn.metrics import log_loss
(x_train.shape, y_train.shape, x_test.shape, y_test.shape)
gb = GradientBoostingClassifier(n_estimators=1000, max_depth=9, subsample=0.8, max_features='log2', min_samples_leaf=9, random_state=42)
gb.fit(... | code |
130003860/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum()
data.dtypes[data.dtypes == 'object']
data.dtypes[data.dtypes == 'object'].isnull() | code |
130003860/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape | code |
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