path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72068232/cell_12 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/emotion-detection-from-text/tweet_emotions.csv')
df.content.iloc[-10:]
df.sentiment.value_counts() | code |
105200230/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import csv
import numpy as np
import re
import pandas as pd
from tqdm.notebook import tqdm
from fuzzywuzzy import fuzz
BANK_FILE = '../input/payment-id-ndsc-2020/bank_statement.csv'
CHECKOUT_FILE = '../input/payment-id-ndsc-2020/checkout.csv'
bank = pd.read_csv(BANK_FILE)
checkout = pd.read_csv(CH... | code |
105200230/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import csv
import numpy as np
import re
import pandas as pd
from tqdm.notebook import tqdm
from fuzzywuzzy import fuzz
BANK_FILE = '../input/payment-id-ndsc-2020/bank_statement.csv'
CHECKOUT_FILE = '../input/payment-id-ndsc-2020/checkout.csv'
bank = pd.read_csv(BANK_FILE)
checkout = pd.read_csv(CH... | code |
105200230/cell_5 | [
"text_plain_output_1.png"
] | from fuzzywuzzy import fuzz
from tqdm.notebook import tqdm
import csv
import pandas as pd
import csv
import numpy as np
import re
import pandas as pd
from tqdm.notebook import tqdm
from fuzzywuzzy import fuzz
BANK_FILE = '../input/payment-id-ndsc-2020/bank_statement.csv'
CHECKOUT_FILE = '../input/payment-id-ndsc-20... | code |
18100538/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 pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True}) | code |
18100538/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.head() | code |
2036992/cell_42 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import D... | code |
2036992/cell_21 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder(... | code |
2036992/cell_23 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder(... | code |
2036992/cell_44 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import D... | code |
2036992/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | df_train.head() | code |
2036992/cell_40 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import D... | code |
2036992/cell_29 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.L... | code |
2036992/cell_39 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import D... | code |
2036992/cell_41 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import D... | code |
2036992/cell_7 | [
"text_html_output_1.png"
] | df_test.head() | code |
2036992/cell_45 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import D... | code |
2036992/cell_49 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import D... | code |
2036992/cell_18 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.... | code |
2036992/cell_51 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import D... | code |
2036992/cell_28 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.L... | code |
2036992/cell_16 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.... | code |
2036992/cell_38 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import D... | code |
2036992/cell_43 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import D... | code |
2036992/cell_14 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.... | code |
1005471/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
ta... | code |
1005471/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
ta... | code |
1005471/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
target = 'interest_level'
train_data.iloc[0] | code |
1005471/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
target = 'interest_level'
train_data.iloc[0]
uniq_interest_levels = list(train_data[target].unique())
inter... | code |
1005471/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 |
1005471/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
ta... | code |
1005471/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
ta... | code |
1005471/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
target = 'interest_level'
train_data.iloc[0]
bathroom_df = train_data[['bathrooms', 'interest_level']]
bath... | code |
32072743/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_test.loc[df_test['Province_State'].isnull(), 'Province_State'] = 'None'
df_test.isnull().sum() | code |
32072743/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.tail() | code |
32072743/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[d... | code |
32072743/cell_44 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[d... | code |
32072743/cell_40 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[d... | code |
32072743/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[d... | code |
32072743/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
print('Number of unique province_country groups in test file: {}'.format(len(test_province_country_groups.groups.keys()))... | code |
32072743/cell_45 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[d... | code |
32072743/cell_49 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[d... | code |
32072743/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum() | code |
32072743/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
df_train.head() | code |
32072743/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[d... | code |
32072743/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[d... | code |
32072743/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.info() | code |
32072743/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.gro... | code |
32072743/cell_36 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[d... | code |
121148680/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath = '/kaggle/input/er-fast-track'
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv(f'{filepath}/heart.csv')
df.head(5) | code |
122264339/cell_4 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
count = 0
for i in b:
count = count + 1
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
a | code |
122264339/cell_6 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
count = 0
for i in b:
count = count + 1
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
a
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
b = set(a)
b
a = '... | code |
122264339/cell_2 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b | code |
122264339/cell_8 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
count = 0
for i in b:
count = count + 1
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
a
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
b = set(a)
b
a = '... | code |
122264339/cell_3 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
count = 0
for i in b:
count = count + 1
print(count) | code |
122264339/cell_5 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
count = 0
for i in b:
count = count + 1
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
a
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
b = set(a)
b | code |
129039294/cell_21 | [
"text_plain_output_1.png"
] | import glob
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 glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None,... | code |
129039294/cell_13 | [
"text_plain_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for fi... | code |
129039294/cell_9 | [
"text_html_output_1.png"
] | import glob
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', ... | code |
129039294/cell_25 | [
"image_output_1.png"
] | import glob
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
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv... | code |
129039294/cell_4 | [
"image_output_1.png"
] | import glob
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files | code |
129039294/cell_20 | [
"text_html_output_1.png"
] | import glob
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 glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None,... | code |
129039294/cell_19 | [
"text_plain_output_1.png"
] | import glob
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 glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None,... | code |
129039294/cell_7 | [
"text_html_output_1.png"
] | import glob
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', ... | code |
129039294/cell_18 | [
"text_plain_output_1.png"
] | import glob
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 glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None,... | code |
129039294/cell_28 | [
"image_output_1.png"
] | import glob
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
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv... | code |
129039294/cell_8 | [
"text_html_output_1.png"
] | import glob
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', ... | code |
129039294/cell_3 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
129039294/cell_17 | [
"text_html_output_1.png"
] | import glob
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 glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None,... | code |
129039294/cell_24 | [
"image_output_1.png"
] | import glob
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
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv... | code |
129039294/cell_14 | [
"text_html_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for fi... | code |
129039294/cell_22 | [
"text_plain_output_1.png"
] | import glob
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
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv... | code |
129039294/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for fi... | code |
129039294/cell_5 | [
"image_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for fi... | code |
16123001/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subs... | code |
16123001/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
Health_df['Indicator Category'].unique() | code |
16123001/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.head() | code |
16123001/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subs... | code |
16123001/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.describe() | code |
16123001/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_squared_log_error, r2_score
from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df... | code |
16123001/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
lm = LinearRegression()
lm.fit(X_train, Y_train) | code |
16123001/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
list(Health_df['Source'].unique()) | code |
16123001/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16123001/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique())) | code |
16123001/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum() | code |
16123001/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.info() | code |
16123001/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
Health_df[Health_df['Indicator Category'] == 'Demographi... | code |
16123001/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
Health_df['Source'].value_counts() | code |
16123001/cell_27 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
lm = LinearRegression()
lm.fit(X_train, Y_train)
print('Intercept value:', lm.intercept_)
print('Coefficient values:', lm.coef_) | code |
16123001/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
Health_df[Health_df['Value'] == 80977] | code |
16123001/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
list(Health_df['Indicator'].unique()) | code |
121149196/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 |
121149196/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
if df.isnull().values.any():
print('There are empty cells in the dataframe')
else:
print('There are no empty cells in the dataframe') | code |
121149196/cell_5 | [
"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('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
duplicates_P = df[df.duplicated(['PatientId'])]
if duplicates_P.empty:
print('There are no duplicates in the PatientId')
else:
print(f'There are {len(duplicates)} d... | code |
17134452/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from plotly.tools import make_subplots
import advertools as adv
import pandas as pd
import plotly.graph_objs as go
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs ... | code |
17134452/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | lang_football = {'en': 'football', 'fr': 'football', 'de': 'fußball', 'es': 'fútbol', 'it': 'calcio', 'pt-BR': 'futebol', 'nl': 'voetbal'}
lang_football
len(lang_football) | code |
17134452/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
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