path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105191248/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 |
105191248/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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_15 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_16 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
youtube.head() | code |
105191248/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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_14 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_10 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_12 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count | code |
2042995/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess im... | code |
322963/cell_2 | [
"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 |
322963/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
zika_df = pd.read_csv(os.path.join('..', 'input', 'cdc_zika.csv'), low_memory=False)
keep_rows = pd.notnull(zika_df['report_date'])
zika_df = zika_df[keep_rows]
print('Removed {:d} out of {:d} rows with missing report_date.'.format(len(k... | code |
122264653/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | Person1 = [['Maths', 'Science', 'Entrepreneurship'], 'B', 'Blue', '42.5']
Person1[0][0] | code |
122264653/cell_4 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = dict()
type(age)
age = {'Ragul': 23, 'Joe': 15, 'Venkat': 32}
type(age) | code |
122264653/cell_2 | [
"text_plain_output_1.png"
] | age = {}
type(age) | code |
122264653/cell_11 | [
"text_plain_output_1.png"
] | marks = {'Ragul': 23, 'Joe': 15, 'Venkat': [34, 44, 56]}
marks['Venkat'] | code |
122264653/cell_8 | [
"text_plain_output_1.png"
] | Person1 = [['Maths', 'Science', 'Entrepreneurship'], 'B', 'Blue', '42.5']
Person1[0] | code |
122264653/cell_15 | [
"text_plain_output_1.png"
] | marks = {'Ragul': 23, 'Joe': 15, 'Venkat': [34, 44, 56]}
marks['Venkat']
marks = {'Ragul': 23, 'Joe': 15, 'Venkat': [34, 44, 56]}
marks['Venkat'][2]
for i in marks:
print(i) | code |
122264653/cell_16 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = dict()
type(age)
age = {'Ragul': 23, 'Joe': 15, 'Venkat': 32}
type(age)
age = ['Venkat']
for i in age:
print(i, age[i]) | code |
122264653/cell_3 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = dict()
type(age) | code |
122264653/cell_14 | [
"text_plain_output_1.png"
] | marks = {'Ragul': 23, 'Joe': 15, 'Venkat': [34, 44, 56]}
marks['Venkat']
marks = {'Ragul': 23, 'Joe': 15, 'Venkat': [34, 44, 56]}
marks['Venkat'][2]
marks | code |
122264653/cell_12 | [
"text_plain_output_1.png"
] | marks = {'Ragul': 23, 'Joe': 15, 'Venkat': [34, 44, 56]}
marks['Venkat']
marks = {'Ragul': 23, 'Joe': 15, 'Venkat': [34, 44, 56]}
marks['Venkat'][2] | code |
73071424/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
states = {-1: 'Loss', 0: 'Draw', 1: 'Win'}
states
transition = np.array([[0.65, 0.1, 0.25], [0.3, 0.5, 0.2], [0.35, 0.1, 0.55]])
transition
n = 15
br = 0
points = 0
start_state = 0
prev_state = start_state
while n:
if prev_state == 0:
points += 1
elif prev_state ==... | code |
73071424/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
states = {-1: 'Loss', 0: 'Draw', 1: 'Win'}
states
transition = np.array([[0.65, 0.1, 0.25], [0.3, 0.5, 0.2], [0.35, 0.1, 0.55]])
transition
n = 15
br = 0
points = 0
start_state = 0
prev_state = start_state
while n:
if prev_state == 0:
points += 1
elif prev_state ==... | code |
73071424/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
transition = np.array([[0.65, 0.1, 0.25], [0.3, 0.5, 0.2], [0.35, 0.1, 0.55]])
transition | code |
73071424/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
states = {-1: 'Loss', 0: 'Draw', 1: 'Win'}
states
transition = np.array([[0.65, 0.1, 0.25], [0.3, 0.5, 0.2], [0.35, 0.1, 0.55]])
transition
n = 15
br = 0
points = 0
start_state = 0
prev_state = start_state
while n:
if prev_state == 0:
points += 1
elif prev_state ==... | code |
73071424/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
states = {-1: 'Loss', 0: 'Draw', 1: 'Win'}
states
transition = np.array([[0.65, 0.1, 0.25], [0.3, 0.5, 0.2], [0.35, 0.1, 0.55]])
transition
n = 15
br = 0
points = 0
start_state = 0
prev_state = start_state
while n:
if prev_state == 0:
points += 1
elif prev_state ==... | code |
73071424/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
states = {-1: 'Loss', 0: 'Draw', 1: 'Win'}
states
transition = np.array([[0.65, 0.1, 0.25], [0.3, 0.5, 0.2], [0.35, 0.1, 0.55]])
transition
n = 15
br = 0
points = 0
start_state = 0
print(states[start_state], '-->', end=' ')
prev_state = start_state
while n:
if prev_state == 0:... | code |
73071424/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
states = {-1: 'Loss', 0: 'Draw', 1: 'Win'}
states
transition = np.array([[0.65, 0.1, 0.25], [0.3, 0.5, 0.2], [0.35, 0.1, 0.55]])
transition
n = 15
br = 0
points = 0
start_state = 0
prev_state = start_state
while n:
if prev_state == 0:
points += 1
elif prev_state ==... | code |
73071424/cell_3 | [
"text_plain_output_1.png"
] | states = {-1: 'Loss', 0: 'Draw', 1: 'Win'}
states | code |
73071424/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
states = {-1: 'Loss', 0: 'Draw', 1: 'Win'}
states
transition = np.array([[0.65, 0.1, 0.25], [0.3, 0.5, 0.2], [0.35, 0.1, 0.55]])
transition
n = 15
br = 0
points = 0
start_state = 0
prev_state = start_state
while n:
if prev_state == 0:
points += 1
elif prev_state ==... | code |
73071424/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
states = {-1: 'Loss', 0: 'Draw', 1: 'Win'}
states
transition = np.array([[0.65, 0.1, 0.25], [0.3, 0.5, 0.2], [0.35, 0.1, 0.55]])
transition
n = 15
br = 0
points = 0
start_state = 0
prev_state = start_state
while n:
if prev_state == 0:
points += 1
elif prev_state ==... | code |
73071424/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
transition = np.array([[0.65, 0.1, 0.25], [0.3, 0.5, 0.2], [0.35, 0.1, 0.55]])
transition
import scipy.linalg
values, left = scipy.linalg.eig(transition, right=False, left=True)
print('left eigen vectors =\n', left, '\n')
print('eigen values = \n', values) | code |
326282/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from IPython.display import display
import numpy as np
import pandas as pd
import re
def extract_maritial(name):
""" extract the person's title, and bin it to Mr. Miss. and Mrs.
assuming a Miss, Lady or Countess has more change to survive than a regular married woman."""
re_maritial = ' ([A-Za-z]+\\.) '... | code |
326282/cell_16 | [
"text_plain_output_1.png"
] | from IPython.display import display
from sklearn import cross_validation
from sklearn.feature_selection import RFECV
import numpy as np
import pandas as pd
import re
import xgboost as xgb
def extract_maritial(name):
""" extract the person's title, and bin it to Mr. Miss. and Mrs.
assuming a Miss, Lady or... | code |
326282/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import display
import re
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn import preprocessing
from sklearn import cross_validation
from sklearn.model_selection import KFold
from sklearn.feature_selection import RFECV
from sklearn.grid_search import GridSearchCV | code |
326282/cell_14 | [
"text_html_output_4.png",
"text_plain_output_4.png",
"text_html_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from IPython.display import display
from sklearn import cross_validation
import numpy as np
import pandas as pd
import re
import xgboost as xgb
def extract_maritial(name):
""" extract the person's title, and bin it to Mr. Miss. and Mrs.
assuming a Miss, Lady or Countess has more change to survive than a r... | code |
128039843/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
128039843/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
train_data.isnull().sum().sum() | code |
128039843/cell_23 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
128039843/cell_20 | [
"image_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
128039843/cell_26 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
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)
train_data = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
train_data.isnull().sum().sum()
fig, ax = plt.subplots(5... | code |
128039843/cell_11 | [
"text_html_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)
train_data = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
train_data.isnull().sum().sum()
fig, ax = plt.subplots(5, 5, figsize=(8, 8))
for i in range(5):
for j in r... | code |
128039843/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 |
128039843/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
train_data.head() | code |
128039843/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
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)
train_data ... | code |
128039843/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
train_data.describe() | code |
128039843/cell_16 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
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)
train_data = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
train_data.isnull().sum().sum()
fig, ax = plt.subplots(5... | code |
128039843/cell_24 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
128039843/cell_22 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
128007350/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('../input/toloke... | code |
128007350/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges | code |
128007350/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
nodes['education'].value_counts() | code |
128007350/cell_29 | [
"text_html_output_1.png"
] | from scipy.stats import chi2
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges
import pandas as pd
import numpy as np
import seaborn as... | code |
128007350/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes | code |
128007350/cell_31 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges
import pandas as pd
import numpy as np
import seaborn as... | code |
128007350/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('../input/toloke... | code |
128007350/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('../input/toloke... | code |
128007350/cell_27 | [
"text_html_output_1.png"
] | from scipy.stats import chi2
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges
import pandas as pd
import numpy as np
import seaborn as... | code |
34133139/cell_13 | [
"text_html_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import nltk
from nltk.tokenize import sent_tokenize
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
fp = open('/kaggle/input/hotel-text-data/text.txt')
data = ... | code |
34133139/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import nltk
from nltk.tokenize import sent_tokenize
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
fp = open('/kaggle/input/hotel-te... | code |
34133139/cell_23 | [
"text_plain_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import nltk
from nltk.tokenize import sent_tokenize
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
fp = open('/kaggle/input/hotel-te... | code |
34133139/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.tokenize import sent_tokenize
from statistics import mean
import nltk
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 re
import string
import nltk
from nltk.tokenize import sent_tokenize
tokenizer... | code |
34133139/cell_20 | [
"text_plain_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import nltk
from nltk.tokenize import sent_tokenize
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
fp = open('/kaggle/input/hotel-te... | code |
34133139/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import nltk
from nltk.tokenize import sent_tokenize
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
fp = open('/kaggle/input/hotel-text-data/text.txt')
data = ... | code |
34133139/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.tokenize import sent_tokenize
from statistics import mean
import nltk
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 re
import string
import nltk
from nltk.tokenize import sent_tokenize
tokenizer... | code |
34133139/cell_11 | [
"text_html_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import nltk
from nltk.tokenize import sent_tokenize
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
fp = open('/kaggle/input/hotel-text-data/text.txt')
data = ... | code |
34133139/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 |
34133139/cell_18 | [
"text_plain_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import nltk
from nltk.tokenize import sent_tokenize
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
fp = open('/kaggle/input/hotel-text-data/text.tx... | code |
34133139/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.tokenize import sent_tokenize
from statistics import mean
import nltk
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 re
import string
import nltk
from nltk.tokenize import sent_tokenize
tokenizer... | code |
34133139/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.tokenize import sent_tokenize
from statistics import mean
import nltk
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 re
import string
import nltk
from nltk.tokenize import sent_tokenize
tokenizer... | code |
34133139/cell_15 | [
"text_html_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import nltk
from nltk.tokenize import sent_tokenize
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
fp = open('/kaggle/input/hotel-text-data/text.txt')
data = ... | code |
34133139/cell_16 | [
"text_plain_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
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 re
import nltk
from nltk.tokenize import sent_tokenize
tokenizer = nltk.data.load('tokenizers/punkt/english.... | code |
34133139/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.tokenize import sent_tokenize
from statistics import mean
import nltk
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 re
import string
import nltk
from nltk.tokenize import sent_tokenize
tokenizer... | code |
34133139/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.tokenize import sent_tokenize
from statistics import mean
import nltk
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 re
import string
import nltk
from nltk.tokenize import sent_tokenize
tokenizer... | code |
34133139/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.tokenize import sent_tokenize
from statistics import mean
import nltk
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 re
import string
import nltk
from nltk.tokenize import sent_tokenize
tokenizer... | code |
50224995/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | train = dt.fread(os.path.join(root_path, 'train.csv')).to_pandas().query('weight > 0').pipe(reduce_mem_usage).reset_index(drop=True)
train['action'] = (train.resp > 0).astype(int)
resp_cols = [i for i in train.columns if 'resp' in i]
features_names = [i for i in train.columns if 'feature_' in i]
features_index = list(m... | code |
50224995/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
import janestreet
import lightgbm as lgb
from sklearn.model_selection import StratifiedKFold
params = {'objective': 'binary', 'metrics': ['auc']}
nfolds = 3
kfold = StratifiedKFold(n_splits=nfolds)
lgb_models = list()
import lightgbm as lgb
for k, (train_idx, valid... | code |
50224995/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import random
import numpy as np
import datatable as dt
import pandas as pd
import random
import re
random.seed(28)
import tqdm
import os
import gc
import logging
import optuna
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
plt.style.use('fiveth... | code |
74041737/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'imonth': 'Month', 'iday': 'Day', 'country_txt': 'Country',... | code |
74041737/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.head() | code |
74041737/cell_23 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
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
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.ren... | code |
74041737/cell_20 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
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
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.ren... | code |
74041737/cell_2 | [
"text_html_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 |
74041737/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'imonth': 'Month', 'iday': 'Day', 'country_txt': 'Country',... | code |
74041737/cell_19 | [
"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
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'im... | code |
74041737/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)
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'imonth': 'Month', 'iday': 'Day', 'country_txt': 'Country',... | code |
74041737/cell_18 | [
"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
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'im... | code |
74041737/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'imonth': 'Month', 'iday': 'Day', 'country_txt': 'Country',... | code |
74041737/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'imonth': 'Month', 'iday': 'Day', 'country_txt': 'Country',... | code |
74041737/cell_17 | [
"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
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'im... | code |
74041737/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'imonth': 'Month', 'iday': 'Day', 'country_txt': 'Country',... | code |
74041737/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'imonth': 'Month', 'iday': 'Day', 'country_txt': 'Country',... | code |
74041737/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list()
terror_df.rename(columns={'iyear': 'Year', 'imonth': 'Month', 'iday': 'Day', 'country_txt': 'Country',... | code |
74041737/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
terror_df = pd.read_csv('/kaggle/input/gtd/globalterrorismdb_0718dist.csv', encoding='latin1')
terror_df.columns.to_list() | code |
72121245/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes | code |
72121245/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols ... | code |
72121245/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
... | code |
72121245/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols ... | code |
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