path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
50240297/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_8 | [
"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)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to... | code |
50240297/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_lis... | code |
50240297/cell_24 | [
"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)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
gender_sub_df.info() | code |
320748/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
numClasses = 10
numEig = 28 * 28
picSize = 28 * 28
trainData = pd.read_csv('../input/train.csv')
testData = pd.read_csv('../input/test.csv')
trainData.sort_values(by=['label'], inplace=True)
trainY = trainDat... | code |
320748/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
numClasses = 10
numEig = 28 * 28
picSize = 28 * 28
trainData = pd.read_csv('../input/train.csv')
testData = pd.read_csv('../input/test.csv')
trainData.sort_values(by=['label']... | code |
33105482/cell_13 | [
"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)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([tr... | code |
33105482/cell_9 | [
"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)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([tr... | code |
33105482/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
train.head() | code |
33105482/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test.head() | code |
33105482/cell_19 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kag... | code |
33105482/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 |
33105482/cell_7 | [
"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)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([tr... | code |
33105482/cell_18 | [
"text_plain_output_1.png"
] | y_train.notna() | code |
33105482/cell_8 | [
"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)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([tr... | code |
33105482/cell_15 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/... | code |
33105482/cell_17 | [
"text_html_output_1.png"
] | import math
import math
math.sqrt(len(y_test)) | code |
33105482/cell_24 | [
"text_plain_output_1.png"
] | !head /kaggle/working/submit.csv | code |
33105482/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-20... | code |
33105482/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)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([tr... | code |
33105482/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)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([tr... | code |
33105482/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
print(train.shape)
print(test.shape) | code |
2024900/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | """
Converting numbers into words
TO DO
""" | code |
2024900/cell_30 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_... | code |
2024900/cell_33 | [
"text_plain_output_1.png"
] | from collections import Counter
from collections import Counter
from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import nltk
import re
import re
import string
text1 = "ThIs's ã sent tokenize test . ... | code |
2024900/cell_20 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_... | code |
2024900/cell_11 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_... | code |
2024900/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'))
'\nImportation des librairies\n'
import re
import string
import numpy as np
import nltk
from collections import Counter | code |
2024900/cell_7 | [
"text_plain_output_1.png"
] | from nltk.tokenize import sent_tokenize
german_text = u'Die Orgellandschaft Südniedersachsen umfasst das Gebiet der Landkreise Goslar, Göttingen, Hameln-Pyrmont, Hildesheim, Holzminden, Northeim und Osterode am Harz sowie die Stadt Salzgitter. Über 70 historische Orgeln vom 17. bis 19. Jahrhundert sind in der südniede... | code |
2024900/cell_18 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_... | code |
2024900/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import brown
"""
Method 1 : Using the brown corpus in NLTK and "in" operator
"""
from nltk.corpus import brown
word_list = brown.words()
len(word_list)
word_set = set(word_list)
'looked' in word_set | code |
2024900/cell_28 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import re
import string
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence t... | code |
2024900/cell_8 | [
"text_plain_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance ... | code |
2024900/cell_16 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_... | code |
2024900/cell_3 | [
"text_plain_output_1.png"
] | text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
print(text1) | code |
2024900/cell_24 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import re
import string
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence t... | code |
2024900/cell_22 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import re
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK... | code |
2024900/cell_37 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NL... | code |
2024900/cell_12 | [
"text_plain_output_1.png"
] | """
from nltk.tokenize import PunktSentenceTokenizer
from nltk.corpus import state_union
train_text = state_union.raw("2005-GWBush.txt")
sample_text = state_union.raw("2006-GWBush.txt")
# train the Punkt tokenizer like:
custom_sent_tokenizer = PunktSentenceTokenizer(train_text)
# we can actually tokenize
tokenized = cu... | code |
2024900/cell_5 | [
"text_plain_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance ... | code |
2024900/cell_36 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NL... | code |
49129174/cell_13 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership',... | code |
49129174/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', '... | code |
49129174/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', '... | code |
49129174/cell_30 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Countr... | code |
49129174/cell_20 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'Euro... | code |
49129174/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', '... | code |
49129174/cell_29 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Countr... | code |
49129174/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Countr... | code |
49129174/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', '... | code |
49129174/cell_1 | [
"text_plain_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 |
49129174/cell_18 | [
"text_html_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'Euro... | code |
49129174/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', '... | code |
49129174/cell_15 | [
"text_html_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'Euro... | code |
49129174/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('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', '... | code |
49129174/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
from scipy.stats import mannwhitneyu
from scipy.stats import spearmanr
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', nam... | code |
49129174/cell_22 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
from scipy.stats import mannwhitneyu
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of deve... | code |
89127815/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
89127815/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.describe() | code |
89127815/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 |
89127815/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
89127815/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
128031511/cell_4 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
data_train_db.head() | code |
128031511/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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)
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from scipy import stats
st... | code |
128031511/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 |
128031511/cell_8 | [
"application_vnd.jupyter.stderr_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 sn
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from... | code |
128031511/cell_14 | [
"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)
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from scipy import stats
st... | code |
128031511/cell_10 | [
"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)
import seaborn as sn
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from... | code |
128031511/cell_12 | [
"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 sn
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from... | code |
128031511/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from scipy import stats
stdev_max = 6
pd.set_option('display.max_rows', data_train_db.shape[0] +... | code |
16147672/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklear... | code |
16147672/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16147672/cell_7 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data1 = data_train.copy(deep=True)
data2 = data_test.copy(deep=True)
frame = [data1, data2]
lis = []
lis1 = []
for col i... | code |
16147672/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
type(data_test) | code |
16147672/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data1 = data_train.copy(deep=True)
data2 = data_test.copy(deep=True)
frame = [data1, data2]
lis = []
lis1 = []
for col i... | code |
16147672/cell_12 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import ... | code |
16147672/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data1 = data_train.copy(deep=True)
data2 = data_test.copy(deep=True)
frame = [data1, data2]
lis = []
lis1 = []
print(dat... | code |
1005486/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
df.describe() | code |
1005486/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/epi_r.csv')
df.head() | code |
1005486/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('../input/epi_r.csv')
sorted(list(df.columns)) | code |
1005486/cell_5 | [
"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)
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
df.pivot_table(index=['rating'], columns=['fat'], aggfunc=np.mean) | code |
89131571/cell_13 | [
"text_html_output_1.png"
] | import csv
import pandas as pd
import sqlite3
path = './'
database = path + 'ted-talk-data.sqlite'
conn = sqlite3.connect(database)
create_table = 'CREATE TABLE tedtalk(\n title TEXT,\n author TEXT,\n date DATE,\n views INTEGER,\n likes INTE... | code |
17141744/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(... | code |
17141744/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(... | code |
17141744/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
train.head() | code |
17141744/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(... | code |
17141744/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(... | code |
17141744/cell_19 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(... | code |
17141744/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17141744/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
test['Phrase'][0] | code |
17141744/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(... | code |
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