path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128005164/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(... | code |
128005164/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/... | code |
128005164/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(... | code |
128005164/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV fil... | code |
128005164/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.info() | code |
50238529/cell_25 | [
"text_plain_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].ma... | code |
50238529/cell_34 | [
"text_html_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].ma... | code |
50238529/cell_23 | [
"text_html_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].ma... | code |
50238529/cell_29 | [
"text_plain_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].ma... | code |
50238529/cell_7 | [
"image_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0,... | code |
50238529/cell_32 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].ma... | code |
50238529/cell_35 | [
"text_html_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].ma... | code |
50238529/cell_10 | [
"text_html_output_1.png"
] | import graphviz as graphviz
import lightgbm as lgb
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] =... | code |
50238529/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].ma... | code |
50238529/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import graphviz as graphviz
import lightgbm as lgb
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] =... | code |
50238529/cell_5 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np... | code |
50238529/cell_36 | [
"image_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].ma... | code |
90137326/cell_11 | [
"text_plain_output_1.png"
] | from wikipedia.exceptions import PageError
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Governance', 'Risk and Co... | code |
90137326/cell_19 | [
"text_plain_output_1.png"
] | from wikipedia.exceptions import PageError
import pandas as pd
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Gove... | code |
90137326/cell_18 | [
"text_plain_output_1.png"
] | from wikipedia.exceptions import PageError
import pandas as pd
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Gove... | code |
90137326/cell_15 | [
"text_plain_output_1.png"
] | from wikipedia.exceptions import PageError
import pandas as pd
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Gove... | code |
90137326/cell_16 | [
"text_plain_output_1.png"
] | from wikipedia.exceptions import PageError
import pandas as pd
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Gove... | code |
90137326/cell_17 | [
"text_html_output_1.png"
] | from wikipedia.exceptions import PageError
import pandas as pd
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Gove... | code |
90137326/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from wikipedia.exceptions import PageError
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Governance', 'Risk and Co... | code |
128009779/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment... | code |
128009779/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3) | code |
128009779/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment... | code |
128009779/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.info() | code |
128009779/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment... | code |
128009779/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment... | code |
128009779/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
sns.boxplot(df)
plt.title('Boxplot representation of the dataframe')
plt.show() | code |
128009779/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
plt.pie(work_year_values, labels=work_year_values.index, autopct='%.0f%%')
plt.title('Distri... | code |
128009779/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
plt.pie(exp_level_values, labels=e... | code |
128009779/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
print(f'Shape of the dataframe: {df.shape}') | code |
128009779/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment... | code |
128009779/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment... | code |
128009779/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df[df['salary'] > 30000000] | code |
128009779/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum() | code |
128009779/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.describe() | code |
50215202/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import nltk
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df... | code |
50215202/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(','... | code |
50215202/cell_25 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import nltk
import nltk
import pandas as pd # data processing,... | code |
50215202/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
dfpub | code |
50215202/cell_23 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import nltk
import nltk
import pandas as pd # data processing,... | code |
50215202/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import nltk
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df... | code |
50215202/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
dfpub['browsing_date'] | code |
50215202/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')... | code |
50215202/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 |
50215202/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
print(c)
if '1601' in c:
continue
today = c.split()[0]
arr... | code |
50215202/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row i... | code |
50215202/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1... | code |
50215202/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(','... | code |
50215202/cell_16 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(','... | code |
50215202/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(','... | code |
50215202/cell_22 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import nltk
import nltk
import pandas as pd # data processing,... | code |
50215202/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.dates as mdates
import matplotlib.pylab as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' i... | code |
50215202/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import word2vec
from nltk.corpus import stopwords
import io
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
... | code |
16154375/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupl... | code |
16154375/cell_4 | [
"image_output_1.png"
] | import warnings
import seaborn as sns
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
print('Libraries were loaded.') | code |
16154375/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input... | code |
16154375/cell_33 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupl... | code |
16154375/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupl... | code |
16154375/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupl... | code |
16154375/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupl... | code |
16154375/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input... | code |
16154375/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16154375/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input... | code |
16154375/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupl... | code |
16154375/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input... | code |
16154375/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupli... | code |
16154375/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input... | code |
16154375/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupl... | code |
50239726/cell_13 | [
"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/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data.isnull().sum() | code |
50239726/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
map1 = sns.FacetGrid(train_data, col='Pclass', row='Sex')
map1.map_dataframe(sn... | code |
50239726/cell_20 | [
"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/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
print('Survival rate of adult males:', ((train_data['Survived'] == True) & (train_data['Sex'] == 'male... | code |
50239726/cell_6 | [
"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/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data.head() | code |
50239726/cell_26 | [
"text_plain_output_1.png",
"image_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/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
print('Survival rate of females:', ((train_data['Survived'] == True) & (train_data['Sex'] == 'female')... | code |
50239726/cell_1 | [
"text_plain_output_1.png"
] | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g... | code |
50239726/cell_18 | [
"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/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
print('Survival rate of males:', ((train_data['Survived'] == True) & (train_data['Sex'] == 'male')).su... | code |
50239726/cell_15 | [
"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/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
print('Percertage survived (train_data):', (train_data['Survived'] == True).sum() * 100 / train_data.s... | code |
50239726/cell_17 | [
"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/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
print('Survival rate of females:', ((train_data['Survived'] == True) & (train_data['Sex'] == 'female')... | code |
50239726/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
sns.countplot(x='Survived', hue='Pclass', data=train_data) | code |
50239726/cell_10 | [
"text_html_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/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
print('Total size of train_data:', train_data.shape)
print('Total size of test_data:', test_data.shape) | code |
50239726/cell_12 | [
"text_html_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/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum() | code |
50239726/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.head(20) | code |
309683/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_... | code |
309683/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_... | code |
309683/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_... | code |
32068206/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_57 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_56 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_44 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-... | code |
32068206/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_55 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
32068206/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-... | code |
32068206/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categ... | code |
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