path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72106185/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
train
train.drop('id', axis=1, inplace=True)
na_count = pd.Series([train[col].isna().sum() for col in train.columns], index=train.columns, name='NA Count')
na_count | code |
72106185/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
train
train.drop('id', axis=1, inplace=True)
na_count = pd.Series([train[col].isna().sum() for col in train.columns], index=train.columns, name='NA Count')
na_count
y = train['target']
X = train.drop('target', axis=1)
X | code |
72106185/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
print(train.columns)
train | code |
72106185/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
train
train.drop('id', axis=1, inplace=True)
na_count = pd.Series([train[col].isna().sum() for col in train.columns], index=train.columns, name='NA Count')
na_count
y = train['target']
X = train.drop('target', axis=1)
num_cols = [col for c... | code |
72106185/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
train
train.drop('id', axis=1, inplace=True)
na_count = pd.Series([train[col].isna().sum() for col in train.columns], index=train.columns, name='NA Count')
na_count
y = train['target']
X = tr... | code |
72106185/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
train
train.drop('id', axis=1, inplace=True)
na_count = pd.Series([train[col].isna().sum() for col in train.columns], index=train.columns, name='NA Count')
na_count
y = train['target']
X = tr... | code |
72106185/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
train
train.drop('id', axis=1, inplace=True)
na_count = pd.Series([train[col].isna().sum() for col in train.columns], index=train.columns, name='NA Count')
na_count
y = train['target']
X = tr... | code |
2010808/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Imputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
y = df['Survived']
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch... | code |
2010808/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2010808/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Imputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
y = df['Survived']
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch... | code |
2024726/cell_42 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
plt.figure(figsize=(14, 12))
sns.heatmap(train_df.astype(float).corr(), annot=True) | code |
2024726/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
plt.figure(figsize=(20, 7))
sns.boxplot('Pclass', 'Fare', data=test_df) | code |
2024726/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
print(train_df[train_df['Pclass'] == 1]['Age'].mean())
print(train_df[train_df['Pclass'] == 2]['Age'].mean())
print(train_df[train_df['Pclass'] == 3]['Age'].mean()) | code |
2024726/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sns.countplot('Survived', hue='Sex', data=train_df)
print(train_df[train_df['Sex'] == 'male']['Survived'].value_counts())
print(train_df[train_df['Sex'] == 'male']['Survived'].value_count... | code |
2024726/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.info() | code |
2024726/cell_44 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_x = train_df.drop(['Survived'], axis=1)
train_y = train_df['Survived']
test_x = test_df
train_x.head() | code |
2024726/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
def immute_ages(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):
if Pclass == 1:
return 38
if Pclass == 2:
return 30
else:
return ... | code |
2024726/cell_55 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
def includeFamilySize(df):
df['FamilySize'] = df['SibSp'] + df['Parch'] + 1
df['isAlone'] = 0
df.loc[df['FamilySize'] == 1, 'isAlone'] = 1
def immute_ages(cols):
Age = cols[0]
Pclass = cols... | code |
2024726/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.describe() | code |
2024726/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
print(test_df.columns)
print(train_df.columns) | code |
2024726/cell_48 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_x = train_df.drop(['Survived'], axis=1)
train_y = train_df['Survived']
test_x = test_df
def predic... | code |
2024726/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.head() | code |
2024726/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.head() | code |
2024726/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sns.countplot('Survived', hue='Embarked', data=train_df) | code |
2024726/cell_50 | [
"text_html_output_1.png"
] | from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_x = train_df.drop(['Survived'], axis=1)
train_y = train_df['Survived']
test_x = test_df
def predict(train_x, train_y, ... | code |
2024726/cell_52 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_x = train_df.drop(['Survived'], axis=1)
train_y = train_df['Survived']
test_x = test_df
def pre... | code |
2024726/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassif... | code |
2024726/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_df.describe() | code |
2024726/cell_45 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_x = train_df.drop(['Survived'], axis=1)
train_y = train_df['Survived']
test_x = test_df
test_x.head() | code |
2024726/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_x = train_df.drop(['Survived'], axis=1)
train_y = train_df['Survived']
test_x = test_df
def pr... | code |
2024726/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
print(train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean())
print(train_df[['isAlone', 'Survived']].groupby(['isAlone'], as_index=False).mean()) | code |
2024726/cell_51 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_x = train_df.drop(['Survived'], axis=1)
train_y = train_df['Survived'... | code |
2024726/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
def includeFamilySize(df):
df['FamilySize'] = df['SibSp'] + df['Parch'] + 1
df['isAlone'] = 0
df.loc[df['FamilySize'] == 1, 'isAlone'] = 1
def immute_ages(cols):
Age = cols[0]
Pclass = cols... | code |
2024726/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sns.countplot('Survived', data=train_df)
print(train_df['Survived'].value_counts())
print(train_df['Survived'].value_counts(normalize=True)) | code |
2024726/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
print(train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean()) | code |
2024726/cell_47 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_x = train_df.drop(['Survived'], axis=1)
train_y = train_df['Survived']
test_x = test_df
def pr... | code |
2024726/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_df.head() | code |
2024726/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
print(train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()) | code |
2024726/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
def includeFamilySize(df):
df['FamilySize'] = df['SibSp'] + df['Parch'] + 1
df['isAlone'] = 0
df.loc[df['FamilySize'] == 1, 'isAlone'] = 1
def immute_ages(cols):
Age = cols[0]
Pclass = cols... | code |
2024726/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
print(train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean()) | code |
2024726/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_df[test_df['Fare'].isnull()]
print(test_df[test_df['Pclass'] == 3]['Fare'].mean())
test_df['Fare'].fillna(12.45, inplace=True) | code |
2024726/cell_53 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
def includeFamilySize(df):
df['FamilySize'] = df['SibSp'] + df['Parch'] + 1
df['isAlone'] = 0
df.loc[df['FamilySize'] == 1, 'isAlone'] = 1
def immute_ages(cols):
Age = cols[0]
Pclass = cols... | code |
2024726/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
plt.figure(figsize=(10, 7))
sns.boxplot('Pclass', 'Age', data=train_df) | code |
2024726/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sns.countplot('Embarked', hue='Pclass', data=train_df) | code |
2024726/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_df.info() | code |
88083988/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/grocery-products-purchase-data/Grocery Products Purchase.csv')
df1 = df.melt(var_name='columns', value_name='index')
unique_values = pd.DataFrame(df1['index'].value_counts())
unique_values.index
values = unique_v... | code |
88083988/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/grocery-products-purchase-data/Grocery Products Purchase.csv')
df.info() | code |
88083988/cell_1 | [
"text_plain_output_1.png"
] | !pip install apyori
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
88083988/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/grocery-products-purchase-data/Grocery Products Purchase.csv')
df1 = df.melt(var_name='columns', value_name='index')
unique_values = pd.DataFrame(df1['index'].value_counts())
unique_values.head(10) | code |
88083988/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/grocery-products-purchase-data/Grocery Products Purchase.csv')
df1 = df.melt(var_name='columns', value_name='index')
unique_values = pd.DataFrame(df1['index'].value_counts())
unique_values.index | code |
88083988/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/grocery-products-purchase-data/Grocery Products Purchase.csv')
df | code |
88083988/cell_14 | [
"text_plain_output_1.png"
] | from apyori import apriori
import pandas as pd
df = pd.read_csv('../input/grocery-products-purchase-data/Grocery Products Purchase.csv')
df1 = df.melt(var_name='columns', value_name='index')
unique_values = pd.DataFrame(df1['index'].value_counts())
transacts = []
for i in range(len(df)):
transacts.append([str(... | code |
88083988/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/grocery-products-purchase-data/Grocery Products Purchase.csv')
df1 = df.melt(var_name='columns', value_name='index')
unique_values = pd.DataFrame(df1['index'].value_counts())
unique_values.index
values = unique_v... | code |
88083988/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/grocery-products-purchase-data/Grocery Products Purchase.csv')
df.describe() | code |
34150863/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
import requests
url = 'https://www.worldometers.info/coronavirus/#countries'
header = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36', '... | code |
34150863/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
import requests
url = 'https://www.worldometers.info/coronavirus/#countries'
header = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36', '... | code |
34150863/cell_4 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
init_notebook_mode(connected=True)
cf.go_offline()
import plotl... | code |
34150863/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
import requests
url = 'https://www.worldometers.info/coronavirus/#countries'
header = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36', '... | code |
34150863/cell_19 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
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 plotly.express as px
import plotly.express as px
import requests
import requests
url = 'https://www.worldometers.info... | code |
34150863/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
import requests
url = 'https://www.worldometers.info/coronavirus/#countries'
header = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36', '... | code |
34150863/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
import requests
url = 'https://www.worldometers.info/coronavirus/#countries'
header = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36', '... | code |
34150863/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)
import requests
import requests
url = 'https://www.worldometers.info/coronavirus/#countries'
header = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36', '... | code |
34150863/cell_15 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.express as px
import requests
import requests
url = 'https://www.worldometers.info/coronavirus/#countries'
header = {'User-Ag... | code |
34150863/cell_3 | [
"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 |
34150863/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.express as px
import requests
import requests
url = 'https://www.worldometers.info/coronavirus/#countries'
header = {'User-Ag... | code |
34150863/cell_22 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
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 plotly.express as px
import plotly.express as px
import requests
import requests
url = 'https://www.worldometers.info... | code |
34150863/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)
import requests
import requests
url = 'https://www.worldometers.info/coronavirus/#countries'
header = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36', '... | code |
34150863/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)
import requests
import requests
url = 'https://www.worldometers.info/coronavirus/#countries'
header = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36', '... | code |
18116817/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/cumulative.csv')
koi_pond = ['koi_pdisposition', 'koi_tce_plnt_num', 'koi_tce_delivname', 'kepler_name']
cols = raw_data.columns
for c in cols:
if 'err' in c:
koi_pond.append(c)
data = raw_data.drop(koi... | code |
18116817/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/cumulative.csv')
koi_pond = ['koi_pdisposition', 'koi_tce_plnt_num', 'koi_tce_delivname', 'kepler_name']
cols = raw_data.columns
for c in cols:
if 'err' in c:
koi_pond.append(c)
data = raw_data.drop(koi... | code |
18116817/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/cumulative.csv')
koi_pond = ['koi_pdisposition', 'koi_tce_plnt_num', 'koi_tce_delivname', 'kepler_name']
cols = raw_data.columns
for c in cols:
if 'err' in c:
koi_pond.append(c)
data = raw_data.drop(koi... | code |
18116817/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/cumulative.csv')
print('Here are all the columns:\n')
print(raw_data.columns.tolist()) | code |
18116817/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/cumulative.csv')
koi_pond = ['koi_pdisposition', 'koi_tce_plnt_num', 'koi_tce_delivname', 'kepler_name']
cols = raw_data.columns
for c in cols:
if 'err' in c:
koi_pond.append(c)
data = raw_data.drop(koi... | code |
18116817/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_data = pd.read_csv('../input/cumulative.csv')
koi_pond = ['koi_pdisposition', 'koi_tce_plnt_num', 'koi_tce_delivname', 'kepler_name']
cols = raw_data.columns
for c in cols:
if 'err' in c:
koi_pond.append(c)
data = raw_data.drop(koi... | code |
18116817/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # more plots
raw_data = pd.read_csv('../input/cumulative.csv')
koi_pond = ['koi_pdisposition', 'koi_tce_plnt_num', 'koi_tce_delivname', 'kepler_name']
cols = raw_data.columns
for c in cols:
if 'err' in c:
koi_pon... | code |
50244427/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug... | code |
50244427/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50244427/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50244427/cell_2 | [
"text_html_output_1.png"
] | import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50244427/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug... | code |
50244427/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50244427/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug... | code |
50244427/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug... | code |
50244427/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50244427/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug... | code |
50244427/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
18143144/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
X_test = test_set.drop(['PassengerId', 'Name', 'Parch', 'Ticket', 'Cabin'], axis=1)
print(X_test.head())
X_test = X_test.values | code |
18143144/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
train_set[['Pclass', 'Survived']].groupby(['Pclass']).mean().sort_values(by='Survived', ascendin... | code |
18143144/cell_4 | [
"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_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.head() | code |
18143144/cell_34 | [
"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_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.... | code |
18143144/cell_30 | [
"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_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.... | code |
18143144/cell_33 | [
"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 sns
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.... | code |
18143144/cell_44 | [
"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_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(342 * 100 / 891)
np.round(549 * 100 / 891)
mean ... | code |
18143144/cell_20 | [
"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_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
g = sns.FacetGrid(train_set, col='Surviv... | code |
18143144/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts() | code |
18143144/cell_40 | [
"text_plain_output_1.png",
"image_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_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(342 * 100 / 891)
np.round(549 * 100 / 891)
mean ... | code |
18143144/cell_39 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_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_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(342 * 100 / 891)
np.round(549 * 100 / 891)
mean ... | code |
18143144/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(34... | code |
18143144/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.roun... | code |
18143144/cell_32 | [
"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_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(342 * 100 / 891)
np.round(549 * 100 / 891)
mean ... | code |
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