path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
34120249/cell_4 | [
"image_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses.head(3) | code |
34120249/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_... | code |
34120249/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_... | code |
34120249/cell_33 | [
"image_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_... | code |
34120249/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy... | code |
34120249/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_... | code |
34120249/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_... | code |
34120249/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_... | code |
34120249/cell_32 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_cour... | code |
34120249/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_... | code |
34120249/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_... | code |
34120249/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy... | code |
34120249/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
import os
import re
plt.style.use('ggplot')
sns.set(style='darkgrid', context='notebook')
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(di... | code |
34120249/cell_31 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_cour... | code |
34120249/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses.info() | code |
329956/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
import pandas as pd
import numpy as np
import trueskill as ts
pd.set_option('display.max_rows', len(dfRatings))
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
... | code |
329956/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import trueskill as ts
pd.set_option('display.max_rows', len(dfRatings)) | code |
329956/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
import pandas as pd
import numpy as np
import trueskill as ts
pd.set_option('display.max_rows', len(dfRatings))
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
... | code |
329956/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
import pandas as pd
import numpy as np
import trueskill as ts
pd.set_option('display.max_rows', len(dfRatings))
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
... | code |
32063168/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
clf.feature_importances_ | code |
32063168/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df.info() | code |
32063168/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
from sklearn import metrics
print(metrics.classification_report(y_test, pred)) | code |
32063168/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df[df['RAIN'].isnull()] | code |
32063168/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('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.head() | code |
32063168/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, pred)) | code |
32063168/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32063168/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df = df.dropna()
df.shape | code |
32063168/cell_3 | [
"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('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape | code |
32063168/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train) | code |
32063168/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df = df.dropna()
df.shape
df['RAIN'].value_counts() | code |
32063168/cell_12 | [
"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
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df = df.dropna()
df.shape
df = df.drop('DATE', axis=1)
import seaborn as sns
plt.fi... | code |
32063168/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df['RAIN'].unique() | code |
33118937/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
T... | code |
33118937/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(ex... | code |
33118937/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Sample.head() | code |
33118937/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
T... | code |
33118937/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
T... | code |
33118937/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
T... | code |
33118937/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
T... | code |
33118937/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sam... | code |
33118937/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape | code |
33118937/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = ... | code |
33118937/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
T... | code |
33118937/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sam... | code |
33118937/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = ... | code |
16111026/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16111026/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.head(2) | code |
73077140/cell_4 | [
"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.DataFrame(np.array([['Species 1', np.nan, np.nan, np.nan], ['Species 1', 'Ocotber 2011', np.nan, np.nan], ['Species 1', np.nan, np.nan, 'Decmber 2011'], ['Species 2', 'Ocotber 2011', np.nan, np.nan], ['S... | code |
73077140/cell_2 | [
"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.DataFrame(np.array([['Species 1', np.nan, np.nan, np.nan], ['Species 1', 'Ocotber 2011', np.nan, np.nan], ['Species 1', np.nan, np.nan, 'Decmber 2011'], ['Species 2', 'Ocotber 2011', np.nan, np.nan], ['S... | code |
73077140/cell_3 | [
"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.DataFrame(np.array([['Species 1', np.nan, np.nan, np.nan], ['Species 1', 'Ocotber 2011', np.nan, np.nan], ['Species 1', np.nan, np.nan, 'Decmber 2011'], ['Species 2', 'Ocotber 2011', np.nan, np.nan], ['S... | code |
73077140/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.DataFrame(np.array([['Species 1', np.nan, np.nan, np.nan], ['Species 1', 'Ocotber 2011', np.nan, np.nan], ['Species 1', np.nan, np.nan, 'Decmber 2011'], ['Species 2', 'Ocotber 2011', np.nan, np.nan], ['S... | code |
50238455/cell_6 | [
"text_plain_output_1.png"
] | from sklearn import tree
import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
X = train.drop(['Outcome'], axis=1)
y = train.Outcome
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=6)
clf.fit(X, y) | code |
50238455/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
test = pd.read_csv(DATA_DIR + 'test.csv')
sample_submission = pd.read_csv(DATA_DIR + 'sample_submission.csv')
sample_submission.head() | code |
50238455/cell_1 | [
"text_plain_output_1.png"
] | import os
from sklearn import tree
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 |
50238455/cell_7 | [
"text_html_output_1.png"
] | from IPython.display import SVG
from IPython.display import display
from graphviz import Source
from sklearn import tree
import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
X = train.drop(['Outcome'], axis=1)
y = train.Outcome
clf = tree.DecisionTreeClassifi... | code |
50238455/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
test = pd.read_csv(DATA_DIR + 'test.csv')
test.head() | code |
50238455/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
train.head() | code |
50238455/cell_10 | [
"text_html_output_1.png"
] | from sklearn import tree
import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
X = train.drop(['Outcome'], axis=1)
y = train.Outcome
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=6)
clf.fit(X, y)
test = pd.read_csv(DATA_DIR + 'test.csv')
sub... | code |
50238455/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import tree
import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
X = train.drop(['Outcome'], axis=1)
y = train.Outcome
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=6)
clf.fit(X, y)
test = pd.read_csv(DATA_DIR + 'test.csv')
sub... | code |
1008790/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import xgboost
from sklearn import cross_validation
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression | code |
1008790/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import xgboost
clf = xgboost.XGBClassifier()
clf.fit(xtrain, ytrain)
pred = clf.predict(xtest)
acc = accuracy_score(ytest, pred)
print('%0.2f%%' % (acc * 100.0)) | code |
1008790/cell_5 | [
"text_plain_output_1.png"
] | import xgboost
clf = xgboost.XGBClassifier()
clf.fit(xtrain, ytrain) | code |
34126020/cell_13 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack | code |
34126020/cell_9 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits
fruits.append('grape')
fruits | code |
34126020/cell_25 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0] | code |
34126020/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple') | code |
34126020/cell_6 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana') | code |
34126020/cell_40 | [
"text_plain_output_1.png"
] | a = set('abracadabra')
b = set('alacazam')
a | code |
34126020/cell_29 | [
"text_plain_output_1.png"
] | v = ([1, 2, 3], [3, 2, 1])
v | code |
34126020/cell_39 | [
"text_plain_output_1.png"
] | basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
'crabgrass' in basket | code |
34126020/cell_26 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
t | code |
34126020/cell_48 | [
"text_plain_output_1.png"
] | tel = {'jack': 4098, 'sape': 4139}
tel['guido'] = 4127
tel
del tel['sape']
tel | code |
34126020/cell_11 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits
fruits.append('grape')
fruits
fruits.sort()
fruits
fruits.pop() | code |
34126020/cell_7 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4) | code |
34126020/cell_28 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
t[0] = 88888 | code |
34126020/cell_8 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits | code |
34126020/cell_15 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack
stack.pop()
stack | code |
34126020/cell_16 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack
stack.pop()
stack.pop()
stack.pop()
stack | code |
34126020/cell_38 | [
"text_plain_output_1.png"
] | basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
'orange' in basket | code |
34126020/cell_47 | [
"text_plain_output_1.png"
] | tel = {'jack': 4098, 'sape': 4139}
tel['guido'] = 4127
tel
tel['jack'] | code |
34126020/cell_35 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
x, y, z = t
print(x, y, z) | code |
34126020/cell_46 | [
"text_plain_output_1.png"
] | tel = {'jack': 4098, 'sape': 4139}
tel['guido'] = 4127
tel | code |
34126020/cell_14 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack
stack.pop() | code |
34126020/cell_10 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits
fruits.append('grape')
fruits
fruits.sort()
fruits | code |
34126020/cell_27 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
u = (t, (1, 2, 3, 4, 5))
u | code |
34126020/cell_37 | [
"text_plain_output_1.png"
] | basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
print(basket) | code |
34126020/cell_5 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine') | code |
90149707/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept... | code |
90149707/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
X.head() | code |
90149707/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
df.head() | code |
90149707/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
sns.lmplot(x='area', y='price', data=df, ci=None) | code |
90149707/cell_20 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept... | code |
90149707/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
sns.kdeplot(x='area', data=df) | code |
90149707/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
len(df) | code |
90149707/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept... | code |
90149707/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
sns.kdeplot(x='stories', data=df) | code |
90149707/cell_18 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept... | code |
90149707/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df.info() | code |
90149707/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y) | code |
90149707/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y)
model.score(X, y) | code |
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