path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90149707/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df.head() | code |
90149707/cell_17 | [
"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_14 | [
"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']
y.head() | code |
90149707/cell_5 | [
"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='stories', y='price', data=df, ci=None) | code |
1008271/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.metrics import log_loss
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(n_estimators=1000)
from sklearn.tree import DecisionTreeClassifier
clf.fit(X_train, y_train)
y_val_pred = clf.predict_proba(X_val)
log_loss(y_val, y_val_pr... | code |
1008271/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
trn = pd.read_json(open('../input/train.json', 'r'))
tst = pd.read_json(open('../input/test.json', 'r'))
list(trn.columns.values)
list(trn.columns.values) | code |
1008271/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
trn = pd.read_json(open('../input/train.json', 'r'))
tst = pd.read_json(open('../input/test.json', 'r'))
list(trn.columns.values) | code |
1008271/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 |
1008271/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
trn = pd.read_json(open('../input/train.json', 'r'))
tst = pd.read_json(open('../input/test.json', 'r'))
list(trn.columns.values)
trn.head() | code |
1008271/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
trn = pd.read_json(open('../input/train.json', 'r'))
tst = pd.read_json(open('../input/test.json', 'r'))
print('Train set: ', trn.shape)
print('Test set: ', tst.shape) | code |
324967/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
cursor = con.cursor()
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
def load(what='NationalNames'):
assert what in ('NationalNames', ... | code |
324967/cell_4 | [
"text_plain_output_1.png"
] | import sqlite3
con = sqlite3.connect('../input/database.sqlite')
cursor = con.cursor()
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
print(cursor.fetchall()) | code |
324967/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
324967/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
cursor = con.cursor()
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
def load(what='NationalNames'):
assert what in ('NationalNames', ... | code |
105177221/cell_21 | [
"text_plain_output_1.png"
] | from category_encoders import OneHotEncoder, WOEEncoder
from sklearn.base import clone, TransformerMixin, BaseEstimator
from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor
from sklearn.impute import SimpleImputer, IterativeImputer... | code |
105177221/cell_6 | [
"text_plain_output_1.png"
] | from category_encoders import OneHotEncoder, WOEEncoder
from sklearn.impute import SimpleImputer, IterativeImputer, KNNImputer, MissingIndicator
from sklearn.linear_model import BayesianRidge, Ridge, Lasso, HuberRegressor
import gc
import numpy as np
import pandas as pd
def prepreprocessing(df_train, df_test):
... | code |
105177221/cell_19 | [
"text_html_output_1.png"
] | from category_encoders import OneHotEncoder, WOEEncoder
from sklearn.base import clone, TransformerMixin, BaseEstimator
from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
from sklearn.impute import SimpleImputer, IterativeImputer, KNNImputer, MissingIndicator
from sklearn.linear_model import Bayesia... | code |
105177221/cell_7 | [
"text_plain_output_1.png"
] | train, test, columns = prepreprocessing(train, test) | code |
105177221/cell_3 | [
"text_plain_output_1.png"
] | from sklearnex import patch_sklearn
import warnings
import numpy as np
import pandas as pd
import time
import gc
import warnings
warnings.filterwarnings('ignore')
from sklearnex import patch_sklearn
patch_sklearn()
import sklearn
from sklearn.experimental import enable_iterative_imputer
from sklearn.base import clone... | code |
105177221/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from category_encoders import OneHotEncoder, WOEEncoder
from sklearn.base import clone, TransformerMixin, BaseEstimator
from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
from sklearn.impute import SimpleImputer, IterativeImputer, KNNImputer, MissingIndicator
from sklearn.linear_model import Bayesia... | code |
74054242/cell_13 | [
"text_html_output_1.png"
] | from warnings import simplefilter
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
import time
pd.set_option('display.max_columns', None)
pd.set_option('di... | code |
74054242/cell_9 | [
"image_output_1.png"
] | from warnings import simplefilter
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
import time
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', Non... | code |
74054242/cell_23 | [
"text_plain_output_1.png"
] | from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.graphics.tsaplots import plot_pacf
from warnings import simplefilter
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd
import pandas as pd # dat... | code |
74054242/cell_26 | [
"image_output_1.png"
] | from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.graphics.tsaplots import plot_pacf
from statsmodels.tsa.arima_model import ARMA
from warnings import simplefilter
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
... | code |
74054242/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from warnings import simplefilter
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
import time
pd.set_option('display.max_columns', None)
pd.set_option('di... | code |
74054242/cell_19 | [
"text_html_output_1.png"
] | from warnings import simplefilter
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.api as sm
import time
import numpy as np
import pandas as pd
import time
pd.set_option('display.max_col... | code |
74054242/cell_1 | [
"text_plain_output_1.png"
] | from warnings import simplefilter
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
import time
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', None)
pd.set_option('dis... | code |
74054242/cell_7 | [
"image_output_1.png"
] | from warnings import simplefilter
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
import time
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', Non... | code |
74054242/cell_18 | [
"image_output_1.png"
] | from warnings import simplefilter
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
import... | code |
74054242/cell_28 | [
"image_output_1.png"
] | from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.graphics.tsaplots import plot_pacf
from statsmodels.tsa.arima_model import ARMA
from warnings import simplefilter
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
... | code |
74054242/cell_14 | [
"text_html_output_1.png"
] | from warnings import simplefilter
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
import time
pd.set_option('display.max_columns', None)
pd.set_option('di... | code |
74054242/cell_22 | [
"image_output_1.png"
] | from statsmodels.graphics.tsaplots import plot_acf
from warnings import simplefilter
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import... | code |
32068244/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
tmp = data.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
X = data.drop(['Id', 'Species'], axis=1)
y = data['Species']
X_train, X_test, y_train, y_test = train_test_split(X, y... | code |
32068244/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
tmp = data.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
g = sns.violinplot(y='Species', x='SepalLengthCm', data=data, inner='quartile')
plt.show()
g = sns.violinplot(y='Species', x='SepalWidthCm', data=data, inner='quartile')
pl... | code |
32068244/cell_4 | [
"image_output_1.png"
] | data.head() | code |
32068244/cell_6 | [
"text_plain_output_1.png"
] | data.describe() | code |
32068244/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import seaborn as sns
tmp = data.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
g = sns.violinplot(y='Species', x='SepalLengthCm', data=data, inner='quartile')
plt.show()
... | code |
32068244/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 |
32068244/cell_7 | [
"image_output_1.png"
] | data['Species'].value_counts() | code |
32068244/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
tmp = data.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show() | code |
32068244/cell_15 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import seaborn as sns
tmp = data.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='... | code |
32068244/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import seaborn as sns
tmp = data.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='... | code |
32068244/cell_14 | [
"image_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import seaborn as sns
tmp = data.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='... | code |
32068244/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
tmp = data.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
X = data.drop(['Id', 'Species'], axis=1)
y = data['Species']
print(X.shape)
print(y.shape) | code |
32068244/cell_12 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import seaborn as sns
tmp = data.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
g = sns.violinplot(y='Species', x='Sep... | code |
32068244/cell_5 | [
"text_plain_output_1.png"
] | data.info() | code |
33112043/cell_21 | [
"text_plain_output_1.png"
] | from keras import layers
from keras import losses
from keras import models
from keras import optimizers
from keras.utils import to_categorical
import pandas as pd
train_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
train_label = train_data['label']
train_data = train_data.drop('label', axis=1)
... | code |
33112043/cell_9 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
import pandas as pd
train_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
train_label = train_data['label']
train_label_to_cat = to_categorical(train_label)
train_label_to_cat.shape | code |
33112043/cell_19 | [
"text_plain_output_1.png"
] | from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(1... | code |
33112043/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
from keras import optimizers
from keras import models
from keras import layers
from keras import losses
from keras.utils import to_categorical
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirn... | code |
33112043/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
train_data.head(2) | code |
33112043/cell_17 | [
"text_plain_output_1.png"
] | from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(1... | code |
33112043/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
train_data = train_data.drop('label', axis=1)
train_data.shape
train_data = train_data.values.reshape(-1, 28, 28, 1)
train_data.shape | code |
33112043/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
train_data = train_data.drop('label', axis=1)
train_data.shape | code |
33112043/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_data = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
test_data.head(2) | code |
105210284/cell_33 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 10)
games = pd.read_csv('../input/chess-games/games.csv')
games.columns
games.loc[:, 'first_move'] = games.moves.map(lambda x: x.split(' ')[0])
chosen_cols = ['winn... | code |
105210284/cell_28 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 10)
games = pd.read_csv('../input/chess-games/games.csv')
games.columns
games.loc[:, 'first_move'] = games.moves.map(lambda x: x.split(' ')[0])
chosen_cols = ['winn... | code |
105210284/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 10)
games = pd.read_csv('../input/chess-games/games.csv')
games.describe() | code |
105210284/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 10)
games = pd.read_csv('../input/chess-games/games.csv')
games.columns | code |
105210284/cell_43 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 10)
games = pd.read_csv('../input/chess-games/games.csv')
games.columns
games.loc[:, 'first_move'] = games.moves.map(lambda x: x.split(' ')[0])
chosen_cols = ['winn... | code |
105210284/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 10)
games = pd.read_csv('../input/chess-games/games.csv')
games.columns
games.loc[:, 'first_move'] = games.moves.map(lambda x: x.split(' ')[0])
chosen_cols = ['winner', 'increment_code', 'white_rating', 'black_rating', '... | code |
105210284/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 10)
games = pd.read_csv('../input/chess-games/games.csv')
games.head() | code |
105210284/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 10)
games = pd.read_csv('../input/chess-games/games.csv')
games.columns
games.loc[:, 'first_move'] = games.moves.map(lambda x: x.split(' ')[0])
chosen_cols = ['winner', 'increment_code', 'white_rating', 'black_rating', '... | code |
105210284/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 10)
games = pd.read_csv('../input/chess-games/games.csv')
games.columns
games.loc[:, 'first_move'] = games.moves.map(lambda x: x.split(' ')[0])
chosen_cols = ['winn... | code |
33111160/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import glob
import os
def list_columns_in_folder(file_path):
"""List out every column for every file in a folder"""
for dirname, _, filenames in os.walk(file_path):
for filename in filenames:
df = pd.read_csv(os.path.join... | code |
33111160/cell_3 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import glob
import os
def list_columns_in_folder(file_path):
"""List out every column for every file in a folder"""
for dirname, _, filenames in os.walk(file_path):
for filename in filenames:
df = pd.read_csv(os.path.join... | code |
329777/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/StateNames.csv')
df2 = pd.read_csv('../input/NationalNames.csv')
df[df['Name'] == 'Mary'] | code |
329777/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import seaborn as sns
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
17132170/cell_9 | [
"text_plain_output_1.png"
] | from clustergrammer2 import net
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ccle.txt/CCLE.txt', index_col=0)
from ast import literal_eval as make_tuple
cols = df.columns.tolist()
new_cols = [make_tuple(x) for x in cols]
df.columns = new_cols
df.shape
net.load_df(... | code |
17132170/cell_6 | [
"text_plain_output_1.png"
] | from clustergrammer2 import net
show_widget = False
from clustergrammer2 import net
if show_widget == False:
print('\n-----------------------------------------------------')
print('>>> <<<')
print('>>> Please set show_widget to True to see widgets <<<')
pr... | code |
17132170/cell_2 | [
"text_plain_output_1.png"
] | from IPython.display import HTML
import warnings
from IPython.display import HTML
import warnings
warnings.filterwarnings('ignore')
HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/9vqLO6McFwQ?rel=0&controls=0&showinfo=0" frameborder="0" allowfullscreen></iframe>') | code |
17132170/cell_11 | [
"text_html_output_1.png"
] | from clustergrammer2 import net
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ccle.txt/CCLE.txt', index_col=0)
from ast import literal_eval as make_tuple
cols = df.columns.tolist()
new_cols = [make_tuple(x) for x in cols]
df.columns = new_cols
df.shape
net.load_df(... | code |
17132170/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ccle.txt/CCLE.txt', index_col=0)
from ast import literal_eval as make_tuple
cols = df.columns.tolist()
new_cols = [make_tuple(x) for x in cols]
df.columns = new_cols
df.shape | code |
17132170/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
90148768/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
from ast import increment_lineno
import numpy as np
import os
import pandas as pd
import matplotlib.pyplot as plt
dataset = np.loadtxt('../input/ex1data1/ex1data1.txt', delimiter=',')
X = dataset[:, 0]
Y = dataset[:, 1]
m = Y.size
print(m) | code |
90148768/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
from ast import increment_lineno
import numpy as np
import os
import pandas as pd
import matplotlib.pyplot as plt
dataset = np.loadtxt('../input/ex1data1/ex1data1.txt', delimiter=',')
X = dataset[:, 0]
Y = dataset[:, 1]
m = Y.size
X = dataset[:, 0]
Y = dataset[:, 1]... | code |
18149936/cell_13 | [
"image_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'])
df['year'] = [d.year for d in d... | code |
18149936/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'])
df['year'] = [d.year for d in df.date]
df['month'] = [d.month for d in df.date]
years = ... | code |
18149936/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'])
df.head() | code |
18149936/cell_20 | [
"text_html_output_1.png"
] | from pandas.plotting import autocorrelation_plot
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller, kpss
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
im... | code |
18149936/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'])
df['year'] = [d.year for d in df.date]
df['month'] = [d.month for d in df.date]
years = ... | code |
18149936/cell_11 | [
"text_html_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'])
df['year'] = [d.year for d in d... | code |
18149936/cell_18 | [
"image_output_2.png",
"image_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller, kpss
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('https://raw... | code |
18149936/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller, kpss
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', ... | code |
18149936/cell_14 | [
"image_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'])
df['year'] = [d.year for d in d... | code |
18149936/cell_22 | [
"text_plain_output_1.png"
] | from pandas.plotting import autocorrelation_plot
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller, kpss
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
im... | code |
18149936/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'])
df['year'] = [d.year for d in df.date]
df['month'] = [d.month for d in df.date]
years = df['year'].unique()
np.... | code |
16147938/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.shape
df.isna().sum()
df.dtypes
df['TotalCharges'] = df.TotalCharges.convert_objects(convert_numeric=True) | code |
16147938/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.shape
df.isna().sum() | code |
16147938/cell_4 | [
"image_output_1.png"
] | import os
import os
print(os.listdir('../input/churn-prediction')) | code |
16147938/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.ticker as mtick
import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.shape
df.isna().sum()
df.dtypes
df['TotalCharges'] = df.TotalCharges.convert_objects(convert_numeric=True)
df['tenure_range'] = pd.cut(df.tenure, [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,... | code |
16147938/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.head() | code |
16147938/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.ticker as mtick
import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.shape
df.isna().sum()
df.dtypes
df['TotalCharges'] = df.TotalCharges.convert_objects(convert_numeric=True)
df['tenure_range'] = pd.cut(df.tenure, [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,... | code |
16147938/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.ticker as mtick
import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.shape
df.isna().sum()
df.dtypes
df['TotalCharges'] = df.TotalCharges.convert_objects(convert_numeric=True)
df['tenure_range'] = pd.cut(df.tenure, [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,... | code |
16147938/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.shape
df.isna().sum()
df.dtypes
df['Churn'].value_counts() | code |
16147938/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.ticker as mtick
import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.shape
df.isna().sum()
df.dtypes
df['TotalCharges'] = df.TotalCharges.convert_objects(convert_numeric=True)
df['tenure_range'] = pd.cut(df.tenure, [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,... | code |
16147938/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.shape | code |
16147938/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.shape
df.isna().sum()
df.dtypes
df['TotalCharges'] = df.TotalCharges.convert_objects(convert_numeric=True)
df['tenure_range'] = pd.cut(df.tenure, [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75], right=True)
df['tenure_r... | code |
16147938/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/churn-prediction/Churn.csv')
df.shape
df.isna().sum()
df.dtypes | code |
88091003/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import StratifiedKFold, GroupKFold
import numpy as np
import os
import pandas as pd
SEED = 42
DATA_PATH = '../input/birdclef-2022/'
AUDIO_PATH = '../input/birdclef-2022/train_audio'
MEAN = np.array([0.485, 0.456, 0.406])
STD = np.array([0.229, 0.224, 0.225])
NUM_WORKERS = 4
CLASSES = so... | code |
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