path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16146132/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
ca... | code |
16146132/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
ca... | code |
88087082/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
n_features = 300
features = [f'f_{i}' for i in range(n_features)]
train = pd.read_pickle('../input/ubiquant-market-prediction-half-precision-pickle/train.pkl')
plt.figure(figsize=(25, 30))
plt.title('Pearson Correlation', y=1.05, size=15)
sns... | code |
88087082/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
n_features = 300
features = [f'f_{i}' for i in range(n_features)]
train = pd.read_pickle('../input/ubiquant-market-prediction-half-precision-pickle/train.pkl')
print(train.shape)
train.head() | code |
88087082/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 |
34127846/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
import numpy as np
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = ... | code |
34127846/cell_9 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in data... | code |
34127846/cell_20 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
import numpy as np
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = ... | code |
34127846/cell_6 | [
"text_plain_output_1.png"
] | import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
head... | code |
34127846/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in data... | code |
34127846/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in data... | code |
34127846/cell_16 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in data... | code |
34127846/cell_3 | [
"text_plain_output_1.png"
] | import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
dataset[0] | code |
34127846/cell_22 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
import numpy as np
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = ... | code |
34127846/cell_10 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in data... | code |
34127846/cell_12 | [
"text_plain_output_1.png"
] | import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
head... | code |
122258057/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
train_df['Survived'].value_counts(normalize=True).plot(kind='bar', label='Выжившие') | code |
122258057/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.info() | code |
122258057/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.histplot(train_df['Survived']) | code |
122258057/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
pd.plotting.scatter_matrix(train_df[['Age', 'SibSp']], alpha=0.2) | code |
122258057/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
train_df.info() | code |
122258057/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
train_df['Survived'].hist(bins=2) | code |
122258057/cell_48 | [
"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/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.barplot(x='Sex', y='Survived', hue='Embarked', data=train_df)
plt.legend()
... | code |
122258057/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.head(5) | code |
122258057/cell_50 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
f, ax = plt.subplots(figsize=(25, 10))
sns.countplot(x='Age', hue='Survived', d... | code |
122258057/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.pairplot(train_df, vars=['Age', 'SibSp']) | code |
122258057/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df.describe(include='int64') | code |
122258057/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.jointplot(x='Age', y='SibSp', data=train_df) | code |
122258057/cell_47 | [
"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/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.barplot(x='Sex', y='Survived', data=train_df)
plt.legend()
plt.xlabel('пол'... | code |
122258057/cell_43 | [
"text_plain_output_1.png"
] | """
sns.boxplot(y="Fare", x="Pclass", data=train_df, orient="h");
Такой boxplot получается не очень красивым из-за выбросов.**
Опционально: создайте признак `Fare_no_out` (стоимости без выбросов), в котором исключаются стоимости, отличающиеся от средней по классу более чем на 2 стандартных отклонения.
Важно: надо иск... | code |
122258057/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
train_df.info() | code |
122258057/cell_46 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.countplot(x='Sex', hue='Survived', data=train_df)
plt.legend()
plt.xlabel('... | code |
122258057/cell_24 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.displot(train_df['Survived']) | code |
122258057/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum() | code |
122258057/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
plt.hist(x=train_df['Survived']) | code |
122258057/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
train_df.plot.scatter(x='Age', y='SibSp') | code |
122258057/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
plt.scatter(train_df['Age'], train_df['SibSp']) | code |
1009955/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
price = pd.read_csv('../input/price.csv')
price_per_sqft = pd.read_csv('../input/pricepersqft.csv')
flat_price = pd.melt(price, id_vars=['City Code', 'City', 'Metro', 'County', 'State', 'Population Rank'])
flat_price.dropna(inplace=True)
top10 =... | code |
1009955/cell_20 | [
"text_html_output_1.png"
] | flat_grouped = flat_price_sorted.groupby(['City_State'])
value_diff = flat_grouped['value'].agg({'value': ['first', 'last']})
value_diff['value']['last'] - value_diff['value']['first'] | code |
1009955/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | flat_grouped = flat_price_sorted.groupby(['City_State'])
value_diff = flat_grouped['value'].agg({'value': ['first', 'last']}) | code |
1009955/cell_18 | [
"text_html_output_1.png"
] | flat_grouped = flat_price_sorted.groupby(['City_State']) | code |
1009955/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
price = pd.read_csv('../input/price.csv')
price_per_sqft = pd.read_csv('../input/pricepersqft.csv')
flat_price = pd.melt(price, id_vars=['City Code', 'City', 'Metro', 'County', 'State', 'Population Rank'])
flat_price.head() | code |
1009955/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1009955/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
price = pd.read_csv('../input/price.csv')
price_per_sqft = pd.read_csv('../input/pricepersqft.csv')
flat_price = pd.melt(price, id_vars=['City Code', 'City', 'Metro', 'County', 'State', 'Population Rank'])
flat_price.dropna(inplace=True)
flat_pr... | code |
1009955/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
price = pd.read_csv('../input/price.csv')
price_per_sqft = pd.read_csv('../input/pricepersqft.csv')
price.head() | code |
73073936/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
fig, ax = plt.subplots()
ax.scatter(x = training_dataframe['GrLivArea... | code |
73073936/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
fig, ax = plt.subplots()
ax.scatter(x=training_dataframe['GrLivArea']... | code |
73073936/cell_2 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
training_dataframe.head() | code |
73073936/cell_11 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
import warnings
import os
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import math
import matplotlib... | code |
73073936/cell_19 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import warnings
import os
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import ma... | code |
73073936/cell_1 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import warnings
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import math... | code |
73073936/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
fig, ax = plt.subplots()
ax.scatter(x = training_dataframe['GrLivArea... | code |
73073936/cell_16 | [
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import warnings
import os
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import ma... | code |
73073936/cell_14 | [
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import warnings
import os
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import ma... | code |
90146658/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
print(iris.DESCR) | code |
90146658/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys()) | code |
90146658/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
X = iris.data[:, 2:]
y = iris.target
iris = sns.load_dataset('iris')
sns.set_style('whitegrid')
sns.FacetGrid(iris, hue='species', height=6).map(plt.scatter,... | code |
90146658/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
X = iris.data[:, 2:]
y = iris.target
tree_clf = DecisionTreeClassifier(max_depth=2)
tree_clf.fit(X, y) | code |
90146658/cell_17 | [
"text_html_output_1.png"
] | from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
X = iris.data[:, 2:]
y = iris.target
iris = sns.load_dataset('iris... | code |
90146658/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
X = iris.data[:, 2:]
y = iris.target
iris = sns.load_dataset('iris')
sns.set_style('whitegrid')
sns.FacetGrid(iris, hue='species', height=6).map(plt.scatter,... | code |
90146658/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
X = iris.data[:, 2:]
y = iris.target
X[:5, :] | code |
32073950/cell_11 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
crime = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-pr... | code |
32073950/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
... | code |
32073950/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
crime = pd.read_csv('/kaggle/input/... | code |
32073950/cell_15 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
crime = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-pr... | code |
32073950/cell_12 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
crime = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-pr... | code |
32073950/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
crime = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-pr... | code |
73079159/cell_13 | [
"text_html_output_1.png"
] | from datetime import datetime
from gluonts.dataset.common import ListDataset
from gluonts.dataset.field_names import FieldName
from gluonts.model.deepar import DeepAREstimator
from gluonts.mx.trainer import Trainer
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.st... | code |
73079159/cell_9 | [
"text_html_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month',... | code |
73079159/cell_6 | [
"image_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month',... | code |
73079159/cell_29 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from datetime import datetime
from gluonts.dataset.common import ListDataset
from gluonts.dataset.common import ListDataset
from gluonts.dataset.field_names import FieldName
from gluonts.model.deepar import DeepAREstimator
from gluonts.model.deepar import DeepAREstimator
from gluonts.mx import Trainer
from gluon... | code |
73079159/cell_19 | [
"text_plain_output_1.png"
] | item_metrics | code |
73079159/cell_1 | [
"text_plain_output_1.png"
] | ## Install the package
#!pip install --upgrade mxnet-cu101==1.6.0.post0
!pip install --upgrade mxnet==1.6.0
!pip install gluonts | code |
73079159/cell_18 | [
"text_plain_output_1.png"
] | from datetime import datetime
from gluonts.evaluation import Evaluator
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_date... | code |
73079159/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month',... | code |
73079159/cell_15 | [
"text_html_output_1.png"
] | from datetime import datetime
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert... | code |
73079159/cell_3 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from datetime import datetime
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
make.head(3... | code |
73079159/cell_17 | [
"image_output_1.png"
] | from datetime import datetime
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert... | code |
73079159/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv'... | code |
73079159/cell_22 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
import pandas as pd
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity',... | code |
73079159/cell_37 | [
"text_html_output_1.png"
] | item_metrics | code |
73079159/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset... | code |
73079159/cell_36 | [
"text_html_output_1.png"
] | from datetime import datetime
from gluonts.dataset.common import ListDataset
from gluonts.dataset.common import ListDataset
from gluonts.evaluation import Evaluator
from gluonts.evaluation import Evaluator
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import ... | code |
2014978/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | df.Department.groupby(df.Department).count().plot(kind='bar')
Specialization = 'Area of Specialization/Research Interests'
df[Specialization].value_counts().sort_values()[::-1][:20].plot(kind='bar') | code |
2014978/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | df.Department.groupby(df.Department).count().plot(kind='bar') | code |
2014978/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import plotly.plotly as py
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
plt.rcParams['figure.figsize'] = (12, 5)
df = pd.read_csv('../input/Pakistan Intellectual Capi... | code |
2014978/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | df.Department.groupby(df.Department).count().plot(kind='bar')
province = 'Province University Located'
df[province].value_counts().sort_values().plot(kind='bar') | code |
2014978/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | df.Department.groupby(df.Department).count().plot(kind='bar')
df['Country'].value_counts().sort_values()[::-1][1:].plot(kind='bar') | code |
17132381/cell_42 | [
"text_plain_output_1.png"
] | acts = hook_a.stored[0].cpu()
acts.shape | code |
17132381/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.de... | code |
17132381/cell_13 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(S... | code |
17132381/cell_23 | [
"text_html_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.de... | code |
17132381/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
os.listdir('../input') | code |
17132381/cell_26 | [
"text_html_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.de... | code |
17132381/cell_11 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(S... | code |
17132381/cell_7 | [
"image_output_1.png"
] | print('Make sure cudnn is enabled:', torch.backends.cudnn.enabled) | code |
17132381/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import matplotlib.pyplot as plt
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(... | code |
17132381/cell_16 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(S... | code |
17132381/cell_47 | [
"text_plain_output_1.png"
] | acts = hook_a.stored[0].cpu()
acts.shape
grad = hook_g.stored[0][0].cpu()
grad.shape
grad_chan = grad.mean(1).mean(1)
grad_chan.shape
mult = F.relu((acts * grad_chan[..., None, None]).sum(0))
mult.shape | code |
17132381/cell_43 | [
"text_plain_output_1.png"
] | grad = hook_g.stored[0][0].cpu()
grad.shape | code |
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