path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
74048227/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_d... | code |
74048227/cell_3 | [
"text_plain_output_1.png"
] | import os
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn import ensemble, linear_model, metrics, model_selection, neighbors, preprocessing, svm, tree
from sklearn.impute import SimpleImputer
from skl... | code |
74048227/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import ensemble, linear_model,metrics,model_selection,neighbors,preprocessing, svm, tree
from sklearn.impute import SimpleImputer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/... | code |
74048227/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
train... | code |
74048227/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
train... | code |
74048227/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
print(... | code |
130004107/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data.plot(kind='scatter', x='Survived', y='Age', title='Scatter Plot of Survivors Separated by Age') | code |
130004107/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data['Pclass'].value_counts() | code |
130004107/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count() | code |
130004107/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
a = data.filter(['AgeBin', 'Survived'])
b = a.pivot_table(index='AgeBin', columns=['Survived'], aggfunc=len)
b
data[(data['Sex'] == 'male') & (data['Survived'] == 1)]['Pclass'].value_counts().sort_index().plo... | code |
130004107/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data[data['Survived'] == 1]['Age'].value_counts().sort_index().plot(kind='bar', title='Survivors by Age') | code |
130004107/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data[data['Survived'] == 1]['Pclass'].value_counts().sort_index().plot(kind='bar', title='Survivors Separated by Cabin Class') | code |
130004107/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
(data['Age'].min(), data['Age'].max()) | code |
130004107/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
a = data.filter(['AgeBin', 'Survived'])
b = a.pivot_table(index='AgeBin', columns=['Survived'], aggfunc=len)
b
b.plot(kind='bar', stacked=True, xlabel='Age Group', ylabel='Count', title='Survivors by Age Group... | code |
130004107/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data['AgeBin'].value_counts().sort_index().plot(kind='bar', title='Passenger by Age Groups') | code |
130004107/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data | code |
130004107/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data['Sex'].value_counts() | code |
130004107/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
alpha_color = 0.5
data['Pclass'].value_counts().sort_index().plot(kind='bar', alpha=alpha_color, title='Passengers Separated by Cabin Class') | code |
130004107/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
alpha_color = 0.5
data['Sex'].value_counts().sort_index().plot(kind='bar', color=['b', 'r'], alpha=alpha_color, title='Passengers Separated by Sex') | code |
130004107/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
a = data.filter(['AgeBin', 'Survived'])
b = a.pivot_table(index='AgeBin', columns=['Survived'], aggfunc=len)
b
data[data['Sex'] == 'female']['Survived'].value_counts().plot(kind='bar', xlabel='Female Survivor... | code |
130004107/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data[data['Survived'] == 0]['AgeBin'].value_counts().sort_index().plot(kind='bar', title='Death by Age Groups') | code |
130004107/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data['Survived'].value_counts() | code |
130004107/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
alive = data[data['Survived'].eq(1)]['Survived'].value_counts()
round(data[data['Survived'].eq(1)]['Sex'].value_counts().astype(int) * 100 / alive[1], 2) | code |
130004107/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
len(data) | code |
130004107/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
alpha_color = 0.5
data['Survived'].value_counts().plot(kind='bar', title='Number of Survivors') | code |
130004107/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
a = data.filter(['AgeBin', 'Survived'])
b = a.pivot_table(index='AgeBin', columns=['Survived'], aggfunc=len)
b
data[(data['Sex'] == 'female') & (data['Survived'] == 1)]['Pclass'].value_counts().sort_index().p... | code |
130004107/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
a = data.filter(['AgeBin', 'Survived'])
b = a.pivot_table(index='AgeBin', columns=['Survived'], aggfunc=len)
b
data[data['Sex'] == 'male']['Survived'].value_counts().plot(kind='bar', xlabel='Male Survivor', t... | code |
130004107/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data['Survived'].value_counts() * 100 / len(data) | code |
130004107/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data[data['Survived'] == 1]['AgeBin'].value_counts().sort_index().plot(kind='bar', title='Survivors by Age Groups') | code |
72120651/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
countries_dataset = pd.read_csv('../input/countries-of-the-world/countries of the world.csv', decimal=',')
countries_dataset.head() | code |
72120651/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
countries_dataset = pd.read_csv('../input/countries-of-the-world/countries of the world.csv', decimal=',')
print('Shape:', countries_dataset.shape, '\n')
print('Missing values:')
print(countries_dataset.isnull().sum(), '\n')
print('Data types:')
print(countries_dataset.dtypes, '\n') | code |
105201140/cell_2 | [
"text_plain_output_1.png"
] | # build dependency wheels
!pip wheel --verbose --no-binary cython-bbox==0.1.3 cython-bbox -w /kaggle/working/
!pip wheel --verbose --no-binary lap==0.4.0 lap -w /kaggle/working/
!pip wheel --verbose --no-binary loguru-0.6.0 loguru -w /kaggle/working/
!pip wheel --verbose --no-binary thop-0.1.1.post2209072238 thop -w /... | code |
328596/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grou... | code |
328596/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_... | code |
328596/cell_25 | [
"text_plain_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grou... | code |
328596/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.head() | code |
328596/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.count()
test_df = test_df.drop(['Cabin'], ... | code |
328596/cell_6 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean() | code |
328596/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.count() | code |
328596/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grou... | code |
328596/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import random
import numpy as np
import pandas as pd
from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import sklearn.ensemble as ske
import tensorflow as tf
from tensorflow.contrib import skflow | code |
328596/cell_7 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
print(class_sex_grouping['Survi... | code |
328596/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
class_sex_grouping['Survived']... | code |
328596/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_... | code |
328596/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.head() | code |
328596/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.count()
test_df = test_df.drop(['Cabin'], axis=1)
test_df = test_df.dropna()
test_df.count() | code |
328596/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_... | code |
328596/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df['Survived'].mean() | code |
122252822/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
os.chdir('C:\\\\Users\\\\melanie.vercaempt\\\\Documents\\\\Code\\\\train-keyrus-academy-python\\\\data-viz') | code |
122252822/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import date
import os
import geopandas as gpd
import folium
import mapclassify
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import pandas as pd
import plotly.express as px
import re
import seaborn as sns
from shapely.geometry import Point, Poly... | code |
17122803/cell_13 | [
"text_plain_output_1.png"
] | from nltk.tokenize import word_tokenize, sent_tokenize
text = 'Mary had a little lamb. Her fleece was white as snow'
sents = sent_tokenize(text)
print(sents) | code |
17122803/cell_9 | [
"text_plain_output_1.png"
] | text1.concordance('monstrous')
text1.dispersion_plot(['happy', 'sad']) | code |
17122803/cell_25 | [
"image_output_1.png"
] | from nltk.tokenize import word_tokenize, sent_tokenize
import nltk
text2.concordance('monstrous')
text2.similar('monstrous')
text2.common_contexts(['monstrous', 'very'])
text2 = 'Mary closed on closing night when she was in the mood to close.'
nltk.pos_tag(word_tokenize(text2)) | code |
17122803/cell_4 | [
"image_output_1.png"
] | text2.concordance('monstrous') | code |
17122803/cell_6 | [
"text_plain_output_1.png"
] | text2.concordance('monstrous')
text2.similar('monstrous')
text2.common_contexts(['monstrous', 'very']) | code |
17122803/cell_2 | [
"text_plain_output_1.png"
] | from nltk.book import * | code |
17122803/cell_19 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
from string import punctuation
text = 'Mary had a little lamb. Her fleece was white as snow'
customStopWords = set(stopwords.words('english') + list(punctuation))
wordsWOStopwords = [word for word in word_tokenize(text) if wor... | code |
17122803/cell_1 | [
"text_plain_output_1.png"
] | import os
import nltk
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17122803/cell_7 | [
"text_plain_output_1.png"
] | text4.dispersion_plot(['citizens', 'democracy', 'freedom', 'duties', 'America']) | code |
17122803/cell_8 | [
"text_plain_output_1.png"
] | text2.concordance('monstrous')
text2.similar('monstrous')
text2.common_contexts(['monstrous', 'very'])
text2.dispersion_plot(['happy', 'sad']) | code |
17122803/cell_3 | [
"image_output_1.png"
] | text1.concordance('monstrous') | code |
17122803/cell_24 | [
"text_plain_output_1.png"
] | from nltk.stem.lancaster import LancasterStemmer
from nltk.tokenize import word_tokenize, sent_tokenize
text2.concordance('monstrous')
text2.similar('monstrous')
text2.common_contexts(['monstrous', 'very'])
text2 = 'Mary closed on closing night when she was in the mood to close.'
st = LancasterStemmer()
stemmedW... | code |
17122803/cell_14 | [
"text_plain_output_1.png"
] | from nltk.tokenize import word_tokenize, sent_tokenize
text = 'Mary had a little lamb. Her fleece was white as snow'
sents = sent_tokenize(text)
words = [word_tokenize(sent) for sent in sents]
print(words) | code |
17122803/cell_5 | [
"text_plain_output_1.png"
] | text2.concordance('monstrous')
text2.similar('monstrous') | code |
90143099/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers'... | code |
90143099/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesw... | code |
90143099/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesw... | code |
90143099/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 |
90143099/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesw... | code |
90143099/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers'... | code |
1003686/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_train.head() | code |
1003686/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_train['SalePrice'].describe() | code |
1003686/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
sns.distplot(df_train['SalePrice']) | code |
1003686/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
print('How skewed is the data?, Skewness: {}'.format(df_train['SalePrice'].skew()))
print('How sharp is the peak the data?, Kurtosis: {}'.format(df_train['SalePrice'].kurt())) | code |
1003686/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
print(df_train.columns) | code |
32071698/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
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 matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona... | code |
32071698/cell_13 | [
"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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv... | code |
32071698/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Cou... | code |
32071698/cell_2 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
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))
plt.style.use('fivethirtyeight') | code |
32071698/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
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 matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona... | code |
32071698/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Cou... | code |
32071698/cell_15 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
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 matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona... | code |
32071698/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
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 matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona... | code |
32071698/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
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 matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona... | code |
32071698/cell_10 | [
"text_plain_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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv... | code |
32071698/cell_12 | [
"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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv... | code |
73074342/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
cat_features = ['cat' + str(i) for i in range(10)]
num_features = ['cont' + str(i) for i in ... | code |
73074342/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).T | code |
73074342/cell_11 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
# Comparing the datasets length
fig, ax... | code |
73074342/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).T
print(f"{(df['target'] < 5).sum() / ... | code |
73074342/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
# Comparing the datasets length
fig, ax = plt.subplots(figs... | code |
73074342/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import seaborn as sns
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
c... | code |
73074342/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
df.head() | code |
73074342/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
# Comparing the ... | code |
73074342/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
# Comparing the datasets length
fig, ax... | code |
73074342/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
# Comparing the datasets length
fig, ax... | code |
73074342/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
fig, ax = plt.subplots(figsize=(5, 5))
pie = ax.pie([len(df... | code |
128010348/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = df.dropna()
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.drop(['Id'], axis=1)
scaler = MinMaxScaler()
cols_to_scale = df.columns[:-1]
df_norm = pd.DataFr... | code |
128010348/cell_25 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier, Pool
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm ... | code |
128010348/cell_30 | [
"text_plain_output_35.png",
"application_vnd.jupyter.stderr_output_24.png",
"application_vnd.jupyter.stderr_output_16.png",
"application_vnd.jupyter.stderr_output_52.png",
"text_plain_output_43.png",
"text_plain_output_37.png",
"application_vnd.jupyter.stderr_output_32.png",
"text_plain_output_5.png",... | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential, Model, load_model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from tensorflow.keras import regularizers
from tensorflow.keras.layers import Conv2D, MaxPool2D, Activation, Dropout, BatchN... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.