path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2001733/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
zoos = museums['Revenue'][museums['Museum Type'] == 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
other = museums['Revenue'][museums['Mus... | code |
2001733/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
museums.head(5) | code |
2001733/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
zoos = museums['Revenue'][museums['Museum Type'] == 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
other = museums['Revenue'][museums['Mus... | code |
2001733/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
museums['Museum Type'].unique() | code |
2025203/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datetime(airvisit['visit_date']).dt.date
airinfo = pd.read_csv('../input/air_store_info.csv')
airinfo.head() | code |
2025203/cell_25 | [
"text_plain_output_1.png"
] | from mpl_toolkits.basemap import Basemap
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datet... | code |
2025203/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
air.head() | code |
2025203/cell_34 | [
"text_html_output_1.png"
] | from mpl_toolkits.basemap import Basemap
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datet... | code |
2025203/cell_30 | [
"text_html_output_1.png"
] | from mpl_toolkits.basemap import Basemap
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datet... | code |
2025203/cell_33 | [
"text_html_output_1.png"
] | from mpl_toolkits.basemap import Basemap
from sklearn.cluster import KMeans
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/... | code |
2025203/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
air.tail() | code |
2025203/cell_26 | [
"text_plain_output_1.png"
] | from mpl_toolkits.basemap import Basemap
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datet... | code |
2025203/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 |
2025203/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datetime(airvisit['visit_date']).dt.date
len(airvisit['air_store_id'].unique()) | code |
2025203/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datetime(airvisit['visit_date']).dt.date
airinfo = pd.read_csv('../input/air_store_info.csv')
len(airinfo['air_gen... | code |
2025203/cell_32 | [
"text_html_output_1.png"
] | from mpl_toolkits.basemap import Basemap
from sklearn.cluster import KMeans
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/... | code |
2025203/cell_28 | [
"text_html_output_1.png"
] | from mpl_toolkits.basemap import Basemap
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datet... | code |
2025203/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datetime(airvisit['visit_date']).dt.date
airvisit.tail() | code |
2025203/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datetime(airvisit['visit_date']).dt.date
airinfo = pd.read_csv('../input/air_store_info.csv')
len(airinfo['air_sto... | code |
2025203/cell_22 | [
"text_html_output_1.png"
] | from mpl_toolkits.basemap import Basemap
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datet... | code |
2025203/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
len(air['air_store_id'].unique()) | code |
2025203/cell_27 | [
"image_output_1.png"
] | from mpl_toolkits.basemap import Basemap
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datet... | code |
2025203/cell_37 | [
"text_plain_output_1.png"
] | from mpl_toolkits.basemap import Basemap
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
air = pd.read_csv('../input/air_reserve.csv')
airvisit = pd.read_csv('../input/air_visit_data.csv')
airvisit['visit_date'] = pd.to_datet... | code |
2031459/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_df.head() | code |
2031459/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train = train_df[['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', 'SaleCondition']]
t... | code |
2031459/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import learning_curve
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')... | code |
2031459/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train = train_df[['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', 'SaleCondition']]
t... | code |
2031459/cell_15 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train... | code |
2031459/cell_16 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train = train_df[['MSSubClass', 'LotFronta... | code |
2031459/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.head() | code |
2031459/cell_17 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_... | code |
2031459/cell_14 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train = train_df[['MSSubClass', 'L... | code |
2031459/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train = train_df[['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', 'SaleCondition']]
t... | code |
2031459/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.describe() | code |
17111741/cell_4 | [
"text_plain_output_1.png"
] | from PIL import Image
train_cat = '../input/training_set/training_set/cats'
train_dog = '../input/training_set/training_set/dogs'
test_cat = '../input/test_set/test_set/cats'
test_dog = '../input/test_set/test_set/dogs'
image_size = 128
Image.open(train_cat + '/' + 'cat.1.jpg')
Image.open('../input/training_set/trai... | code |
17111741/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import warnings
warnings.filterwarnings('ignore')
import os
print(os.listdir('../input')) | code |
17111741/cell_3 | [
"image_output_1.png"
] | from PIL import Image
train_cat = '../input/training_set/training_set/cats'
train_dog = '../input/training_set/training_set/dogs'
test_cat = '../input/test_set/test_set/cats'
test_dog = '../input/test_set/test_set/dogs'
image_size = 128
Image.open(train_cat + '/' + 'cat.1.jpg') | code |
17111741/cell_12 | [
"image_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
train_cat = '../input/training_set/training_set/cats'
train_dog = '../input/training_set/training_set/dogs'
test_cat = '../input/test_set/test_set/cats'
test_dog = '../input... | code |
17111741/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
train_cat = '../input/training_set/training_set/cats'
train_dog = '../input/training_set/training_set/dogs'
test_cat = '../input/test_set/test_set/cats'
test_dog = '../input/test_set/test_set/dogs'
image_size = 128
Image.open(train_cat + '/' + 'cat.1.jpg')
Image.open('../input/training_set/trai... | code |
49124471/cell_55 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import nltk
import re
import string
def clean_text(text):
text = text.lower().strip()
text = ' '.join([w for w in text.split() if len(w) > 2])
text = re.sub('\\[.*?\\]', '', text)
text = re.sub('https?://\\S+|www\\.\\S+', '', text)
text = re.sub('<.*?>+', '', te... | code |
49124471/cell_29 | [
"text_plain_output_1.png"
] | train_word = train.explode('comment')
word_all_rate = train_word.comment.value_counts(ascending=True)
word_all_rate = word_all_rate[word_all_rate > 10]
word_all_rate | code |
49124471/cell_52 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
import nltk
import re
import string
def clean_text(text):
text = text.lower().strip()
text = ' '.join([w for w in text.split() if len(w) > 2])
text = re.sub('\\[.*?\\]', '', text)
text = re.sub('https?://\\S+|www\\.\\S+', '', text)
text = re.sub('<.*?>+', '', te... | code |
49124471/cell_64 | [
"text_plain_output_1.png"
] | !pip install wordcloud | code |
49124471/cell_68 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
import nltk
import pandas as pd
import re
import string
import numpy as np
import pandas as pd
import re
import string
import nltk
pd.options.mode.chained_assignment = None
original_data = pd.read_csv('../input/boardgamegeek-reviews/bgg-15m-reviews.csv')
comment_rate = pd.DataFr... | code |
49124471/cell_66 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import re
import string
def clean_text(text):
text = text.lower().strip()
text = ' '.join([w for w in text.split() if len(w) > 2])
text = re.sub('\\[.*?\\]', '', text)
text = re.sub('https?://\\S+|www\\.\\S+', '', text)
text = re.sub('<.*?>+', '', text)
... | code |
32070892/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df_y = df.groupby('Year')
df_a = df.groupby('Airport name').sum()
df_a
df[df['Whole year'] == df['Whole year'].max()]['Airport name'] | code |
32070892/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df_y = df.groupby('Year')
df_ys = df_y.sum().drop('Whole year', axis=1)
df_ys
pl... | code |
32070892/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df.head() | code |
32070892/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df.describe().transpose() | code |
32070892/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df_y = df.groupby('Year')
df['Airport name'].nunique() | code |
32070892/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 |
32070892/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df_y = df.groupby('Year')
df_a = df.groupby('Airport name').sum()
df_a
df.iloc[353]
df.iloc[601]
df_a = df.groupby('Airport name')
df_... | code |
32070892/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df_y = df.groupby('Year')
df_ys = df_y.sum().drop('Whole year', axis=1)
df_ys | code |
32070892/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df_y = df.groupby('Year')
df_a = df.groupby('Airport name').sum()
df_a
df_asort = df['Whole year'].sort_values(ascending=False)
df_asort... | code |
32070892/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df_y = df.groupby('Year')
df_a = df.groupby('Airport name').sum()
df_a
df.iloc[353] | code |
32070892/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df_y = df.groupby('Year')
df_a = df.groupby('Airport name').sum()
df_a
df.iloc[353]
df.iloc[601] | code |
32070892/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df_y = df.groupby('Year')
df_a = df.groupby('Airport name').sum()
df_a
df_a1 = df_a['Whole year'].sort_values(ascending=False)
df_a1.hea... | code |
32070892/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df_y = df.groupby('Year')
df_a = df.groupby('Airport name').sum()
df_a | code |
32070892/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/russian-passenger-air-service-20072020/russian_passenger_air_service_2.csv')
df.info() | code |
88078658/cell_3 | [
"text_plain_output_1.png"
] | import math
import math
a = 123456
n_digit = math.floor(math.log10(a) + 1)
print(n_digit) | code |
104118983/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/polynomial/HeightVsWeight.csv')
X = df.iloc[:, :-1].values
Y = df.iloc[:, -1].values
from skl... | code |
104118983/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 |
104118983/cell_7 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/polynomial/HeightVsWeight.csv')
X = df.iloc[:, :-1].values
Y = df.iloc[:, -1].values
from skl... | code |
104118983/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/polynomial/HeightVsWeight.csv')
X = df.iloc[:, :-1].values
Y = df.iloc[:, -1].values
from skl... | code |
104118983/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/polynomial/HeightVsWeight.csv')
df.head(6) | code |
128029773/cell_1 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | model_path = "/kaggle/working/models/hydra/"
!pip install chardet | code |
129018278/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
x_1 = 2 * np.random.rand(100, 1)
x_2 = 50 * np.random.rand(100, 1)
x_3 = 1000 * np.random.rand(100, 1)
y = 3 + 500 * x_1 + 20 * x_2 + x_3
fig, axs = plt.subplots(2, 2)
fig.tight_layout(h_pad=2, w_pad=2)
axs[0, 0].plot(x_1, y, 'k.')
axs[0, 0].set(xlabel='$x_1$', ylabe... | code |
129018278/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
# Create simulated data
x_1 = 2 * np.random.rand(100, 1)
x_2 = 50 * np.random.rand(100, 1)
x_3 = 1000 * np.random.rand(100, 1)
# Calculate target variable based on y = 3 + 500x1 + 20x2 + x3 formula
y = 3 + 500 * x_1 + 20 * x_2 + x_3
# Plot the simulated data
fig, a... | code |
129018278/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
# Create simulated data
x_1 = 2 * np.random.rand(100, 1)
x_2 = 50 * np.random.rand(100, 1)
x_3 = 1000 * np.random.rand(100, 1)
# Calculate target variable based on y = 3 + 500x1 + 20x2 + x3 formula
y = 3 + 500 * x_1 + 20 * x_2 + x_3
# Plot the simulated data
fig, a... | code |
2044063/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
plt.figure(figsize=(12, 8))
sns.countplot(data=df, y='username') | code |
2044063/cell_25 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet... | code |
2044063/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
df.head() | code |
2044063/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet... | code |
2044063/cell_20 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet... | code |
2044063/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
pd.isnull(df).any() | code |
2044063/cell_29 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet... | code |
2044063/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud, STOPWORDS
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2044063/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
df.describe() | code |
2044063/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10] | code |
2044063/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet ', ascending=False)[:10]
topretweets = df.groupby('username')[['retweets']].sum()
topretweets.sort... | code |
2044063/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
df[df['retweets'] == 79537] | code |
2044063/cell_27 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
toptweeps = df.groupby('username')[['tweet ']].count()
toptweeps.sort_values('tweet... | code |
2044063/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('../input/tweets.xlsx', sheet_name='tweets')
df.info() | code |
74040076/cell_11 | [
"image_output_1.png"
] | from matplotlib.colors import ListedColormap
from sklearn.datasets import make_classification, make_blobs,make_gaussian_quantiles, make_circles,make_moons
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm imp... | code |
104114403/cell_42 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../i... | code |
104114403/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first create a correlation matrix and store it in 'corm' variable, and uncomment lines below)
corm = pima.iloc[:,:-1].corr()
masko = np.zeros_like(co... | code |
104114403/cell_25 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first create a correlation matrix and store it in 'corm' variable, and uncomment lines below)
c... | code |
104114403/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.dummy import DummyClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import recall_score, precision_score, accuracy_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima ... | code |
104114403/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.dummy import DummyClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first c... | code |
104114403/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.info() | code |
104114403/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first create a correlation matrix and store... | code |
104114403/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.dummy import DummyClassifier
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first create a correlation matrix and store it in 'co... | code |
104114403/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
from sklearn.metrics import recall_score, precision_score, accuracy_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
... | code |
104114403/cell_50 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first create a correlation matrix and store... | code |
104114403/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.dummy import DummyClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first c... | code |
104114403/cell_51 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
from sklearn.metrics import recall_score, precision_score, accuracy_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
... | code |
104114403/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
corm = pima.iloc[:, :-1].corr()
masko = np.zeros_like(corm, dtype=np.bool)
masko[np.triu_indices_from(masko)] = True
fig, ax = plt.subplots(figsize=(10, 5))
sns.h... | code |
104114403/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
from sklearn.metrics import recall_score, precision_score, accuracy_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
... | code |
104114403/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
pima.describe() | code |
104114403/cell_22 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first create a correlation matrix and store it in 'corm' variable, and uncomment lines below)
corm = pima.iloc[:,:-1].corr()
masko = np.zeros_like(co... | code |
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