path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
89125628/cell_51 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
from sklearn.preprocessing import StandardScaler
details = {'col1': [1, 3, 5, 7, 9], 'col2': [7, 4, 35, 14, 56]}
df = pd.DataFrame(details)
scaler = StandardScaler()
df = scaler.fit_transform(df)
df = pd.DataFrame(df)
plt = df.plot.bar() | code |
89125628/cell_59 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import StandardScaler
details = {'col1': [1, 3, 5, 7, 9], 'col2': [7, 4, 35, 14, 56]}
df = pd.DataFrame(details)
scaler = S... | code |
89125628/cell_58 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import StandardScaler
details = {'col1': [1, 3, 5, 7, 9], 'col2': [7, 4, 35, 14, 56]}
df = pd.DataFrame(details)
scaler = StandardScaler()
df = scaler.fit_t... | code |
129007116/cell_42 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.isnull().sum()
data.isnull().sum().to_frame().rename(columns={0: 'Total No. of Missing Values'})
x = data.YearsExperience
y = data.Salary
w = 9909
b = 21641.1797
def linearRegression(x, w, b):
... | code |
129007116/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.isnull().sum()
data.isnull().sum().to_frame().rename(columns={0: 'Total No. of Missing Values'})
print('Duplicate Values =', data.duplicated().sum()) | code |
129007116/cell_30 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | w = 9909
b = 21641.1797
print('w :', w)
print('b :', b) | code |
129007116/cell_29 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.isnull().sum()
data.isnull().sum().to_frame().rename(columns={0: 'Total No. of Missing Values'})
x = data.YearsExperience
y = data.Salary
plt.title('Salary Data')... | code |
129007116/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.isnull().sum()
data.isnull().sum().to_frame().rename(columns={0: 'Total No. of Missing Values'})
x = data.YearsExperience
y = data.Salary
print('x_train data is')
x | code |
129007116/cell_48 | [
"text_plain_output_1.png"
] | import math
import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.isnull().sum()
data.isnull().sum().to_frame().rename(columns={0: 'Total No. of Missing Values'})
x = data.YearsExperience
y = data.Salary
w = 9909
b = 21641.1797
def linearRegressio... | code |
129007116/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.isnull().sum()
data.isnull().sum().to_frame().rename(columns={0: 'Total No. of Missing Values'})
x = data.YearsExperience
y = data.Salary
w = 9909
b = 21641.1797
def linearRegression(x, w, b):
... | code |
129007116/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.head() | code |
129007116/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.isnull().sum()
data.isnull().sum().to_frame().rename(columns={0: 'Total No. of Missing Values'}) | code |
129007116/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.tail() | code |
129007116/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.isnull().sum() | code |
129007116/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
print('data shape :', data.shape) | code |
129007116/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
print(data.info()) | code |
129007116/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.isnull().sum()
data.isnull().sum().to_frame().rename(columns={0: 'Total No. of Missing Values'})
x = data.YearsExperience
y = data.Salary
print('y_train data is')
y | code |
129007116/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
data.isnull().sum()
data.isnull().sum().to_frame().rename(columns={0: 'Total No. of Missing Values'})
x = data.YearsExperience
y = data.Salary
w = 9909
b = 21641.1797
... | code |
2041173/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import warnings
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
import warnings
warnings.filter... | code |
2041173/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from sklearn.preprocessing import MaxAbsScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.... | code |
2041173/cell_8 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from sklearn.preprocessing import MaxAbsScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.... | code |
2041173/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test[0:10] | code |
105203676/cell_21 | [
"text_html_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.utils.np_utils import to_categorical
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data proces... | code |
105203676/cell_9 | [
"text_plain_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('/kaggle/input/bbc-news/BBC News Train.csv')
df.describe(include='object') | code |
105203676/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from keras.utils.np_utils import to_categorical
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/bbc-news/BBC News Train.csv')
num_of_categories = 45000
shuffled = df.reindex(np.random.... | code |
105203676/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.ut... | code |
105203676/cell_20 | [
"text_html_output_1.png"
] | from keras.utils.np_utils import to_categorical
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/bbc-news/BBC News Train.csv')
num_of_categories = 45000
shuffled = df.reindex(np.random.... | code |
105203676/cell_29 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.ut... | code |
105203676/cell_26 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.utils.np_utils import to_categorical
import ... | code |
105203676/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/bbc-news/BBC News Train.csv')
plt.hist(x=df['length']) | code |
105203676/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 |
105203676/cell_7 | [
"text_plain_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('/kaggle/input/bbc-news/BBC News Train.csv')
df.info() | code |
105203676/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/bbc-news/BBC News Train.csv')
df['Category'].value_counts() | code |
105203676/cell_28 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.utils.np_utils import to_categorical
import ... | code |
105203676/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/bbc-news/BBC News Train.csv')
print(df['Category'].value_counts())
sns.countplot(data=df, x='Category', palette='RdBu')
plt.title('The Dis... | code |
105203676/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/bbc-news/BBC News Train.csv')
df[['length', 'polarity', 'subjectivity']] | code |
105203676/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/bbc-news/BBC News Train.csv')
plt.rcParams['figure.figsize'] = (10, 4)
plt.subplot(1, 2, 1)
sns.distplot(df['polarity'])
plt.subplot(1, 2,... | code |
105203676/cell_35 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.ut... | code |
105203676/cell_31 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.ut... | code |
105203676/cell_14 | [
"text_plain_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('/kaggle/input/bbc-news/BBC News Train.csv')
df[['length', 'polarity', 'Text']] | code |
105203676/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_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('/kaggle/input/bbc-news/BBC News Train.csv')
print('Shpe of Data', df.shape)
df.head(10) | code |
32074163/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.info() | code |
32074163/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.sort_values(by=['saledate'], inplace=True, ascending=True... | code |
32074163/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('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df.head() | code |
32074163/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.sort_values(by=['saledate'], inplace=True, ascending=True... | code |
32074163/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.sort_values(by=['saledate'], inplace=True, ascending=True... | code |
32074163/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('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df.SalePrice.plot.hist() | code |
32074163/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv') | code |
32074163/cell_19 | [
"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('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.sort_values(by=['saledate'], inplace=True, ascending=True... | code |
32074163/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 |
32074163/cell_18 | [
"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('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.sort_values(by=['saledate'], inplace=True, ascending=True... | code |
32074163/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df.info() | code |
32074163/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.sort_values(by=['saledate'], inplace=True, ascending=True... | code |
32074163/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('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.sort_values(by=['saledate'], inplace=True, ascending=True... | code |
32074163/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.sort_values(by=['saledate'], inplace=True, ascending=True... | code |
32074163/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.head() | code |
32074163/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.sort_values(by=['saledate'], inplace=True, ascending=True... | code |
32074163/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('../input/bluebook-for-bulldozers/TrainAndValid.csv')
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])
df.saledate.head(20) | code |
32074163/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/bluebook-for-bulldozers/TrainAndValid.csv')
fig, ax = plt.subplots()
ax.scatter(df['saledate'][:1000], df['SalePrice'][:1000]) | code |
1005908/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import itertools as it
import pandas as pd
import re
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.cha... | code |
1005908/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import itertools as it
import pandas as pd
import re
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.cha... | code |
1005908/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import itertools as it
import pandas as pd
import re
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.cha... | code |
1005908/cell_6 | [
"text_plain_output_1.png"
] | import itertools as it
import pandas as pd
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.chain.from_iterable(feature_total))
len(feature_total)
uniq_feature_total ... | code |
1005908/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import itertools as it
import pandas as pd
import re
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.cha... | code |
1005908/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import itertools as it
import pandas as pd
import re
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.cha... | code |
1005908/cell_7 | [
"text_plain_output_1.png"
] | import itertools as it
import pandas as pd
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.chain.from_iterable(feature_total))
len(feature_total)
uniq_feature_total ... | code |
1005908/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import itertools as it
import pandas as pd
import re
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.cha... | code |
1005908/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import log_loss
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import pandas as pd
train = pd.read_json('../input/train.json')
train['listing_id'] = train... | code |
1005908/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import itertools as it
import pandas as pd
import re
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.cha... | code |
1005908/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import itertools as it
import pandas as pd
import re
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.cha... | code |
1005908/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import itertools as it
import pandas as pd
import re
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.cha... | code |
1005908/cell_5 | [
"text_plain_output_1.png"
] | import itertools as it
import pandas as pd
train = pd.read_json('../input/train.json')
train['listing_id'] = train['listing_id'].apply(str)
feature_total = []
train['features'].apply(lambda x: feature_total.append(x))
feature_total = list(it.chain.from_iterable(feature_total))
len(feature_total) | code |
73095165/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | Project | code |
33120729/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_tra... | code |
33120729/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_tra... | code |
33120729/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_training, df_testing], axis=0, ignore_index=True)
df
def s... | code |
33120729/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
print(df_training.head(3))
print(df_testing.head(3)) | code |
33120729/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_tra... | code |
33120729/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
df_training.head() | code |
33120729/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_training, df_testing], axis=0, ignore_index=True)
df
df.is... | code |
33120729/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_tra... | code |
33120729/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_tra... | code |
33120729/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_training, df_testing], axis=0, ignore_index=True)
df
df['t... | code |
33120729/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_tra... | code |
33120729/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_tra... | code |
33120729/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_testing.head() | code |
33120729/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_tra... | code |
33120729/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_tra... | code |
33120729/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_training, df_testing], axis=0, ignore_index=True)
df
df['S... | code |
33120729/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_tra... | code |
33120729/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
url = '../input/dataquest2020/energy_train.csv'
df_training = pd.read_csv(url)
url = '../input/dataquest2020/energy_test.csv'
df_testing = pd.read_csv(url)
df_training['source'] = 'train'
df_testing['source'] = 'test'
df = pd.concat([df_training, df_testing], axis=0, ignore_index=True)
df | code |
129001503/cell_4 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from icevision.all import * | code |
129001503/cell_2 | [
"text_plain_output_1.png"
] | # Download IceVision installation script:
!wget https://raw.githubusercontent.com/airctic/icevision/master/icevision_install.sh
# Choose installation target: cuda11 or cuda10 or cpu
!bash icevision_install.sh cuda10 master | code |
129001503/cell_1 | [
"text_plain_output_1.png"
] | !python --version | code |
129001503/cell_3 | [
"text_plain_output_1.png"
] | import IPython
import IPython
IPython.Application.instance().kernel.do_shutdown(True) | code |
129001503/cell_5 | [
"text_plain_output_1.png"
] | print("Let's begin!") | code |
129039718/cell_4 | [
"image_output_11.png",
"text_plain_output_100.png",
"text_plain_output_334.png",
"image_output_239.png",
"image_output_98.png",
"text_plain_output_445.png",
"image_output_337.png",
"text_plain_output_201.png",
"text_plain_output_261.png",
"image_output_121.png",
"image_output_180.png",
"image_... | import cv2
import glob
mask_directory = '/kaggle/input/mask-images/testMasks'
mask_names = glob.glob('/kaggle/input/mask-images/testMasks/*.tif')
mask_names = sorted(mask_names, key=lambda x: (len(x), x))
masks = [cv2.imread(mask, 0) for mask in mask_names]
for i in range(len(masks)):
print(i)
plt.imshow(mas... | code |
129039718/cell_3 | [
"text_plain_output_1.png"
] | import cv2
import glob
mask_directory = '/kaggle/input/mask-images/testMasks'
mask_names = glob.glob('/kaggle/input/mask-images/testMasks/*.tif')
mask_names = sorted(mask_names, key=lambda x: (len(x), x))
print(mask_names[0:6])
masks = [cv2.imread(mask, 0) for mask in mask_names]
print(len(masks)) | code |
90108947/cell_21 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import math
import matplotlib.pylab as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (... | code |
90108947/cell_9 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '/kaggle/input/110-1-ntut-dl-app-hw3/IXIC.csv'
df = pd.read_csv(data)
df
df.shape
new_df = df.filter(['Clo... | code |
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