path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73083438/cell_11 | [
"text_html_output_1.png"
] | from termcolor import colored
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = train.drop(['target'], axis=1)
num_col = list(train.select_dtypes(include='float64').columns)
cat... | code |
73083438/cell_7 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.info() | code |
73083438/cell_18 | [
"text_plain_output_1.png"
] | from termcolor import colored
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = train.drop(['target'], axis=1)
num_col = list(train.select_dtypes(include='float64').columns)
cat... | code |
73083438/cell_28 | [
"text_plain_output_1.png"
] | from termcolor import colored
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = tr... | code |
73083438/cell_8 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum() | code |
73083438/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = train.drop(['target'], axis=1)
list(test.columns) == list(features.columns)
test.info() | code |
73083438/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = train.drop(['target'], axis=1)
list(test.columns) == list(features.columns)
test.isnull().sum() | code |
73083438/cell_31 | [
"text_plain_output_1.png"
] | from termcolor import colored
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = tr... | code |
73083438/cell_24 | [
"text_html_output_1.png"
] | from termcolor import colored
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = train.drop(['target'], axis=1)
num_col = ... | code |
73083438/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = train.drop(['target'], axis=1)
list(test.columns) == list(features.columns)
test.describe() | code |
73083438/cell_27 | [
"text_plain_output_1.png"
] | from termcolor import colored
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = tr... | code |
73083438/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.head() | code |
34133665/cell_6 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34133665/cell_17 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMRegressor
from math import sqrt
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_squared_log_error
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing ... | code |
34133665/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv', index_col=[0])
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv', index_col=[0])
sample = pd.read_csv('/kaggle/input... | code |
74062774/cell_21 | [
"text_html_output_1.png"
] | from imblearn.over_sampling import SMOTE
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as ms
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('seaborn')
pd.set_option('display.max_columns', None)
data = pd.read_csv('../i... | code |
74062774/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('seaborn')
pd.set_option('display.max_columns', None)
data = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
data
data.isna().sum() | code |
74062774/cell_23 | [
"text_html_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as ms
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pypl... | code |
74062774/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import recall_score
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as ms
import pandas as pd
import numpy as np
im... | code |
74062774/cell_33 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_curve
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as ms
import pandas as pd
import numpy as np
impor... | code |
74062774/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as ms
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('seaborn')
pd.set_option('display.max_columns', None)
data = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
dat... | code |
74062774/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('seaborn')
pd.set_option('display.max_columns', None)
data = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
data
data.isna().sum()
data = data.drop... | code |
74062774/cell_29 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as ms
import pandas as pd
import numpy as np
... | code |
74062774/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as ms
import pandas as pd
import numpy as n... | code |
74062774/cell_2 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('seaborn')
pd.set_option('display.max_columns', None)
data = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
data | code |
74062774/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('seaborn')
pd.set_option('display.max_columns', None)
data = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
data
data.isna().sum()
data = data.drop... | code |
74062774/cell_32 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as ms
import pandas as pd
import numpy as np
i... | code |
74062774/cell_28 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.mode... | code |
74062774/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('seaborn')
pd.set_option('display.max_columns', None)
data = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
data
data.info() | code |
74062774/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('seaborn')
pd.set_option('display.max_columns', None)
data = pd.read_csv('../input/hotel-booking/hotel_booking.csv')
data
data.isna().sum()
data = data.drop... | code |
74062774/cell_31 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_score
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as ms
import pandas as pd
import numpy as np... | code |
74062774/cell_22 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as ms
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pypl... | code |
74062774/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import sklearn.model_selection as ms
import pandas ... | code |
129008932/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/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000 | code |
129008932/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('/kaggle/input/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000
wii_average_sales = over10000[over10000['Platform'] == 'Wii']['Global_Sales'].mean()
other_platforms_average_sales = over10000[ove... | code |
129008932/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/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000
over10000['Publisher'].value_counts().index[0] | code |
129008932/cell_2 | [
"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 |
129008932/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/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000
top_selling_game_sales = over10000['NA_Sales'].max()
mean_sales = over10000['NA_Sales'].mean()
std_sales = over10000['NA_Sales'].s... | code |
129008932/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/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000
over10000['Platform'].value_counts().index[0] | code |
129008932/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/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000
na_median_sales = over10000['NA_Sales'].median()
ten_games_surrounding_median = over10000[over10000['NA_Sales'].between(na_median... | code |
129008932/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('/kaggle/input/videogamesales/vgsales.csv')
df | code |
129008932/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('/kaggle/input/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000
top_3_publishers_total_sales = over10000.groupby('Publisher')['Global_Sales'].sum().nlargest(3)
platform_sales = over10000.groupb... | code |
129008932/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/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000
na_median_sales = over10000['NA_Sales'].median()
print('The median for North American video game sales is:', na_median_sales) | code |
129008932/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('/kaggle/input/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000
top_3_publishers_total_sales = over10000.groupby('Publisher')['Global_Sales'].sum().nlargest(3)
print('Top 3 publishers with the h... | code |
129008932/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_csv('/kaggle/input/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000
over10000['Genre'].value_counts().index[0] | code |
129008932/cell_12 | [
"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/videogamesales/vgsales.csv')
df
over10000 = df[df['Global_Sales'] > 0.01]
over10000
over10000[['Name', 'Global_Sales']].sort_values('Global_Sales', ascending=False)[0:20] | code |
128027348/cell_13 | [
"text_plain_output_1.png"
] | from gensim.models import keyedvectors
import gensim
from gensim.models import keyedvectors
w2v = keyedvectors.load_word2vec_format('/kaggle/input/tencent/tencent-ailab-embedding-zh-d100-v0.2.0-s/tencent-ailab-embedding-zh-d100-v0.2.0-s.txt', binary=False)
w2v[['的', '在']] | code |
128027348/cell_4 | [
"text_plain_output_1.png"
] | import io
import pandas as pd
root_path = '/kaggle/input/test-train'
train_path = '/kaggle/input/test-train/train_clean.txt'
import pandas as pd
import io
with open('/kaggle/input/test-train/train_clean.txt', 'r') as f:
train_text = f.read()
train_data = pd.read_csv(io.StringIO(train_text), sep='\t', names=['labe... | code |
128027348/cell_6 | [
"text_plain_output_1.png"
] | from collections import Counter
import io
import os
import pandas as pd
root_path = '/kaggle/input/test-train'
train_path = '/kaggle/input/test-train/train_clean.txt'
import pandas as pd
import io
with open('/kaggle/input/test-train/train_clean.txt', 'r') as f:
train_text = f.read()
train_data = pd.read_csv(io.... | code |
128027348/cell_2 | [
"text_plain_output_1.png"
] | import io
import pandas as pd
root_path = '/kaggle/input/test-train'
train_path = '/kaggle/input/test-train/train_clean.txt'
import pandas as pd
import io
with open('/kaggle/input/test-train/train_clean.txt', 'r') as f:
train_text = f.read()
train_data = pd.read_csv(io.StringIO(train_text), sep='\t', names=['labe... | code |
128027348/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | pad_id = 923
print(pad_id) | code |
128027348/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | print(len(data_input))
print(len(data_input[7])) | code |
128027348/cell_16 | [
"text_plain_output_1.png"
] | from collections import Counter
from gensim.models import keyedvectors
import io
import numpy as np
import os
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as Data
root_path = '/kaggle/input/test-train'
train_path = ... | code |
128027348/cell_3 | [
"text_plain_output_1.png"
] | import io
import pandas as pd
root_path = '/kaggle/input/test-train'
train_path = '/kaggle/input/test-train/train_clean.txt'
import pandas as pd
import io
with open('/kaggle/input/test-train/train_clean.txt', 'r') as f:
train_text = f.read()
train_data = pd.read_csv(io.StringIO(train_text), sep='\t', names=['labe... | code |
128027348/cell_14 | [
"text_plain_output_1.png"
] | from gensim.models import keyedvectors
import gensim
from gensim.models import keyedvectors
w2v = keyedvectors.load_word2vec_format('/kaggle/input/tencent/tencent-ailab-embedding-zh-d100-v0.2.0-s/tencent-ailab-embedding-zh-d100-v0.2.0-s.txt', binary=False)
print(len(w2v.key_to_index)) | code |
34139450/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
df_train = pd.read_csv('titanic/titanic.csv')
df_train.head() | code |
73089201/cell_13 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from matplotlib.colors import ListedColormap
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import Axes3D
from pandas.plotting import autocorrelation_plot
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas a... | code |
73089201/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import scipy.io as sp
import seaborn as sns
import scipy.io as sp
import numpy as np
import pandas as pd
def load_data(file):
file = sp.loadmat(file)
load = file['o']
data = pd.DataFrame(load['data'][0, 0])
marker = pd.DataFrame(load['marker'][0, 0... | code |
73089201/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import scipy.io as sp
import seaborn as sns
import scipy.io as sp
import numpy as np
import pandas as pd
def load_data(file):
file = sp.loadmat(file)
load = file['o']
data = pd.DataFrame(load['data'][0, 0])
marker = pd.DataFrame(load['marker'][0, 0... | code |
73089201/cell_11 | [
"image_output_1.png"
] | from pandas.plotting import autocorrelation_plot
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import scipy.io as sp
import seaborn as sns
import seaborn as sns
import scipy.io as sp
import numpy as np
import pandas as pd
def load_data(file):
file = ... | code |
73089201/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import scipy.io as sp
import scipy.io as sp
import numpy as np
import pandas as pd
def load_data(file):
file = sp.loadmat(file)
load = file['o']
data = pd.DataFrame(load['data'][0, 0])
marker = pd.DataFrame(load['marker'][0, 0])
datadf = pd.concat([data, marker], axis=1)
da... | code |
73089201/cell_7 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import scipy.io as sp
import seaborn as sns
import scipy.io as sp
import numpy as np
import pandas as pd
def load_data(file):
file = sp.loadmat(file)
load = file['o']
data = pd.DataFrame(load['data'][0, 0])
marker = pd.DataFrame(load['marker'][0, 0... | code |
73089201/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import scipy.io as sp
import seaborn as sns
import scipy.io as sp
import numpy as np
import pandas as pd
def load_data(file):
file = sp.loadmat(file)
load = file['o']
data = pd.DataFrame(load['data'][0, 0])
marker = pd.DataFrame(load['marker'][0, 0... | code |
73089201/cell_16 | [
"image_output_1.png"
] | from matplotlib.colors import ListedColormap
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import Axes3D
from pandas.plotting import autocorrelation_plot
from sklearn.decomposition import PCA, NMF
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotl... | code |
73089201/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import scipy.io as sp
import scipy.io as sp
import numpy as np
import pandas as pd
def load_data(file):
file = sp.loadmat(file)
load = file['o']
data = pd.DataFrame(load['data'][0, 0])
marker = pd.DataFrame(load['marker'][0, 0])
datadf = pd.concat([data, marker], axis=1)
da... | code |
73089201/cell_17 | [
"image_output_1.png"
] | from matplotlib.colors import ListedColormap
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import Axes3D
from pandas.plotting import autocorrelation_plot
from sklearn.decomposition import PCA, NMF
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotl... | code |
73089201/cell_14 | [
"image_output_1.png"
] | from matplotlib.colors import ListedColormap
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import Axes3D
from pandas.plotting import autocorrelation_plot
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas a... | code |
73089201/cell_10 | [
"text_plain_output_1.png"
] | from pandas.plotting import autocorrelation_plot
import matplotlib.pyplot as plt
import pandas as pd
import scipy.io as sp
import seaborn as sns
import scipy.io as sp
import numpy as np
import pandas as pd
def load_data(file):
file = sp.loadmat(file)
load = file['o']
data = pd.DataFrame(load['data'][0,... | code |
73089201/cell_12 | [
"text_plain_output_1.png"
] | from matplotlib.colors import ListedColormap
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import Axes3D
from pandas.plotting import autocorrelation_plot
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas a... | code |
104127284/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.layers import GRU, Input, Dense, Activation, RepeatVector, Bidirectional, LSTM, Dropout, Embedding
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text... | code |
104127284/cell_9 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import matplotlib as plt
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
import seaborn as sns
df = pd.read_csv('../input/nlp-getting-started/train.csv')
df_test = pd.read_csv('../input/nlp-getting-started/test.csv')
... | code |
104127284/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 |
104127284/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib as plt
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
import seaborn as sns
df = pd.read_csv('../input/nlp-getting-started/train.csv')
df_test = pd.read_csv('../input/nlp-getting-started/test.csv')
... | code |
32068545/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/data-without-drift/train_clean.csv')
test = pd.read_csv('/kaggle/input/data-without-drift/test_clean.csv')
batch_indices = [slice(500000 * i, 500000 * (i + 1)) for i in range(10)]... | code |
32068545/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/data-without-drift/train_clean.csv')
test = pd.read_csv('/kaggle/input/data-without-drift/test_clean.csv')
train.head() | code |
32068545/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 |
32068545/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/data-without-drift/train_clean.csv')
test = pd.read_csv('/kaggle/input/data-without-drift/test_clean.csv')
batch_indice... | code |
32068545/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/data-without-drift/train_clean.csv')
test = pd.read_csv('/kaggle/input/data-without-drift/test_clean.csv')
batch_indice... | code |
32068545/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from scipy.optimize import minimize
from sklearn.metrics import f1_score, classification_report
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/data-without-drift/train_clean.csv')
test = pd.read_csv('/k... | code |
32068545/cell_10 | [
"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)
train = pd.read_csv('/kaggle/input/data-without-drift/train_clean.csv')
test = pd.read_csv('/kaggle/input/data-without-drift/test_clean.csv')
batch_indices = [slice(500000 * i, 500000 * (i + 1)) for i in range(10)]... | code |
32068545/cell_12 | [
"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)
import seaborn as sns
train = pd.read_csv('/kaggle/input/data-without-drift/train_clean.csv')
test = pd.read_csv('/kaggle/input/data-without-drift/test_clean.csv')
batch_indices = [slice(500000 * i, 500000 * (i + ... | code |
32068545/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/data-without-drift/train_clean.csv')
test = pd.read_csv('/kaggle/input/data-without-drift/test_clean.csv')
train.info() | code |
17098455/cell_21 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
import string
def process(text):
text = text.lower()
text = ''.join([t for t in text if t not in string.punctuation])
text = ... | code |
17098455/cell_13 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import string
df = pd.DataFrame(pd.read_csv('../input/spam.csv', encoding='latin-1')[['v1', 'v2']])
df.columns = ['Label', 'Message']
df.groupby('Label').describe()
def process(text):
text = text.... | code |
17098455/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.DataFrame(pd.read_csv('../input/spam.csv', encoding='latin-1')[['v1', 'v2']])
df.columns = ['Label', 'Message']
df.groupby('Label').describe()
sns.countplot(data=df, x='Label') | code |
17098455/cell_23 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
import string
def process(text):
text = text.lower()
text = ''.join([t for t in text if t not in string.punctuation])
text = ... | code |
17098455/cell_20 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
import string
def process(text):
text = text.lower()
text = ''.join([t for t in text if t not in string.punctuation])
text = ... | code |
17098455/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.DataFrame(pd.read_csv('../input/spam.csv', encoding='latin-1')[['v1', 'v2']])
df.columns = ['Label', 'Message']
df.head() | code |
17098455/cell_1 | [
"text_plain_output_1.png"
] | import os
print(os.listdir('../input'))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import string
from nltk.corpus import stopwords
from nltk import PorterStemmer as Stemmer | code |
17098455/cell_7 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import string
def process(text):
text = text.lower()
text = ''.join([t for t in text if t not in string.punctuation])
text = [t for t in text.split() if t not in stopwords.words('english')]
st = Stemmer()
text = [st.stem(t) for t in text]
return text
process(... | code |
17098455/cell_18 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
import string
def process(text):
text = text.lower()
text = ''.join([t for t in text if t not in string.punctuation])
text = ... | code |
17098455/cell_8 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import pandas as pd
import string
df = pd.DataFrame(pd.read_csv('../input/spam.csv', encoding='latin-1')[['v1', 'v2']])
df.columns = ['Label', 'Message']
df.groupby('Label').describe()
def process(text):
text = text.lower()
text = ''.join([t for t in text if t not in strin... | code |
17098455/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.DataFrame(pd.read_csv('../input/spam.csv', encoding='latin-1')[['v1', 'v2']])
df.columns = ['Label', 'Message']
df.groupby('Label').describe() | code |
17098455/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import string
df = pd.DataFrame(pd.read_csv('../input/spam.csv', encoding='latin-1')[['v1', 'v2']])
df.columns = ['Label', 'Message']
df.groupby('Label').describe()
def process(text):
text = text.... | code |
17098455/cell_22 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
import string
def process(text):
text = text.lower()
text = ''.join([t for t i... | code |
17098455/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.DataFrame(pd.read_csv('../input/spam.csv', encoding='latin-1')[['v1', 'v2']])
df.columns = ['Label', 'Message']
df.groupby('Label').describe()
mess = df.iloc[2]['Message']
print(mess) | code |
74067865/cell_13 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd
import plotly.graph_objects as go
import plotly.graph_objects as go
healthsysdf = pd.read_csv('../input/world-bank-wdi-212-health-systems/2.12_Health_systems.csv')
healthsysdf = healthsysdf.drop(columns='Province_State')
healthsysdf ... | code |
74067865/cell_9 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd
import plotly.graph_objects as go
import plotly.graph_objects as go
healthsysdf = pd.read_csv('../input/world-bank-wdi-212-health-systems/2.12_Health_systems.csv')
healthsysdf = healthsysdf.drop(columns='Province_State')
healthsysdf ... | code |
74067865/cell_11 | [
"text_html_output_2.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd
import plotly.graph_objects as go
import plotly.graph_objects as go
healthsysdf = pd.read_csv('../input/world-bank-wdi-212-health-systems/2.12_Health_systems.csv')
healthsysdf = healthsysdf.drop(columns='Province_State')
healthsysdf ... | code |
74067865/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import math
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import matplotlib as mpl
import matplotlib.pyplot as plt
from plotly.subplots import make_subplots
import plotly.graph_objects... | code |
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