path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73075873/cell_7 | [
"text_plain_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)
data = pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')
import matplotlib.pyplot as plt
total = list(data.Risk_Flag.value_counts())
Flag0 = total[0]
Flag1 = total[1]
plt.fig... | code |
73075873/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')
import matplotlib.pyplot as plt
total ... | code |
73075873/cell_32 | [
"text_plain_output_1.png"
] | from imblearn.combine import SMOTETomek
from imblearn.over_sampling import ADASYN
from imblearn.under_sampling import TomekLinks
from imblearn.over_sampling import ADASYN
ada = ADASYN(random_state=42)
X_ada, y_ada = ada.fit_resample(X_train, y_train)
from imblearn.combine import SMOTETomek
from imblearn.under_sampl... | code |
73075873/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')
import matplotlib.pyplot as plt
total ... | code |
73075873/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')
data.info() | code |
73075873/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')
import matplotlib.pyplot as plt
total ... | code |
73075873/cell_10 | [
"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
data = pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')
import matplotlib.pyplot as plt
total = list(data.Risk_Flag.value_counts())
Flag0 = total[0]
F... | code |
73075873/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.ensemble import BalancedRandomForestClassifier
from sklearn.metrics import f1_score, accuracy_score, roc_auc_score, plot_roc_curve, plot_confusion_matrix
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seabo... | code |
73075873/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')
import matplotlib.pyplot as plt
total ... | code |
73075873/cell_36 | [
"text_plain_output_1.png"
] | from imblearn.combine import SMOTETomek
from imblearn.ensemble import BalancedRandomForestClassifier
from imblearn.over_sampling import ADASYN
from imblearn.under_sampling import TomekLinks
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score, accuracy_score, roc_auc_score, plot... | code |
105186160/cell_13 | [
"text_plain_output_1.png"
] | working_path = Path.cwd()
folders = ('train', 'test')
labels = ('0', '1')
im = Image.open(working_path / 'train' / '0' / '3002.png')
im | code |
105186160/cell_9 | [
"image_output_1.png"
] | working_path = Path.cwd()
folders = ('train', 'test')
labels = ('0', '1')
input_path = Path('/kaggle/input')
train_image_paths = sorted(input_path.rglob('train/*.png'))
test_image_paths = sorted(input_path.rglob('test/*.png'))
train_image_paths | code |
105186160/cell_2 | [
"image_output_1.png"
] | !pip install -Uqq fastai | code |
105186160/cell_11 | [
"text_plain_output_1.png"
] | working_path = Path.cwd()
folders = ('train', 'test')
labels = ('0', '1')
input_path = Path('/kaggle/input')
train_image_paths = sorted(input_path.rglob('train/*.png'))
test_image_paths = sorted(input_path.rglob('test/*.png'))
try:
for image_path in train_image_paths:
if '_1' in image_path.stem:
... | code |
105186160/cell_1 | [
"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 |
105186160/cell_7 | [
"text_plain_output_1.png"
] | working_path = Path.cwd()
folders = ('train', 'test')
labels = ('0', '1')
working_path / folders[0] / labels[0] | code |
105186160/cell_18 | [
"text_plain_output_1.png"
] | working_path = Path.cwd()
folders = ('train', 'test')
labels = ('0', '1')
im = Image.open(working_path / 'train' / '0' / '3002.png')
im
training_images = get_image_files(working_path / 'train')
training_images
image = Image.open(training_images[1])
image
testing_images = get_image_files(working_path / 'test')
len(t... | code |
105186160/cell_15 | [
"text_plain_output_1.png"
] | working_path = Path.cwd()
folders = ('train', 'test')
labels = ('0', '1')
training_images = get_image_files(working_path / 'train')
training_images | code |
105186160/cell_16 | [
"image_output_1.png"
] | working_path = Path.cwd()
folders = ('train', 'test')
labels = ('0', '1')
im = Image.open(working_path / 'train' / '0' / '3002.png')
im
training_images = get_image_files(working_path / 'train')
training_images
image = Image.open(training_images[1])
image | code |
105186160/cell_17 | [
"text_plain_output_1.png"
] | working_path = Path.cwd()
folders = ('train', 'test')
labels = ('0', '1')
testing_images = get_image_files(working_path / 'test')
len(testing_images) | code |
105186160/cell_14 | [
"text_plain_output_1.png"
] | working_path = Path.cwd()
folders = ('train', 'test')
labels = ('0', '1')
input_path = Path('/kaggle/input')
train_image_paths = sorted(input_path.rglob('train/*.png'))
test_image_paths = sorted(input_path.rglob('test/*.png'))
try:
for image_path in train_image_paths:
if '_1' in image_path.stem:
... | code |
105186160/cell_12 | [
"text_plain_output_1.png"
] | working_path = Path.cwd()
folders = ('train', 'test')
labels = ('0', '1')
(working_path / 'train' / '0' / '3002.png').exists() | code |
74041457/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
Xtrain = pd.read_csv('../input/sept-2021-filled/train_new.csv')
test = pd.read_csv('../input/sept-2021-filled/test_new.csv')
(Xtrain.shape, test.shape) | code |
74041457/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
Xtrain = pd.read_csv('../input/sept-2021-filled/train_new.csv')
test = pd.read_csv('../input/sept-2021-filled/test_new.csv')
Xtrain.head() | code |
74041457/cell_11 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd
Xtrain = pd.read_csv('../input/sept-2021-filled/train_new.csv')
test = pd.read_csv('../input/sept-2021-filled/test_new.csv')
(Xtrain.shape, test.shape)
y = Xt... | code |
74041457/cell_1 | [
"text_plain_output_1.png"
] | import optuna
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import optuna
from optuna.samplers import TPESampler
import catboost
from xgboost import XGBClassif... | code |
74041457/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
Xtrain = pd.read_csv('../input/sept-2021-filled/train_new.csv')
test = pd.read_csv('../input/sept-2021-filled/test_new.csv')
pd.read_csv('../input/tabular-playground-series-sep-2021/sample_solution.csv') | code |
74041457/cell_17 | [
"text_html_output_1.png"
] | from functools import partial
from optuna.samplers import TPESampler
from sklearn.decomposition import PCA
from sklearn.metrics import accuracy_score,roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler... | code |
74041457/cell_5 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
Xtrain = pd.read_csv('../input/sept-2021-filled/train_new.csv')
test = pd.read_csv('../input/sept-2021-filled/test_new.csv')
(Xtrain.shape, test.shape)
test.head() | code |
48163599/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_dir = '/kaggle/input/stanford-covid-vaccine/'
train = pd.read_json(data_dir + 'train.json', lines=True)
test = pd.read_json(data_dir + 'test.json', lines=True)
sample_df = pd.read_csv(data_dir + 'sample_submission.csv')
test.head(10) | code |
48163599/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_dir = '/kaggle/input/stanford-covid-vaccine/'
train = pd.read_json(data_dir + 'train.json', lines=True)
test = pd.read_json(data_dir + 'test.json', lines=True)
sample_df = pd.read_csv(data_dir + 'sample_submission.csv')
test.shape | code |
48163599/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 |
48163599/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_dir = '/kaggle/input/stanford-covid-vaccine/'
train = pd.read_json(data_dir + 'train.json', lines=True)
test = pd.read_json(data_dir + 'test.json', lines=True)
sample_df = pd.read_csv(data_dir + 'sample_sub... | code |
48163599/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_dir = '/kaggle/input/stanford-covid-vaccine/'
train = pd.read_json(data_dir + 'train.json', lines=True)
test = pd.read_json(data_dir + 'test.json', lines=True)
sample_df = pd.read_csv(data_dir + 'sample_submission.csv')
len(test['sequence'][0... | code |
48163599/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_dir = '/kaggle/input/stanford-covid-vaccine/'
train = pd.read_json(data_dir + 'train.json', lines=True)
test = pd.read_json(data_dir + 'test.json', lines=True)
sample_df = pd.read_csv(data_dir + 'sample_submission.csv')
train.shape | code |
122264608/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow_decision_forests as tfdf
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
df[label] = df[label].map(classes.index)
df = df.iloc[:, 1:]
def split_dataset(dataset, test_ratio=0.15):
... | code |
122264608/cell_9 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow_decision_forests as tfdf
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
df[label] = df[label].map(classes.index)
df = df.iloc[:, 1:]
def split_dataset(dataset, test_ratio=0.15):
... | code |
122264608/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
print(f'Label classes: {classes}')
df[label] = df[label].map(classes.index) | code |
122264608/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
df = df.iloc[:, 1:]
df.head() | code |
122264608/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow_decision_forests as tfdf
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
df[label] = df[label].map(classes.index)
df = df.iloc[:, 1:]
def split_dataset(dataset, test_ratio=0.15):
... | code |
122264608/cell_1 | [
"text_plain_output_1.png"
] | !pip install tensorflow_decision_forests wurlitzer | code |
122264608/cell_7 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow_decision_forests as tfdf
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
df[label] = df[label].map(classes.index)
df = df.iloc[:, 1:]
def split_dataset(dataset, test_ratio=0.15):
... | code |
122264608/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow_decision_forests as tfdf
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
df[label] = df[label].map(classes.index)
df = df.iloc[:, 1:]
def split_datase... | code |
122264608/cell_8 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow_decision_forests as tfdf
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
df[label] = df[label].map(classes.index)
df = df.iloc[:, 1:]
def split_dataset(dataset, test_ratio=0.15):
... | code |
122264608/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
df.head() | code |
122264608/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow_decision_forests as tfdf
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
df[label] = df[label].map(classes.index)
df = df.iloc[:, 1:]
def split_dataset(dataset, test_ratio=0.15):
... | code |
122264608/cell_14 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow_decision_forests as tfdf
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
df[label] = df[label].map(classes.index)
df = df.iloc[:, 1:]
def split_datase... | code |
122264608/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow_decision_forests as tfdf
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
df[label] = df[label].map(classes.index)
df = df.iloc[:, 1:]
def split_dataset(dataset, test_ratio=0.15):
... | code |
122264608/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow_decision_forests as tfdf
df = pd.read_csv('/kaggle/input/playground-series-s3e10/train.csv')
label = 'Class'
classes = df[label].unique().tolist()
df[label] = df[label].map(classes.index)
df = df.iloc[:, 1:]
def split_dataset(dataset, test_ratio=0.15):
... | code |
2011423/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.info() | code |
2011423/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
np.set_printoptions(suppress=True, linewidth=300)
pd.options.display.float_format = lambda x: '%0.6f' % x
print(check_output(['ls', '../input']).decode('utf-8')) | code |
2011423/cell_5 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.png",
"image_output_20.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png"... | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
columns = [c for c in data_df.columns if not c in ('class', 'y')]
single_val_c = {}
for i, c in enumerate(columns):
if data_df[c].nuniqu... | code |
32066544/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | (x_train.shape, y_train.shape) | code |
32066544/cell_9 | [
"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)
fashion_mnist_df = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
fashion_mnist_df = fashion_mnist_df.sample(frac=0.3).reset_index(drop=True)
import matplotlib.pyplot as plt
LOOKUP = {0: 'T-shir... | code |
32066544/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_mnist_df = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
fashion_mnist_df.head(10) | code |
32066544/cell_11 | [
"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)
fashion_mnist_df = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
fashion_mnist_df = fashion_mnist_df.sample(frac=0.3).reset_index(drop=True)
import matplotlib.pyplot as plt
LOOKUP = {0: 'T-shir... | code |
32066544/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 |
32066544/cell_8 | [
"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)
fashion_mnist_df = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
fashion_mnist_df = fashion_mnist_df.sample(frac=0.3).reset_index(drop=True)
import matplotlib.pyplot as plt
LOOKUP = {0: 'T-shir... | code |
32066544/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_mnist_df = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
fashion_mnist_df['label'].unique() | code |
32066544/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)
fashion_mnist_df = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
fashion_mnist_df = fashion_mnist_df.sample(frac=0.3).reset_index(drop=True)
import matplotlib.pyplot as plt
LOOKUP = {0: 'T-shir... | code |
2032622/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.rep... | code |
2032622/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.rep... | code |
2032622/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.replace(('cp', 'im', 'pp', 'imU', 'om', 'omL', 'imL', 'imS... | code |
2032622/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.replace(('cp', 'im', 'pp', 'imU', 'om', 'omL', 'imL', 'imS... | code |
2032622/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.rep... | code |
2032622/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.replace(('cp', 'im', 'pp', 'imU', 'om', 'omL', 'imL', 'imS... | code |
2032622/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.rep... | code |
2032622/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.rep... | code |
106212034/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_movies.csv')
credit = pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_credits.csv')
credit.head(3) | code |
106212034/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import ast
from sklearn.feature_extraction.text import CountVectorizer
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106212034/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_movies.csv')
credit = pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_credits.csv')
movies = movies[['id', 'title', 'overview', 'tagline', 'genres', 'keywords']]
movies = ... | code |
106212034/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_movies.csv')
credit = pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_credits.csv')
movies.head(3) | code |
106212034/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_movies.csv')
cre... | code |
106212034/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_movies.csv')
credit = pd.read_csv('/kaggle/input/tmdb-movie-metadata/tmdb_5000_credits.csv')
movies = movies[['id', 'title', 'overview', 'tagline', 'genres', 'keywords']]
movies = ... | code |
329711/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import csv
from sklearn.ensemble import RandomForestClassifier
def munge_data(df):
"""fill in missing values and convert characters to numerical"""
... | code |
329711/cell_2 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import csv
from sklearn.ensemble import RandomForestClassifier
def munge_data(df):
"""fill in missing values and convert characters to numerical"""
df['Sex'] = df['Sex'].map({'fem... | code |
329711/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import cross_validation
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import csv
from sklearn.ensemble import RandomForestClassifier
def munge_data(df):
"""fill in missing values and... | code |
90129873/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # Data processing, CSV file I/O (e.g. pd.read_csv)
krenth311 = pd.read_csv('../input/dataset/krenth311.csv')
krenth316 = pd.read_csv('../input/dataset/krenth316.csv')
merge = pd.concat([krenth311, krenth316])
merge.to_csv('merge.csv', i... | code |
90129873/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cufflinks as cf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import dates as md
import seaborn as sns
import plotly.graph_objs as go
import plotly
import cufflinks as cf
cf.set_config_file(offline=True)
import os | code |
17108074/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
sku_category_filepath = '../input/sku-category/sku_category.csv'
sku_category = pd.read_csv(sku_category_filepath, sep=None, decimal=',', engine='python')
sku_category.drop('compare', axis=1, inplace=True)
sku_category.drop('sector', axis=1, inplace=True)
sku = sku_category.copy()
for i in range(1,... | code |
17108074/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
sku_category_filepath = '../input/sku-category/sku_category.csv'
sku_category = pd.read_csv(sku_category_filepath, sep=None, decimal=',', engine='python')
sku_category.drop('compare', axis=1, inplace=True)
sku_category.drop('sector', axis=1, inplace=True)
sku = sku_category.copy()
for i in range(1,... | code |
17108074/cell_8 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
sku_category_filepath = '../input/sku-category/sku_category.csv'
sku_category = pd.read_csv(sku_category_filepath, sep=None, decimal=',', engine='python')
sku_category.drop('compare', axis=1, inplace=True)
sku_category.drop('sector', axis=1, inplace=True)
sku = sku_category.copy... | code |
17108074/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
sku_category_filepath = '../input/sku-category/sku_category.csv'
sku_category = pd.read_csv(sku_category_filepath, sep=None, decimal=',', engine='python')
sku_category.drop('compare', axis=1, inplace=True)
sku_category.drop('sector', axis=1, inplace=True)
sku = sku_category.copy()
for i in range(1,... | code |
17108074/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
sku_category_filepath = '../input/sku-category/sku_category.csv'
sku_category = pd.read_csv(sku_category_filepath, sep=None, decimal=',', engine='python')
sku_category.drop('compare', axis=1, inplace=True)
sku_category.drop('sector', axis=1, inplace=True)
sku = sku_category.copy()
for i in range(1,... | code |
32068625/cell_6 | [
"text_html_output_1.png"
] | import networkx as nx
import plotly.graph_objects as go
import sys
import plotly.graph_objects as go
import networkx as nx
node_list = list(['Chloroquine phosphate', 'Spike (S) antibody', 'IL-6 antibody', 'Remdesivir', 'Favipiravir', 'Fluorouracil', 'Ribavirin', 'Acyclovir', 'Ritonavir', 'Lopinavir', 'Kaletra', 'Daru... | code |
32068625/cell_7 | [
"text_html_output_2.png"
] | import networkx as nx
import networkx as nx
import numpy as np
import plotly.graph_objects as go
import plotly.graph_objects as go
import sys
import plotly.graph_objects as go
import networkx as nx
node_list = list(['Chloroquine phosphate', 'Spike (S) antibody', 'IL-6 antibody', 'Remdesivir', 'Favipiravir', 'Fluor... | code |
32068625/cell_8 | [
"text_html_output_1.png"
] | import networkx as nx
import networkx as nx
import networkx as nx
import numpy as np
import numpy as np
import plotly.graph_objects as go
import plotly.graph_objects as go
import plotly.graph_objects as go
import sys
import plotly.graph_objects as go
import networkx as nx
node_list = list(['Chloroquine phosphat... | code |
16113855/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/champs-scalar-coupling/train.csv')
test = pd.read_csv('../input/champs-scalar-coupling/test.csv')
sub = pd.read_csv('../input/champs-scalar-coupling/sample_submission.csv')
train_d... | code |
16113855/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16113855/cell_15 | [
"text_plain_output_1.png"
] | from numpy.random import permutation
import lightgbm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/champs-scalar-coupling/train.csv')
test = pd.read_csv('../input/champs-scalar-coupling/test.csv')
sub = pd.read_csv('../input/... | code |
128045262/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from IPython.display import Image
import plotly.express as px
from IPython.display import Image
from wordcloud import WordCloud, STOPWORDS
df = pd.read_csv('/kagg... | code |
128045262/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from IPython.display import Image
import plotly.express as px
from IPython.display import Image
from wordcloud import WordCloud, STOPWORDS
df = pd.read_csv('/kagg... | code |
128045262/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from IPython.display import Image
import plotly.express as px
from IPython.display import Image
from wordcloud import WordCloud, STOPWORDS
df = pd.read_csv('/kagg... | code |
128045262/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from IPython.display import Image
import plotly.express as px
from IPython.display import Image
from wordcloud import WordCloud, STOPWORDS
df = pd.read_csv('/kagg... | code |
128045262/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from IPython.display import Image
import plotly.express as px
from IPython.display import Image
from wordcloud import WordCloud, STOPWORDS
df = pd.read_csv('/kagg... | code |
128045262/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from IPython.display import Image
import plotly.express as px
from IPython.display import Image
from wordcloud import WordCloud, STOPWORDS
df = pd.read_csv('/kagg... | code |
128045262/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from IPython.display import Image
import plotly.express as px
from IPython.display import Image
from wordcloud import WordCloud, STOPWORDS
df = pd.read_csv('/kagg... | code |
128045262/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from IPython.display import Image
import plotly.express as px
from IPython.display import Image
from wordcloud import WordCloud, STOPWORDS
df = pd.read_csv('/kagg... | code |
128045262/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from IPython.display import Image
import plotly.express as px
from IPython.display import Image
from wordcloud import WordCloud, STOPWORDS
df = pd.read_csv('/kagg... | code |
129014537/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
RANDOM_STATE = 12... | code |
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