path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17109112/cell_10 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import os
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
vgg_format = transforms.Compose([transforms.CenterCrop(224), transforms.ToTensor(), normalize])
dsets = {x: dataset... | code |
17109112/cell_12 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import os
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
vgg_format = transforms.Compose([transforms.CenterCrop(224), transforms.ToTensor(), normalize])
dsets = {x: dataset... | code |
17109112/cell_5 | [
"text_plain_output_1.png"
] | import torch
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Using gpu : %s ' % torch.cuda.is_available()) | code |
17109112/cell_36 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import torch
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
inputs_try.shape
model_vgg = models.vgg16(pretrained=True)
inputs_try, lables_try = (inputs_try.to(device), labels_try.to(device))
model_vgg = model_vgg.to(de... | code |
74040532/cell_9 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from astropy.io import fits
from skimage import data, io, filters
import matplotlib.pyplot as plt
import skimage
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-al... | code |
74040532/cell_6 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from astropy.io import fits
from skimage import data, io, filters
import matplotlib.pyplot as plt
import skimage
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-al... | code |
74040532/cell_1 | [
"text_plain_output_1.png"
] | !pip install astropy
!pip install specutils | code |
74040532/cell_7 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from astropy.io import fits
from skimage import data, io, filters
import matplotlib.pyplot as plt
import skimage
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-al... | code |
74040532/cell_8 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from astropy.io import fits
from skimage import data, io, filters
import matplotlib.pyplot as plt
import skimage
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-al... | code |
74040532/cell_10 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from astropy.io import fits
from skimage import data, io, filters
import matplotlib.pyplot as plt
import skimage
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-al... | code |
2004239/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_train = pd.read_csv('../input/train.csv', usecols=[1, 2, 3, 4, 5], dtype={'onpromotion': bool}, converters={'unit_sales': lambda u: np.log1p(float(u)) if float(u) > 0 else 0}, parse_dates=['date'], skiprows=range(1, 66458909))
df_test = pd.read_csv('../input/test.csv', usecol... | code |
2004239/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_train = pd.read_csv('../input/train.csv', usecols=[1, 2, 3, 4, 5], dtype={'onpromotion': bool}, converters={'unit_sales': lambda u: np.log1p(float(u)) if float(u) > 0 else 0}, parse_dates=['date'], skiprows=range(1, 66458909))
df_test = pd.read_csv('../input/test.csv', usecol... | code |
2004239/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_train = pd.read_csv('../input/train.csv', usecols=[1, 2, 3, 4, 5], dtype={'onpromotion': bool}, converters={'unit_sales': lambda u: np.log1p(float(u)) if float(u) > 0 else 0}, parse_dates=['date'], skiprows=range(1, 66458909))
df_test = pd.read_csv('../input/test.csv', usecol... | code |
18110494/cell_21 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='Same', activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(64, (5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, (3, ... | code |
18110494/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
x_train = train.drop(labels=['label'], axis=1)
x_train = x_train / 255.0
test = test / 255.0
x_train = x_train.values.reshape(-1, 28, 28, 1)
test = test.values.res... | code |
18110494/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
y_train = train['label']
y_train.value_counts() | code |
18110494/cell_23 | [
"text_html_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential
from keras.optimizers import RMSprop
import keras
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
y_train... | code |
18110494/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
test.head() | code |
18110494/cell_19 | [
"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)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
x_train = train.drop(labels=['label'], axis=1)
x_train = x_train / 255.0
test = test / 255.0
x_train = x_train.values.reshape(-1, ... | code |
18110494/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import keras | code |
18110494/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
y_train = train['label']
y_train.head() | code |
18110494/cell_24 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential
from keras.optimizers import RMSprop
import keras
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
y_train... | code |
18110494/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
x_train = train.drop(labels=['label'], axis=1)
x_train.isnull().describe() | code |
18110494/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
train.head() | code |
90135546/cell_21 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnul... | code |
90135546/cell_13 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape | code |
90135546/cell_9 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape | code |
90135546/cell_23 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_d... | code |
90135546/cell_6 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.head() | code |
90135546/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentimen... | code |
90135546/cell_11 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.info() | code |
90135546/cell_52 | [
"text_plain_output_1.png"
] | from cuml.linear_model import LogisticRegression
from cuml.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVectorizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentime... | code |
90135546/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 |
90135546/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.info() | code |
90135546/cell_49 | [
"text_plain_output_1.png"
] | from cuml.linear_model import LogisticRegression
from cuml.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVectorizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentime... | code |
90135546/cell_18 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape
test_df.isnull(... | code |
90135546/cell_51 | [
"text_plain_output_1.png"
] | from cuml.linear_model import LogisticRegression
from cuml.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVectorizer
import cudf as pd
import cupy as cp
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/k... | code |
90135546/cell_28 | [
"text_plain_output_1.png"
] | import string
import string
import string
import re
string.punctuation | code |
90135546/cell_8 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.describe() | code |
90135546/cell_15 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnul... | code |
90135546/cell_16 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape
test_df.isnull(... | code |
90135546/cell_17 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnul... | code |
90135546/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentimen... | code |
90135546/cell_24 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_d... | code |
90135546/cell_10 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.head() | code |
90135546/cell_12 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.describe() | code |
90135546/cell_36 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
stopwords.words('english') | code |
34138455/cell_9 | [
"image_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epoc... | code |
34138455/cell_6 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epoc... | code |
34138455/cell_11 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epoc... | code |
34138455/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epoc... | code |
34138455/cell_8 | [
"text_plain_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epoc... | code |
34138455/cell_10 | [
"text_html_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epoc... | code |
34138455/cell_5 | [
"text_html_output_4.png",
"text_plain_output_4.png",
"text_html_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
table.head() | code |
50224544/cell_7 | [
"text_html_output_1.png"
] | from time import time
import cv2
import numpy as np
import pandas as pd
def breaker():
pass
def head(x, no_of_ele=5):
pass
def getImages(file_path=None, file_names=None, size=None):
images = []
for name in file_names:
try:
image = cv2.imread(file_path + name + '.jpg', cv2.IMREAD_... | code |
50224544/cell_18 | [
"text_plain_output_1.png"
] | from time import time
from torch.utils.data import Dataset
import cv2
import numpy as np
import pandas as pd
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
from torch import nn, optim
from torch.utils.data import Dataset
from torch.utils.data import DataLoader as ... | code |
17117664/cell_4 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
type(parsed_file) | code |
17117664/cell_20 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
df.Cdc.unique()
df['Cdc'] = df['Cdc'].astype('str')... | code |
17117664/cell_6 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
type(parsed_file[5]) | code |
17117664/cell_19 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
df.Cdc.unique()
df['Cdc'] = df['Cdc'].astype('str')... | code |
17117664/cell_8 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
df.head() | code |
17117664/cell_16 | [
"text_html_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
df.Cdc.unique()
df['Cdc'] = df['Cdc'].astype('str')... | code |
17117664/cell_10 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
for val, col in zip(df.iloc[0], df.columns):
pri... | code |
17117664/cell_12 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
df.Cdc.unique() | code |
17096461/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
paths = ['../input/train.csv', '../input/test.csv']
target_name = 'SalePrice'
rd = Reader(sep=',')
df = rd.train_test_split(paths, target_name)
dft = Drift_thresholder()
df = dft.fit_transform(df)
rmse = make_scorer(lambda y_true, y_pred: np.sqrt(np.sum((y_true - y_pred) ** 2) / ... | code |
17096461/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | paths = ['../input/train.csv', '../input/test.csv']
target_name = 'SalePrice'
rd = Reader(sep=',')
df = rd.train_test_split(paths, target_name)
dft = Drift_thresholder()
df = dft.fit_transform(df) | code |
17096461/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install mlbox | code |
17096461/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17096461/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
rmse = make_scorer(lambda y_true, y_pred: np.sqrt(np.sum((y_true - y_pred) ** 2) / len(y_true)), greater_is_better=False, needs_proba=False)
opt = Optimiser(scoring=rmse, n_folds=3) | code |
17096461/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
paths = ['../input/train.csv', '../input/test.csv']
target_name = 'SalePrice'
rd = Reader(sep=',')
df = rd.train_test_split(paths, target_name)
dft = Drift_thresholder()
df = dft.fit_transform(df)
rmse = make_scorer(lambda y_true, y_pred: np.sqrt(np.sum((y_true - y_pred) ** 2) / ... | code |
17096461/cell_3 | [
"text_plain_output_1.png"
] | from mlbox.preprocessing import *
from mlbox.optimisation import *
from mlbox.prediction import * | code |
17096461/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
paths = ['../input/train.csv', '../input/test.csv']
target_name = 'SalePrice'
submit = pd.read_csv('../input/sample_submission.csv', sep=',')
preds = pd.read_csv('save/' + target_name + '_predictions.csv')
submit[target_name] = preds[target_name +... | code |
17096461/cell_5 | [
"text_plain_output_1.png"
] | paths = ['../input/train.csv', '../input/test.csv']
target_name = 'SalePrice'
rd = Reader(sep=',')
df = rd.train_test_split(paths, target_name) | code |
32062473/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = Fals... | code |
32062473/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = Fals... | code |
32062473/cell_33 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = Fals... | code |
32062473/cell_26 | [
"text_html_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = Fals... | code |
32062473/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 |
32062473/cell_35 | [
"text_html_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = Fals... | code |
32062473/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = Fals... | code |
32062473/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = Fals... | code |
32062473/cell_27 | [
"text_html_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = Fals... | code |
2044577/cell_21 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('.... | code |
2044577/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes | code |
2044577/cell_11 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes
def clean_data(dataset):
data = dataset.dropna()
data = data.drop('Passeng... | code |
2044577/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data... | code |
2044577/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import classification_report
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from sklearn.... | code |
2044577/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes
def clean_data(dataset):
data = dataset.dropna()
data = data.drop('Passeng... | code |
2044577/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
dataset.head()
actual_data = dataset
actual_data | code |
2044577/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes
def clea... | code |
2044577/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes
def clean_data(dataset):
data = dataset.dropna()
data = data.drop('PassengerId', axis=1)
data... | code |
129035264/cell_13 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape
train.duplicated().sum()
women = train.loc[train.Sex == 'female']['Survived']
women_sur_rate = sum(women) / len(women)
men = train.loc[train.Sex == 'mal... | code |
129035264/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.shape
test.info() | code |
129035264/cell_4 | [
"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/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.head() | code |
129035264/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"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/titanic/train.csv')
train.shape | code |
129035264/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.shape
test.duplicated().sum() | code |
129035264/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 |
129035264/cell_7 | [
"text_plain_output_1.png",
"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/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.shape | code |
129035264/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape
train.info() | code |
129035264/cell_15 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape
train.duplicated().sum()
women = train.loc[train.Sex == 'female']['Survived']
women_sur_rate = sum(women) / len(women)
men = train.loc[train.Sex == 'mal... | code |
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