path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128017162/cell_28 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
test = pd.read_csv('/kaggle/input/hackerearth/dataset/test.csv')
preds_list = test['Image']
preds_list | code |
128017162/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
num = len(train['Class'].unique())
print('Total Labels : ', str(num)) | code |
128017162/cell_15 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
import pandas as pd
import visualkeras as vk
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
num = len(train['Class'].unique())
model = S... | code |
128017162/cell_16 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import plot_model, load_img, to_categorical
import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')... | code |
128017162/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
train['Class'].unique() | code |
128017162/cell_14 | [
"text_html_output_1.png"
] | from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
num = len(train['Class'].unique())
model = Sequential()
model.add(Conv... | code |
128017162/cell_22 | [
"image_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matp... | code |
128017162/cell_27 | [
"image_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import nump... | code |
128017162/cell_37 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import nump... | code |
128017162/cell_36 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import nump... | code |
122265193/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/air-concentration-for-the-chernobyl-disaster/data.csv')
data.info() | code |
122265193/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/air-concentration-for-the-chernobyl-disaster/data.csv')
m = data['I_131_(Bq/m3)'].str.contains('L|\\?', regex=True, na=False)
data.loc[m, 'I_131_(Bq/m3)'] = None
m = data['Cs_134_(Bq/m3)'].str.contains('N|\\?', regex=T... | code |
122265193/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/air-concentration-for-the-chernobyl-disaster/data.csv')
print(sorted(data['I_131_(Bq/m3)'].unique())[-5:])
print(sorted(data['Cs_134_(Bq/m3)'].unique())[-5:])
print(sorted(data['Cs_137_(Bq/m3)'].unique())[-5:]) | code |
122265193/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/air-concentration-for-the-chernobyl-disaster/data.csv')
data | code |
122265193/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/air-concentration-for-the-chernobyl-disaster/data.csv')
m = data['I_131_(Bq/m3)'].str.contains('L|\\?', regex=True, na=False)
data.loc[m, 'I_131_(Bq/m3)'] = None
m = data['Cs_134_(Bq/m3)'].str.contains('N|\\?', regex=T... | code |
16154606/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pytorch_pretrained_bert import BertConfig
from pytorch_pretrained_bert import BertTokenizer, BertForSequenceClassification, BertAdam
from tqdm import tqdm
import numpy as np
import pandas as pd
import torch
device = torch.device('cuda')
def convert_lines(example, max_seq_length, tokenizer):
max_seq_leng... | code |
16154606/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pytorch_pretrained_bert import BertConfig
from pytorch_pretrained_bert import BertTokenizer, BertForSequenceClassification, BertAdam
from tqdm import tqdm
import numpy as np
import pandas as pd
import torch
device = torch.device('cuda')
def convert_lines(example, max_seq_length, tokenizer):
max_seq_leng... | code |
16154606/cell_7 | [
"text_plain_output_1.png"
] | from pytorch_pretrained_bert import BertConfig
from pytorch_pretrained_bert import BertTokenizer, BertForSequenceClassification, BertAdam
from tqdm import tqdm
import numpy as np
import torch
device = torch.device('cuda')
def convert_lines(example, max_seq_length, tokenizer):
max_seq_length -= 2
all_token... | code |
73095926/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import model_selection
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.value_counts()
from sklearn import model_selection
df['kfold'] = -1
df = df.sample(frac=1).reset_index(drop=True)
y = df.income.values
kf = m... | code |
73095926/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.value_counts() | code |
73095926/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 |
73095926/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import metrics
from sklearn import model_selection
from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import xgboost as xgb
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.valu... | code |
73095926/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.value_counts()
df.head() | code |
73095926/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import metrics
from sklearn import model_selection
from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.value_counts()
from sklearn import model_se... | code |
73095926/cell_12 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import metrics
from sklearn import model_selection
from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import xgboost as xgb
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.valu... | code |
73095926/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.value_counts()
df['income'].isnull().sum() | code |
90157865/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import plotly.express as px
vaults = pd.read_csv('../input/psolvaults/output.csv')
solPrice = 80
vaults['debt'] = vaults['debtAmount'] / 10 ** vaults['decimal']
vaults['debtValue'] = vaults['debt'] * solPrice
vaults['collateral'] = vaults['collateralAmount'] / 10 ** vaults['decimal']
vaults['collateralValue'] = vaults[... | code |
73088459/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,auc,classification_report,confusion_matrix,mean_squared_error, precision_score, recall_score,roc_curve
from sklearn.model_selection import cross_val_score,cross_val_predict,cross_validate,train_test_split,GridSearchCV,KFold,RepeatedKFold,learning_curve,RandomizedSearchCV,Stra... | code |
73088459/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from scipy.stats import norm, randint
from math import ceil
import time
import os
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import seaborn as sns
from catboost import CatBoostRegressor
from sklearn.compose import ColumnTransformer
from sklea... | code |
73088459/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('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy()
fulltest_df = test.copy()
print('Data Import Complete')
prepro... | code |
73088459/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,auc,classification_report,confusion_matrix,mean_squared_error, precision_score, recall_score,roc_curve
from sklearn.model_selection import cross_val_score,cross_val_predict,cross_validate,train_test_split,GridSearchCV,KFold,RepeatedKFold,learning_curve,RandomizedSearchCV,Stra... | code |
73088459/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy... | code |
73088459/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy... | code |
73088459/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score,cross_val_predict,cross_validate,train_test_split,GridSearchCV,KFold,RepeatedKFold,learning_curve,RandomizedSearchCV,StratifiedKFold
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file ... | code |
73088459/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy... | code |
2008154/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
img_size = 64
channel_size = 1
print('Training shape:', X_train.shape)
print(X_train.shape[0], 'sample,', X_train.shape[1], 'x', X_train.shape[2], 'size grayscale image.\n')
print('Test shape:', X_test.shape)
print(X_test.shape[0], 'sample,', X_test.shape[1], 'x', X_test.shape[2], 'size... | code |
2008154/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
print(check_output(['ls', '../input/Sign-language-digits-dataset']).decode('utf8')) | code |
90108999/cell_21 | [
"text_plain_output_1.png"
] | y_test_temp = y_test.reshape(1, len(y_test))[0]
print(type(y_test_temp))
print(y_test_temp.shape) | code |
90108999/cell_9 | [
"image_output_1.png"
] | import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
... | code |
90108999/cell_6 | [
"image_output_1.png"
] | import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
... | code |
90108999/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, la... | code |
90108999/cell_19 | [
"text_plain_output_1.png"
] | x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
cuml_model = cuRFC(max_features=1.0, n_bins=8, n_estimators=40)
cuml_model.fit(x_train.astype('float32'), y_train.astype('float32'))
y_rfc_predict = cuml_model.predict(x_test... | code |
90108999/cell_18 | [
"text_plain_output_1.png"
] | x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
cuml_model = cuRFC(max_features=1.0, n_bins=8, n_estimators=40)
cuml_model.fit(x_train.astype('float32'), y_train.astype('float32')) | code |
90108999/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from cuml.metrics.confusion_matrix import confusion_matrix
from sklearn import metrics
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
import seaborn as sns
image_size = 200
labels = ['PNEUMONIA', 'NORMAL'... | code |
90108999/cell_35 | [
"text_plain_output_1.png"
] | from cuml.metrics.confusion_matrix import confusion_matrix
from sklearn import metrics
from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import f1_score
from sklearn.metrics... | code |
90108999/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labe... | code |
90108999/cell_14 | [
"text_plain_output_1.png"
] | x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
print(x_train.shape)
print(x_test.shape)
print(x_val.shape) | code |
90108999/cell_22 | [
"text_plain_output_1.png"
] | import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
... | code |
90108999/cell_27 | [
"text_plain_output_1.png"
] | from cuml.metrics.confusion_matrix import confusion_matrix
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
import seaborn as sns
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
... | code |
90108999/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
print(x_val.shape)
print(y_val.shape) | code |
18146033/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.val... | code |
18146033/cell_20 | [
"text_plain_output_1.png"
] | from keras.applications.densenet import DenseNet121
from keras.layers import (Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D)
from keras.models import Model
from tqdm import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import... | code |
18146033/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.val... | code |
18146033/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import PIL
import cv2
from PIL import Image, ImageOps
from keras.models import Sequential, load_model
from keras.layers import Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D
from keras.applications.densenet import DenseNe... | code |
18146033/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.val... | code |
18146033/cell_19 | [
"text_plain_output_1.png"
] | from keras.applications.densenet import DenseNet121
from keras.layers import (Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D)
from keras.models import Model
from tqdm import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import... | code |
18146033/cell_16 | [
"text_plain_output_1.png"
] | from keras.applications.densenet import DenseNet121
from keras.layers import (Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D)
from keras.models import Model
from tqdm import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import... | code |
18146033/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.val... | code |
18146033/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.val... | code |
72062529/cell_7 | [
"text_plain_output_1.png"
] | # @title Install dependencies
# @markdown Download dataset, modules, and files needed for the tutorial from GitHub.
# @markdown Download from OSF. Original repo: https://github.com/colleenjg/neuromatch_ssl_tutorial.git
import os, sys, importlib
REPO_PATH = "neuromatch_ssl_tutorial"
download_str = "Downloading"
if os.pa... | code |
72062529/cell_17 | [
"text_plain_output_1.png"
] | from IPython.display import IFrame
from IPython.display import IFrame
from IPython.display import YouTubeVideo
from IPython.display import display, Image # to visualize images
from ipywidgets import widgets
import ipywidgets as widgets # interactive display
from ipywidgets import widgets
out2 = widgets.Outp... | code |
72062529/cell_14 | [
"text_html_output_1.png"
] | from neuromatch_ssl_tutorial.modules import data, load, models, plot_util
from neuromatch_ssl_tutorial.modules import data, load, models, plot_util
import matplotlib.pyplot as plt
import numpy as np
import random
import torch
import torch
# @title Plotting functions
# @markdown Function to plot a histogram of R... | code |
72062529/cell_5 | [
"text_plain_output_1.png"
] | from IPython.display import IFrame
from IPython.display import IFrame
IFrame(src=f'https://mfr.ca-1.osf.io/render?url=https://osf.io/wvt34/?direct%26mode=render%26action=download%26mode=render', width=854, height=480) | code |
16112056/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
healt... | code |
16112056/cell_4 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum()
... | code |
16112056/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum() | code |
16112056/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
healt... | code |
16112056/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input'))
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
h... | code |
16112056/cell_8 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum()
... | code |
16112056/cell_3 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum()
... | code |
16112056/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
healt... | code |
128011806/cell_4 | [
"text_plain_output_1.png"
] | model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary() | code |
128011806/cell_34 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
import tensorflow as tf
model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary()
class_names = ['happy', 'sad']
data = np.ndarray(shape=(48, 48, 1), dtype=np.fl... | code |
128011806/cell_33 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
import tensorflow as tf
model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary()
class_names = ['happy', 'sad']
data = np.ndarray(shape=(48, 48, 1), dtype=np.fl... | code |
128011806/cell_19 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
import tensorflow as tf
model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary()
class_names = ['happy', 'sad']
data = np.ndarray(shape=(48, 48, 1), dtype=np.fl... | code |
128011806/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
import tensorflow as tf | code |
128011806/cell_18 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
import tensorflow as tf
model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary()
class_names = ['happy', 'sad']
data = np.ndarray(shape=(48, 48, 1), dtype=np.fl... | code |
128011806/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np
import tensorflow as tf
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
data_tensor = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor = tf.expand_dims(data_tensor, axis=0)
data_tensor2 = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor2 = tf.expand_dims(data_tensor2, axi... | code |
128011806/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import tensorflow as tf
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
data_tensor = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor = tf.expand_dims(data_tensor, axis=0)
data_tensor.shape | code |
128011806/cell_27 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
import tensorflow as tf
model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary()
class_names = ['happy', 'sad']
data = np.ndarray(shape=(48, 48, 1), dtype=np.fl... | code |
128011806/cell_12 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
image = Image.open('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/dataset/sad/PrivateTest_568359.jpg').convert('RGB')
gray_image = image.convert('L')
size = (4... | code |
122253838/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
url = 'https://data.boston.gov/api/3/action/datastore_search?resource_id=c13199bf-49a1-488d-b8e9-55e49523ef81&limit=90000'
js = pd.read_json(url)
df = pd.DataFrame(js['result']['records'])
df = df.set_index('timestamp')
df.columns = df.columns.str.lower()
df = df.drop(['_id'], axis=1)
df.index = ... | code |
73083713/cell_21 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum()
customer_data_cleaned = customer_data.dropna()
from datetime import date
def get_age(birthyear):
... | code |
73083713/cell_13 | [
"text_html_output_1.png"
] | from datetime import date
import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum()
customer_data_cleaned = customer_data.dropna()
from datetime import date
def get_age(birthyear):
... | code |
73083713/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum() | code |
73083713/cell_23 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum()
customer_data_cleaned = customer_data.dropna()
from datetime import date
def get_age(birthyear):
... | code |
73083713/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.head() | code |
73083713/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape | code |
73083713/cell_32 | [
"text_html_output_1.png"
] | from datetime import date
import numpy as np
import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum()
customer_data_cleaned = customer_data.dropna()
from datetime import date
def get... | code |
73083713/cell_15 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum()
customer_data_cleaned = customer_data.dropna()
from datetime import date
def get_age(birthyear):
... | code |
33104665/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
plt.figure(figsize = (16,8))
#Let's verify the correlation of each value
ax = sb.heatmap(Ydata[['views', 'likes', 'disli... | code |
33104665/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
Ydata['trending_date'].head() | code |
33104665/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
plt.figure(figsize=(16, 8))
ax = sb.heatmap(Ydata[['views', 'likes', 'dislikes', 'comment_count']].corr(), annot=True, an... | code |
33104665/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 |
33104665/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.info() | code |
33104665/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull())) | code |
33104665/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
column_list = ['views', 'likes', 'dislikes', 'comment_count']
corr_matrix = Ydata[column_list].corr()
corr_matrix | code |
33104665/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
sns.countplot(x='likes', data=Ydata) | code |
33104665/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.head() | code |
50244797/cell_13 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zi... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.