path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
89135215/cell_15 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = pd.read_csv('../input/h... | code |
89135215/cell_16 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = pd.read_csv('../input/h... | code |
89135215/cell_17 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = pd.read_csv('../input/h... | code |
89135215/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = pd.read_csv('../input/h... | code |
89135215/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
house_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
house_test = pd.read_csv('../input/house-prices-advanced-regression-t... | code |
72076990/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
test.isna(... | code |
72076990/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
... | code |
72076990/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols ... | code |
72076990/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
print(f'Train Shape: {train.shape}\nTest Shape: {test.shape}') | code |
72076990/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
... | code |
72076990/cell_6 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols | code |
72076990/cell_2 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.preprocessing import OrdinalEncoder
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
import lightgbm as lgb
from sklearn.metrics import mean_square... | code |
72076990/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
... | code |
72076990/cell_7 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols ... | code |
72076990/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols ... | code |
72076990/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
... | code |
72076990/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test.isna().sum()[test.isna().sum() > 0] | code |
72076990/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submissi... | code |
72076990/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
... | code |
72076990/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for ... | code |
72076990/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols ... | code |
72076990/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes | code |
72081792/cell_9 | [
"text_plain_output_1.png"
] | from tqdm.auto import tqdm
from transformers import TFAutoModel, AutoTokenizer
import pandas as pd
import pathlib
import tensorflow as tf
ROOT_PATH = pathlib.Path('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/')
MODEL = 'distilbert-base-multilingual-cased'
BATCH_SIZE = 32
EPOCHS = 1
MAX_DOC_LENGT... | code |
72081792/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pathlib
ROOT_PATH = pathlib.Path('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/')
MODEL = 'distilbert-base-multilingual-cased'
BATCH_SIZE = 32
EPOCHS = 1
MAX_DOC_LENGTH = 256
train_df = pd.read_csv(ROOT_PATH / 'jigsaw-toxic-comment-train.csv')
valid_df = pd.read_csv(ROOT_... | code |
72081792/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pathlib
ROOT_PATH = pathlib.Path('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/')
MODEL = 'distilbert-base-multilingual-cased'
BATCH_SIZE = 32
EPOCHS = 1
MAX_DOC_LENGTH = 256
train_df = pd.read_csv(ROOT_PATH / 'jigsaw-toxic-comment-train.csv')
valid_df = pd.read_csv(ROOT_... | code |
72081792/cell_15 | [
"text_plain_output_1.png"
] | from tqdm.auto import tqdm
from transformers import TFAutoModel, AutoTokenizer
import pandas as pd
import pathlib
import tensorflow as tf
import tensorflow.keras as keras
ROOT_PATH = pathlib.Path('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/')
MODEL = 'distilbert-base-multilingual-cased'
BATCH_... | code |
72081792/cell_17 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tqdm.auto import tqdm
from transformers import TFAutoModel, AutoTokenizer
import pandas as pd
import pathlib
import tensorflow as tf
import tensorflow.keras as keras
ROOT_PATH = pathlib.Path('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/')
MODEL = 'distilbert-base-multilingual-cased'
BATCH_... | code |
72081792/cell_12 | [
"text_html_output_1.png"
] | (x[0].shape, x[1].shape) | code |
128026857/cell_13 | [
"image_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rc = {'axes.facecolor': '#F8F8F8', 'figure.facecolor': '#F8F8F8', 'axes.edge... | code |
128026857/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
df_orig_train = pd.read_csv('/kaggle/input/vector-borne-disease-prediction/trainn.csv')
df_orig_test = pd.read_csv('/kag... | code |
128026857/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
df_orig_train = pd.read_csv('/kaggle/input/vector-borne-disease-prediction/trainn.csv')
df_orig_test = pd.read_csv('/kaggle/input/vector-borne-disease-pr... | code |
128026857/cell_29 | [
"text_plain_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rc = {'axes.facecolor': '#F8F8F8', 'figure.facecolor': '#F8F8F8', 'axes.edge... | code |
128026857/cell_26 | [
"text_plain_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rc = {'axes.facecolor': '#F8F8F8', 'figure.facecolor': '#F8F8F8', 'axes.edge... | code |
128026857/cell_11 | [
"text_plain_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rc = {'axes.facecolor': '#F8F8F8', 'figure.facecolor': '#F8F8F8', 'axes.edgecolor': '#000000', 'gri... | code |
128026857/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
df_orig_train = pd.read_csv('/kaggle/input/vector-borne-disease-prediction/trainn.csv')
df_orig_test = pd.read_csv('/kag... | code |
128026857/cell_15 | [
"text_plain_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rc = {'axes.facecolor': '#F8F8F8', 'figure.facecolor': '#F8F8F8', 'axes.edge... | code |
128026857/cell_31 | [
"text_html_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rc = {'axes.facecolor': '#F8F8F8', 'figure.facecolor': '#F8F8F8', 'axes.edge... | code |
128026857/cell_14 | [
"text_html_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rc = {'axes.facecolor': '#F8F8F8', 'figure.facecolor': '#F8F8F8', 'axes.edge... | code |
128026857/cell_22 | [
"image_output_11.png",
"text_plain_output_56.png",
"text_plain_output_35.png",
"image_output_24.png",
"text_plain_output_43.png",
"image_output_46.png",
"text_plain_output_37.png",
"image_output_25.png",
"text_plain_output_5.png",
"text_plain_output_48.png",
"text_plain_output_30.png",
"image_... | from colorama import Style, Fore
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rc = {'axes.facecolor': '#F8F8F8', 'figure.facecolor': '#F8F8F8', 'axes.edge... | code |
128026857/cell_27 | [
"image_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rc = {'axes.facecolor': '#F8F8F8', 'figure.facecolor': '#F8F8F8', 'axes.edge... | code |
90131759/cell_16 | [
"text_plain_output_1.png"
] | from _csv import reader
from numpy import mean
from numpy import std
from scipy.stats import norm
import pandas as pd
import random
def load_csv(filename):
dataset = list()
with open(filename, 'r') as file:
csv_reader = reader(file)
for row in csv_reader:
if not row:
... | code |
130015160/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import tensorflow as tf
x_train = x_train / 255
x_test = x_test / 255
i = tf.keras.Input((28, 28, 1))
x = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(i)
x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)
x = tf.keras.layers.Conv2D(32, (3, 3), activat... | code |
130015160/cell_2 | [
"text_plain_output_1.png"
] | from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data() | code |
130015160/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
from tensorflow import keras
from keras.datasets import mnist
import matplotlib.pyplot as plt | code |
130015160/cell_7 | [
"text_plain_output_1.png"
] | import tensorflow as tf
x_train = x_train / 255
x_test = x_test / 255
i = tf.keras.Input((28, 28, 1))
x = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(i)
x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)
x = tf.keras.layers.Conv2D(32, (3, 3), activation='tanh', padding='same')(x)
x ... | code |
130015160/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import tensorflow as tf
x_train = x_train / 255
x_test = x_test / 255
i = tf.keras.Input((28, 28, 1))
x = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(i)
x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)
x = tf.keras.layers.Conv2D(32, (3, 3), activat... | code |
130015160/cell_5 | [
"text_plain_output_1.png"
] | import tensorflow as tf
i = tf.keras.Input((28, 28, 1))
x = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(i)
x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)
x = tf.keras.layers.Conv2D(32, (3, 3), activation='tanh', padding='same')(x)
x = tf.keras.layers.MaxPooling2D((2, 2), padding=... | code |
88090019/cell_1 | [
"text_plain_output_1.png"
] | !pip install -U timm | code |
88090019/cell_17 | [
"text_plain_output_1.png"
] | from torch.utils.data.dataset import Dataset
import cv2
import os
import timm
import torch
conf = {'batch': 16, 'image_dir': '../input/dog-image-dsg/photo/photo', 'image_size': 224, 'tta': 1, 'num_classes': 73, 'num_workers': 2, 'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
modeldef = [{'mdl': 'convne... | code |
88090019/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
infer_df = pd.read_csv('../input/dog-image-dsg/test.csv')
infer_df.head() | code |
72073661/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_test = pd.r... | code |
2003218/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/spooky-author-identification/train.csv')
test_data = pd.read_csv('../input/spooky-author-identification/test.csv')
train_data.describe() | code |
2003218/cell_3 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import nltk
import pandas as pd
train_data = pd.read_csv('../input/spooky-author-identification/train.csv')
test_data = pd.read_csv('../input/spooky-author-identification/test.csv')
def preprocess_text(text, remove_list):
"""
tokens = nltk.pos_tag(nltk.word_tokenize(text))
... | code |
88085043/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import pandas as pd
def load_stock(name):
df = pd.read_csv('../input/stock-market-data/stock_market_data/nasdaq/csv/{}.csv'.format(name))
df.set_index('Date', inplace=True)
return df
names = ['AAL', 'AAPL']
stocks ... | code |
88085043/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
def load_stock(name):
df = pd.read_csv('../input/stock-market-data/stock_market_data/nasdaq/csv/{}.csv'.format(name))
df.set_index('Date', inplace=True)
return df
names = ['AAL', 'AAPL']
stocks = [load_stock(n) for n in names]
df = stocks[0][-1500:... | code |
88085043/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
def load_stock(name):
df = pd.read_csv('../input/stock-market-data/stock_market_data/nasdaq/csv/{}.csv'.format(name))
df.set_index('Date', inplace=True)
return df
names = ['AAL', 'AAPL']
stocks = [load_stock(n) for n in names]
df = stocks[0][-1500:... | code |
88085043/cell_7 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
def load_stock(name):
df = pd.read_csv('../input/stock-market-data/stock_market_data/nasdaq/csv/{}.csv'.format(name))
df.set_index('Date', inplace=True)
return df
names = ['AAL', 'AAPL']
stocks = [load_stock(n) for n in names]
df = stocks[0][-1500:... | code |
88085043/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode
from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectKBest
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.neur... | code |
88085043/cell_10 | [
"text_html_output_1.png"
] | from sklearn.feature_selection import SelectKBest
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import pandas as pd
def load_stock(name):
df = pd.read_csv('../input/stock-market-data/stock_market_data/nasdaq/csv/{}.csv'.format(name))
df.set_index('Date', inplace=... | code |
106198232/cell_21 | [
"image_output_1.png"
] | import cv2
import h5py
import matplotlib.pyplot as plt
import numpy as np
import random
event = h5py.File('../input/insar-dataset/insar_data_event.hdf5', 'r')
disp = np.zeros(shape=(16000, 40, 40))
sim = np.zeros(shape=(16000, 9, 40, 40, 1))
topo = np.zeros(shape=(16000, 1, 40, 40, 1))
for i in range(len(event.k... | code |
106198232/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
disp = np.zeros(shape=(16000, 40, 40))
sim = np.zeros(shape=(16000, 9, 40, 40, 1))
topo = np.zeros(shape=(16000, 1, 40, 40, 1))
print(sim[0][1].shape) | code |
106198232/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import random
disp = np.zeros(shape=(16000, 40, 40))
sim = np.zeros(shape=(16000, 9, 40, 40, 1))
topo = np.zeros(shape=(16000, 1, 40, 40, 1))
for i in range(100):
ind = random.randint(0, 15999)
img = disp[ind]
plt.subplots(10, 5, figsize=(20, 30))
for i in... | code |
106198232/cell_30 | [
"image_output_2.png",
"image_output_1.png"
] | from skimage.filters import gaussian
from skimage.filters import median
from skimage.morphology import disk
from skimage.restoration import denoise_bilateral
import cv2
import h5py
import matplotlib.pyplot as plt
import numpy as np
import random
event = h5py.File('../input/insar-dataset/insar_data_event.hdf5',... | code |
106198232/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import os
os.listdir('../input') | code |
106198232/cell_19 | [
"text_plain_output_1.png"
] | import cv2
import h5py
import matplotlib.pyplot as plt
import numpy as np
import random
event = h5py.File('../input/insar-dataset/insar_data_event.hdf5', 'r')
disp = np.zeros(shape=(16000, 40, 40))
sim = np.zeros(shape=(16000, 9, 40, 40, 1))
topo = np.zeros(shape=(16000, 1, 40, 40, 1))
for i in range(len(event.k... | code |
106198232/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import random
disp = np.zeros(shape=(16000, 40, 40))
sim = np.zeros(shape=(16000, 9, 40, 40, 1))
topo = np.zeros(shape=(16000, 1, 40, 40, 1))
plt.subplots(10, 5, figsize=(20, 30))
for i in range(100):
ind = random.randint(0, 15999)
img = disp[ind]
plt.s... | code |
106198232/cell_15 | [
"image_output_1.png"
] | import numpy as np
disp = np.zeros(shape=(16000, 40, 40))
sim = np.zeros(shape=(16000, 9, 40, 40, 1))
topo = np.zeros(shape=(16000, 1, 40, 40, 1))
img3 = sim[0][1].reshape(40, 40)
print(img3.shape) | code |
106198232/cell_16 | [
"text_plain_output_1.png"
] | import h5py
import matplotlib.pyplot as plt
import numpy as np
import random
event = h5py.File('../input/insar-dataset/insar_data_event.hdf5', 'r')
disp = np.zeros(shape=(16000, 40, 40))
sim = np.zeros(shape=(16000, 9, 40, 40, 1))
topo = np.zeros(shape=(16000, 1, 40, 40, 1))
for i in range(len(event.keys())):
... | code |
106198232/cell_17 | [
"text_plain_output_1.png"
] | import cv2
import h5py
import matplotlib.pyplot as plt
import numpy as np
import random
event = h5py.File('../input/insar-dataset/insar_data_event.hdf5', 'r')
disp = np.zeros(shape=(16000, 40, 40))
sim = np.zeros(shape=(16000, 9, 40, 40, 1))
topo = np.zeros(shape=(16000, 1, 40, 40, 1))
for i in range(len(event.k... | code |
106198232/cell_24 | [
"image_output_1.png"
] | from skimage.filters import gaussian
import cv2
import h5py
import matplotlib.pyplot as plt
import numpy as np
import random
event = h5py.File('../input/insar-dataset/insar_data_event.hdf5', 'r')
disp = np.zeros(shape=(16000, 40, 40))
sim = np.zeros(shape=(16000, 9, 40, 40, 1))
topo = np.zeros(shape=(16000, 1, 4... | code |
106198232/cell_14 | [
"image_output_1.png"
] | import h5py
import numpy as np
event = h5py.File('../input/insar-dataset/insar_data_event.hdf5', 'r')
disp = np.zeros(shape=(16000, 40, 40))
sim = np.zeros(shape=(16000, 9, 40, 40, 1))
topo = np.zeros(shape=(16000, 1, 40, 40, 1))
for i in range(len(event.keys())):
for j in range(100):
disp[i * 100 + j] ... | code |
106198232/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage.filters import gaussian
from skimage.filters import median
from skimage.morphology import disk
import cv2
import h5py
import matplotlib.pyplot as plt
import numpy as np
import random
event = h5py.File('../input/insar-dataset/insar_data_event.hdf5', 'r')
disp = np.zeros(shape=(16000, 40, 40))
sim =... | code |
122256098/cell_21 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum()
train_x = train[['Pclass', 'Sex']]
train_y = train[['Survived']]
train_x['Sex'].replace('male', 1, inplace=True)
train_x['Sex'].replac... | code |
122256098/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum()
train_x = train[['Pclass', 'Sex']]
train_y = train[['Survived']]
train_x['Sex'].replace('male', 1, inplace=True)
train_x['Sex'].replac... | code |
122256098/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum()
train_x = train[['Pclass', 'Sex']]
train_x['Sex'].replace('male', 1, inplace=True)
train_x['Sex'].replace('female', 0, inplace=True)
train_x.head() | code |
122256098/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
op = test[['PassengerId']]
op.to_csv('Submission.csv', index=False) | code |
122256098/cell_4 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.head() | code |
122256098/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum()
train_x = train[['Pclass', 'Sex']]
train_y = train[['Survived']]
train_x['Sex'].replace('male', 1, inplace=True)
train_x['Sex'].replac... | code |
122256098/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum() | code |
122256098/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
test_x = test[['Pclass', 'Sex']]
test_x['Sex'].replace('male', 1, inplace=True)
test_x['Sex'].replace('female', 0, inplace=True)
test_x.head() | code |
122256098/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum()
train_x = train[['Pclass', 'Sex']]
train_x.head() | code |
122256098/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
test_x = test[['Pclass', 'Sex']]
test_x['Sex'].replace('male', 1, inplace=True)
test_x['Sex'].replace('female', 0, inplace=True)
test_x.head() | code |
122256098/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum()
train_y = train[['Survived']]
train_y.head() | code |
122256098/cell_15 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum()
train_x = train[['Pclass', 'Sex']]
train_y = train[['Survived']]
train_x['Sex'].replace('male', 1, inplace=True)
train_x['Sex'].replac... | code |
122256098/cell_16 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
test.head() | code |
122256098/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
test_x = test[['Pclass', 'Sex']]
test_x.head() | code |
122256098/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
op = test[['PassengerId']]
op.head() | code |
122256098/cell_14 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum()
train_x = train[['Pclass', 'Sex']]
train_y = train[['Survived']]
train_x['Sex'].replace('male', 1, inplace=True)
train_x['Sex'].replac... | code |
122256098/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum()
train_x = train[['Pclass', 'Sex']]
train_y = train[['Survived']]
train_x['Sex'].replace('male', 1, inplace=True)
train_x['Sex'].replace('female', 0, inplace=True)
tr_x, cv_x, tr_y, cv_y ... | code |
122256098/cell_12 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('train.csv')
train.isnull().sum()
train_x = train[['Pclass', 'Sex']]
train_y = train[['Survived']]
train_x['Sex'].replace('male', 1, inplace=True)
train_x['Sex'].replac... | code |
122256098/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.describe() | code |
122248046/cell_21 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.shape[0]
df = df.drop('Destination', axis=1)... | code |
122248046/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum() | code |
122248046/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.shape[0]
df = df.drop('Destination', axis=1)
popular_cities = df['... | code |
122248046/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum() | code |
122248046/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.shape[0]
df = df.drop('Destination', axis=1)
duractions_count = df['Duration (days)'].value_counts(... | code |
122248046/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import plotly.express as px
from plotly.offline import iplot
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
122248046/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.shape[0] | code |
122248046/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.shape[0]
df = df.drop('Destination', axis=1)
popular_cities = df['... | code |
122248046/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.shape[0]
df.info() | code |
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