path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
33106981/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel_num = hotel.dtypes[hotel.dtypes != 'object']
hotel_num = hotel_num.index.to_list()
Date_Drop = {'is_canceled', 'company'}
hotel_num = [... | code |
33106981/cell_15 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel_num = hotel.dtypes[hotel.dtypes != 'object']
hotel_num = hotel_nu... | code |
33106981/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape | code |
33106981/cell_22 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",... | from collections import Counter
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)
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel_num = hotel.dtypes[hotel.dtypes ... | code |
33106981/cell_27 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel_num = hot... | code |
33106981/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel.info() | code |
34141447/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)
df_train_path = '/kaggle/input/digit-recognizer/train.csv'
df_test_path = '/kaggle/input/digit-recognizer/test.csv'
X_train = pd.read_csv(df_train_path)
X_test = pd.read_csv(df_test_path)
y_train = X_train['label']
X_train = X_train.drop('label', a... | code |
34141447/cell_25 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from torch import nn, optim
from torch.autograd import Variable
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import torch
import torch.nn.functional as F
use_gpu = torch.cuda.is_available()
use_gpu
df_train_path = '/kaggle/input/dig... | code |
34141447/cell_4 | [
"text_plain_output_1.png"
] | import torch
use_gpu = torch.cuda.is_available()
use_gpu | code |
34141447/cell_23 | [
"text_plain_output_1.png"
] | from torch import nn, optim
from torch.autograd import Variable
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import torch
import torch.nn.functional as F
use_gpu = torch.cuda.is_available()
use_gpu
df_train_path = '/kaggle/input/dig... | code |
34141447/cell_29 | [
"text_plain_output_1.png"
] | from torch import nn, optim
from torch.autograd import Variable
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import torch
import torch.nn.functional as F
use_gpu = torch.cuda.is_available()
use_gp... | code |
34141447/cell_18 | [
"text_plain_output_1.png"
] | from torch import nn, optim
import torch
import torch.nn.functional as F
use_gpu = torch.cuda.is_available()
use_gpu
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 6, (5, 5), padding=2)
self.conv2 = nn.Conv2d(6, 16, (5, 5))
sel... | code |
34141447/cell_28 | [
"text_plain_output_1.png"
] | from torch import nn, optim
from torch.autograd import Variable
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import torch
import torch.nn.functional as F
use_gpu = torch.cuda.is_available()
use_gp... | code |
34141447/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train_path = '/kaggle/input/digit-recognizer/train.csv'
df_test_path = '/kaggle/input/digit-recognizer/test.csv'
X_train = pd.read_csv(df_train_path)
X_test = pd.read_csv(df_test_path)
y_tr... | code |
34141447/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import torch
use_gpu = torch.cuda.is_available()
use_gpu
df_train_path = '/kaggle/input/digit-recognizer/train.csv'
df_test_path = '/kaggle/input/digit-recognizer/test.csv'
X_train = pd.read... | code |
34141447/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import torch
from torch import nn, optim
import torch.nn.functional as F
from torch.autograd import Variable
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
pri... | code |
34141447/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train_path = '/kaggle/input/digit-recognizer/train.csv'
df_test_path = '/kaggle/input/digit-recognizer/test.csv'
X_train = pd.read_csv(df_train_path)
X_test = pd.read_csv(df_test_path)
y_tr... | code |
34141447/cell_27 | [
"text_plain_output_1.png"
] | from torch import nn, optim
from torch.autograd import Variable
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import torch
import torch.nn.functional as F
use_gpu = torch.cuda.is_available()
use_gpu
df_train_path = '/kaggle/input/dig... | code |
34141447/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train_path = '/kaggle/input/digit-recognizer/train.csv'
df_test_path = '/kaggle/input/digit-recognizer/test.csv'
X_train = pd.read_csv(df_train_path)
X_test = pd.read_csv(df_test_path)
y_tr... | code |
122255805/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ibutton2016/Temp2016.csv', skiprows=[i for i in range(1, 1096)], skipfooter=1112, engine='python', parse_dates... | code |
122255805/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 |
122255805/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ibutton2016/Temp2016.csv', skiprows=[i for i in range(1, 1096)], skipfooter=1112, engine='python', parse_dates=['Var1'], index_col=['Var1'])
df... | code |
2016103/cell_4 | [
"text_plain_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from scipy.stats import linregress
from subprocess import check_output
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import ipl... | code |
2016103/cell_2 | [
"text_plain_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from subprocess import check_output
import pandas as pd
import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
from subproc... | code |
2016103/cell_7 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from scipy.stats import linregress
from subprocess import check_output
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tool... | code |
2016103/cell_5 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from scipy.stats import linregress
from subprocess import check_output
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tool... | code |
90102830/cell_5 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import gc
import glob
import multiprocessing
import numpy as np
import os
import random
import torch
from tqdm.auto import tqdm
import os
import sys
import random
import numpy as np
import pandas as pd
import glob
import gc
gc.enable()
from joblib import Parallel, delayed
import torch
import torch.nn as nn
from ... | code |
33102990/cell_13 | [
"text_plain_output_1.png"
] | from glob import glob
from keras import layers
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Dense
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import matplotlib.pyplot as plt
train_path = '/kaggle/input/signatur... | code |
33102990/cell_4 | [
"image_output_1.png"
] | from glob import glob
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import matplotlib.pyplot as plt
train_path = '/kaggle/input/signature/signature/Train/'
test_path = '/kaggle/input/signature/signature/Test/'
img = load_img(train_path + 'forged/f138.png')
plt.figure()
plt.imshow(... | code |
33102990/cell_20 | [
"text_plain_output_1.png"
] | from glob import glob
from keras import layers
from keras.applications.resnet50 import preprocess_input
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Dense
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import matp... | code |
33102990/cell_6 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
train_path = '/kaggle/input/signature/signature/Train/'
test_path = '/kaggle/input/signature/signature/Test/'
train_data = ImageDataGenerator().flow_from_directory(train_path, target_size=(224, 224), class_mode='binary')
test_data = Imag... | code |
33102990/cell_11 | [
"text_plain_output_1.png"
] | from glob import glob
from keras import layers
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Dense
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
from keras.utils import plot_model
import matplotlib.pyplot as plt
... | code |
33102990/cell_19 | [
"image_output_1.png"
] | from glob import glob
from keras import layers
from keras.applications.resnet50 import preprocess_input
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Dense
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import matp... | code |
33102990/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import sys
import csv
import os
import math
import json, codecs
import numpy as np
import pandas as pd
import cv2 as cv
import matplotlib.pyplot as plt
from zipfile import ZipFile
import shutil
from glob import glob
from PIL import Image
from PIL import ImageFilter
from sklearn.model_selection import train_t... | code |
33102990/cell_8 | [
"image_output_1.png"
] | from keras.applications.resnet50 import preprocess_input
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
train_path = '/kaggle/input/signature/signature/Train/'
test_path = '/kaggle/input/signature/signature/Test/'
train_datagen = ImageDataGenerator(shear_range=10, zoom_range=0.2, ho... | code |
33102990/cell_16 | [
"image_output_1.png"
] | from glob import glob
from keras import layers
from keras.applications.resnet50 import preprocess_input
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Dense
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import matp... | code |
33102990/cell_17 | [
"text_plain_output_1.png"
] | from glob import glob
from keras import layers
from keras.applications.resnet50 import preprocess_input
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Dense
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import matp... | code |
33102990/cell_14 | [
"text_plain_output_1.png"
] | from glob import glob
from keras import layers
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Dense
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import matplotlib.pyplot as plt
train_path = '/kaggle/input/signatur... | code |
33102990/cell_22 | [
"image_output_1.png"
] | from glob import glob
from keras import layers
from keras.applications.resnet50 import preprocess_input
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Dense
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import matp... | code |
33102990/cell_10 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from glob import glob
from keras import layers
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Dense
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import matplotlib.pyplot as plt
train_path = '/kaggle/input/signatur... | code |
32071593/cell_13 | [
"text_html_output_1.png",
"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_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission_file = pd.read_csv('/kaggle/input/covid19-glob... | code |
32071593/cell_6 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission_file = pd.read_csv('/kaggle/input/covid19-glob... | code |
32071593/cell_11 | [
"text_html_output_2.png",
"text_html_output_1.png",
"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_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission_file = pd.read_csv('/kaggle/input/covid19-glob... | code |
32071593/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 |
32071593/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission_file = pd.read_csv('/kaggle/input/covid19-glob... | code |
32071593/cell_8 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission_file = pd.read_csv('/kaggle/input/covid19-glob... | code |
32071593/cell_14 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-glob... | code |
32071593/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission_file = pd.rea... | code |
32071593/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission_file = pd.read_csv('/kaggle/input/covid19-glob... | code |
2036553/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import keras
from keras import Sequential
from keras.layers import Conv2D, MaxPooling2D... | code |
2036553/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from keras.optimizers import Adam
import numpy as np # linear algebra
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
fro... | code |
2036553/cell_1 | [
"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)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import keras
from keras import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from keras.... | code |
2036553/cell_3 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
... | code |
128002832/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
df.isna().sum().sum()
def plot_features(df):
fig, axs = plt.subplots(figsize=(16, 10), ncols=3, nrows=6)
for count, col in enumerate(df.columns):... | code |
128002832/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
df.isna().sum().sum()
def plot_features(df):
fig, axs = plt.subplots(figsize=(16, 10), ncols=3, nrows=6)
for count, col in enumerate(df.columns):
sns.boxplo... | code |
128002832/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
df.head(2) | code |
128002832/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
df.isna().sum().sum()
def plot_features(df):
fig, axs = plt.subplots(figsize=(16, 10), ncols=3, nrows=6)
for count, col in enumerate(df.columns):
sns.boxplot... | code |
128002832/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
df.i... | code |
128002832/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
128002832/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
model = LinearRegres... | code |
128002832/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
df.isna().sum().sum()
def plot_features(df):
fig, axs = plt.subplots(figsize=(16, 10), ncols=3, nrows=6)
for count, col in enumerate(df.columns):
sns.boxplo... | code |
128002832/cell_15 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
df.isna().sum().sum()
def plot_features(df):
fig, axs = plt.subplots(figsize=(16, 10), ncols=3,... | code |
128002832/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
df.i... | code |
128002832/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
df.isna().sum().sum()
def plot_features(df):
fig, axs = plt.subplots(figsize=(16, 10), ncols=3, nrows=6)
for count, col in enumerate(df.columns):... | code |
128002832/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
df.isna().sum().sum() | code |
90134529/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELECT\n timestamp\n FROM \n ... | code |
90134529/cell_6 | [
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from datetime import datetime, timedelta
from google.cloud import bigquery
from scipy.stats import norm
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query =... | code |
90134529/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELECT\n timestamp\n FROM \n `bigquery-public-data.bitcoin_... | code |
90134529/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELECT\n timestamp\n FROM \n ... | code |
90134529/cell_5 | [
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from datetime import datetime, timedelta
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELE... | code |
2002221/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
print(medals.info())
medals.head() | code |
2002221/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
medal_counts = medals['NOC'].value_counts()
print('The total medals: %d' % medal_counts.sum())
print('\nTop 15 countries:\n', medal_counts.head(15)) | code |
2019264/cell_21 | [
"image_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict
import missingno as msno # plotting missing data
import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplo... | code |
2019264/cell_13 | [
"text_plain_output_1.png"
] | import missingno as msno # plotting missing data
import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from sklearn import metr... | code |
2019264/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.preprocessing import Imputer
from... | code |
2019264/cell_4 | [
"text_plain_output_1.png"
] | dataset.hist(bins=50, figsize=(20, 20))
plt.show() | code |
2019264/cell_30 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict
from sklearn.preprocessing import PolynomialFeatures
from sklearn.tree import DecisionTreeRegresso... | code |
2019264/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict
from sklearn.preprocessing import PolynomialFeatures
from sklearn.tree import DecisionTreeRegresso... | code |
2019264/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.preprocessing import Imputer
from... | code |
2019264/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.preprocessing import Imputer
from... | code |
2019264/cell_11 | [
"text_html_output_1.png"
] | import missingno as msno # plotting missing data
import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from sklearn import metr... | code |
2019264/cell_7 | [
"image_output_1.png"
] | import missingno as msno # plotting missing data
import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from sklearn import metr... | code |
2019264/cell_24 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict
from sklearn.preprocessing import PolynomialFeatures
import missingno as msno # plotting missing data
import pandas as pd # data processing
import n... | code |
2019264/cell_14 | [
"image_output_1.png"
] | import missingno as msno # plotting missing data
import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from sklearn import metr... | code |
2019264/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict
from sklearn.preprocessing import PolynomialFeatures
from sklearn.tree import DecisionTreeRegressor
import missingno as msno # plotting missing data
... | code |
2019264/cell_37 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict
from sklearn.preprocessing import PolynomialFeatures
from sklearn.tree import DecisionTreeRegresso... | code |
2019264/cell_12 | [
"image_output_1.png"
] | import missingno as msno # plotting missing data
import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from sklearn import metr... | code |
106208751/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, ax... | code |
106208751/cell_9 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, axis=1)
data_clear.isnull().sum()
data_clear = data_clear... | code |
106208751/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, ax... | code |
106208751/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(l... | code |
106208751/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, ax... | code |
106208751/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(l... | code |
106208751/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, axis=1)
data_clear.info()
data_clear.isnull().sum() | code |
106208751/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(l... | code |
106208751/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, axis=1)
data_clear.isnull... | code |
106208751/cell_3 | [
"image_output_1.png"
] | #Installing the libraries
!pip install pandas
!pip install seaborn
!pip install numpy
!pip install matplotlib | code |
106208751/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, ax... | code |
106208751/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, axis=1)
data_clear.isnull().sum()
data_clear = data_clear... | code |
106208751/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, ax... | code |
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