path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106192280/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust_male = df_cust[df_cust['Genre'] == 'Male']
df_cust_female = df_cust[df_cust['Genre'] == ... | code |
106192280/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns | code |
106192280/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
plt.figure(figsize=(6, 6))
df_cust['Genre'].value_counts().plot(kind='pie', autopct='%1.0f%%', shadow=True, explode=[0,... | code |
106192280/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust_male = df_cust[df_cust['Genre'] == 'Male']
df_cust_female = df_cust[df_cust['Genre'] == ... | code |
106192280/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust.head() | code |
106192280/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust[['CustomerID', 'Genre']].groupby('Genre').count() | code |
106192280/cell_17 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust['Genre'].value_counts() | code |
106192280/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust_male = df_cust[df_cust['Genre'] == 'Male']
df_cust_female = df_cust[df_cust['Genre'] == ... | code |
106192280/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust_male = df_cust[df_cust['Genre'] == 'Male']
df_cust_female = df_cust[df_cust['Genre'] == ... | code |
106192280/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust['Genre'].value_counts().index[0] | code |
106192280/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum() | code |
106192280/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust_male = df_cust[df_cust['Genre'] == 'Male']
df_cust_female = df_cust[df_cust['Genre'] == ... | code |
106192280/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
np.mean(df_cust) | code |
106192280/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.info() | code |
18102745/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
col = ['LotArea', 'SalePrice']
two = data[col]
features = ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'Bs... | code |
18102745/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
print(data.columns)
col = ['LotArea', 'SalePrice']
two = data[col] | code |
18102745/cell_11 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
col = ['LotArea', 'SalePrice']
two = data[col]
features = ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',... | code |
18102745/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
col = ['LotArea', 'SalePrice']
two = data[col]
features = ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'Bs... | code |
18102745/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
col = ['LotArea', 'SalePrice']
two = data[col]
data.head() | code |
18102745/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
col = ['LotArea', 'SalePrice']
two = data[col]
features = ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'Bs... | code |
18102745/cell_12 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
col = ['LotArea', 'SalePrice']
two = data[col]
features = ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',... | code |
18102745/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
col = ['LotArea', 'SalePrice']
two = data[col]
features = ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'Bs... | code |
73066361/cell_4 | [
"text_html_output_1.png"
] | from sklearn import model_selection
import pandas as pd
import numpy as np
import pandas as pd
from sklearn import model_selection
train = pd.read_csv('../input/30daysml/train.csv/train.csv')
train['kfold'] = -1
kf = model_selection.KFold(n_splits=10, shuffle=True, random_state=0)
for fold, (train_indicies, valid_i... | code |
73066361/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn import model_selection
train = pd.read_csv('../input/30daysml/train.csv/train.csv')
train['kfold'] = -1
train.head() | code |
73066361/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn import model_selection
train = pd.read_csv('../input/30daysml/train.csv/train.csv')
train.head() | code |
121154736/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import cv2
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)
import tensorflow as tf
df = pd.read_csv('... | code |
121154736/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import cv2
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)
import tensorflow as tf
df = pd.read_csv('... | code |
34134672/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.drop(['Name'], axis=1, inplace=True)
train.drop(['Cabin'], axis=1, inplace=True)
train.drop(['Ticket'], axis=1, inplace=True)
test.dr... | code |
34134672/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.drop(['Name'], axis=1, inpl... | code |
34134672/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 |
34134672/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.drop(['Name'], axis=1, inplace=True)
train.drop(['Cabin'], axis=1, inplace=True)... | code |
34134672/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.drop(['Name'], axis=1, inpl... | code |
34134672/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.drop(['Name'], axis=1, inplace=True)
train.drop(['Cabin'], axis=1, inplace=True)... | code |
34134672/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.drop(['Name'], axis=1, inplace=True)
train.drop(['Cabin'], axis=1, inplace=True)
train.drop(['Ticket'], axis=1, inplace=True)
test.dr... | code |
34134672/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.drop(['Name'], axis=1, inplace=True)
train.drop(['Cabin'], axis=1, inplace=True)
tr... | code |
16167842/cell_4 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
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
from matplotlib import pyplot as plt
import seaborn as sns
import os
data_dir = '../input/champs-scalar-coupling' if 'champs-scalar-coupling' in os.listdir('../i... | code |
16167842/cell_6 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import numpy as np # linear algebra
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
from matplotlib import pyplot as plt
import seaborn as sns
import os
data_dir = '../input/champs-scalar-coupling' if 'champs-scalar-coupling' in os.listdir('../i... | code |
16167842/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input'))
data_dir = '../input/champs-scalar-coupling' if 'champs-scalar-coupling' in os.listdir('../input/') else '../input' | code |
16167842/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
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
from matplotlib import pyplot as plt
import seaborn as sns
import os
data_dir = '../input/champs-scalar-coupling' if 'champs-scalar-coupling' in os.listdir('../i... | code |
16167842/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
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
from matplotlib import pyplot as plt
import seaborn as sns
import os
data_dir = '../input/champs-scalar-coupling' if 'champs-scalar-coupling' in os.listdir('../i... | code |
16167842/cell_5 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
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
from matplotlib import pyplot as plt
import seaborn as sns
import os
data_dir = '../input/champs-scalar-coupling' if 'champs-scalar-coupling' in os.listdir('../i... | code |
74058130/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/segment-road/Train (1).csv')
test = pd.read_csv('../input/segment-road/Test (1).csv')
sub = pd.read_csv('../input/segment-road/SampleSubmission.csv')
train.head() | code |
74058130/cell_18 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
from keras.applications import VGG19,ResNet50,Xception,InceptionResNetV2,InceptionV3,ResNet152V2
import efficientnet.tfkeras as efn
import numpy as np
import os
import pandas as pd
import tensorflow as tf
def seed_everything(seed=0):
random.seed(seed)
np.random.... | code |
74058130/cell_8 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
import numpy as np
import os
import tensorflow as tf
def seed_everything(seed=0):
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
seed = 42
def auto_selec... | code |
74058130/cell_15 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
from keras.applications import VGG19,ResNet50,Xception,InceptionResNetV2,InceptionV3,ResNet152V2
import efficientnet.tfkeras as efn
import numpy as np
import os
import pandas as pd
import tensorflow as tf
def seed_everything(seed=0):
random.seed(seed)
np.random.... | code |
74058130/cell_17 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
from keras.applications import VGG19,ResNet50,Xception,InceptionResNetV2,InceptionV3,ResNet152V2
import efficientnet.tfkeras as efn
import numpy as np
import os
import pandas as pd
import tensorflow as tf
def seed_everything(seed=0):
random.seed(seed)
np.random.... | code |
74058130/cell_10 | [
"text_html_output_1.png"
] | from kaggle_datasets import KaggleDatasets
import numpy as np
import os
import pandas as pd
import tensorflow as tf
def seed_everything(seed=0):
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
seed... | code |
72121714/cell_12 | [
"image_output_11.png",
"application_vnd.jupyter.stderr_output_27.png",
"application_vnd.jupyter.stderr_output_35.png",
"application_vnd.jupyter.stderr_output_24.png",
"image_output_24.png",
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_52.png",
"application_vnd.... | from torchvision.utils import make_grid
from tqdm import tqdm
import copy
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn as nn
import torch.nn.functional as F
import torchvision.datasets as datasets
import torchvision.transforms as transforms... | code |
34129243/cell_4 | [
"image_output_2.png",
"image_output_1.png"
] | df.hist(bins=20, figsize=(20, 15))
plt.show()
correlation_matrix = df.corr()
fig = plt.figure(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=0.8, square=True)
plt.show() | code |
34129243/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler,OneHotEncoder
fro... | code |
34129243/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TRAIN_DATA = '/kaggle/input/cais-exec-team-in-house/train.csv'
SUBMISSIONS_DATA = '/kaggle/input/cais-exec-team-in-house/sampleSubmission.csv'
TEST_DATA = '/kaggle/input/cais-exec-team-in-house/test.csv'
df = pd.read_csv(TRAIN_DATA, index_col='id')... | code |
34129243/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TRAIN_DATA = '/kaggle/input/cais-ex... | code |
34129243/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TRAIN_DATA = '/kaggle/input/cais-exec-team-in-house/train.csv'
SUBMISSIONS_DATA = '/kaggle/input/cais-exec-team-in-house/sampleSubmission.csv'
TEST_DATA = '/kaggle/input/cais-exec-team-in-house/test.csv'
df = pd.read_csv(TRAIN_DATA, index_col='id')... | code |
34129243/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
TRAIN_DATA = '/kaggle/input/cais-exec-team-in-house/train.csv'
SUBMISSIONS_DATA = '/kag... | code |
32062015/cell_4 | [
"text_html_output_1.png"
] | from multiprocessing.pool import ThreadPool
from pyearth import Earth
from sklearn.preprocessing import PolynomialFeatures
import gc
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import warnings
import pandas as pd
import numpy as np
import gc
import warnings
warnings.filterwarn... | code |
32062015/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import gc
import warnings
warnings.filterwarnings('ignore')
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
train.rename(columns={'Country_Region': 'Country', 'Province_State': 'State', 'ConfirmedCases': 'Confi... | code |
32062015/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import warnings
import pandas as pd
import numpy as np
import gc
import warnings
warnings.filterwarnings('ignore')
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
train.rename(columns={'Country_Region': '... | code |
105176374/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
trainData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TRAIN.csv', index_col=0)
testData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TEST-Without TARGET.csv', index_col=0)
trainData.isna().sum()
TrainCols = list(trainData.columns.valu... | code |
105176374/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
trainData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TRAIN.csv', index_col=0)
testData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TEST-Without TARGET.csv', index_col=0)
print(trainData.shape)
print(testData.shape) | code |
105176374/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
trainData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TRAIN.csv', index_col=0)
testData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TEST-Without TARGET.csv', index_col=0)
trainData.isna().sum() | code |
105176374/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
trainData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TRAIN.csv', index_col=0)
testData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TEST-Without TARGET.csv', index_col=0)
trainData.head() | code |
105176374/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
trainData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TRAIN.csv', index_col=0)
testData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TEST-Without TARGET.csv', index_col=0)
trainData.isna().sum()
trainData.describe() | code |
105176374/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
trainData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TRAIN.csv', index_col=0)
testData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TEST-Without TARGET.csv', index_col=0)
trainData.isna().sum()
TrainCols = list(trainData.columns.valu... | code |
105176374/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
trainData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TRAIN.csv', index_col=0)
testData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TEST-Without TARGET.csv', index_col=0)
testData.head() | code |
105176374/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
trainData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TRAIN.csv', index_col=0)
testData = pd.read_csv('../input/santander-dataset/Santander Customer Satisfaction - TEST-Without TARGET.csv', index_col=0)
trainData.info()
print()
testData.info() | code |
17122208/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/data.csv')
print(df.keys()) | code |
17122208/cell_23 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
17122208/cell_33 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
17122208/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import warnings
warnings.filterwarnings('ignore')
from matplotlib import pyplot as plt
import seaborn as sns
sns.set_style('darkgrid') | code |
17122208/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
17122208/cell_19 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
17122208/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/data.csv')
df.head() | code |
17122208/cell_28 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
17122208/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
17122208/cell_35 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
17122208/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
17122208/cell_27 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
17122208/cell_37 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
17122208/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/data.csv')
def make_histogram(column, bins=None, kde=False, norm_hist=False):
"""
This function returns a seaborn histogram based on an inputted dataset column.
:param column: column of dataset
:param bins: list of bin values of the... | code |
105205632/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly_express as px
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
fig_o = px.pie(names = well_prod.index , values = well_prod.values , labels ={"names":"Well ",... | code |
105205632/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly_express as px
import missingno as msn
plt.style.use('bmh')
df = pd.read_excel('../input/volve-production-data/Volve production data.xls... | code |
105205632/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.head() | code |
105205632/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
well_prod_g = df.groupby('NPD_WELL_BORE_NAME')['BORE_GAS_VOL'].sum()
well_prod_g
well_prod_w = df.groupby('NPD_WELL_BORE_N... | code |
105205632/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
well_prod_g = df.groupby('NPD_WELL_BORE_NAME')['BORE_GAS_VOL'].sum()
well_prod_g
well_prod_w = df.groupby('NPD_WELL_BORE_N... | code |
105205632/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
well_prod_g = df.groupby('NPD_WELL_BORE_NAME')['BORE_GAS_VOL'].sum()
well_prod_g
well_prod_w = df.groupby('NPD_WELL_BORE_N... | code |
105205632/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum() | code |
105205632/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
well_prod_g = df.groupby('NPD_WELL_BORE_NAME')['BORE_GAS_VOL'].sum()
well_prod_g
well_prod_w = df.groupby('NPD_WELL_BORE_N... | code |
105205632/cell_2 | [
"text_plain_output_1.png"
] | !pip install openpyxl | code |
105205632/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly_express as px
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
fig_o = px.pie(names=well_prod.index, values=well_prod.values, labels={'names': 'Well ', 'valu... | code |
105205632/cell_19 | [
"text_html_output_2.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
well_prod_g = df.groupby('NPD_WELL_BORE_NAME')['BORE_GAS_VOL'].sum()
well_prod_g
well_prod_w = df.groupby('NPD_WELL_BORE_N... | code |
105205632/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly_express as px
import missingno as msn
plt.style.use('bmh')
df = pd.read_excel('../input/volve-production-data/Volve production data.xls... | code |
105205632/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import plotly_express as px
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
fig_o = px.pie(names = well_prod.index , values = well_prod.values , labels ={"names":"Well ",... | code |
105205632/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
well_prod_g = df.groupby('NPD_WELL_BORE_NAME')['BORE_GAS_VOL'].sum()
well_prod_g
well_prod_w = df.groupby('NPD_WELL_BORE_N... | code |
105205632/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
well_prod_g = df.groupby('NPD_WELL_BORE_NAME')['BORE_GAS_VOL'].sum()
well_prod_g
well_prod_w = df.groupby('NPD_WELL_BORE_N... | code |
105205632/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
well_prod_g = df.groupby('NPD_WELL_BORE_NAME')['BORE_GAS_VOL'].sum()
well_prod_g
well_prod_w = df.groupby('NPD_WELL_BORE_N... | code |
105205632/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod | code |
105205632/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
df.isna().sum()
well_prod = df.groupby('NPD_WELL_BORE_NAME')['BORE_OIL_VOL'].sum()
well_prod
well_prod_g = df.groupby('NPD_WELL_BORE_NAME')['BORE_GAS_VOL'].sum()
well_prod_g | code |
105205632/cell_5 | [
"text_plain_output_1.png"
] | import missingno as msn
import pandas as pd
df = pd.read_excel('../input/volve-production-data/Volve production data.xlsx')
msn.matrix(df) | code |
90127845/cell_4 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.read_csv(os.path.j... | code |
90127845/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.r... | code |
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