path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
129012199/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
most_common_pub = df.groupby('Publisher').count().sort_values(by='Rank', ascending=False).head(1).index[0]
most_common_pub
... | code |
129012199/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 |
129012199/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
most_common_pub = df.groupby('Publisher').count().sort_values(by='Rank', ascending=False).head(1).index[0]
most_common_pub
... | code |
129012199/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
most_common_pub = df.groupby('Publisher').count().sort_values(by='Rank', ascending=False).head(1).index[0]
most_common_pub
... | code |
129012199/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df | code |
129012199/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
most_common_pub = df.groupby('Publisher').count().sort_values(by='Rank', ascending=False).head(1).index[0]
most_common_pub
... | code |
129012199/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
most_common_pub = df.groupby('Publisher').count().sort_values(by='Rank', ascending=False).head(1).index[0]
most_common_pub | code |
16115005/cell_13 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
fraud_data_path = '../input/credit_fraud_sytn.csv'
fraud_data = pd.read_csv(fraud_data_path)
fraud_data.columns
y = fraud_data.isFradulent
feature_names = ['Average Amount/transaction/day', 'Transaction_amount', 'isForeignTransac... | code |
16115005/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
fraud_data_path = '../input/credit_fraud_sytn.csv'
fraud_data = pd.read_csv(fraud_data_path)
fraud_data.columns | code |
16115005/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
fraud_data_path = '../input/credit_fraud_sytn.csv'
fraud_data = pd.read_csv(fraud_data_path)
fraud_data.columns
fraud_data.head() | code |
16115005/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
fraud_data_path = '../input/credit_fraud_sytn.csv'
fraud_data = pd.read_csv(fraud_data_path)
print('Setup Complete') | code |
16115005/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
fraud_data_path = '../input/credit_fraud_sytn.csv'
fraud_data = pd.read_csv(fraud_data_path)
fraud_data.columns
y = fraud_data.isFradulent
feature_names = ['Average Amount/transaction/day', 'Transaction_amount', 'isForeignTransaction', 'isHighRiskCountry']
X = fraud_data[featu... | code |
16115005/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
fraud_data_path = '../input/credit_fraud_sytn.csv'
fraud_data = pd.read_csv(fraud_data_path)
fraud_data.columns
y = fraud_data... | code |
16115005/cell_15 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
fraud_data_path = '../input/credit_fraud_sytn.csv'
fraud_data = pd.read_csv(fraud_data_path)
fraud_data.columns
y = fraud_data.isFradulent
feature_names = ['Average Amount/transaction/day', 'Transaction_amount', 'isForeignTransac... | code |
16115005/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
fraud_data_path = '../input/credit_fraud_sytn.csv'
fraud_data = pd.read_csv(fraud_data_path)
fraud_data.columns
y = fraud_data.isFradulent
feature_names = ['Average Amount/transact... | code |
16115005/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
fraud_data_path = '../input/credit_fraud_sytn.csv'
fraud_data = pd.read_csv(fraud_data_path)
fraud_data.columns
y = fraud_data.isFradulent
feature_names = ['Average Amount/transaction/day', 'Transaction_amount', 'isForeignTransaction', 'isHighRiskCountry']
X = fraud_data[featu... | code |
34133327/cell_42 | [
"text_plain_output_1.png"
] | from scipy import stats
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
impor... | code |
34133327/cell_21 | [
"text_html_output_1.png"
] | from scipy import stats
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
customers_null = custo... | code |
34133327/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
customers_null = customers
for col in customers... | code |
34133327/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
customers_null = customers
for col in customers... | code |
34133327/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.info() | code |
34133327/cell_33 | [
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
import seaborn as sns #biblioteca utilizada para cira... | code |
34133327/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum() | code |
34133327/cell_40 | [
"text_html_output_1.png"
] | from scipy import stats
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
impor... | code |
34133327/cell_29 | [
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
import seaborn as sns #biblioteca utilizada para cira... | code |
34133327/cell_26 | [
"text_html_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
import seaborn as sns #biblioteca utilizada para cira... | code |
34133327/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
customers_null = customers
for col in customers... | code |
34133327/cell_45 | [
"text_html_output_1.png"
] | from scipy import stats
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para ... | code |
34133327/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
boxplot = customers.boxplot(column=['Age', 'Annual Income (k$)', 'Spending Score (1-100)']) | code |
34133327/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
customers_null = customers
for col in customers... | code |
34133327/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
customers.describe() | code |
34133327/cell_38 | [
"text_html_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
import seaborn as sns #biblioteca utilizada para cira... | code |
34133327/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.head() | code |
34133327/cell_31 | [
"text_html_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
import seaborn as sns #biblioteca utilizada para cira... | code |
34133327/cell_46 | [
"text_html_output_1.png"
] | from scipy import stats
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para ... | code |
34133327/cell_24 | [
"text_html_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
import seaborn as sns #biblioteca utilizada para cira... | code |
34133327/cell_14 | [
"text_html_output_1.png"
] | import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
customers_null = customers
for col in customers... | code |
34133327/cell_22 | [
"text_html_output_1.png"
] | from scipy import stats
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
customers_null = custo... | code |
34133327/cell_10 | [
"text_html_output_1.png"
] | import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
customers_null = customers
for col in customers... | code |
34133327/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt #biblioteca para criar gráficos "comuns" ao estilo Matplot
import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
import seaborn as sns #biblioteca utilizada para cira... | code |
34133327/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np #biblioteca utilizada para trabalhar com vetores
import pandas as pd #biblioteca para trabalhar com dataframes (planilhas excel)
customers = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
customers.isnull().sum()
customers_null = customers
for col in customers... | code |
18102956/cell_13 | [
"text_plain_output_1.png"
] | from scipy.interpolate import griddata
from sklearn.metrics import mean_squared_error
from sklearn.multioutput import MultiOutputRegressor
from sklearn.neighbors import KNeighborsRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindf = pd.read_... | code |
18102956/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
from sklearn.multioutput import MultiOutputRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.neighbors import KNeighborsRegressor
import matplotlib.pyplot as plt | code |
18102956/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.interpolate import griddata
from sklearn.multioutput import MultiOutputRegressor
from sklearn.neighbors import KNeighborsRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindf = pd.read_csv('../input/train.csv')
testdf = pd.read_csv('... | code |
18102956/cell_3 | [
"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)
traindf = pd.read_csv('../input/train.csv')
testdf = pd.read_csv('../input/test.csv')
totalRows = traindf[' y'].max()
totalCols = traindf['x'].max()
print('room size ', totalRows, ' ', totalCols)
trainData = traindf.iloc[:, 2:]
trainlabels = traind... | code |
18102956/cell_14 | [
"text_plain_output_1.png"
] | from scipy.interpolate import griddata
from sklearn.metrics import mean_squared_error
from sklearn.multioutput import MultiOutputRegressor
from sklearn.neighbors import KNeighborsRegressor
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g.... | code |
121150304/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/playground-series-s3e8/test.csv')
train = pd.read_csv('/kaggle/input/playground-series-s3e8/train.csv')
sub = pd.read_csv('/kaggle/input/playground-series-s3e8/sample_submission.csv')
train.dtypes.value_counts().p... | code |
121150304/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/playground-series-s3e8/test.csv')
train = pd.read_csv('/kaggle/input/playground-series-s3e8/train.csv')
sub = pd.read_csv('/kaggle/input/playground-series-s3e8/sample_submission.csv')
train.info() | code |
121150304/cell_25 | [
"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
test = pd.read_csv('/kaggle/input/playground-series-s3e8/test.csv')
train = pd.read_csv('/kaggle/input/playground-series-s3e8/train.csv')
sub = pd.read_csv('/kaggle/input/playground-series-s3e... | code |
121150304/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import matplotlib.pyplot as plt
import time
import seaborn as sns
from sklearn import metrics
import os
from lightgbm import LGBMC... | code |
121150304/cell_23 | [
"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
test = pd.read_csv('/kaggle/input/playground-series-s3e8/test.csv')
train = pd.read_csv('/kaggle/input/playground-series-s3e8/train.csv')
sub = pd.read_csv('/kaggle/input/playground-series-s3e... | code |
121150304/cell_20 | [
"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
test = pd.read_csv('/kaggle/input/playground-series-s3e8/test.csv')
train = pd.read_csv('/kaggle/input/playground-series-s3e8/train.csv')
sub = pd.read_csv('/kaggle/input/playground-series-s3e... | code |
121150304/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/playground-series-s3e8/test.csv')
train = pd.read_csv('/kaggle/input/playground-series-s3e8/train.csv')
sub = pd.read_csv('/kaggle/input/playground-series-s3e8/sample_submission.csv')
test | code |
121150304/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)
import seaborn as sns
test = pd.read_csv('/kaggle/input/playground-series-s3e8/test.csv')
train = pd.read_csv('/kaggle/input/playground-series-s3e8/train.csv')
sub = pd.read_csv('/kaggle/input/playground-series-s3e... | code |
121150304/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/playground-series-s3e8/test.csv')
train = pd.read_csv('/kaggle/input/playground-series-s3e8/train.csv')
sub = pd.read_csv('/kaggle/input/playground-series-s3e8/sample_submission.csv')
train | code |
121150304/cell_16 | [
"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
test = pd.read_csv('/kaggle/input/playground-series-s3e8/test.csv')
train = pd.read_csv('/kaggle/input/playground-series-s3e8/train.csv')
sub = pd.read_csv('/kaggle/input/playground-series-s3e... | code |
121150304/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/playground-series-s3e8/test.csv')
train = pd.read_csv('/kaggle/input/playground-series-s3e8/train.csv')
sub = pd.read_csv('/kaggle/input/playground-series-s3e8/sample_submission.csv')
train.isnull().sum() | code |
122244134/cell_13 | [
"text_plain_output_1.png"
] | from pathlib import Path
from sentence_transformers import SentenceTransformer
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from tqdm.notebook import tqdm
import numpy as np
import pandas as pd
import timm
import torch
class CFG:
model_path = '/kaggle/input/stable-dif... | code |
122244134/cell_9 | [
"text_plain_output_1.png"
] | from pathlib import Path
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from tqdm.notebook import tqdm
import numpy as np
import pandas as pd
import timm
import torch
class CFG:
model_path = '/kaggle/input/stable-diffusion-vit-baseline-train/vit_base_patch16_224.pth'
... | code |
122244134/cell_23 | [
"text_html_output_1.png"
] | prompt_embeddings.shape | code |
122244134/cell_19 | [
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from scipy import spatial
from sentence_transformers import SentenceTransformer
from torch import nn
from torch.optim import Adam
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from tqdm.notebook import tqdm
import numpy as np
... | code |
122244134/cell_16 | [
"text_plain_output_1.png"
] | from pathlib import Path
from scipy import spatial
from sentence_transformers import SentenceTransformer
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from tqdm.notebook import tqdm
import numpy as np
import pandas as pd
import timm
import torch
class CFG:
model_path ... | code |
122244134/cell_17 | [
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from sentence_transformers import SentenceTransformer
from torch import nn
from torch.optim import Adam
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from tqdm.notebook import tqdm
import numpy as np
import pandas as pd
impor... | code |
122244134/cell_22 | [
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from sentence_transformers import SentenceTransformer
from torch import nn
from torch.optim import Adam
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from tqdm.notebook import tqdm
import numpy as np
import pandas as pd
impor... | code |
128022928/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/monthly-milk-production-pounds/monthlyMilkProduction.csv')
df.columns = ['Month', 'Production/Cow']
df = df[:-1]
df['Month'] = pd.to_datetime(df['Month'])
df.set_index('Month', inplace=True)
display(df.head())
display(df.tail()) | code |
128022928/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
iris = sns.load_dataset('iris')
g = sns.PairGrid(iris, hue='species')
g = g.map_diag(sns.histplot)
g = g.map_offdiag(sns.scatterplot)
g = g.add_legend() | code |
128022928/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
iris = sns.load_dataset('iris')
display(iris) | code |
128022928/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
sns.boxplot(data=titanic, x='class', y='fare') | code |
128022928/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
import pandas as pd
df = pd.read_csv('/kaggle/input/monthly-milk-production-pounds/monthlyMilkProduction.csv')
df.columns = ['Month', 'Production/Cow']
df = df[:-1]
df['Month'] = pd.to_datetime(df['Month'])
df.set_index('Month', inplace=True)
from statsmodels.t... | code |
128022928/cell_6 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
sns.lineplot(data=titanic, y='fare', x='embarked') | code |
128022928/cell_29 | [
"text_plain_output_1.png"
] | from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(train, order=(1, 1, 2))
model_fit = model.fit() | code |
128022928/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
iris = sns.load_dataset('iris')
sns.lmplot(data=iris, x='sepal_width', y='petal_width') | code |
128022928/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
sns.barplot(data=titanic, x='fare', y='embarked') | code |
128022928/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
corpus = ['I live in Mumbai', 'I like Mumbai', 'I dont like Pune']
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names_out())
print(X) | code |
128022928/cell_3 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
display(titanic) | code |
128022928/cell_17 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
corpus = ['I live in Mumbai', 'I like Mumbai', 'I dont like Pune']
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
corpus = ['you were born with potential', 'you were born with g... | code |
128022928/cell_31 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from math import sqrt
from sklearn.metrics import mean_squared_error
from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(train, order=(1, 1, 2))
model_fit = model.fit()
predictions = model_fit.forecast(steps=len(test))[0]
rmse = sqrt(mean_squared_error(test.values, predictions))
print(f'RMSE: {rmse}') | code |
128022928/cell_24 | [
"image_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
import pandas as pd
df = pd.read_csv('/kaggle/input/monthly-milk-production-pounds/monthlyMilkProduction.csv')
df.columns = ['Month', 'Production/Cow']
df = df[:-1]
df['Month'] = pd.to_datetime(df['Month'])
df.set_... | code |
128022928/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/monthly-milk-production-pounds/monthlyMilkProduction.csv')
df.columns = ['Month', 'Production/Cow']
df = df[:-1]
df['Month'] = pd.to_datetime(df['Month'])
df.set_index('Month', inplace=True)
df.plot() | code |
128022928/cell_10 | [
"text_html_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
iris = sns.load_dataset('iris')
sns.scatterplot(data=iris, x='sepal_length', y='petal_length').set_title('scatter plot for “sepal_length” and “petal_length”') | code |
128022928/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
iris = sns.load_dataset('iris')
sns.regplot(data=iris, x='petal_length', y='petal_width').set_title('reg plot between petal_width and petal_length') | code |
128022928/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
sns.violinplot(data=titanic, x='age', y='sex') | code |
90104745/cell_9 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/voicegender/voice.csv')
data | code |
90104745/cell_25 | [
"image_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/voicegender/voice.csv')
data.isnull().sum()
data.shape
new_data = data.drop(['sfm', 'kurt', 'meandom', 'meanfreq', 'dfrange', 'modindx'], axis=1)
new_data | code |
90104745/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | n_samples = 1000
n_features = 10
n_classes = 2
n_estimators = 25
max_depth = 10
model = cuRF(max_depth=max_depth, n_estimators=n_estimators, random_state=0)
trained_RF = model.fit(X_train, y_train) | code |
90104745/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
import cuml as np
import numpy as np # linear algebra
n_samples = 1000
n_features = 10
n_classes = 2
n_estimators = 25
max_depth = 10
model = cuRF(max_depth=max_depth, n_estimators=n_estimators, random_state=0)
trained_RF = model.... | code |
90104745/cell_20 | [
"text_plain_output_1.png"
] | !pip install mglearn | code |
90104745/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import mglearn
import pandas
import seaborn
import seaborn
import matplotlib.pyplot as plt
import pandas
df_pandas = pandas.read_csv('../input/voicegender/voice.csv')
# performing the visualization in pandas Data Frame
male = df_pandas.loc[df_pandas['label']=='male']
female = df_pa... | code |
90104745/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas
import seaborn
import seaborn
import matplotlib.pyplot as plt
import pandas
df_pandas = pandas.read_csv('../input/voicegender/voice.csv')
plt.figure(figsize=(21, 21))
seaborn.heatmap(df_pandas.corr(), annot=True, cmap='viridis', linewidth=0.5) | code |
90104745/cell_7 | [
"text_plain_output_1.png"
] | import cudf as pd
import cuml as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy
import seaborn as sns
import sklearn
import sys
import sys
import scipy
print('Environment specification:\n')
print('python', '%s.%s.%s' % sys.version_info[... | code |
90104745/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
import cuml as np
import numpy as np # linear algebra
n_samples = 1000
n_features = 10
n_classes = 2
n_estimators = 25
max_depth = 10
model = cuRF(max_depth=max_depth, n_estimators=n_estimators, random_state=0)
trained_RF = model.... | code |
90104745/cell_28 | [
"text_html_output_1.png"
] | from cuml.model_selection import train_test_split
from cuml.model_selection import train_test_split
from sklearn.model_selection import cross_val_score,train_test_split
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/voicegender/voice.csv')
dat... | code |
90104745/cell_15 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/voicegender/voice.csv')
data.isnull().sum()
data.shape | code |
90104745/cell_16 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/voicegender/voice.csv')
data.isnull().sum()
data.shape
print('Total number of labels: {}'.format(data.shape[0])) | code |
90104745/cell_17 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/voicegender/voice.csv')
data.isnull().sum()
data.shape
print('Number of male: {}'.format(data[data.label == 'male'].shape[0]))
print('Number of female: {}'.format(data[data.label == 'female'].shape... | code |
90104745/cell_31 | [
"text_plain_output_1.png"
] | n_samples = 1000
n_features = 10
n_classes = 2
n_estimators = 25
max_depth = 10
model = cuRF(max_depth=max_depth, n_estimators=n_estimators, random_state=0)
trained_RF = model.fit(X_train, y_train)
predictions = model.predict(X_test) | code |
90104745/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import mglearn
import pandas
import seaborn
import seaborn
import matplotlib.pyplot as plt
import pandas
df_pandas = pandas.read_csv('../input/voicegender/voice.csv')
# performing the visualization in pandas Data Frame
male = df_pandas.loc[df_pandas['label']=='male']
female = df_pa... | code |
90104745/cell_14 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/voicegender/voice.csv')
data.isnull().sum()
data.info() | code |
90104745/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import mglearn
import pandas
import seaborn
import seaborn
import matplotlib.pyplot as plt
import pandas
df_pandas = pandas.read_csv('../input/voicegender/voice.csv')
male = df_pandas.loc[df_pandas['label'] == 'male']
female = df_pandas.loc[df_pandas['label'] == 'female']
fig, axes ... | code |
90104745/cell_12 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/voicegender/voice.csv')
data.isnull().sum() | code |
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