path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
34121580/cell_19 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
df2 = pd.read_csv('../... | code |
34121580/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
x = df1['Active']
y = target['Deceased']
model = sm.OLS(y, x).fit()
predictions = model.predict(x)
model.summary() | code |
34121580/cell_28 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
df2 = pd.read_csv('../... | code |
34121580/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
labels = list(df1.Stat... | code |
34121580/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['D... | code |
34121580/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = ta... | code |
34121580/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['D... | code |
34121580/cell_12 | [
"text_html_output_1.png"
] | from pandas.plotting import andrews_curves
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
... | code |
128027656/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF... | code |
128027656/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF... | code |
128027656/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF... | code |
128027656/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df | code |
128027656/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF... | code |
128027656/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] =... | code |
128027656/cell_2 | [
"text_html_output_1.png"
] | pip install basemap | code |
128027656/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF... | code |
128027656/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF... | code |
128027656/cell_1 | [
"text_plain_output_1.png"
] | !pip install zipcodes | code |
128027656/cell_7 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum() | code |
128027656/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF... | code |
128027656/cell_3 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification
from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
128027656/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF... | code |
128027656/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF... | code |
128027656/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF... | code |
16118182/cell_21 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().... | code |
16118182/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
... | code |
16118182/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)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum() | code |
16118182/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape | code |
16118182/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
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
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()... | code |
16118182/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.info() | code |
16118182/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T | code |
16118182/cell_19 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().... | code |
16118182/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16118182/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T | code |
16118182/cell_18 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().... | code |
16118182/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T | code |
16118182/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
... | code |
16118182/cell_17 | [
"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)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().... | code |
16118182/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(... | code |
16118182/cell_22 | [
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().... | code |
16118182/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]]) | code |
16118182/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr | code |
16118182/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns | code |
122244202/cell_25 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import BatchNormalization, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
(X_train.shape, X_val.shape, y_train.shape, y_val.shape)
datagen = ImageDataGenerator(featurewise_c... | code |
122244202/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv')
submission = pd.read_csv('../input/digit-recognizer/sample_submission.csv')
train_data.head() | code |
122244202/cell_29 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import BatchNormalization, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('..... | code |
122244202/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 |
122244202/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv')
submission = pd.read_csv('../input/digit-recognizer/sample_submission.csv')
train_data.isna().any().describe() | code |
122244202/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv')
submission = pd.read_csv('../input/digit-recognizer/sample_submission.csv')
test_data.isna().any().describe() | code |
122244202/cell_16 | [
"text_html_output_1.png"
] | (X_train.shape, X_val.shape, y_train.shape, y_val.shape) | code |
122244202/cell_22 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import BatchNormalization, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.models import Sequential
model = Sequential([Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(28, 28, 1)), BatchNormalization(), Conv2D(32, (3, 3), padding='same', activation='... | code |
122244202/cell_27 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import BatchNormalization, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
(X_train.shape, X_val.shape, y_train.shape, y_val.shape)
datagen =... | code |
18132352/cell_9 | [
"text_plain_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import pandas as pd
import re
import pandas as pd
df = pd.read_csv('../input/b... | code |
18132352/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
import pandas as pd
import re
import pandas as pd
df = pd.read_csv('../input/bbc-text.csv')
import re
from nltk.stem.wordnet import WordNetLemmatizer
stop_words = ['in', 'of', 'at', 'a', 'the']
def pre_process(text):
text = str(text).lower()
text = re.sub('((\... | code |
18132352/cell_2 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/bbc-text.csv')
df.head(10) | code |
18132352/cell_11 | [
"text_html_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import re
im... | code |
18132352/cell_12 | [
"text_html_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
impor... | code |
18132352/cell_5 | [
"image_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
import matplotlib.pyplot as plt
import pandas as pd
import re
import pandas as pd
df = pd.read_csv('../input/bbc-text.csv')
import re
from nltk.stem.wordnet import WordNetLemmatizer
stop_words = ['in', 'of', 'at', 'a', 'the']
def pre_process(text):
text = str(tex... | code |
73074059/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_c... | code |
73074059/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_c... | code |
73074059/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
print(f'Número de Linhas e Colunas: {df.shape}')
df.head() | code |
73074059/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo'... | code |
73074059/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) *... | code |
73074059/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) *... | code |
73074059/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_c... | code |
73074059/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100 | code |
73074059/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_c... | code |
73074059/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_c... | code |
73074059/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_c... | code |
73074059/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) *... | code |
73074059/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_c... | code |
73074059/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo'... | code |
73074059/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) *... | code |
73074059/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.head() | code |
32068693/cell_9 | [
"text_html_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/... | code |
32068693/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.re... | code |
32068693/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
d... | code |
32068693/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
print(df.shape, '\n', df.head()) | code |
32068693/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/co... | code |
32068693/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting... | code |
32068693/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
import random
from sklearn.metrics import mean_squared_error
from sklearn import metrics
from sklearn.linear_model import ElasticNet
from sklearn.model_selection import RandomizedSearchCV
import pickle
from sklearn.model_selection import... | code |
32068693/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/co... | code |
32068693/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting... | code |
32068693/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.re... | code |
32068693/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.re... | code |
32068693/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
test.head() | code |
32068693/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting... | code |
32068693/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/... | code |
32068693/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.re... | code |
128018474/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
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/btcusd/data1.csv')
df['Datetime'] = [i for i in range(len(df['Datetime']))]
new_df = df[['Open', 'Volum... | code |
128018474/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/btcusd/data1.csv')
print(df.head(10))
df['Datetime'] = [i for i in range(len(df['Datetime']))]
new_df = df[['Open', 'Volume']]
sns.lmplot(data=new_df, x='Open', y='Volume', order=2, ci=None)
plt.show() | code |
128022704/cell_6 | [
"text_plain_output_1.png"
] | array_list = array('B')
for i in range(ELEMENTS_LIMIT):
array_list.append(i) | code |
128022704/cell_11 | [
"text_plain_output_1.png"
] | data = array_list[:]
for i in range(ELEMENTS_LIMIT - 1):
_ = data.pop() | code |
128022704/cell_1 | [
"text_plain_output_1.png"
] | !python --version | code |
128022704/cell_7 | [
"text_plain_output_1.png"
] | deque_list = deque()
for i in range(ELEMENTS_LIMIT):
deque_list.append(i) | code |
128022704/cell_8 | [
"text_plain_output_1.png"
] | from array import array
from collections import deque
from sys import getsizeof
ELEMENTS_LIMIT = 2 ** 8 - 1
def fill_and_print_details(x):
for i in range(ELEMENTS_LIMIT):
x.append(i)
usual_list = []
array_list = array('B')
deque_list = deque()
fill_and_print_details(usual_list)
fill_and_print_details(ar... | code |
128022704/cell_15 | [
"text_plain_output_1.png"
] | data = array_list[:]
for i in range(ELEMENTS_LIMIT - 1):
_ = data.pop(0) | code |
128022704/cell_16 | [
"text_plain_output_1.png"
] | data = deque_list.copy()
for i in range(ELEMENTS_LIMIT - 1):
_ = data.popleft() | code |
128022704/cell_14 | [
"text_plain_output_1.png"
] | data = usual_list.copy()
for i in range(ELEMENTS_LIMIT - 1):
_ = data.pop(0) | code |
128022704/cell_10 | [
"text_plain_output_1.png"
] | data = usual_list.copy()
for i in range(ELEMENTS_LIMIT - 1):
_ = data.pop() | code |
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