path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
33096822/cell_17 | [
"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)
type(avo['Date'].iloc[0])
avo['Date'] = pd.to_datetime(avo['Date'])
type(avo['Date'].iloc[0])
avo['Month'] = avo['Date'].apply(lambda x: x.month)
avo.groupby('Month').mean()['AveragePrice'].sort_values(ascending=Fa... | code |
33096822/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
avo['Month'] = avo['Date'].apply(lambda x: x.month)
avo.groupby('Month').mean()['AveragePrice'].sort_values(ascending=False).index[0]
avo.drop(avo.loc[avo['region'] == 'TotalUS'].index, inplace=True)
avo.groupby('region').sum()['Total Volume']
avo.groupby('region').sum()['Total Volume... | code |
33096822/cell_12 | [
"text_html_output_1.png"
] | avo['Month'] = avo['Date'].apply(lambda x: x.month)
avo.groupby('Month').mean()['AveragePrice'].sort_values(ascending=False).index[0]
avo.drop(avo.loc[avo['region'] == 'TotalUS'].index, inplace=True)
avo.groupby('region').sum()['Total Volume']
avo.groupby('region').sum()['Total Volume'].idxmax() | code |
33096822/cell_5 | [
"text_plain_output_1.png"
] | import seaborn as sns
sns.pairplot(avo) | code |
72065541/cell_13 | [
"text_html_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if train[col].isnull().sum() != 0] | code |
72065541/cell_4 | [
"image_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
print('The shape of the dataset is {}.\n\n'.format(train.shape))
train.head(10) | code |
72065541/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if trai... | code |
72065541/cell_20 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
test = pd.read_csv(os.path.join(dataset_path, 'test.csv'))
[col for col in train.columns if train[col].isnull().... | code |
72065541/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.describe() | code |
72065541/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if trai... | code |
72065541/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
test = pd.read_csv(os.path.join(dataset_path, 'test.csv'))
print('The shape of the dataset is {}.\n\n'.format(test.shape))
test.head(5) | code |
72065541/cell_19 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().su... | code |
72065541/cell_7 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.info() | code |
72065541/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
test = pd.read_csv(os.path.join(dataset_path, 'test.csv'))
[col for col in test.columns if test[col].isnull().sum() != 0] | code |
72065541/cell_17 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
test = pd.read_csv(os.path.join(dataset_path, 'test.csv'))
[col for col in train.columns if train[col].isnull().... | code |
72065541/cell_14 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().su... | code |
72065541/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if trai... | code |
72065541/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if trai... | code |
72065541/cell_36 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
test = pd.read_csv(os.path.join(dataset_path, 'test.csv'))
[col for col in train.columns if train[col].isnull().... | code |
128044716/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d | code |
128044716/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, ... | code |
128044716/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import numpy as np
import time
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = ... | code |
128044716/cell_30 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, ... | code |
128044716/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c | code |
128044716/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, ... | code |
128044716/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, ... | code |
128044716/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2) | code |
128044716/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b | code |
128044716/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a | code |
128044716/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, ... | code |
128044716/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a | code |
128044716/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape | code |
128044716/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim | code |
128044716/cell_35 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import numpy as np
import time
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = ... | code |
128044716/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, ... | code |
128044716/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, ... | code |
128044716/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a | code |
128044716/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c) | code |
128044716/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
c | code |
128044716/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, ... | code |
128044716/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a.shape | code |
128020226/cell_13 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import display
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import os
import pandas as pd
import torch
import torch.nn as nn
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path... | code |
128020226/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from IPython.display import display
import os
import pandas as pd
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH, TEST_FILE), index_col=0)
target... | code |
128020226/cell_6 | [
"text_plain_output_1.png"
] | from IPython.display import display
import matplotlib.pyplot as plt
import os
import pandas as pd
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH... | code |
128020226/cell_11 | [
"text_plain_output_1.png"
] | from IPython.display import display
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import os
import pandas as pd
import torch
import torch.nn as nn
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path... | code |
128020226/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import torch
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | code |
128020226/cell_3 | [
"image_output_1.png"
] | import os
import pandas as pd
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH, TEST_FILE), index_col=0)
target = 'yield'
train_df.info() | code |
128020226/cell_14 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import display
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import os
import pandas as pd
import torch
import torch.nn as nn
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path... | code |
128020226/cell_5 | [
"text_plain_output_1.png"
] | from IPython.display import display
import os
import pandas as pd
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH, TEST_FILE), index_col=0)
target... | code |
33120852/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_d... | code |
33120852/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.head() | code |
33120852/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum() | code |
33120852/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_d... | code |
33120852/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_d... | code |
33120852/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 |
33120852/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_d... | code |
33120852/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_d... | code |
33120852/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)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_d... | code |
33120852/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)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_d... | code |
33120852/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_d... | code |
33120852/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_d... | code |
33120852/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_d... | code |
33120852/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape | code |
16144913/cell_3 | [
"text_plain_output_1.png"
] | !ls ../input | code |
333538/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', encoding='utf-8', low_memory=False)
data['Age'].dropna().describe() | code |
333538/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
333538/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', encoding='utf-8', low_memory=False)
bins = np.arange(data['Income'].dropna().min(), data['... | code |
333538/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', encoding='utf-8', low_memory=False)
data['Income'].dropna().describe() | code |
333538/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', encoding='utf-8', low_memory=False)
bins = np.arange(data['Income'].dropna().min(), data['... | code |
73061409/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape | code |
73061409/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
na... | code |
73061409/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
fig = go.Bar(x=names, y=values... | code |
73061409/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
f... | code |
73061409/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
na... | code |
73061409/cell_14 | [
"text_html_output_2.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
f... | code |
73061409/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
f... | code |
73061409/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
f... | code |
73061409/cell_5 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.head() | code |
88092308/cell_21 | [
"text_plain_output_1.png"
] | from implicit.evaluation import mean_average_precision_at_k
from scipy.sparse import coo_matrix
import implicit
import numpy as np
import pandas as pd
transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
all_customers = customers['customer_id'].unique().tolist()
all_articles = arti... | code |
88092308/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
transactions['t_dat'].max() | code |
88092308/cell_4 | [
"text_plain_output_1.png"
] | transactions = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv', dtype={'article_id': str}, parse_dates=['t_dat'])
sample_submission = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
customers = pd.read_csv('../input/h-and-m-personaliz... | code |
88092308/cell_6 | [
"text_plain_output_1.png"
] | customers | code |
88092308/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | # Installing latest implicit library for ALS
!pip install --upgrade implicit | code |
88092308/cell_11 | [
"text_plain_output_1.png"
] | from scipy.sparse import coo_matrix
import numpy as np
transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
all_customers = customers['customer_id'].unique().tolist()
all_articles = articles['article_id'].unique().tolist()
customer_ids = dict(list(enumerate(all_customers)))
article_id... | code |
88092308/cell_19 | [
"text_plain_output_1.png"
] | from implicit.evaluation import mean_average_precision_at_k
from scipy.sparse import coo_matrix
import implicit
import numpy as np
import pandas as pd
transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
all_customers = customers['customer_id'].unique().tolist()
all_articles = arti... | code |
88092308/cell_18 | [
"text_plain_output_1.png"
] | from implicit.evaluation import mean_average_precision_at_k
from scipy.sparse import coo_matrix
import implicit
import numpy as np
import pandas as pd
transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
all_customers = customers['customer_id'].unique().tolist()
all_articles = arti... | code |
88092308/cell_8 | [
"text_plain_output_1.png"
] | transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape | code |
88092308/cell_15 | [
"text_html_output_1.png"
] | best_map12 = 0
for factors in [40, 50, 60, 100, 200, 500, 1000]:
for iterations in [3, 12, 14, 15, 20]:
for regularization in [0.01]:
map12 = validate(matrices, factors, iterations, regularization, show_progress=False)
if map12 > best_map12:
best_map12 = map12
... | code |
88092308/cell_16 | [
"text_html_output_1.png"
] | best_params | code |
88092308/cell_22 | [
"text_plain_output_1.png"
] | df_preds = submit(model, csr_train, transactions_customers, heng_df) | code |
88092308/cell_10 | [
"text_plain_output_1.png"
] | transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
all_customers = customers['customer_id'].unique().tolist()
all_articles = articles['article_id'].unique().tolist()
customer_ids = dict(list(enumerate(all_customers)))
article_ids = dict(list(enumerate(all_articles)))
transactions['cus... | code |
88092308/cell_12 | [
"text_html_output_1.png"
] | model = implicit.als.AlternatingLeastSquares(factors=10, iterations=2, use_gpu=True, calculate_training_loss=True, random_state=7)
model.fit(coo_train) | code |
88092308/cell_5 | [
"text_plain_output_1.png"
] | articles | code |
104121949/cell_21 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
age2 = {'a': 3, 'b': 6, 'c': 9}
age.update(age2)
age | code |
104121949/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | marks = {'Rahul': 23, 'Joe': 15, 'Venkat': {'Section1': 12, 'Section2': 15, 'Section3': 22}}
marks | code |
104121949/cell_25 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
age2 = {'a': 3, 'b': 6, 'c': 9}
age.update(age2)
age.pop('c')
age.clear()
age | code |
104121949/cell_4 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
age | code |
104121949/cell_23 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
age2 = {'a': 3, 'b': 6, 'c': 9}
age.update(age2)
age.pop('c')
age | code |
104121949/cell_2 | [
"text_plain_output_1.png"
] | age = {}
type(age) | code |
104121949/cell_11 | [
"text_plain_output_1.png"
] | marks = {'Rahul': 23, 'Joe': 15, 'Venkat': {'Section1': 12, 'Section2': 15, 'Section3': 22}}
marks.keys() | code |
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