path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
104131002/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("###########... | code |
104131002/cell_3 | [
"text_html_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 |
104131002/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("###########... | code |
104131002/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("###########... | code |
104131002/cell_22 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seabo... | code |
104131002/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
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("###########... | code |
104131002/cell_27 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import sea... | code |
104131002/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("###########... | code |
18115772/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
df_train.drop_duplicates(keep='first', subset=['date_time'], inplace=True)
df_train.shape
df_train.drop(['date_time'], inplace=True, axis=1)
df_train.info() | code |
18115772/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
df_train.describe() | code |
18115772/cell_11 | [
"text_html_output_1.png"
] | from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import m | code |
18115772/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18115772/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
df_train.drop_duplicates(keep='first', subset=['date_time'], inplace=True)
df_train.shape
df_train.drop(['date_time'], inplace=True, axis=1)
df_train.head(5) | code |
18115772/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
print('The shape of the train dataset is' + str(df_train.shape))
print('The shape of the test dataset is' + str(df_test.shape)) | code |
18115772/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
df_train.drop_duplicates(keep='first', subset=['date_time'], inplace=True)
df_train.shape
df_train.drop(['date_time'], inplace=True, axis=1)
df_train.head(10) | code |
18115772/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
df_test.describe() | code |
74049349/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
print(f'Train Shape: {train_df.shape}')
print(f'Test Shape : {test_df.shape}') | code |
74049349/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.... | code |
74049349/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.... | code |
74049349/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
train_df.describe().transpose() | code |
74049349/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.... | code |
74049349/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
train_df.head() | code |
74049349/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
plt.style.available | code |
74049349/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.... | code |
74049349/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
train_df.info() | code |
74049349/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
test_df.info() | code |
74049349/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.... | code |
74049349/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
sns.countplot('Survived', data=... | code |
74049349/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
features.head() | code |
74049349/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.... | code |
74049349/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.... | code |
2006313/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_o... | code |
2006313/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = ... | code |
2006313/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
print(price.head()) | code |
2006313/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
cols_with_missing | code |
2006313/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_of_interest]
two_columns_of_data.describe() | code |
2006313/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
cols_with_missing
df = pd.read_csv(main_file_path)
target = df.SalePrice
predictors = df.drop(['SalePrice'], a... | code |
2006313/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
print(df.describe()) | code |
2006313/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_of_interest]
y = price
columns_of_interest = ['L... | code |
2006313/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomF... | code |
2006313/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import Imputer
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'... | code |
2006313/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
print(df.columns) | code |
2006313/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, pre... | code |
2006313/cell_14 | [
"text_html_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
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle... | code |
2006313/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_of_interest]
y = price
columns_of_interest = ['L... | code |
2006313/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import Imputer
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'... | code |
2006313/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics... | code |
2006313/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_s... | code |
128018979/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.s... | code |
128018979/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.seed(0)
x = np.random.rand(100, 1)
y = 2 + 3 * x + np.random.rand(100, 1)
model = LinearRegression()
mode... | code |
128018979/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.s... | code |
128018979/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
import n... | code |
128048760/cell_9 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptograph... | code |
128048760/cell_4 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text ... | code |
128048760/cell_6 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptograph... | code |
128048760/cell_11 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptograph... | code |
128048760/cell_19 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
import torch
import torch
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')... | code |
128048760/cell_18 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
import torch
import torch
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')... | code |
128048760/cell_15 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
import torch
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i lo... | code |
128048760/cell_3 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased') | code |
128048760/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptograph... | code |
128048760/cell_12 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptograph... | code |
128048760/cell_5 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
from transformers import AutoTokenizer, GPT2Model... | code |
89141968/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique(... | code |
89141968/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How likely do you feel yourself vulnerable or lonely?'].mode() | code |
89141968/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How often do you get offended or angry or start crying ?'].mode() | code |
89141968/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How comfortable are you in talking about your mental health?'].mode() | code |
89141968/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['Have you taken any therapy or medication in the near past for mental health?'].mode() | code |
89141968/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data | code |
89141968/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique(... | code |
89141968/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['Do you feel bad about yourself — or that you are a failure or have let yourself or your family down?'].mode() | code |
89141968/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique(... | code |
89141968/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum() | code |
89141968/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique(... | code |
89141968/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How likely do you feel yourself vulnerable or lonely?'].mode() | code |
89141968/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How many hours do you spend per day on watching mobile phone, laptop, computer, television, etc.?'].mode() | code |
89141968/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean() | code |
89141968/cell_49 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique(... | code |
89141968/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique(... | code |
89141968/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How comfortable are you in talking about your mental health?'].unique() | code |
89141968/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['Has the COVID-19 pandemic affected your mental well being?'].mode() | code |
89141968/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique(... | code |
89141968/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['If sad, how likely are you to take an appointment with a psychologist or a counsellor for your current mental state?'].mode() | code |
89141968/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique(... | code |
89141968/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique(... | code |
89141968/cell_53 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique(... | code |
89141968/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['(If sad)have you been in the same mental state for the past few days?'].mode() | code |
17117770/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
model = SVC(gamma='auto')
features = list(train.columns)
features.remove('label')
model.fit(train_df[features], train_df['label'])
model.score(valid_df[features], valid_df[... | code |
17117770/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17117770/cell_10 | [
"text_html_output_1.png"
] | from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
model = SVC(gamma='auto')
features = list(train.columns)
features.remove('label')
model.fit(train_df[features], train_df['label']) | code |
17117770/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
train.head() | code |
34121580/cell_21 | [
"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']
df2 = pd.read_csv('../... | code |
34121580/cell_25 | [
"text_html_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_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
print(df1) | code |
34121580/cell_23 | [
"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']
df2 = pd.read_csv('../... | code |
34121580/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
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', 'De... | code |
34121580/cell_20 | [
"text_html_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_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
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', 'De... | code |
34121580/cell_26 | [
"text_plain_output_1.png",
"image_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_11 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
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()
ta... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.