path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
121154415/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
import matplotlib.pyplot as plt
from copy import deepcopy
import warnings
warnings.filterwarnings('ignore')
import random
random.seed(42)
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
pr... | code |
121154415/cell_32 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
... | code |
121154415/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
121154415/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
121154415/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
... | code |
121154415/cell_35 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
... | code |
121154415/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
121154415/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
121154415/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
121154415/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
... | code |
121154415/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
... | code |
121154415/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
121154415/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
122248445/cell_13 | [
"text_plain_output_1.png"
] | !pip install scikit-learn | code |
122248445/cell_9 | [
"text_plain_output_1.png"
] | import os
import shutil
MAIN_DIR = '/kaggle/input/bach-breast-cancer-histology-images/ICIAR2018_BACH_Challenge/ICIAR2018_BACH_Challenge/Photos'
BASE_DIR = '/kaggle/working/bach-train'
TRAIN_DIR = os.path.join(BASE_DIR, 'training')
VAL_DIR = os.path.join(BASE_DIR, 'validation')
TEST_DIR = os.path.join(BASE_DIR, 'test... | code |
122248445/cell_4 | [
"text_plain_output_1.png"
] | import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Device:', tpu.master())
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
except:
strategy = tf.distri... | code |
122248445/cell_2 | [
"text_html_output_1.png"
] | !apt-get update && apt-get install -y python3-opencv
!pip install opencv-python
!pip install seaborn
import cv2
import seaborn as sns | code |
122248445/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/init-bach-eda/bach_data.csv', index_col=0)
df.head() | code |
122248445/cell_15 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import shutil
MAIN_DIR = '/kaggle/input/bach-breast-cancer-histology-images/ICIAR2018_BACH_Challenge/ICIAR2018_BACH_Challenge/Photos'
BASE_DIR = '/kaggle/working/bach-train'
TRAIN_DIR = os.path.join(BASE_DIR, 'training')
VAL_DIR = os.path.join(BASE_DIR, 'validation')
TEST_DIR = os.path... | code |
122248445/cell_3 | [
"text_plain_output_1.png"
] | import tensorflow as tf
from tensorflow.keras.preprocessing.image import img_to_array, ImageDataGenerator, load_img
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D, Dropout, Flatten, BatchNormalization
from tensorflow.keras.activations import softmax
from t... | code |
122248445/cell_17 | [
"text_html_output_1.png"
] | print('Training size: ', len(x_train), len(y_train))
print('Val size: ', len(x_val), len(y_val))
print('Test size: ', len(x_test), len(y_test)) | code |
122248445/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
import shutil
MAIN_DIR = '/kaggle/input/bach-breast-cancer-histology-images/ICIAR2018_BACH_Challenge/ICIAR2018_BACH_Challenge/Photos'
BASE_DIR = '/kaggle/working/bach-train'
TRAIN_DIR = os.path.join(BASE_DIR, 'training')
VAL_DIR = os.path.join(BASE_DIR, 'validation')
TEST_DIR = os.path... | code |
122254156/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('nba_games.csv', index_col=0)
df | code |
89138263/cell_20 | [
"text_plain_output_1.png"
] | 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)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detec... | code |
89138263/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv... | code |
89138263/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv... | code |
89138263/cell_2 | [
"text_plain_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, cross_validate
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, VotingClassifier
from sklearn.preprocessing imp... | code |
89138263/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 |
89138263/cell_47 | [
"text_plain_output_1.png"
] | 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)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detec... | code |
89138263/cell_46 | [
"text_plain_output_1.png"
] | 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)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detec... | code |
89138263/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv... | code |
2033671/cell_4 | [
"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'
data = pd.read_csv(main_file_path)
home_sale_price = data.SalePrice
column_interest = ['BsmtFinSF1', 'SalePrice']
two_column_data = data[column_... | code |
2033671/cell_6 | [
"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 |
2033671/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
print(data.columns)
home_sale_price = data.SalePrice
print(home_sale_price.head())
column_interest = ['BsmtFinSF1', 'SalePrice']
two_column_data = data[column_interest]
two_column_data.describe() | code |
2033671/cell_7 | [
"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 |
2033671/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
home_sale_price = data.SalePrice
column_interest = ['BsmtFinSF1', 'SalePrice']
two_column_data = data[column_interest]
y = data.SalePrice
Cost_predictors = [... | code |
2033671/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
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'
data = pd.read_csv(main_file_path)
home_sale_price = data.SalePrice
column_interest = ['B... | code |
16134883/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
print('Showing Meta Data :')
data.info() | code |
16134883/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.... | code |
16134883/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape | code |
16134883/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
... | code |
16134883/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'o... | code |
16134883/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.... | code |
16134883/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
type(data) | code |
16134883/cell_29 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'o... | code |
16134883/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
dataset.corr() | code |
16134883/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts() | code |
16134883/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
dataset.head() | code |
16134883/cell_32 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
pd.isnull(data).sum()
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if... | code |
16134883/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
print('Descriptive Statastics of our Data:')
data.describe().T | code |
16134883/cell_38 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
... | code |
16134883/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import seaborn as sns
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
16134883/cell_31 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'o... | code |
16134883/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
... | code |
16134883/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
dataset.head() | code |
16134883/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
pd.isnull(data).sum() | code |
16134883/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.... | code |
16134883/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
pd.isnull(data).sum()
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if... | code |
16134883/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts() | code |
16134883/cell_5 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"... | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.head() | code |
16134883/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
pd.isnull(data).sum()
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if... | code |
2011240/cell_42 | [
"text_plain_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, ... | code |
2011240/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='person', aspect=4)
fig.map(sns.kd... | code |
2011240/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
def split(passenger):
age, sex = passenger
if age < 16:
return 'child'
else:
return sex
titanic['person'] = titanic[['Age', 'Sex']].apply(split, axis=1)
titanic[0:10] | code |
2011240/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
sns.factorplot('Sex', data=titanic, hue='Pclass', kind='count') | code |
2011240/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
deck = titanic['Cabin'].dropna()
deck.head() | code |
2011240/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.info() | code |
2011240/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, ... | code |
2011240/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.head() | code |
2011240/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, ... | code |
2011240/cell_33 | [
"text_plain_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, ... | code |
2011240/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic['Survivor'] = titanic.Survived.map({0: 'No', 1: 'Yes'})
titanic.head() | code |
2011240/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='person', aspect=4)
fig.map(sns.kd... | code |
2011240/cell_40 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic['Alone'].loc[titanic['Alone'] > 0] = 'With Family'
titanic['Alone'].loc[titanic['Alone'] == 0] = 'Alone' | code |
2011240/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic['Alone'] | code |
2011240/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic.head() | code |
2011240/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend() | code |
2011240/cell_45 | [
"text_html_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, ... | code |
2011240/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['person'].value_counts() | code |
2011240/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.head() | code |
2011240/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
sns.factorplot('Sex', data=titanic, kind='count', color='red') | code |
2011240/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Age'].hist(bins=70, color='blue') | code |
2011240/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
titanic = pd.read_csv('../input/train.csv')
plt.hist(titanic['Age'].dropna(), bins=70) | code |
2011240/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic.head() | code |
2011240/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.head() | code |
2011240/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Age'].mean() | code |
2011240/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
sns.factorplot('Pclass', hue='person', data=titanic, kind='count') | code |
2011240/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
sns.factorplot('Pclass', data=titanic, hue='Sex', kind='count') | code |
2011240/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, ... | code |
2011240/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.describe() | code |
2011240/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.head() | code |
128024882/cell_13 | [
"text_plain_output_1.png"
] | from statsmodels.sandbox.regression import gmm
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('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
instrument_df = data[['cons_lagged1', 'interest_lagged_1', 'lagged_cons_growth']]
end... | code |
128024882/cell_9 | [
"text_plain_output_1.png"
] | from statsmodels.sandbox.regression import gmm
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('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
instrument_df = data[['cons_lagged1', 'interest_lagged_1', 'lagged_cons_growth']]
end... | code |
128024882/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
data.head() | code |
128024882/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 |
128024882/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
data.columns | code |
128024882/cell_10 | [
"text_html_output_1.png"
] | from statsmodels.sandbox.regression import gmm
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('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
instrument_df = data[['cons_lagged1', 'interest_lagged_1', 'lagged_cons_growth']]
end... | code |
128024882/cell_12 | [
"text_plain_output_1.png"
] | from statsmodels.sandbox.regression import gmm
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('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
instrument_df = data[['cons_lagged1', 'interest_lagged_1', 'lagged_cons_growth']]
end... | code |
74061207/cell_9 | [
"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/sales-store-product-details/Salesstore.csv')
df.isna().sum()
plot_1=sns.histplot(data=df, x='Ship_Mode')
plt.show()
plot_2=sns.histplot(data=df, x='Order_Priority')
plt.show()
plot_3=sns.histplot(data=df, x='C... | code |
74061207/cell_4 | [
"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
df = pd.read_csv('/kaggle/input/sales-store-product-details/Salesstore.csv')
df.isna().sum() | code |
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