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stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
106198328/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.info() | code |
106198328/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.isna().mean()
round(stock_data.isna().mean().sum(), 2)
stock_data.isna().sum().sum()
stock_data.isnull().sum()
perc = 1
min_count = int((100 - perc) / 100 * stock_data.shape[0] + 1)
stock_data = stock_data.dropna(axis=1, thresh=min_count)
stock_data.isna().mean().sum()
for col in stock_data:
stock_data.loc[stock_data[col].isnull() == True, col] = stock_data[col].mean()
stock_data.isnull().sum() | code |
106198328/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.isna().mean()
round(stock_data.isna().mean().sum(), 2)
stock_data.isna().sum().sum()
stock_data.isnull().sum()
perc = 1
min_count = int((100 - perc) / 100 * stock_data.shape[0] + 1)
stock_data = stock_data.dropna(axis=1, thresh=min_count)
stock_data.isna().mean().sum() | code |
106198328/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.isna().mean()
round(stock_data.isna().mean().sum(), 2) | code |
106198328/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data.isna().mean()
round(stock_data.isna().mean().sum(), 2)
stock_data.isna().sum().sum()
stock_data.isnull().sum() | code |
106198328/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
stock_data = pd.read_csv('../input/ibovespa-index/ibovespa_indexq.csv')
stock_data | code |
130014120/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
ds_df = pd.read_csv(train_file_path)
print('Full train dataset shape is {}'.format(ds_df.shape))
print('Dataset head:')
ds_df.head(10) | code |
130014120/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
ds_df = pd.read_csv(train_file_path)
(ds_df['SalePrice'].max() - ds_df['SalePrice'].min()) / ds_df.shape[0] | code |
130014120/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
ds_df = pd.read_csv(train_file_path)
(ds_df['SalePrice'].max() - ds_df['SalePrice'].min()) / ds_df.shape[0]
sns.kdeplot(data=ds_df['SalePrice']) | code |
130014120/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
ds_df = pd.read_csv(train_file_path)
(ds_df['SalePrice'].max() - ds_df['SalePrice'].min()) / ds_df.shape[0]
sns.kdeplot(np.log10(ds_df['SalePrice'])) | code |
130014120/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import math
train_file_path = '../input/house-prices-advanced-regression-techniques/train.csv'
test_file_path = '../input/house-prices-advanced-regression-techniques/test.csv'
print('Done') | code |
130014120/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
ds_df = pd.read_csv(train_file_path)
ds_df['SalePrice'].isna().sum() | code |
122249621/cell_9 | [
"text_plain_output_1.png"
] | ! pip install google-colab | code |
122249621/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/data/BBox_List_2017.csv')
df_train = df_train.rename(columns={'Image Index': 'filename', 'Bbox [x': 'x', 'h]': 'h', 'Finding Label': 'class'})
df_train = df_train.drop(['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'], axis=1)
df_train.head() | code |
122249621/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/data/BBox_List_2017.csv')
df_train.head() | code |
122249621/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/data/BBox_List_2017.csv')
df_train = df_train.rename(columns={'Image Index': 'filename', 'Bbox [x': 'x', 'h]': 'h', 'Finding Label': 'class'})
df_train = df_train.drop(['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'], axis=1)
df_train['bbox'] = df_train[['x', 'y', 'w', 'h']].apply(list, axis=1)
df_train = df_train.drop(columns=['x', 'y', 'w', 'h']).groupby('filename', as_index=False).agg(list)
df_train.head(1) | code |
122249621/cell_3 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/data/BBox_List_2017.csv')
print(f"количество изображений {df_train['Image Index'].nunique()}")
df_train.head(1) | code |
106192159/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt | code |
106192159/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt[['Pid1_', 'Pid2_']] = tt['PassengerId'].str.split('_', expand=True).astype('int')
tt | code |
106192159/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test | code |
106192159/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt
categorical = tt.select_dtypes(['object', 'category']).columns.to_list()
categorical | code |
106192159/cell_30 | [
"text_html_output_1.png"
] | from feature_engine.imputation import MeanMedianImputer
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt
categorical = tt.select_dtypes(['object', 'category']).columns.to_list()
categorical
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
from feature_engine.imputation import MeanMedianImputer
median_imputer = MeanMedianImputer(imputation_method='median', variables=numerical)
median_imputer.fit(tt[numerical]) | code |
106192159/cell_33 | [
"image_output_1.png"
] | from feature_engine.imputation import MeanMedianImputer
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt
categorical = tt.select_dtypes(['object', 'category']).columns.to_list()
categorical
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
from feature_engine.imputation import MeanMedianImputer
median_imputer = MeanMedianImputer(imputation_method='median', variables=numerical)
median_imputer.fit(tt[numerical])
tt_num = median_imputer.transform(tt[numerical])
tt_num
from feature_engine import transformation as vt
power_tf = vt.YeoJohnsonTransformer(variables=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'sum_exp_', 'mean_exp_'])
power_tf.fit(tt_num)
tt_num = power_tf.transform(tt_num)
tt_num | code |
106192159/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100 | code |
106192159/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_train.isnull().sum()
df_train.isnull().mean() * 100 | code |
106192159/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt
categorical = tt.select_dtypes(['object', 'category']).columns.to_list()
categorical
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
columns=['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'sum_exp_', 'mean_exp_']
q, r = divmod(len(columns), 2)
fig, ax=plt.subplots(q, 2, figsize=(18,10))
for i in range(0,len(columns)):
q, r =divmod(i, 2)
sns.kdeplot(data=tt[numerical], x=columns[i], ax=ax[q, r])
plt.show()
columns = ['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'sum_exp_', 'mean_exp_']
q, r = divmod(len(columns), 2)
fig, ax = plt.subplots(q, 2, figsize=(18, 10))
for i in range(0, len(columns)):
q, r = divmod(i, 2)
sns.boxplot(data=tt[numerical], x=columns[i], ax=ax[q, r])
plt.show() | code |
106192159/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt
categorical = tt.select_dtypes(['object', 'category']).columns.to_list()
categorical
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt[numerical] | code |
106192159/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
for i in tt.columns:
print('{} ------------------------------------> {}'.format(i, tt[i].nunique())) | code |
106192159/cell_1 | [
"text_plain_output_1.png"
] | !pip install feature_engine | code |
106192159/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_test.isnull().mean() * 100 | code |
106192159/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical | code |
106192159/cell_32 | [
"image_output_1.png"
] | from feature_engine.imputation import MeanMedianImputer
import matplotlib.pyplot as plt
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt
categorical = tt.select_dtypes(['object', 'category']).columns.to_list()
categorical
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
columns=['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'sum_exp_', 'mean_exp_']
q, r = divmod(len(columns), 2)
fig, ax=plt.subplots(q, 2, figsize=(18,10))
for i in range(0,len(columns)):
q, r =divmod(i, 2)
sns.kdeplot(data=tt[numerical], x=columns[i], ax=ax[q, r])
plt.show()
columns=['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'sum_exp_', 'mean_exp_']
q, r =divmod(len(columns), 2)
fig, ax=plt.subplots(q, 2, figsize=(18,10))
for i in range(0,len(columns)):
q, r =divmod(i, 2)
sns.boxplot(data=tt[numerical], x=columns[i], ax=ax[q, r])
plt.show()
from feature_engine.imputation import MeanMedianImputer
median_imputer = MeanMedianImputer(imputation_method='median', variables=numerical)
median_imputer.fit(tt[numerical])
tt_num = median_imputer.transform(tt[numerical])
tt_num
columns = ['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'sum_exp_', 'mean_exp_']
q, r = divmod(len(columns), 2)
fig, ax = plt.subplots(q, 2, figsize=(18, 10))
for i in range(0, len(columns)):
q, r = divmod(i, 2)
sns.kdeplot(data=tt, x=columns[i], ax=ax[q, r])
sns.kdeplot(data=tt_num, x=columns[i], ax=ax[q, r], color='red')
plt.show() | code |
106192159/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt
categorical = tt.select_dtypes(['object', 'category']).columns.to_list()
categorical
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
columns = ['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'sum_exp_', 'mean_exp_']
q, r = divmod(len(columns), 2)
fig, ax = plt.subplots(q, 2, figsize=(18, 10))
for i in range(0, len(columns)):
q, r = divmod(i, 2)
sns.kdeplot(data=tt[numerical], x=columns[i], ax=ax[q, r])
plt.show() | code |
106192159/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['sum_exp_'] = tt['RoomService'] + tt['FoodCourt'] + tt['ShoppingMall'] + tt['Spa'] + tt['VRDeck']
tt | code |
106192159/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['mean_exp_'] = tt['sum_exp_'] / tt['Pid2_']
tt | code |
106192159/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train | code |
106192159/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_'] | code |
106192159/cell_31 | [
"text_plain_output_1.png"
] | from feature_engine.imputation import MeanMedianImputer
import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt
categorical = tt.select_dtypes(['object', 'category']).columns.to_list()
categorical
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
from feature_engine.imputation import MeanMedianImputer
median_imputer = MeanMedianImputer(imputation_method='median', variables=numerical)
median_imputer.fit(tt[numerical])
tt_num = median_imputer.transform(tt[numerical])
tt_num | code |
106192159/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt
categorical = tt.select_dtypes(['object', 'category']).columns.to_list()
categorical
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical | code |
106192159/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt[['Fname_', 'Lname_']] = tt['Name'].str.split(' ', expand=True)
tt | code |
106192159/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt | code |
106192159/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt['Age_cat_'] = pd.cut(tt.Age, bins=[0, 5, 12, 18, 50, 150], labels=['Toddler/Baby', 'Child', 'Teen', 'Adult', 'Elderly'])
tt['Age_cat_']
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt.isnull().mean() * 100
tt = tt.set_index('PassengerId')
tt
categorical = tt.select_dtypes(['object', 'category']).columns.to_list()
categorical
numerical = tt.select_dtypes(exclude=['object', 'category']).columns.to_list()
numerical
tt[numerical].columns | code |
106192159/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_test = pd.read_csv('../input/spaceship-titanic/test.csv')
df_test
df_train.isnull().sum()
df_train.isnull().mean() * 100
df_test.isnull().mean() * 100
tt = pd.concat([df_train.drop('Transported', axis=1), df_test])
tt
tt[['Cabin1_', 'Cabin2_', 'Cabin3_']] = tt['Cabin'].str.split('/', expand=True)
tt | code |
106192159/cell_5 | [
"image_output_1.png"
] | import pandas as pd # tt processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/spaceship-titanic/train.csv')
df_train
df_train.isnull().sum() | code |
88102916/cell_21 | [
"text_plain_output_1.png"
] | import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
dataset.plot(kind='box', subplots=True, layout=(3, 3), figsize=(15, 8))
plt.show() | code |
88102916/cell_13 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes | code |
88102916/cell_9 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape | code |
88102916/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
import seaborn as sns
corr = dataset.corr()
dataset.head() | code |
88102916/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import Normalizer
import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
import seaborn as sns
corr = dataset.corr()
X = dataset.drop('Outcome', axis=1).values
y = dataset['Outcome'].values
from sklearn.preprocessing import Normalizer
print(Normalizer().fit_transform(X)[0]) | code |
88102916/cell_30 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
import seaborn as sns
corr = dataset.corr()
X = dataset.drop('Outcome', axis=1).values
y = dataset['Outcome'].values
mms = MinMaxScaler(feature_range=(0, 1))
mms.fit(X)
X_scaled = mms.transform(X)
print(X[1])
print(X_scaled[0]) | code |
88102916/cell_20 | [
"text_html_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
dataset.plot(kind='kde', subplots=True, layout=(3, 3), figsize=(15, 8)) | code |
88102916/cell_40 | [
"text_plain_output_1.png"
] | import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
import seaborn as sns
corr = dataset.corr()
X = dataset.drop('Outcome', axis=1).values
y = dataset['Outcome'].values
dataset.head() | code |
88102916/cell_29 | [
"image_output_1.png"
] | import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
import seaborn as sns
corr = dataset.corr()
X = dataset.drop('Outcome', axis=1).values
y = dataset['Outcome'].values
X[0] | code |
88102916/cell_26 | [
"image_output_1.png"
] | import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
import seaborn as sns
corr = dataset.corr()
X = dataset.drop('Outcome', axis=1).values
y = dataset['Outcome'].values
print(X.shape)
print(y.shape) | code |
88102916/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, chi2
import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
raw = open(path, 'rt', encoding='utf8')
header = raw.readline()
data = np.loadtxt(raw, delimiter=',')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
import seaborn as sns
corr = dataset.corr()
X = dataset.drop('Outcome', axis=1).values
y = dataset['Outcome'].values
from sklearn.feature_selection import SelectKBest, chi2
names = dataset.columns[:-1]
skb = SelectKBest(score_func=chi2, k=5)
skb.fit(X, y)
argsort = np.argsort(skb.scores_)[::-1]
print(skb.scores_)
print(names[argsort][:5]) | code |
88102916/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import csv
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88102916/cell_19 | [
"text_html_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
dataset.hist(figsize=(15, 8)) | code |
88102916/cell_7 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
raw = open(path, 'rt', encoding='utf8')
header = raw.readline()
data = np.loadtxt(raw, delimiter=',')
print(data.shape)
print(header) | code |
88102916/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
import seaborn as sns
corr = dataset.corr()
X = dataset.drop('Outcome', axis=1).values
y = dataset['Outcome'].values
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
ss.fit(X)
X_ss = ss.transform(X)
print(X[0])
print(X_ss[0]) | code |
88102916/cell_15 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset['Outcome'].value_counts() | code |
88102916/cell_16 | [
"text_html_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr() | code |
88102916/cell_17 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew() | code |
88102916/cell_14 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.describe() | code |
88102916/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
import seaborn as sns
corr = dataset.corr()
plt.figure(figsize=(8, 8))
sns.heatmap(corr, xticklabels=dataset.columns, yticklabels=dataset.columns)
plt.show() | code |
88102916/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import Binarizer
import csv
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.dtypes
dataset.corr()
dataset.skew()
import matplotlib.pyplot as plt
import seaborn as sns
corr = dataset.corr()
X = dataset.drop('Outcome', axis=1).values
y = dataset['Outcome'].values
from sklearn.preprocessing import Binarizer
print(Binarizer().fit_transform(X)[0]) | code |
88102916/cell_12 | [
"text_plain_output_1.png"
] | import csv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')
dataset = pd.read_csv(path)
dataset.shape
dataset.head(10) | code |
88102916/cell_5 | [
"text_html_output_1.png"
] | import csv
import numpy as np # linear algebra
path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
print(header)
data = list(reader)
data = np.array(data).astype('float')
print(data[0])
print(data.shape) | code |
121152041/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_2022.dtypes
data_2022.corr().style.background_gradient(cmap='coolwarm', axis=None)
data_2022.head(5) | code |
121152041/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes | code |
121152041/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape | code |
121152041/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.head(10) | code |
121152041/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_2022.dtypes | code |
121152041/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 |
121152041/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull() | code |
121152041/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_2022.dtypes
data_2022.corr().style.background_gradient(cmap='coolwarm', axis=None)
columns_to_drop = ['Whisker-high', 'Whisker-low', 'Dystopia (1.83) + residual', 'Explained by: Generosity', 'Explained by: Perceptions of corruption']
data_2022.drop(columns_to_drop, axis='columns', inplace=True)
column_rename = {'Explained by: GDP per capita': 'GDP_per_capita', 'Explained by: Social support': 'Social_support', 'Explained by: Healthy life expectancy': 'life_expectancy', 'Explained by: Freedom to make life choices': 'Freedom', 'Explained by: Generosity': 'Generosity', 'Happiness score': 'Happiness_score_2022'}
data_2022.rename(columns=column_rename, inplace=True)
data_2022
data_2022.sort_values(by='RANK', inplace=True)
plt.figure(figsize=(11, 9))
sns.barplot(y=data_2022['Country'][:10], x=df_2022['Happiness_score_2022'][:10], palette='inferno')
plt.show() | code |
121152041/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
print(data_2022.shape)
data_2022.isnull().sum() | code |
121152041/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_2022.dtypes
data_2022.corr().style.background_gradient(cmap='coolwarm', axis=None)
columns_to_drop = ['Whisker-high', 'Whisker-low', 'Dystopia (1.83) + residual', 'Explained by: Generosity', 'Explained by: Perceptions of corruption']
data_2022.drop(columns_to_drop, axis='columns', inplace=True)
column_rename = {'Explained by: GDP per capita': 'GDP_per_capita', 'Explained by: Social support': 'Social_support', 'Explained by: Healthy life expectancy': 'life_expectancy', 'Explained by: Freedom to make life choices': 'Freedom', 'Explained by: Generosity': 'Generosity', 'Happiness score': 'Happiness_score_2022'}
data_2022.rename(columns=column_rename, inplace=True)
data_2022 | code |
121152041/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_2022.dtypes
data_2022.corr().style.background_gradient(cmap='coolwarm', axis=None)
columns_to_drop = ['Whisker-high', 'Whisker-low', 'Dystopia (1.83) + residual', 'Explained by: Generosity', 'Explained by: Perceptions of corruption']
data_2022.drop(columns_to_drop, axis='columns', inplace=True)
column_rename = {'Explained by: GDP per capita': 'GDP_per_capita', 'Explained by: Social support': 'Social_support', 'Explained by: Healthy life expectancy': 'life_expectancy', 'Explained by: Freedom to make life choices': 'Freedom', 'Explained by: Generosity': 'Generosity', 'Happiness score': 'Happiness_score_2022'}
data_2022.rename(columns=column_rename, inplace=True)
data_2022
data_2022.sort_values(by='RANK', inplace=True)
data_2022.head(10) | code |
121152041/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_2022.dtypes
data_2022.corr().style.background_gradient(cmap='coolwarm', axis=None)
columns_to_drop = ['Whisker-high', 'Whisker-low', 'Dystopia (1.83) + residual', 'Explained by: Generosity', 'Explained by: Perceptions of corruption']
data_2022.drop(columns_to_drop, axis='columns', inplace=True)
column_rename = {'Explained by: GDP per capita': 'GDP_per_capita', 'Explained by: Social support': 'Social_support', 'Explained by: Healthy life expectancy': 'life_expectancy', 'Explained by: Freedom to make life choices': 'Freedom', 'Explained by: Generosity': 'Generosity', 'Happiness score': 'Happiness_score_2022'}
data_2022.rename(columns=column_rename, inplace=True)
data_2022
data_2022.sort_values(by='RANK', inplace=True)
data_2022.head(5) | code |
121152041/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_2022.dtypes
data_2022.corr().style.background_gradient(cmap='coolwarm', axis=None)
columns_to_drop = ['Whisker-high', 'Whisker-low', 'Dystopia (1.83) + residual', 'Explained by: Generosity', 'Explained by: Perceptions of corruption']
data_2022.drop(columns_to_drop, axis='columns', inplace=True)
data_2022.head(5) | code |
121152041/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath_1 = '/kaggle/input/world-happiness-report-2022/World Happiness Report 2022.csv'
data_2022 = pd.read_csv(filepath_1)
data_2022.shape
data_2022.notnull()
data_2022.isnull().sum()
data_2022.dtypes
data_2022 = data_2022.round(2)
data_2022.dtypes
data_2022.corr().style.background_gradient(cmap='coolwarm', axis=None) | code |
1004254/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import ggplot
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
menu = pd.read_csv('../input/menu.csv')
menu.head(5)
df = menu | code |
1004254/cell_8 | [
"text_html_output_1.png"
] | df.sort_values(by='Protein', ascending=False).head(10) | code |
1004254/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | print(menu.describe()) | code |
1004254/cell_12 | [
"text_plain_output_1.png"
] | df.sort_values(by='Protein', ascending=False).head(10)
df.sort_values(by='Protein/Sugar', ascending=False).head(10) | code |
1004254/cell_5 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.pivot_table(df, index=['Category'], values=['Protein'], aggfunc=np.max).plot(kind='bar') | code |
106199411/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import StandardScaler, MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competitive-data-science-predict-future-sales/'
files = ['sales_train.csv', 'items.csv', 'shops.csv', 'item_categories.csv', 'test.csv']
sales_train, items, shops, item_categories, test = read_data(path, files)
def remove_outliers(df: pd.DataFrame, max_price: int, max_cnt: int):
df = df[df['item_price'] > 0]
df = df[df['item_price'] < max_price]
df = df[df['item_cnt_day'] > 0]
df = df[df['item_cnt_day'] < max_cnt]
return df
sales_train = remove_outliers(sales_train, 50000, 1000)
def remove_shop_duplicates(df_train: pd.DataFrame, df_test: pd.DataFrame, shop_dups: dict):
for shop1, shop2 in shop_dups.items():
df_train.loc[df_train['shop_id'] == shop1, 'shop_id'] = shop2
df_test.loc[df_test['shop_id'] == shop1, 'shop_id'] = shop2
shop_dups = {0: 57, 1: 58, 10: 11, 39: 40}
remove_shop_duplicates(sales_train, test, shop_dups)
shps = list(sales_train.shop_id.unique())
shps.sort()
fig = plt.figure(figsize=(20, 50))
for i in range(len(shps)):
plt.subplot(12, 5, i + 1)
plt.plot(sales_train[sales_train.shop_id == shps[i]].groupby(['date_block_num'])['item_cnt_day'].sum())
plt.title('Shop {} sales'.format(shps[i]))
shops_cluster = []
for shop in shps:
temp = [shop]
for month in range(34):
temp += [sales_train[(sales_train.shop_id == shop) & (sales_train.date_block_num == month)]['item_cnt_day'].sum()]
shops_cluster.append(np.array(temp))
shops_cluster = pd.DataFrame(np.vstack(shops_cluster), columns=['shop'] + ['{}'.format(i) for i in range(34)])
shops_cluster
cat_sales = sales_train.merge(items[['item_id', 'item_category_id']], on='item_id', how='left')
fc = FeatureClustering('item_category_id', cat_sales, 34)
fc.plot_graphs(cat_sales)
fc.create_data(cat_sales, StandardScaler())
fc.show_metrics()
fc.plot_centres(4)
fc.plot_clusters() | code |
106199411/cell_9 | [
"image_output_1.png"
] | from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from tslearn.clustering import TimeSeriesKMeans
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competitive-data-science-predict-future-sales/'
files = ['sales_train.csv', 'items.csv', 'shops.csv', 'item_categories.csv', 'test.csv']
sales_train, items, shops, item_categories, test = read_data(path, files)
def remove_outliers(df: pd.DataFrame, max_price: int, max_cnt: int):
df = df[df['item_price'] > 0]
df = df[df['item_price'] < max_price]
df = df[df['item_cnt_day'] > 0]
df = df[df['item_cnt_day'] < max_cnt]
return df
sales_train = remove_outliers(sales_train, 50000, 1000)
def remove_shop_duplicates(df_train: pd.DataFrame, df_test: pd.DataFrame, shop_dups: dict):
for shop1, shop2 in shop_dups.items():
df_train.loc[df_train['shop_id'] == shop1, 'shop_id'] = shop2
df_test.loc[df_test['shop_id'] == shop1, 'shop_id'] = shop2
shop_dups = {0: 57, 1: 58, 10: 11, 39: 40}
remove_shop_duplicates(sales_train, test, shop_dups)
shps = list(sales_train.shop_id.unique())
shps.sort()
fig = plt.figure(figsize=(20, 50))
for i in range(len(shps)):
plt.subplot(12, 5, i + 1)
plt.plot(sales_train[sales_train.shop_id == shps[i]].groupby(['date_block_num'])['item_cnt_day'].sum())
plt.title('Shop {} sales'.format(shps[i]))
shops_cluster = []
for shop in shps:
temp = [shop]
for month in range(34):
temp += [sales_train[(sales_train.shop_id == shop) & (sales_train.date_block_num == month)]['item_cnt_day'].sum()]
shops_cluster.append(np.array(temp))
shops_cluster = pd.DataFrame(np.vstack(shops_cluster), columns=['shop'] + ['{}'.format(i) for i in range(34)])
shops_cluster
scaler = StandardScaler()
shops_scaled = scaler.fit_transform(shops_cluster.iloc[:, 1:].T).T
distortions = []
silhouette = []
K = range(1, 10)
for k in K:
kmeanModel = TimeSeriesKMeans(n_clusters=k, metric='dtw', n_jobs=6, max_iter=10)
kmeanModel.fit(shops_scaled)
distortions.append(kmeanModel.inertia_)
if k > 1:
silhouette.append(silhouette_score(shops_scaled, kmeanModel.labels_))
n_clusters = 4
ts_kmeans_dtw = TimeSeriesKMeans(n_clusters=n_clusters, metric='dtw', n_jobs=6, max_iter=10)
ts_kmeans_dtw.fit(shops_scaled)
plt.figure(figsize=(12, 8))
for cluster_number in range(n_clusters):
plt.plot(ts_kmeans_dtw.cluster_centers_[cluster_number, :, 0].T, label=cluster_number)
plt.title('Cluster centroids')
plt.legend()
plt.show() | code |
106199411/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competitive-data-science-predict-future-sales/'
files = ['sales_train.csv', 'items.csv', 'shops.csv', 'item_categories.csv', 'test.csv']
sales_train, items, shops, item_categories, test = read_data(path, files)
def remove_outliers(df: pd.DataFrame, max_price: int, max_cnt: int):
df = df[df['item_price'] > 0]
df = df[df['item_price'] < max_price]
df = df[df['item_cnt_day'] > 0]
df = df[df['item_cnt_day'] < max_cnt]
return df
sales_train = remove_outliers(sales_train, 50000, 1000)
def remove_shop_duplicates(df_train: pd.DataFrame, df_test: pd.DataFrame, shop_dups: dict):
for shop1, shop2 in shop_dups.items():
df_train.loc[df_train['shop_id'] == shop1, 'shop_id'] = shop2
df_test.loc[df_test['shop_id'] == shop1, 'shop_id'] = shop2
shop_dups = {0: 57, 1: 58, 10: 11, 39: 40}
remove_shop_duplicates(sales_train, test, shop_dups)
shps = list(sales_train.shop_id.unique())
shps.sort()
fig = plt.figure(figsize=(20, 50))
for i in range(len(shps)):
plt.subplot(12, 5, i + 1)
plt.plot(sales_train[sales_train.shop_id == shps[i]].groupby(['date_block_num'])['item_cnt_day'].sum())
plt.title('Shop {} sales'.format(shps[i]))
shops_cluster = []
for shop in shps:
temp = [shop]
for month in range(34):
temp += [sales_train[(sales_train.shop_id == shop) & (sales_train.date_block_num == month)]['item_cnt_day'].sum()]
shops_cluster.append(np.array(temp))
shops_cluster = pd.DataFrame(np.vstack(shops_cluster), columns=['shop'] + ['{}'.format(i) for i in range(34)])
shops_cluster | code |
106199411/cell_2 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competitive-data-science-predict-future-sales/'
files = ['sales_train.csv', 'items.csv', 'shops.csv', 'item_categories.csv', 'test.csv']
sales_train, items, shops, item_categories, test = read_data(path, files)
sales_train.head() | code |
106199411/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from tslearn.clustering import TimeSeriesKMeans
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competitive-data-science-predict-future-sales/'
files = ['sales_train.csv', 'items.csv', 'shops.csv', 'item_categories.csv', 'test.csv']
sales_train, items, shops, item_categories, test = read_data(path, files)
def remove_outliers(df: pd.DataFrame, max_price: int, max_cnt: int):
df = df[df['item_price'] > 0]
df = df[df['item_price'] < max_price]
df = df[df['item_cnt_day'] > 0]
df = df[df['item_cnt_day'] < max_cnt]
return df
sales_train = remove_outliers(sales_train, 50000, 1000)
def remove_shop_duplicates(df_train: pd.DataFrame, df_test: pd.DataFrame, shop_dups: dict):
for shop1, shop2 in shop_dups.items():
df_train.loc[df_train['shop_id'] == shop1, 'shop_id'] = shop2
df_test.loc[df_test['shop_id'] == shop1, 'shop_id'] = shop2
shop_dups = {0: 57, 1: 58, 10: 11, 39: 40}
remove_shop_duplicates(sales_train, test, shop_dups)
shps = list(sales_train.shop_id.unique())
shps.sort()
fig = plt.figure(figsize=(20, 50))
for i in range(len(shps)):
plt.subplot(12, 5, i + 1)
plt.plot(sales_train[sales_train.shop_id == shps[i]].groupby(['date_block_num'])['item_cnt_day'].sum())
plt.title('Shop {} sales'.format(shps[i]))
shops_cluster = []
for shop in shps:
temp = [shop]
for month in range(34):
temp += [sales_train[(sales_train.shop_id == shop) & (sales_train.date_block_num == month)]['item_cnt_day'].sum()]
shops_cluster.append(np.array(temp))
shops_cluster = pd.DataFrame(np.vstack(shops_cluster), columns=['shop'] + ['{}'.format(i) for i in range(34)])
shops_cluster
scaler = StandardScaler()
shops_scaled = scaler.fit_transform(shops_cluster.iloc[:, 1:].T).T
distortions = []
silhouette = []
K = range(1, 10)
for k in K:
kmeanModel = TimeSeriesKMeans(n_clusters=k, metric='dtw', n_jobs=6, max_iter=10)
kmeanModel.fit(shops_scaled)
distortions.append(kmeanModel.inertia_)
if k > 1:
silhouette.append(silhouette_score(shops_scaled, kmeanModel.labels_))
plt.figure(figsize=(10, 4))
plt.plot(K, distortions, 'bx-')
plt.xlabel('k')
plt.ylabel('Distortion')
plt.title('Elbow Method')
plt.show()
plt.figure(figsize=(10, 4))
plt.plot(K[1:], silhouette, 'bx-')
plt.xlabel('k')
plt.ylabel('Silhouette score')
plt.title('Silhouette')
plt.show() | code |
106199411/cell_10 | [
"text_html_output_1.png"
] | from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from tslearn.clustering import TimeSeriesKMeans
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competitive-data-science-predict-future-sales/'
files = ['sales_train.csv', 'items.csv', 'shops.csv', 'item_categories.csv', 'test.csv']
sales_train, items, shops, item_categories, test = read_data(path, files)
def remove_outliers(df: pd.DataFrame, max_price: int, max_cnt: int):
df = df[df['item_price'] > 0]
df = df[df['item_price'] < max_price]
df = df[df['item_cnt_day'] > 0]
df = df[df['item_cnt_day'] < max_cnt]
return df
sales_train = remove_outliers(sales_train, 50000, 1000)
def remove_shop_duplicates(df_train: pd.DataFrame, df_test: pd.DataFrame, shop_dups: dict):
for shop1, shop2 in shop_dups.items():
df_train.loc[df_train['shop_id'] == shop1, 'shop_id'] = shop2
df_test.loc[df_test['shop_id'] == shop1, 'shop_id'] = shop2
shop_dups = {0: 57, 1: 58, 10: 11, 39: 40}
remove_shop_duplicates(sales_train, test, shop_dups)
shps = list(sales_train.shop_id.unique())
shps.sort()
fig = plt.figure(figsize=(20, 50))
for i in range(len(shps)):
plt.subplot(12, 5, i + 1)
plt.plot(sales_train[sales_train.shop_id == shps[i]].groupby(['date_block_num'])['item_cnt_day'].sum())
plt.title('Shop {} sales'.format(shps[i]))
shops_cluster = []
for shop in shps:
temp = [shop]
for month in range(34):
temp += [sales_train[(sales_train.shop_id == shop) & (sales_train.date_block_num == month)]['item_cnt_day'].sum()]
shops_cluster.append(np.array(temp))
shops_cluster = pd.DataFrame(np.vstack(shops_cluster), columns=['shop'] + ['{}'.format(i) for i in range(34)])
shops_cluster
scaler = StandardScaler()
shops_scaled = scaler.fit_transform(shops_cluster.iloc[:, 1:].T).T
distortions = []
silhouette = []
K = range(1, 10)
for k in K:
kmeanModel = TimeSeriesKMeans(n_clusters=k, metric='dtw', n_jobs=6, max_iter=10)
kmeanModel.fit(shops_scaled)
distortions.append(kmeanModel.inertia_)
if k > 1:
silhouette.append(silhouette_score(shops_scaled, kmeanModel.labels_))
n_clusters = 4
ts_kmeans_dtw = TimeSeriesKMeans(n_clusters=n_clusters, metric='dtw', n_jobs=6, max_iter=10)
ts_kmeans_dtw.fit(shops_scaled)
shops_cluster['cluster'] = ts_kmeans_dtw.predict(shops_scaled)
for cluster in range(n_clusters):
print('=================================================================================')
print(f' Cluster number: {cluster}')
print('=================================================================================')
plot_cluster_shops(shops_cluster[shops_cluster.cluster == cluster]) | code |
106199411/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
def read_data(path: str, files: list):
dataframes = []
for file in files:
dataframes.append(pd.read_csv(path + file))
return dataframes
path = '../input/competitive-data-science-predict-future-sales/'
files = ['sales_train.csv', 'items.csv', 'shops.csv', 'item_categories.csv', 'test.csv']
sales_train, items, shops, item_categories, test = read_data(path, files)
def remove_outliers(df: pd.DataFrame, max_price: int, max_cnt: int):
df = df[df['item_price'] > 0]
df = df[df['item_price'] < max_price]
df = df[df['item_cnt_day'] > 0]
df = df[df['item_cnt_day'] < max_cnt]
return df
sales_train = remove_outliers(sales_train, 50000, 1000)
shps = list(sales_train.shop_id.unique())
shps.sort()
fig = plt.figure(figsize=(20, 50))
for i in range(len(shps)):
plt.subplot(12, 5, i + 1)
plt.plot(sales_train[sales_train.shop_id == shps[i]].groupby(['date_block_num'])['item_cnt_day'].sum())
plt.title('Shop {} sales'.format(shps[i])) | code |
88102789/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='alive', hue='embark_town', palette='deep') | code |
88102789/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='alive', hue='class', palette='deep') | code |
88102789/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.jointplot(data=df, x='age', y='fare', kind='scatter', color='c') | code |
88102789/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
df.info() | code |
88102789/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='class', hue='sex') | code |
88102789/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='embark_town', palette='Set2') | code |
88102789/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.kdeplot(df['age'], shade=True, color='m') | code |
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