path
stringlengths 13
17
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sequencelengths 1
873
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stringlengths 0
40.4k
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stringclasses 1
value |
|---|---|---|---|
1005893/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
import csv
import tflearn
import tensorflow as tf
from keras.utils.np_utils import to_categorical | code |
1005893/cell_5 | [
"text_plain_output_1.png"
] | from keras.utils.np_utils import to_categorical
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tflearn
df_trn = pd.read_csv('../input/train.csv')
df_tst = pd.read_csv('../input/test.csv')
x_trn = df_trn.ix[:, 1:].values
y_trn = df_trn.ix[:, 0].values
y_trn_cat = to_categorical(y_trn)
tf.reset_default_graph()
net = tflearn.input_data([None, 784])
net = tflearn.fully_connected(net, 256, activation='ReLU')
net = tflearn.fully_connected(net, 128, activation='ReLU')
net = tflearn.fully_connected(net, 64, activation='ReLU')
net = tflearn.fully_connected(net, 10, activation='softmax')
net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1, loss='categorical_crossentropy')
model = tflearn.DNN(net)
model.fit(x_trn, y_trn_cat, validation_set=0, show_metric=True, batch_size=1000, n_epoch=100) | code |
326660/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
train_types = {'Agencia_ID': np.uint16, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
test_types = {'Agencia_ID': np.uint16, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'id': np.uint32}
df_train = pd.read_csv('../input/train.csv', usecols=train_types.keys(), dtype=train_types)
df_test = pd.read_csv('../input/test.csv', usecols=test_types.keys(), dtype=test_types)
df_client = pd.read_csv('../input/cliente_tabla.csv')
df_product = pd.read_csv('../input/producto_tabla.csv')
df_town = pd.read_csv('../input/town_state.csv')
print('Train Data\n', df_train.head(1), '\n')
print('Test Data\n', df_test.head(1), '\n')
print('Client Data\n', df_client.head(1), '\n')
print('Product Data\n', df_product.head(1), '\n')
print('Town Data\n', df_town.head(1), '\n') | code |
326660/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
train_types = {'Agencia_ID': np.uint16, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
test_types = {'Agencia_ID': np.uint16, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'id': np.uint32}
df_train = pd.read_csv('../input/train.csv', usecols=train_types.keys(), dtype=train_types)
df_test = pd.read_csv('../input/test.csv', usecols=test_types.keys(), dtype=test_types)
df_client = pd.read_csv('../input/cliente_tabla.csv')
df_product = pd.read_csv('../input/producto_tabla.csv')
df_town = pd.read_csv('../input/town_state.csv')
sns.distplot(np.log1p(df_train['Demanda_uni_equil']), kde=False) | code |
326660/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
train_types = {'Agencia_ID': np.uint16, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
test_types = {'Agencia_ID': np.uint16, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'id': np.uint32}
df_train = pd.read_csv('../input/train.csv', usecols=train_types.keys(), dtype=train_types)
df_test = pd.read_csv('../input/test.csv', usecols=test_types.keys(), dtype=test_types)
df_client = pd.read_csv('../input/cliente_tabla.csv')
df_product = pd.read_csv('../input/producto_tabla.csv')
df_town = pd.read_csv('../input/town_state.csv')
agencies_subset = np.zeros(len(df_train))
for i in range(4):
this_agency = df_train['Agencia_ID'].unique()[i]
agencies_subset += df_train['Agencia_ID'] == this_agency
print(agencies_subset) | code |
18141740/cell_4 | [
"image_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
df_source = pd.read_csv('../input/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, inplace=True)
df_source_time = df_source.copy()
df_source_time['rep_time'] = df_source_time['rep_date'].apply(lambda x: dt.datetime.strptime(x.strftime('%H:%M'), '%H:%M'))
df_source_time.drop(['rep_date'], axis=1, inplace=True)
df_source = df_source.set_index('rep_date')
df_source_time = df_source_time.set_index('rep_time')
df_source_time['C9'].plot()
plt.show() | code |
18141740/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
from pandas import DataFrame
import datetime as dt
import matplotlib as mpl
import matplotlib.pyplot as plt
import os
import lowess as lo
print(os.listdir('../input')) | code |
18141740/cell_3 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | import datetime as dt
import pandas as pd
df_source = pd.read_csv('../input/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, inplace=True)
df_source_time = df_source.copy()
df_source_time['rep_time'] = df_source_time['rep_date'].apply(lambda x: dt.datetime.strptime(x.strftime('%H:%M'), '%H:%M'))
df_source_time.drop(['rep_date'], axis=1, inplace=True)
df_source = df_source.set_index('rep_date')
df_source_time = df_source_time.set_index('rep_time')
print('Rows found in the DataFrame:\n{}\n'.format(len(df_source.index)))
display(df_source.tail(3))
display(df_source_time.tail(3)) | code |
18141740/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from pandas import DataFrame
import datetime as dt
import lowess as lo
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_source = pd.read_csv('../input/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, inplace=True)
df_source_time = df_source.copy()
df_source_time['rep_time'] = df_source_time['rep_date'].apply(lambda x: dt.datetime.strptime(x.strftime('%H:%M'), '%H:%M'))
df_source_time.drop(['rep_date'], axis=1, inplace=True)
df_source = df_source.set_index('rep_date')
df_source_time = df_source_time.set_index('rep_time')
v_window = 8
k_out = 1.5
k_norm = 1.5
i = df_source_time.index.shape[0]
x = np.linspace(-10, 10, i)
def f_out(x):
name = x.index[0]
if x[name] > x[name + '_lo'] + k_out * x[name + '_std_first_step']:
x[name + '_adj'] = np.nan
elif x[name] < x[name + '_lo'] - k_out * x[name + '_std_first_step']:
x[name + '_adj'] = np.nan
else:
x[name + '_adj'] = x[name]
return x
def f_low(df_x):
df_res = DataFrame(df_x)
name = df_res.columns[0]
i = df_x.index.shape[0]
x = np.linspace(-10, 10, i)
df_res[name + '_lo'] = lo.lowess(x, df_x.values, x)
df_res[name + '_std_first_step'] = df_x.rolling(window=v_window, min_periods=0).std().fillna(method='bfill').shift(-int(v_window / 2))
df_res = df_res.apply(f_out, axis=1)
df_res[name + '_adj_first_step'] = df_res[name + '_adj'].fillna(method='bfill')
df_res[name + '_adj'] = lo.lowess(x, np.array(df_res[name + '_adj_first_step']), x)
df_res[name + '_std'] = df_res[name + '_adj_first_step'].rolling(window=v_window, min_periods=0).std().fillna(method='bfill').shift(-int(v_window / 2))
return df_res
l = list(df_source_time.columns)
print('Список полученных для анализа фич:\n{}'.format(l))
for name in l:
df = f_low(df_source_time[name].sort_index(axis=0))
display(df.head())
fig, ax = plt.subplots(1, figsize=(12, 9))
ax.plot(df[name], 'b.', label='Original')
ax.plot(df[name + '_lo'] + k_out * df[name + '_std_first_step'], 'g', label='Границы фильтрации выбросов')
ax.plot(df[name + '_lo'] - k_out * df[name + '_std_first_step'], 'g', label='Границы фильтрации выбросов')
ax.plot(df[name + '_lo'], 'r', label='Восстановленный график на первом шаге')
ax.plot(df[name + '_adj'] + k_norm * df[name + '_std'], 'k', label='Верхняя граница нормального трафика')
ax.plot(df[name + '_adj'] - k_norm * df[name + '_std'], 'k', label='Нижняя граница нормального трафика')
ax.plot(df[name + '_adj'], 'y', label='Восстановленный график на втором шаге')
ax.set_title(name)
plt.legend()
plt.show() | code |
73081315/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30days-folds/train_folds.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.head() | code |
73081315/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30days-folds/train_folds.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
sum(train.isnull().sum())
y = train['target']
features = train.drop(['target'], axis=1)
features.head() | code |
73081315/cell_7 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd
import time
train = pd.read_csv('../input/30days-folds/train_folds.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
sum(train.isnull().sum())
y = train['target']
features = train.drop(['target'], axis=1)
final_predictions = []
ordinal_encoder = OrdinalEncoder()
model = RandomForestRegressor(random_state=1)
for fold in range(5):
X_test = test.copy()
X_train = train[train.kfold != fold].reset_index(drop=True)
X_valid = train[train.kfold == fold].reset_index(drop=True)
y_train = X_train['target']
y_valid = X_valid['target']
X_train.drop(['target', 'kfold'], axis=1, inplace=True)
X_valid.drop(['target', 'kfold'], axis=1, inplace=True)
object_cols = [col for col in X_train.columns if X_train[col].dtype == 'object']
X_train[object_cols] = ordinal_encoder.fit_transform(X_train[object_cols])
X_valid[object_cols] = ordinal_encoder.transform(X_valid[object_cols])
X_test[object_cols] = ordinal_encoder.transform(X_test[object_cols])
model = XGBRegressor(random_state=fold, n_jobs=4)
start_time = time.time()
model.fit(X_train, y_train)
end_time = time.time()
preds_valid = model.predict(X_valid)
preds_test = model.predict(X_test)
final_predictions.append(preds_test)
print(fold, round(mean_squared_error(y_valid, preds_valid, squared=False), 4), end_time - start_time, sep=' - ') | code |
73081315/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30days-folds/train_folds.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
sum(train.isnull().sum()) | code |
72099958/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, figsize=(10,10))
ax=ax.ravel()
for index, col in enumerate(cat):
sns.boxplot(x='age',y=col,data=df, ax=ax[index])
df['site'] = df['site'].apply(lambda x: str(x))
fig,ax=plt.subplots(figsize=(15,15))
sns.heatmap(df.corr(),annot=True)
plt.show()
#to a limited degree, body dimensions are to some degree correlated with age
num = ['hdlngth', 'skullw', 'totlngth', 'taill', 'footlgth', 'earconch', 'eye', 'chest', 'belly']
fig, ax = plt.subplots(3, 3, figsize=(10, 10), constrained_layout=True)
ax = ax.ravel()
for index, col in enumerate(num):
sns.histplot(x=col, data=df, ax=ax[index], kde=True)
ax[index].set_title(f'Skewness:{df[col].skew(axis=0)}') | code |
72099958/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
df['site'] = df['site'].apply(lambda x: str(x))
df.info() | code |
72099958/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
df.info() | code |
72099958/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, figsize=(10,10))
ax=ax.ravel()
for index, col in enumerate(cat):
sns.boxplot(x='age',y=col,data=df, ax=ax[index])
df['site'] = df['site'].apply(lambda x: str(x))
fig,ax=plt.subplots(figsize=(15,15))
sns.heatmap(df.corr(),annot=True)
plt.show()
#to a limited degree, body dimensions are to some degree correlated with age
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
sns.histplot(x=col,data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[col].skew(axis = 0)}')
#Some regression techniques don't work well skewed data, so we are doing this to detech is that's the case
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
log = (f'{col}_log')
df[log] = df[col].apply(lambda x:np.log(x+1))
sns.histplot(x=f'{col}_log',data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[log].skew(axis = 0)}')
df['age_log'] = df['age'].apply(lambda x: np.log(x + 1))
df.dropna(axis=0, inplace=True)
df.columns | code |
72099958/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, figsize=(10,10))
ax=ax.ravel()
for index, col in enumerate(cat):
sns.boxplot(x='age',y=col,data=df, ax=ax[index])
df['site'] = df['site'].apply(lambda x: str(x))
fig,ax=plt.subplots(figsize=(15,15))
sns.heatmap(df.corr(),annot=True)
plt.show()
#to a limited degree, body dimensions are to some degree correlated with age
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
sns.histplot(x=col,data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[col].skew(axis = 0)}')
#Some regression techniques don't work well skewed data, so we are doing this to detech is that's the case
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
log = (f'{col}_log')
df[log] = df[col].apply(lambda x:np.log(x+1))
sns.histplot(x=f'{col}_log',data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[log].skew(axis = 0)}')
df['age_log'] = df['age'].apply(lambda x: np.log(x + 1))
df.dropna(axis=0, inplace=True)
df.info() | code |
72099958/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
for col in cat:
print(f'In {col}: {df[col].unique()}') | code |
72099958/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 |
72099958/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig, ax = plt.subplots(3, figsize=(10, 10))
ax = ax.ravel()
for index, col in enumerate(cat):
sns.boxplot(x='age', y=col, data=df, ax=ax[index]) | code |
72099958/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, figsize=(10,10))
ax=ax.ravel()
for index, col in enumerate(cat):
sns.boxplot(x='age',y=col,data=df, ax=ax[index])
df['site'] = df['site'].apply(lambda x: str(x))
fig,ax=plt.subplots(figsize=(15,15))
sns.heatmap(df.corr(),annot=True)
plt.show()
#to a limited degree, body dimensions are to some degree correlated with age
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
sns.histplot(x=col,data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[col].skew(axis = 0)}')
#Some regression techniques don't work well skewed data, so we are doing this to detech is that's the case
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
log = (f'{col}_log')
df[log] = df[col].apply(lambda x:np.log(x+1))
sns.histplot(x=f'{col}_log',data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[log].skew(axis = 0)}')
df['age_log'] = df['age'].apply(lambda x: np.log(x + 1))
df.head() | code |
72099958/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, figsize=(10,10))
ax=ax.ravel()
for index, col in enumerate(cat):
sns.boxplot(x='age',y=col,data=df, ax=ax[index])
df['site'] = df['site'].apply(lambda x: str(x))
fig,ax=plt.subplots(figsize=(15,15))
sns.heatmap(df.corr(),annot=True)
plt.show()
#to a limited degree, body dimensions are to some degree correlated with age
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
sns.histplot(x=col,data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[col].skew(axis = 0)}')
#Some regression techniques don't work well skewed data, so we are doing this to detech is that's the case
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
log = (f'{col}_log')
df[log] = df[col].apply(lambda x:np.log(x+1))
sns.histplot(x=f'{col}_log',data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[log].skew(axis = 0)}')
sns.histplot(x='age', data=df, kde=True)
plt.title(f'Skewness:{df.age.skew(axis=0)}')
plt.show() | code |
72099958/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, figsize=(10,10))
ax=ax.ravel()
for index, col in enumerate(cat):
sns.boxplot(x='age',y=col,data=df, ax=ax[index])
df['site'] = df['site'].apply(lambda x: str(x))
fig,ax=plt.subplots(figsize=(15,15))
sns.heatmap(df.corr(),annot=True)
plt.show()
#to a limited degree, body dimensions are to some degree correlated with age
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
sns.histplot(x=col,data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[col].skew(axis = 0)}')
#Some regression techniques don't work well skewed data, so we are doing this to detech is that's the case
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
log = (f'{col}_log')
df[log] = df[col].apply(lambda x:np.log(x+1))
sns.histplot(x=f'{col}_log',data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[log].skew(axis = 0)}')
df['age_log'] = df['age'].apply(lambda x: np.log(x + 1))
sns.histplot(x='age_log', data=df, kde=True)
plt.title(f'Skewness:{df.age_log.skew(axis=0)}')
plt.show() | code |
72099958/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/openintro-possum/possum.csv')
df | code |
72099958/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, figsize=(10,10))
ax=ax.ravel()
for index, col in enumerate(cat):
sns.boxplot(x='age',y=col,data=df, ax=ax[index])
df['site'] = df['site'].apply(lambda x: str(x))
fig,ax=plt.subplots(figsize=(15,15))
sns.heatmap(df.corr(),annot=True)
plt.show()
#to a limited degree, body dimensions are to some degree correlated with age
num = ['hdlngth',
'skullw',
'totlngth',
'taill',
'footlgth',
'earconch',
'eye',
'chest',
'belly']
#numerical columns EDA
fig,ax=plt.subplots(3,3, figsize=(10,10),constrained_layout=True)
ax=ax.ravel()
for index, col in enumerate(num):
sns.histplot(x=col,data=df,ax=ax[index],
kde=True)
ax[index].set_title(f'Skewness:{df[col].skew(axis = 0)}')
#Some regression techniques don't work well skewed data, so we are doing this to detech is that's the case
num = ['hdlngth', 'skullw', 'totlngth', 'taill', 'footlgth', 'earconch', 'eye', 'chest', 'belly']
fig, ax = plt.subplots(3, 3, figsize=(10, 10), constrained_layout=True)
ax = ax.ravel()
for index, col in enumerate(num):
log = f'{col}_log'
df[log] = df[col].apply(lambda x: np.log(x + 1))
sns.histplot(x=f'{col}_log', data=df, ax=ax[index], kde=True)
ax[index].set_title(f'Skewness:{df[log].skew(axis=0)}') | code |
72099958/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/openintro-possum/possum.csv')
df
cat = ['sex', 'Pop', 'site']
fig,ax=plt.subplots(3, figsize=(10,10))
ax=ax.ravel()
for index, col in enumerate(cat):
sns.boxplot(x='age',y=col,data=df, ax=ax[index])
df['site'] = df['site'].apply(lambda x: str(x))
fig, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(df.corr(), annot=True)
plt.show() | code |
17137459/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 6))
sns.set(style="whitegrid")
ax = sns.violinplot(x=df1['Age'], y=df1['Purchase'], palette='Set2')
produtos = df1['Product_ID'].value_counts().head(10)
plt.figure(figsize=(16, 6))
for i, v in produtos.iteritems():
plt.bar(i, v, label = i)
plt.text(i, v, v, va='bottom', ha='center')
plt.title('Produtos mais comprados')
plt.show()
occupation = df1['Occupation'].value_counts().head(5)
aux = pd.DataFrame
for i, v in occupation.iteritems():
if aux.empty:
aux = df1[df1['Occupation'] == i]
else:
aux = aux.append(df1[df1['Occupation'] == i])
plt.figure(figsize=(20, 10))
sns.boxenplot(x=aux['Occupation'], y=aux['Purchase'], hue=aux['Age']) | code |
17137459/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 6))
sns.set(style="whitegrid")
ax = sns.violinplot(x=df1['Age'], y=df1['Purchase'], palette='Set2')
produtos = df1['Product_ID'].value_counts().head(10)
plt.figure(figsize=(16, 6))
for i, v in produtos.iteritems():
plt.bar(i, v, label=i)
plt.text(i, v, v, va='bottom', ha='center')
plt.title('Produtos mais comprados')
plt.show() | code |
17137459/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
df1.head(10) | code |
17137459/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 6))
sns.set(style="whitegrid")
ax = sns.violinplot(x=df1['Age'], y=df1['Purchase'], palette='Set2')
produtos = df1['Product_ID'].value_counts().head(10)
plt.figure(figsize=(16, 6))
for i, v in produtos.iteritems():
plt.bar(i, v, label = i)
plt.text(i, v, v, va='bottom', ha='center')
plt.title('Produtos mais comprados')
plt.show()
occupation = df1['Occupation'].value_counts().head(5)
aux = pd.DataFrame
for i, v in occupation.iteritems():
if aux.empty:
aux = df1[df1['Occupation'] == i]
else:
aux = aux.append(df1[df1['Occupation'] == i])
purchase = df1[df1['Purchase'] > 9000]
plt.figure(figsize=(16, 6))
sns.catplot(x='Marital_Status', y='Purchase', hue='Marital_Status', margin_titles=True, kind='box', col='Occupation', data=purchase, aspect=0.4, col_wrap=7) | code |
17137459/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 6))
sns.set(style='whitegrid')
ax = sns.violinplot(x=df1['Age'], y=df1['Purchase'], palette='Set2') | code |
73068056/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
df_train.head() | code |
73068056/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
X = df_train.copy()
X_test = df_test.copy()
y = X.pop('target')
s = X_train.dtypes == 'object'
object_cols = list(s[s].index)
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[object_cols]))
OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[object_cols]))
OH_cols_test = pd.DataFrame(OH_encoder.transform(X_test[object_cols]))
OH_cols_train.index = X_train.index
OH_cols_valid.index = X_valid.index
OH_cols_test.index = X_test.index
num_X_train = X_train.drop(object_cols, axis=1)
num_X_valid = X_valid.drop(object_cols, axis=1)
num_X_test = X_test.drop(object_cols, axis=1)
OH_X_train = pd.concat([num_X_train, OH_cols_train], axis=1)
OH_X_valid = pd.concat([num_X_valid, OH_cols_valid], axis=1)
OH_X_test = pd.concat([num_X_test, OH_cols_test], axis=1)
X_train = OH_X_train
X_valid = OH_X_valid
X_test = OH_X_test
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
print(mean_squared_error(y_valid, preds_valid, squared=False)) | code |
73068056/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import OrdinalEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from category_encoders import MEstimateEncoder
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.feature_selection import mutual_info_regression
from sklearn.model_selection import KFold, cross_val_score
from xgboost import XGBRegressor
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73068056/cell_27 | [
"text_html_output_1.png"
] | s = X_train.dtypes == 'object'
object_cols = list(s[s].index)
print('Categorical variables:')
print(object_cols) | code |
34139290/cell_13 | [
"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
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2] for i in df['publication_date']]
df['decade'] = [int(i) // 10 * 10 for i in df['publication_year']]
df_lang_year = df.groupby(['decade', 'language_code']).count().reset_index()
df_lang_year
plt.figure(figsize=(20, 10))
plt.xlabel('Year')
plt.ylabel('Number of Books')
ax1 = sns.lineplot(x='decade', y='bookID', hue='language_code', data=df_lang_year)
ax1.set_ylabel('Number of Books')
ax1.set_xlabel('Decade') | code |
34139290/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns | code |
34139290/cell_25 | [
"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
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2] for i in df['publication_date']]
df['decade'] = [int(i) // 10 * 10 for i in df['publication_year']]
df_lang_year = df.groupby(['decade', 'language_code']).count().reset_index()
df_lang_year
plt.figure(figsize=(20,10))
plt.xlabel('Year')
plt.ylabel('Number of Books')
ax1 = sns.lineplot(x="decade", y="bookID",
hue="language_code", #style="event",
data=df_lang_year)
ax1.set_ylabel('Number of Books')
ax1.set_xlabel('Decade')
x = df.groupby('language_code')['bookID'].count().reset_index().sort_values(by = 'bookID',ascending=False)
plt.figure(figsize=(15,10))
ax1 = sns.barplot(x = 'language_code',
y = 'bookID',
data = x)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
ax1.set_yscale("log")
# ax1.set_ticklabels(x['bookID'], minor=False)
plt.figure(figsize=(15,15))
chart = sns.countplot(
data=df,
x='language_code'
)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
df['updated_language'] = ['en' if i in ('eng','en-US', 'en-GB', 'en-CA') else i for i in df['language_code']]
x = df.groupby('updated_language')['bookID'].count().reset_index().sort_values(by = 'bookID',ascending=False)
plt.figure(figsize=(15,10))
ax1 = sns.barplot(x = 'updated_language',
y = 'bookID',
data = x)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
ax1.set_yscale("log")
# ax1.set_ticklabels(x['bookID'], minor=False)
authors = df.groupby('authors')['bookID'].count().reset_index().sort_values(by = 'bookID', ascending = False).head(10)
plt.figure(figsize=(15,10))
au = sns.barplot(x = 'authors',
y = 'bookID',
data = authors)
au.set_xlabel('Authors')
au.set_ylabel('Number of Books')
# Other way to rotate labels
# au.set_xticklabels(au.get_xticklabels(),
# rotation=45,
# fontweight='light',
# fontsize='x-large')
plt.xticks(
rotation=45,
horizontalalignment='right',
fontweight='light',
fontsize='x-large'
)
df['average_rating_rounded'] = df['average_rating'].round(1)
plt.figure(figsize=(20, 15))
ax1 = sns.countplot(data=df, x='average_rating_rounded')
ax1.set_xlabel('Average Rating')
ax1.set_ylabel('Number of Books') | code |
34139290/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.head() | code |
34139290/cell_20 | [
"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
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2] for i in df['publication_date']]
df['decade'] = [int(i) // 10 * 10 for i in df['publication_year']]
df_lang_year = df.groupby(['decade', 'language_code']).count().reset_index()
df_lang_year
plt.figure(figsize=(20,10))
plt.xlabel('Year')
plt.ylabel('Number of Books')
ax1 = sns.lineplot(x="decade", y="bookID",
hue="language_code", #style="event",
data=df_lang_year)
ax1.set_ylabel('Number of Books')
ax1.set_xlabel('Decade')
x = df.groupby('language_code')['bookID'].count().reset_index().sort_values(by = 'bookID',ascending=False)
plt.figure(figsize=(15,10))
ax1 = sns.barplot(x = 'language_code',
y = 'bookID',
data = x)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
ax1.set_yscale("log")
# ax1.set_ticklabels(x['bookID'], minor=False)
plt.figure(figsize=(15,15))
chart = sns.countplot(
data=df,
x='language_code'
)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
df['updated_language'] = ['en' if i in ('eng', 'en-US', 'en-GB', 'en-CA') else i for i in df['language_code']]
x = df.groupby('updated_language')['bookID'].count().reset_index().sort_values(by='bookID', ascending=False)
plt.figure(figsize=(15, 10))
ax1 = sns.barplot(x='updated_language', y='bookID', data=x)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
ax1.set_yscale('log') | code |
34139290/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
print(df.dtypes) | code |
34139290/cell_18 | [
"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
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2] for i in df['publication_date']]
df['decade'] = [int(i) // 10 * 10 for i in df['publication_year']]
df_lang_year = df.groupby(['decade', 'language_code']).count().reset_index()
df_lang_year
plt.figure(figsize=(20,10))
plt.xlabel('Year')
plt.ylabel('Number of Books')
ax1 = sns.lineplot(x="decade", y="bookID",
hue="language_code", #style="event",
data=df_lang_year)
ax1.set_ylabel('Number of Books')
ax1.set_xlabel('Decade')
x = df.groupby('language_code')['bookID'].count().reset_index().sort_values(by = 'bookID',ascending=False)
plt.figure(figsize=(15,10))
ax1 = sns.barplot(x = 'language_code',
y = 'bookID',
data = x)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
ax1.set_yscale("log")
# ax1.set_ticklabels(x['bookID'], minor=False)
plt.figure(figsize=(15, 15))
chart = sns.countplot(data=df, x='language_code')
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books') | code |
34139290/cell_16 | [
"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
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2] for i in df['publication_date']]
df['decade'] = [int(i) // 10 * 10 for i in df['publication_year']]
df_lang_year = df.groupby(['decade', 'language_code']).count().reset_index()
df_lang_year
plt.figure(figsize=(20,10))
plt.xlabel('Year')
plt.ylabel('Number of Books')
ax1 = sns.lineplot(x="decade", y="bookID",
hue="language_code", #style="event",
data=df_lang_year)
ax1.set_ylabel('Number of Books')
ax1.set_xlabel('Decade')
x = df.groupby('language_code')['bookID'].count().reset_index().sort_values(by='bookID', ascending=False)
plt.figure(figsize=(15, 10))
ax1 = sns.barplot(x='language_code', y='bookID', data=x)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
ax1.set_yscale('log') | code |
34139290/cell_22 | [
"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
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2] for i in df['publication_date']]
df['decade'] = [int(i) // 10 * 10 for i in df['publication_year']]
df_lang_year = df.groupby(['decade', 'language_code']).count().reset_index()
df_lang_year
plt.figure(figsize=(20,10))
plt.xlabel('Year')
plt.ylabel('Number of Books')
ax1 = sns.lineplot(x="decade", y="bookID",
hue="language_code", #style="event",
data=df_lang_year)
ax1.set_ylabel('Number of Books')
ax1.set_xlabel('Decade')
x = df.groupby('language_code')['bookID'].count().reset_index().sort_values(by = 'bookID',ascending=False)
plt.figure(figsize=(15,10))
ax1 = sns.barplot(x = 'language_code',
y = 'bookID',
data = x)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
ax1.set_yscale("log")
# ax1.set_ticklabels(x['bookID'], minor=False)
plt.figure(figsize=(15,15))
chart = sns.countplot(
data=df,
x='language_code'
)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
df['updated_language'] = ['en' if i in ('eng','en-US', 'en-GB', 'en-CA') else i for i in df['language_code']]
x = df.groupby('updated_language')['bookID'].count().reset_index().sort_values(by = 'bookID',ascending=False)
plt.figure(figsize=(15,10))
ax1 = sns.barplot(x = 'updated_language',
y = 'bookID',
data = x)
ax1.set_xlabel('Language Code')
ax1.set_ylabel('Number of Books')
ax1.set_yscale("log")
# ax1.set_ticklabels(x['bookID'], minor=False)
authors = df.groupby('authors')['bookID'].count().reset_index().sort_values(by='bookID', ascending=False).head(10)
plt.figure(figsize=(15, 10))
au = sns.barplot(x='authors', y='bookID', data=authors)
au.set_xlabel('Authors')
au.set_ylabel('Number of Books')
plt.xticks(rotation=45, horizontalalignment='right', fontweight='light', fontsize='x-large') | code |
34139290/cell_12 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.rename(columns={' num_pages': 'num_pages'}, inplace=True)
df.columns
df['publication_year'] = [i.split('/')[2] for i in df['publication_date']]
df['decade'] = [int(i) // 10 * 10 for i in df['publication_year']]
df_lang_year = df.groupby(['decade', 'language_code']).count().reset_index()
df_lang_year | code |
34139290/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/goodreadsbooks/books.csv', error_bad_lines=False)
df.describe(include='all') | code |
18116881/cell_63 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
test_set.at[152, 'Fare'] = np.nanmedian(test_set[(test_set['Pclass'] == 3) & (test_set['Embarked'] == 'S')]['Fare'])
sns.distplot(test_set['Fare']) | code |
18116881/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set.info() | code |
18116881/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
def missing_zero_values_table(df):
zero_val = (df == 0.0).astype(int).sum(axis=0)
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mz_table = pd.concat([zero_val, mis_val, mis_val_percent], axis=1)
mz_table = mz_table.rename(columns={0: 'Zero Values', 1: 'Missing Values', 2: '% of Total Values'})
mz_table['Data Type'] = df.dtypes
mz_table = mz_table[mz_table.iloc[:, 1] != 0].sort_values('% of Total Values', ascending=False).round(1)
return mz_table
missing_zero_values_table(test_set) | code |
18116881/cell_57 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
test_set.at[152, 'Fare'] = np.nanmedian(test_set[(test_set['Pclass'] == 3) & (test_set['Embarked'] == 'S')]['Fare'])
test_set['Title'].value_counts() | code |
18116881/cell_56 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
test_set.at[152, 'Fare'] = np.nanmedian(test_set[(test_set['Pclass'] == 3) & (test_set['Embarked'] == 'S')]['Fare'])
def extract_title(name):
return name.split(',')[1].split()[0].strip()
def refine_title(title):
if title in ['Mr.', 'Sir.', 'Major.', 'Dr.', 'Capt.']:
return 'mr'
elif title == 'Master.':
return 'master'
elif title in ['Miss.', 'Ms.']:
return 'miss'
elif title in ['Mrs.', 'Lady.']:
return 'mrs'
else:
return 'other'
test_set['Title'] = test_set['Name'].apply(extract_title)
test_set['Title'] = test_set['Title'].apply(refine_title) | code |
18116881/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
def missing_zero_values_table(df):
zero_val = (df == 0.0).astype(int).sum(axis=0)
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mz_table = pd.concat([zero_val, mis_val, mis_val_percent], axis=1)
mz_table = mz_table.rename(columns={0: 'Zero Values', 1: 'Missing Values', 2: '% of Total Values'})
mz_table['Data Type'] = df.dtypes
mz_table = mz_table[mz_table.iloc[:, 1] != 0].sort_values('% of Total Values', ascending=False).round(1)
return mz_table
missing_zero_values_table(train_set) | code |
18116881/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set['Embarked'][61] = 'S'
train_set['Embarked'][829] = 'S' | code |
18116881/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set['Name'] | code |
18116881/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
plt.show() | code |
18116881/cell_65 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
def missing_zero_values_table(df):
zero_val = (df == 0.0).astype(int).sum(axis=0)
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mz_table = pd.concat([zero_val, mis_val, mis_val_percent], axis=1)
mz_table = mz_table.rename(columns={0: 'Zero Values', 1: 'Missing Values', 2: '% of Total Values'})
mz_table['Data Type'] = df.dtypes
mz_table = mz_table[mz_table.iloc[:, 1] != 0].sort_values('% of Total Values', ascending=False).round(1)
return mz_table
test_set.at[152, 'Fare'] = np.nanmedian(test_set[(test_set['Pclass'] == 3) & (test_set['Embarked'] == 'S')]['Fare'])
fare_bins = [-np.inf, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, np.inf]
fare_labels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
train_set['FareBin'] = pd.cut(train_set['Fare'], bins=fare_bins, labels=fare_labels)
test_set['FareBin'] = pd.cut(test_set['Fare'], bins=fare_bins, labels=fare_labels) | code |
18116881/cell_48 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
def extract_title(name):
return name.split(',')[1].split()[0].strip()
train_set['Title'] = train_set['Name'].apply(extract_title) | code |
18116881/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
test_set.at[152, 'Fare'] = np.nanmedian(test_set[(test_set['Pclass'] == 3) & (test_set['Embarked'] == 'S')]['Fare'])
train_set['FamilySize'] = train_set['SibSp'] + train_set['Parch'] + 1
test_set['FamilySize'] = test_set['SibSp'] + test_set['Parch'] + 1 | code |
18116881/cell_54 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set['Title'].value_counts() | code |
18116881/cell_67 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
test_set.at[152, 'Fare'] = np.nanmedian(test_set[(test_set['Pclass'] == 3) & (test_set['Embarked'] == 'S')]['Fare'])
test_set.head() | code |
18116881/cell_60 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
sns.distplot(train_set['Fare']) | code |
18116881/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set.head() | code |
18116881/cell_49 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set['Title'].value_counts() | code |
18116881/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
plt.show() | code |
18116881/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set[train_set['Age'].isna()]['Sex'].value_counts() | code |
18116881/cell_58 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set.head() | code |
18116881/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set[(train_set['Pclass'] == 1) & (train_set['Embarked'] == 'Q')]['Sex'].value_counts() | code |
18116881/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set.describe() | code |
18116881/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
plt.show() | code |
18116881/cell_38 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
test_set.at[152, 'Fare'] = np.nanmedian(test_set[(test_set['Pclass'] == 3) & (test_set['Embarked'] == 'S')]['Fare'])
test_set['Fare'][152] | code |
18116881/cell_66 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set.head() | code |
18116881/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
test_set[test_set['Fare'].isna()] | code |
18116881/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
plt.show() | code |
18116881/cell_53 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
def refine_title(title):
if title in ['Mr.', 'Sir.', 'Major.', 'Dr.', 'Capt.']:
return 'mr'
elif title == 'Master.':
return 'master'
elif title in ['Miss.', 'Ms.']:
return 'miss'
elif title in ['Mrs.', 'Lady.']:
return 'mrs'
else:
return 'other'
train_set['Title'] = train_set['Title'].apply(refine_title) | code |
18116881/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
train_set[train_set['Embarked'].isna()] | code |
18116881/cell_37 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax = ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 80, 5)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Age', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Pclass', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
bins = np.arange(0, 550, 50)
g = sns.FacetGrid(train_set, row='Sex', col='Embarked', hue='Survived', margin_titles=True, size=3, aspect=1.1)
g.map(sns.distplot, 'Fare', kde=False, bins=bins, hist_kws=dict(alpha=0.6))
g.add_legend()
test_set.at[152, 'Fare'] = np.nanmedian(test_set[(test_set['Pclass'] == 3) & (test_set['Embarked'] == 'S')]['Fare']) | code |
18116881/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
cat_cols = ['Survived', 'Sex', 'Pclass', 'Embarked', 'Parch', 'SibSp']
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.3, hspace=0.3)
for i in range(2):
for j in range(3):
c = i * 3 + j
ax = axs[i][j]
sns.countplot(train_set[cat_cols[c]], hue=train_set['Survived'], ax=ax)
ax.set_title(cat_cols[c], fontsize=14, fontweight='bold')
ax.grid() | code |
18116881/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv') | code |
128014857/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug | code |
128014857/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in name_cols:
df_Dr = df_drug.groupby(['Drug', name_col]).size().reset_index(name='Count')
df_drug.corr()
df_drug['Na_to_K_Bigger_Than_15'] = [1 if i >= 15.015 else 0 for i in df_drug.Na_to_K]
df_drug.head() | code |
128014857/cell_44 | [
"text_html_output_1.png"
] | from sklearn import linear_model, naive_bayes, neighbors, svm
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from typing import Tuple
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
def find_boxplot_boundaries(
col: pd.Series, Na_to_K_coeff: float = 1.5
) -> Tuple[float, float]:
"""Findx minimum and maximum in boxplot.
Args:
col: a pandas serires of input.
Na_to_K_coeff: Na_to_K coefficient in box plot
"""
Q1 = col.quantile(0.25)
Q3 = col.quantile(0.75)
IQR = Q3 - Q1
lower = Q1 - Na_to_K_coeff * IQR
upper = Q3 + Na_to_K_coeff * IQR
return lower, upper
class BoxplotOutlierClipper(BaseEstimator, TransformerMixin):
def __init__(self, Na_to_K_coeff: float = 1.5):
self.Na_to_K = Na_to_K_coeff
self.lower = None
self.upper = None
def fit(self, X: pd.Series):
self.lower, self.upper = find_boxplot_boundaries(X, self.Na_to_K)
return self
def transform(self, X):
return X.clip(self.lower, self.upper)
X_train = pd.get_dummies(X_train)
X_test = pd.get_dummies(X_test)
log_reg = linear_model.LogisticRegression(max_iter=5000)
log_reg.fit(X_train, y_train)
log_reg_acc = 100 * log_reg.score(X_test, y_test)
y_pred = log_reg.predict(X_test)
print('Logistic Regression Predictions: \n', log_reg.predict(X_test), '\n Accuracy:', log_reg_acc, '%')
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred)) | code |
128014857/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in name_cols:
df_Dr = df_drug.groupby(['Drug', name_col]).size().reset_index(name='Count')
fig, axes = plt.subplots(1, 2, figsize=(9,6))
sns.swarmplot(ax=axes[0], x = "Drug", y = 'Age', data = df_drug, color = "green")
# plt.legend(df_drug['Drug'].value_counts().index)
sns.swarmplot(ax=axes[1], x = "Drug", y = "Na_to_K", data = df_drug)
# plt.legend(df_drug['Drug'].value_counts().index)
df_drug.corr()
clipped_Na_to_K = BoxplotOutlierClipper().fit_transform(df_drug["Na_to_K"])
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))
df_drug['Na_to_K'].hist(bins=50, ax=axes[0])
clipped_Na_to_K.hist(bins=50, ax=axes[1])
#clipped_Na_to_K.to_frame().boxplot(ax=axes[2],vert=True)
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))
sns.boxplot(ax=axes[0], x = df_drug['Na_to_K'])
sns.boxplot(ax=axes[1], x = clipped_Na_to_K)
df_drug['Na_to_K_Bigger_Than_15'] = [1 if i >= 15.015 else 0 for i in df_drug.Na_to_K]
df_drug['Age > 50'] = [1 if i >= 50 else 0 for i in df_drug.Age]
df_drug = df_drug.drop(['Na_to_K', 'Age'], axis=1)
X = df_drug.drop(['Drug'], axis=1)
y = df_drug['Drug']
sns.set_theme(style='darkgrid')
sns.countplot(y=y_train, data=df_drug)
sns.color_palette('husl', 8)
plt.ylabel('Drug Type')
plt.xlabel('Total')
plt.show() | code |
128014857/cell_2 | [
"text_plain_output_1.png",
"image_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 |
128014857/cell_19 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in name_cols:
df_Dr = df_drug.groupby(['Drug', name_col]).size().reset_index(name='Count')
fig, axes = plt.subplots(1, 2, figsize=(9,6))
sns.swarmplot(ax=axes[0], x = "Drug", y = 'Age', data = df_drug, color = "green")
# plt.legend(df_drug['Drug'].value_counts().index)
sns.swarmplot(ax=axes[1], x = "Drug", y = "Na_to_K", data = df_drug)
# plt.legend(df_drug['Drug'].value_counts().index)
df_drug.corr()
clipped_Na_to_K = BoxplotOutlierClipper().fit_transform(df_drug["Na_to_K"])
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))
df_drug['Na_to_K'].hist(bins=50, ax=axes[0])
clipped_Na_to_K.hist(bins=50, ax=axes[1])
#clipped_Na_to_K.to_frame().boxplot(ax=axes[2],vert=True)
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))
sns.boxplot(ax=axes[0], x=df_drug['Na_to_K'])
sns.boxplot(ax=axes[1], x=clipped_Na_to_K) | code |
128014857/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
df_drug['Age'].describe() | code |
128014857/cell_18 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in name_cols:
df_Dr = df_drug.groupby(['Drug', name_col]).size().reset_index(name='Count')
fig, axes = plt.subplots(1, 2, figsize=(9,6))
sns.swarmplot(ax=axes[0], x = "Drug", y = 'Age', data = df_drug, color = "green")
# plt.legend(df_drug['Drug'].value_counts().index)
sns.swarmplot(ax=axes[1], x = "Drug", y = "Na_to_K", data = df_drug)
# plt.legend(df_drug['Drug'].value_counts().index)
df_drug.corr()
clipped_Na_to_K = BoxplotOutlierClipper().fit_transform(df_drug['Na_to_K'])
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))
df_drug['Na_to_K'].hist(bins=50, ax=axes[0])
clipped_Na_to_K.hist(bins=50, ax=axes[1]) | code |
128014857/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
for name_col in name_cols:
print('\n')
plt.figure(dpi=200)
print(df_drug[name_col].value_counts())
sns.countplot(x=df_drug[name_col])
plt.title(str(name_col) + ' Counts')
plt.show() | code |
128014857/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in name_cols:
df_Dr = df_drug.groupby(['Drug', name_col]).size().reset_index(name='Count')
fig, axes = plt.subplots(1, 2, figsize=(9,6))
sns.swarmplot(ax=axes[0], x = "Drug", y = 'Age', data = df_drug, color = "green")
# plt.legend(df_drug['Drug'].value_counts().index)
sns.swarmplot(ax=axes[1], x = "Drug", y = "Na_to_K", data = df_drug)
# plt.legend(df_drug['Drug'].value_counts().index)
df_drug.corr()
sns.boxplot(data=df_drug[['Na_to_K', 'Age']]) | code |
128014857/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in name_cols:
df_Dr = df_drug.groupby(['Drug', name_col]).size().reset_index(name='Count')
df_drug.corr()
df_drug['Na_to_K_Bigger_Than_15'] = [1 if i >= 15.015 else 0 for i in df_drug.Na_to_K]
df_drug['Age > 50'] = [1 if i >= 50 else 0 for i in df_drug.Age]
df_drug = df_drug.drop(['Na_to_K', 'Age'], axis=1)
df_drug.head() | code |
128014857/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in name_cols:
df_Dr = df_drug.groupby(['Drug', name_col]).size().reset_index(name='Count')
df_drug.corr() | code |
128014857/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in name_cols:
print('\n')
plt.figure(dpi=200)
df_Dr = df_drug.groupby(['Drug', name_col]).size().reset_index(name='Count')
print(df_Dr)
sns.barplot(x='Drug', y='Count', hue=name_col, data=df_Dr)
plt.title(str(name_col) + '--Drugs')
plt.show() | code |
128014857/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
name_cols = ['Sex', 'Drug', 'BP', 'Cholesterol']
name_cols = ['Sex', 'BP', 'Cholesterol']
for name_col in name_cols:
df_Dr = df_drug.groupby(['Drug', name_col]).size().reset_index(name='Count')
fig, axes = plt.subplots(1, 2, figsize=(9, 6))
sns.swarmplot(ax=axes[0], x='Drug', y='Age', data=df_drug, color='green')
sns.swarmplot(ax=axes[1], x='Drug', y='Na_to_K', data=df_drug) | code |
128014857/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
df_drug.info() | code |
128014857/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.base import BaseEstimator, TransformerMixin
from typing import Tuple
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_drug = pd.read_csv('../input/drugsets/drug200.csv')
df_drug
def find_boxplot_boundaries(
col: pd.Series, Na_to_K_coeff: float = 1.5
) -> Tuple[float, float]:
"""Findx minimum and maximum in boxplot.
Args:
col: a pandas serires of input.
Na_to_K_coeff: Na_to_K coefficient in box plot
"""
Q1 = col.quantile(0.25)
Q3 = col.quantile(0.75)
IQR = Q3 - Q1
lower = Q1 - Na_to_K_coeff * IQR
upper = Q3 + Na_to_K_coeff * IQR
return lower, upper
class BoxplotOutlierClipper(BaseEstimator, TransformerMixin):
def __init__(self, Na_to_K_coeff: float = 1.5):
self.Na_to_K = Na_to_K_coeff
self.lower = None
self.upper = None
def fit(self, X: pd.Series):
self.lower, self.upper = find_boxplot_boundaries(X, self.Na_to_K)
return self
def transform(self, X):
return X.clip(self.lower, self.upper)
X_train = pd.get_dummies(X_train)
X_test = pd.get_dummies(X_test)
X_train | code |
49129249/cell_21 | [
"text_plain_output_1.png"
] | from imutils import paths
from tqdm import tqdm
import torchvision.transforms as transforms
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
def create_img_df(dir):
img_list = list(paths.list_images(dir))
data = pd.DataFrame(columns=['File_name', 'Target'])
for i, ipaths in tqdm(enumerate(img_list), total=len(img_list)):
data.loc[i, 'image_path'] = ipaths
data.loc[i, 'File_name'] = os.path.basename(ipaths)
data.loc[i, 'Target'] = os.path.split(os.path.dirname(ipaths))[-1]
return data
train_csv = create_img_df(train_dir)
transformer = transforms.Compose([transforms.Resize(130), transforms.CenterCrop(129), transforms.ToTensor()])
train_dataset = heroDataset(train_csv, train_dir, transform=transformer)
train_dataset[1][0].shape | code |
49129249/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from imutils import paths
from tqdm import tqdm
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
def create_img_df(dir):
img_list = list(paths.list_images(dir))
data = pd.DataFrame(columns=['File_name', 'Target'])
for i, ipaths in tqdm(enumerate(img_list), total=len(img_list)):
data.loc[i, 'image_path'] = ipaths
data.loc[i, 'File_name'] = os.path.basename(ipaths)
data.loc[i, 'Target'] = os.path.split(os.path.dirname(ipaths))[-1]
return data
train_csv = create_img_df(train_dir)
train_csv.tail(2) | code |
49129249/cell_23 | [
"text_plain_output_1.png"
] | from PIL import Image
from imutils import paths
from torch.utils.data import Dataset, random_split, DataLoader
from tqdm import tqdm
import torchvision.transforms as transforms
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
label_enc = {'Ant-Man': 0, 'Aquaman': 1, 'Avengers': 2, 'Batman': 3, 'Black Panther': 4, 'Captain America': 5, 'Catwoman': 6, 'Ghost Rider': 7, 'Hulk': 8, 'Iron Man': 9, 'Spiderman': 10, 'Superman': 11}
label_deco = {0: 'Ant-Man', 1: 'Aquaman', 2: 'Avengers', 3: 'Batman', 4: 'Black Panther', 5: 'Captain America', 6: 'Catwoman', 7: 'Ghost Rider', 8: 'Hulk', 9: 'Iron Man', 10: 'Spiderman', 11: 'Superman'}
def create_img_df(dir):
img_list = list(paths.list_images(dir))
data = pd.DataFrame(columns=['File_name', 'Target'])
for i, ipaths in tqdm(enumerate(img_list), total=len(img_list)):
data.loc[i, 'image_path'] = ipaths
data.loc[i, 'File_name'] = os.path.basename(ipaths)
data.loc[i, 'Target'] = os.path.split(os.path.dirname(ipaths))[-1]
return data
train_csv = create_img_df(train_dir)
#counting number of images under each category
plt.figure(figsize=(10,6))
g=sns.countplot(train_csv['Target'])
g.set_xticklabels(g.get_xticklabels(),rotation=40);
def encode_label(label):
target = torch.zeros(12, dtype=torch.float)
target[int(label)] = 1.0
return target
def decode_target(target, text_labels=False, threshold=0.5):
label = None
for i, x in enumerate(target):
if x >= threshold:
label = i
break
if text_labels:
return f'{label_deco[label]}({label})'
return label
transformer = transforms.Compose([transforms.Resize(130), transforms.CenterCrop(129), transforms.ToTensor()])
class heroDataset(Dataset):
def __init__(self, csv_file, root_dir, transform=None):
self.df = csv_file
self.transform = transform
self.root_dir = root_dir
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
row = self.df.loc[idx]
img_id, img_label = (row['File_name'], row['Target'])
img = Image.open(row['image_path'])
if self.transform:
img = self.transform(img)
return (img, encode_label(img_label))
train_dataset = heroDataset(train_csv, train_dir, transform=transformer)
def show_sample(img, target):
pass
show_sample(*train_dataset[250]) | code |
49129249/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from torch.utils.data import Dataset, random_split, DataLoader
batch_size = 50
input_size = 129 * 129
output_size = 12
train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=2, pin_memory=True)
val_dl = DataLoader(val_ds, batch_size * 2, num_workers=2, pin_memory=True)
for a, b in val_dl:
print(a.shape, b.shape, sep='\n')
break | code |
49129249/cell_20 | [
"text_html_output_1.png"
] | from imutils import paths
from tqdm import tqdm
import torchvision.transforms as transforms
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
def create_img_df(dir):
img_list = list(paths.list_images(dir))
data = pd.DataFrame(columns=['File_name', 'Target'])
for i, ipaths in tqdm(enumerate(img_list), total=len(img_list)):
data.loc[i, 'image_path'] = ipaths
data.loc[i, 'File_name'] = os.path.basename(ipaths)
data.loc[i, 'Target'] = os.path.split(os.path.dirname(ipaths))[-1]
return data
train_csv = create_img_df(train_dir)
transformer = transforms.Compose([transforms.Resize(130), transforms.CenterCrop(129), transforms.ToTensor()])
train_dataset = heroDataset(train_csv, train_dir, transform=transformer)
train_dataset[250] | code |
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