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|---|---|---|---|
128005164/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(exclude=['number']).columns.tolist()
cat_columns
train.head() | code |
128005164/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(exclude=['number']).columns.tolist()
cat_columns
X = train.drop(['Survived', 'PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1)
y = train['Survived']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred_dt = dt.predict(X_test)
accuracy_score(y_test, y_pred_dt)
print(classification_report(y_test, y_pred_dt)) | code |
128005164/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(exclude=['number']).columns.tolist()
cat_columns | code |
128005164/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(exclude=['number']).columns.tolist()
cat_columns
X = train.drop(['Survived', 'PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1)
y = train['Survived']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
from sklearn.ensemble import RandomForestClassifier
rf_c = RandomForestClassifier(n_estimators=10, criterion='entropy')
rf_c.fit(X_train, y_train)
y_pred_rf_c = rf_c.predict(X_test)
accuracy_score(y_test, y_pred_rf_c)
print(classification_report(y_test, y_pred_rf_c)) | code |
128005164/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.info() | code |
50238529/cell_25 | [
"text_plain_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
new_data = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Pclass': [1, 3], 'Age': [17, 20], 'FamilyMembers': [1, 0], 'Female': [1, 0]})
new_data
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3)
predictions = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Log-odds': [3.362, -1.861]})
predictions['Probability of survival'] = np.exp(predictions['Log-odds']) / (1 + np.exp(predictions['Log-odds']))
predictions
lgbfit.predict(np.array(new_data[model_columns]))
alldata = pd.concat([train[model_columns], new_data[model_columns]]).reset_index(drop=True)
explainer = shap.TreeExplainer(lgbfit)
shap_values = explainer.shap_values(alldata)
print(explainer.expected_value)
print(np.exp(explainer.expected_value) / (1 + np.exp(explainer.expected_value))) | code |
50238529/cell_34 | [
"text_html_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
new_data = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Pclass': [1, 3], 'Age': [17, 20], 'FamilyMembers': [1, 0], 'Female': [1, 0]})
new_data
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3)
predictions = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Log-odds': [3.362, -1.861]})
predictions['Probability of survival'] = np.exp(predictions['Log-odds']) / (1 + np.exp(predictions['Log-odds']))
predictions
lgbfit.predict(np.array(new_data[model_columns]))
alldata = pd.concat([train[model_columns], new_data[model_columns]]).reset_index(drop=True)
explainer = shap.TreeExplainer(lgbfit)
shap_values = explainer.shap_values(alldata)
shap.initjs()
shap.dependence_plot('Pclass', shap_values[1], alldata[model_columns]) | code |
50238529/cell_23 | [
"text_html_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
new_data = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Pclass': [1, 3], 'Age': [17, 20], 'FamilyMembers': [1, 0], 'Female': [1, 0]})
new_data
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3)
predictions = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Log-odds': [3.362, -1.861]})
predictions['Probability of survival'] = np.exp(predictions['Log-odds']) / (1 + np.exp(predictions['Log-odds']))
predictions
lgbfit.predict(np.array(new_data[model_columns]))
alldata = pd.concat([train[model_columns], new_data[model_columns]]).reset_index(drop=True)
explainer = shap.TreeExplainer(lgbfit)
shap_values = explainer.shap_values(alldata) | code |
50238529/cell_29 | [
"text_plain_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
new_data = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Pclass': [1, 3], 'Age': [17, 20], 'FamilyMembers': [1, 0], 'Female': [1, 0]})
new_data
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3)
predictions = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Log-odds': [3.362, -1.861]})
predictions['Probability of survival'] = np.exp(predictions['Log-odds']) / (1 + np.exp(predictions['Log-odds']))
predictions
lgbfit.predict(np.array(new_data[model_columns]))
alldata = pd.concat([train[model_columns], new_data[model_columns]]).reset_index(drop=True)
explainer = shap.TreeExplainer(lgbfit)
shap_values = explainer.shap_values(alldata)
shap.initjs()
shap.force_plot(explainer.expected_value[1], shap_values[1][892], alldata.iloc[892]) | code |
50238529/cell_7 | [
"image_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3) | code |
50238529/cell_32 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
new_data = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Pclass': [1, 3], 'Age': [17, 20], 'FamilyMembers': [1, 0], 'Female': [1, 0]})
new_data
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3)
predictions = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Log-odds': [3.362, -1.861]})
predictions['Probability of survival'] = np.exp(predictions['Log-odds']) / (1 + np.exp(predictions['Log-odds']))
predictions
lgbfit.predict(np.array(new_data[model_columns]))
alldata = pd.concat([train[model_columns], new_data[model_columns]]).reset_index(drop=True)
explainer = shap.TreeExplainer(lgbfit)
shap_values = explainer.shap_values(alldata)
shap.initjs()
shap.force_plot(explainer.expected_value[1], shap_values[1], alldata[model_columns]) | code |
50238529/cell_35 | [
"text_html_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
new_data = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Pclass': [1, 3], 'Age': [17, 20], 'FamilyMembers': [1, 0], 'Female': [1, 0]})
new_data
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3)
predictions = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Log-odds': [3.362, -1.861]})
predictions['Probability of survival'] = np.exp(predictions['Log-odds']) / (1 + np.exp(predictions['Log-odds']))
predictions
lgbfit.predict(np.array(new_data[model_columns]))
alldata = pd.concat([train[model_columns], new_data[model_columns]]).reset_index(drop=True)
explainer = shap.TreeExplainer(lgbfit)
shap_values = explainer.shap_values(alldata)
shap.initjs()
shap.dependence_plot('Female', shap_values[1], alldata[model_columns]) | code |
50238529/cell_10 | [
"text_html_output_1.png"
] | import graphviz as graphviz
import lightgbm as lgb
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3)
def get_a_graph(tree_index=0):
gv1 = lgb.create_tree_digraph(lgbfit, tree_index=tree_index, show_info='data_percentage')
gv1s = gv1.source.replace('Column_0', 'Female').replace('Column_1', 'Pclass').replace('Column_2', 'Age').replace('Column_3', 'FamilyMembers')
graph = graphviz.Source(gv1s)
return graph
get_a_graph(tree_index=0) | code |
50238529/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
new_data = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Pclass': [1, 3], 'Age': [17, 20], 'FamilyMembers': [1, 0], 'Female': [1, 0]})
new_data
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3)
predictions = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Log-odds': [3.362, -1.861]})
predictions['Probability of survival'] = np.exp(predictions['Log-odds']) / (1 + np.exp(predictions['Log-odds']))
predictions
lgbfit.predict(np.array(new_data[model_columns]))
alldata = pd.concat([train[model_columns], new_data[model_columns]]).reset_index(drop=True)
explainer = shap.TreeExplainer(lgbfit)
shap_values = explainer.shap_values(alldata)
shap.initjs()
shap.force_plot(explainer.expected_value[1], shap_values[1][891], alldata.iloc[891]) | code |
50238529/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import graphviz as graphviz
import lightgbm as lgb
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3)
def get_a_graph(tree_index=0):
gv1 = lgb.create_tree_digraph(lgbfit, tree_index=tree_index, show_info='data_percentage')
gv1s = gv1.source.replace('Column_0', 'Female').replace('Column_1', 'Pclass').replace('Column_2', 'Age').replace('Column_3', 'FamilyMembers')
graph = graphviz.Source(gv1s)
return graph
get_a_graph(tree_index=1) | code |
50238529/cell_5 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
new_data = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Pclass': [1, 3], 'Age': [17, 20], 'FamilyMembers': [1, 0], 'Female': [1, 0]})
new_data | code |
50238529/cell_36 | [
"image_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
import shap
import numpy as np
import pandas as pd
import lightgbm as lgb
import graphviz as graphviz
import shap
train = pd.read_csv('../input/titanic/train.csv')
train['FamilyMembers'] = train['SibSp'] + train['Parch']
train['Female'] = train['Sex'].map({'male': 0, 'female': 1}).astype(np.int8)
train['Pclass_cat'] = (train['Pclass'] - 1).astype(np.int8)
train['Adult'] = (train['Age'] > 16).astype(np.int8)
train['Adult'].values[train['Age'].isna()] = 1
train['Age'] = train.groupby(['Pclass', 'Female'])['Age'].transform(lambda x: x.fillna(x.median()))
new_data = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Pclass': [1, 3], 'Age': [17, 20], 'FamilyMembers': [1, 0], 'Female': [1, 0]})
new_data
continuous_features = ['Pclass', 'Age', 'FamilyMembers']
discrete_features = ['Female']
model_columns = discrete_features + continuous_features
ids_of_categorical = [0]
X = np.array(train[model_columns])
y = np.array(train['Survived']).flatten()
dtrain = lgb.Dataset(X, label=y)
params = {'objective': 'binary', 'metric': 'binary_error', 'verbosity': -1, 'boosting_type': 'gbdt', 'seed': 538, 'learning_rate': 1, 'num_leaves': 4, 'feature_fraction': 1.0, 'bagging_fraction': 1.0, 'bagging_freq': 15, 'min_child_samples': 5}
lgbfit = lgb.train(params, dtrain, categorical_feature=ids_of_categorical, verbose_eval=True, num_boost_round=3)
predictions = pd.DataFrame({'Person': ['Rose', 'Jack'], 'Log-odds': [3.362, -1.861]})
predictions['Probability of survival'] = np.exp(predictions['Log-odds']) / (1 + np.exp(predictions['Log-odds']))
predictions
lgbfit.predict(np.array(new_data[model_columns]))
alldata = pd.concat([train[model_columns], new_data[model_columns]]).reset_index(drop=True)
explainer = shap.TreeExplainer(lgbfit)
shap_values = explainer.shap_values(alldata)
shap.initjs()
shap.dependence_plot('Age', shap_values[1], alldata[model_columns]) | code |
90137326/cell_11 | [
"text_plain_output_1.png"
] | from wikipedia.exceptions import PageError
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Governance', 'Risk and Compliance', 'Human Centric Security', 'Identity and Access Management', 'Infrastructure and Virtualization Security', 'Mobile Security', 'Network Security', 'Physical and Facility Security', 'Security Operations Center', 'Forensics and Incident Response', 'Threat and Vulnerability Management', 'Web Security']
result = []
count = 0
def create_data(search_term):
results_list = wikipedia.search(search_term, results=20)
wiki_search_results = []
for each_result in results_list:
wiki_page_result = {}
try:
wiki_page_obj = wikipedia.page(each_result)
except PageError:
continue
except wikipedia.DisambiguationError as e:
s = 1
p = wikipedia.page(s)
wiki_page_result['title'] = wiki_page_obj.title
wiki_page_result['content'] = wiki_page_obj.content
wiki_page_result['url'] = wiki_page_obj.url
wiki_page_result['links'] = wiki_page_obj.links
wiki_search_results.append(wiki_page_result)
return wiki_search_results
for index in topic_list:
outcome = create_data(index)
result.append(outcome)
count += 1
print(len(result)) | code |
90137326/cell_19 | [
"text_plain_output_1.png"
] | from wikipedia.exceptions import PageError
import pandas as pd
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Governance', 'Risk and Compliance', 'Human Centric Security', 'Identity and Access Management', 'Infrastructure and Virtualization Security', 'Mobile Security', 'Network Security', 'Physical and Facility Security', 'Security Operations Center', 'Forensics and Incident Response', 'Threat and Vulnerability Management', 'Web Security']
result = []
count = 0
def create_data(search_term):
results_list = wikipedia.search(search_term, results=20)
wiki_search_results = []
for each_result in results_list:
wiki_page_result = {}
try:
wiki_page_obj = wikipedia.page(each_result)
except PageError:
continue
except wikipedia.DisambiguationError as e:
s = 1
p = wikipedia.page(s)
wiki_page_result['title'] = wiki_page_obj.title
wiki_page_result['content'] = wiki_page_obj.content
wiki_page_result['url'] = wiki_page_obj.url
wiki_page_result['links'] = wiki_page_obj.links
wiki_search_results.append(wiki_page_result)
return wiki_search_results
for index in topic_list:
outcome = create_data(index)
result.append(outcome)
count += 1
p = []
for i in result:
for j in i:
p.append(j)
final_df = pd.DataFrame(p)
final_df.shape
final_df.columns
final_df.title[0]
final_df.content[0] | code |
90137326/cell_18 | [
"text_plain_output_1.png"
] | from wikipedia.exceptions import PageError
import pandas as pd
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Governance', 'Risk and Compliance', 'Human Centric Security', 'Identity and Access Management', 'Infrastructure and Virtualization Security', 'Mobile Security', 'Network Security', 'Physical and Facility Security', 'Security Operations Center', 'Forensics and Incident Response', 'Threat and Vulnerability Management', 'Web Security']
result = []
count = 0
def create_data(search_term):
results_list = wikipedia.search(search_term, results=20)
wiki_search_results = []
for each_result in results_list:
wiki_page_result = {}
try:
wiki_page_obj = wikipedia.page(each_result)
except PageError:
continue
except wikipedia.DisambiguationError as e:
s = 1
p = wikipedia.page(s)
wiki_page_result['title'] = wiki_page_obj.title
wiki_page_result['content'] = wiki_page_obj.content
wiki_page_result['url'] = wiki_page_obj.url
wiki_page_result['links'] = wiki_page_obj.links
wiki_search_results.append(wiki_page_result)
return wiki_search_results
for index in topic_list:
outcome = create_data(index)
result.append(outcome)
count += 1
p = []
for i in result:
for j in i:
p.append(j)
final_df = pd.DataFrame(p)
final_df.shape
final_df.columns
final_df.title[0] | code |
90137326/cell_15 | [
"text_plain_output_1.png"
] | from wikipedia.exceptions import PageError
import pandas as pd
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Governance', 'Risk and Compliance', 'Human Centric Security', 'Identity and Access Management', 'Infrastructure and Virtualization Security', 'Mobile Security', 'Network Security', 'Physical and Facility Security', 'Security Operations Center', 'Forensics and Incident Response', 'Threat and Vulnerability Management', 'Web Security']
result = []
count = 0
def create_data(search_term):
results_list = wikipedia.search(search_term, results=20)
wiki_search_results = []
for each_result in results_list:
wiki_page_result = {}
try:
wiki_page_obj = wikipedia.page(each_result)
except PageError:
continue
except wikipedia.DisambiguationError as e:
s = 1
p = wikipedia.page(s)
wiki_page_result['title'] = wiki_page_obj.title
wiki_page_result['content'] = wiki_page_obj.content
wiki_page_result['url'] = wiki_page_obj.url
wiki_page_result['links'] = wiki_page_obj.links
wiki_search_results.append(wiki_page_result)
return wiki_search_results
for index in topic_list:
outcome = create_data(index)
result.append(outcome)
count += 1
p = []
for i in result:
for j in i:
p.append(j)
final_df = pd.DataFrame(p)
final_df.shape | code |
90137326/cell_16 | [
"text_plain_output_1.png"
] | from wikipedia.exceptions import PageError
import pandas as pd
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Governance', 'Risk and Compliance', 'Human Centric Security', 'Identity and Access Management', 'Infrastructure and Virtualization Security', 'Mobile Security', 'Network Security', 'Physical and Facility Security', 'Security Operations Center', 'Forensics and Incident Response', 'Threat and Vulnerability Management', 'Web Security']
result = []
count = 0
def create_data(search_term):
results_list = wikipedia.search(search_term, results=20)
wiki_search_results = []
for each_result in results_list:
wiki_page_result = {}
try:
wiki_page_obj = wikipedia.page(each_result)
except PageError:
continue
except wikipedia.DisambiguationError as e:
s = 1
p = wikipedia.page(s)
wiki_page_result['title'] = wiki_page_obj.title
wiki_page_result['content'] = wiki_page_obj.content
wiki_page_result['url'] = wiki_page_obj.url
wiki_page_result['links'] = wiki_page_obj.links
wiki_search_results.append(wiki_page_result)
return wiki_search_results
for index in topic_list:
outcome = create_data(index)
result.append(outcome)
count += 1
p = []
for i in result:
for j in i:
p.append(j)
final_df = pd.DataFrame(p)
final_df.shape
final_df.columns | code |
90137326/cell_17 | [
"text_html_output_1.png"
] | from wikipedia.exceptions import PageError
import pandas as pd
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Governance', 'Risk and Compliance', 'Human Centric Security', 'Identity and Access Management', 'Infrastructure and Virtualization Security', 'Mobile Security', 'Network Security', 'Physical and Facility Security', 'Security Operations Center', 'Forensics and Incident Response', 'Threat and Vulnerability Management', 'Web Security']
result = []
count = 0
def create_data(search_term):
results_list = wikipedia.search(search_term, results=20)
wiki_search_results = []
for each_result in results_list:
wiki_page_result = {}
try:
wiki_page_obj = wikipedia.page(each_result)
except PageError:
continue
except wikipedia.DisambiguationError as e:
s = 1
p = wikipedia.page(s)
wiki_page_result['title'] = wiki_page_obj.title
wiki_page_result['content'] = wiki_page_obj.content
wiki_page_result['url'] = wiki_page_obj.url
wiki_page_result['links'] = wiki_page_obj.links
wiki_search_results.append(wiki_page_result)
return wiki_search_results
for index in topic_list:
outcome = create_data(index)
result.append(outcome)
count += 1
p = []
for i in result:
for j in i:
p.append(j)
final_df = pd.DataFrame(p)
final_df.shape
final_df.columns
final_df | code |
90137326/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from wikipedia.exceptions import PageError
import wikipedia
topic_list = ['Application Security', 'Backup Business Continuity and Recovery', 'Change Control', 'Communication Security', 'Cryptography', 'Encryption and Key Management', 'Data Security', 'Endpoint Security', 'General Security', 'Governance', 'Risk and Compliance', 'Human Centric Security', 'Identity and Access Management', 'Infrastructure and Virtualization Security', 'Mobile Security', 'Network Security', 'Physical and Facility Security', 'Security Operations Center', 'Forensics and Incident Response', 'Threat and Vulnerability Management', 'Web Security']
result = []
count = 0
def create_data(search_term):
results_list = wikipedia.search(search_term, results=20)
wiki_search_results = []
for each_result in results_list:
wiki_page_result = {}
try:
wiki_page_obj = wikipedia.page(each_result)
except PageError:
continue
except wikipedia.DisambiguationError as e:
s = 1
p = wikipedia.page(s)
wiki_page_result['title'] = wiki_page_obj.title
wiki_page_result['content'] = wiki_page_obj.content
wiki_page_result['url'] = wiki_page_obj.url
wiki_page_result['links'] = wiki_page_obj.links
wiki_search_results.append(wiki_page_result)
return wiki_search_results
for index in topic_list:
outcome = create_data(index)
result.append(outcome)
count += 1 | code |
128009779/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment_type'].value_counts()
most_frequent_titles = df['job_title'].value_counts().sort_values(ascending=False)
plt.xticks(rotation=45)
sns.displot(df['salary_in_usd']).set(title='Distribution of the salaries *in US dollars*')
plt.show() | code |
128009779/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3) | code |
128009779/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment_type'].value_counts()
most_frequent_titles = df['job_title'].value_counts().sort_values(ascending=False)
plt.xticks(rotation=45)
salary_curr_values = df['salary_currency'].value_counts()
most_frequent_residences = df['employee_residence'].value_counts().sort_values(ascending=False)
most_frequent_residences[:10].plot(kind='bar')
plt.xticks(rotation=45)
plt.ylabel('Count')
plt.xlabel('Employee residence')
plt.title('Most frequent employees residences')
plt.show() | code |
128009779/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.info() | code |
128009779/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment_type'].value_counts()
most_frequent_titles = df['job_title'].value_counts().sort_values(ascending=False)
plt.xticks(rotation=45)
sns.displot(df['salary']).set(title='Distribution of the salaries')
plt.show() | code |
128009779/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment_type'].value_counts()
most_frequent_titles = df['job_title'].value_counts().sort_values(ascending=False)
most_frequent_titles[:10].plot(kind='bar')
plt.xticks(rotation=45)
plt.ylabel('Count')
plt.xlabel('Job title')
plt.title('Most frequent job titles')
plt.show() | code |
128009779/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
sns.boxplot(df)
plt.title('Boxplot representation of the dataframe')
plt.show() | code |
128009779/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
plt.pie(work_year_values, labels=work_year_values.index, autopct='%.0f%%')
plt.title('Distribution of years of work')
plt.show() | code |
128009779/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
plt.pie(exp_level_values, labels=exp_level_values.index, autopct='%.0f%%')
plt.title('Distribution of levels of experience')
plt.show() | code |
128009779/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
print(f'Shape of the dataframe: {df.shape}') | code |
128009779/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment_type'].value_counts()
plt.pie(empl_type_values, labels=empl_type_values.index, autopct='%.0f%%')
plt.title('Distribution of type of employment')
plt.show() | code |
128009779/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum()
work_year_values = df['work_year'].value_counts()
exp_level_values = df['experience_level'].value_counts()
empl_type_values = df['employment_type'].value_counts()
most_frequent_titles = df['job_title'].value_counts().sort_values(ascending=False)
plt.xticks(rotation=45)
salary_curr_values = df['salary_currency'].value_counts()
plt.pie(salary_curr_values, labels=salary_curr_values.index, autopct='%.0f%%')
plt.title('Distribution of the different currencies')
plt.show() | code |
128009779/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df[df['salary'] > 30000000] | code |
128009779/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.isnull().sum() | code |
128009779/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ai-ml-data-salaries/salaries.csv')
df.sample(3)
df.describe() | code |
50215202/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import nltk
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1 if today not in array else array[today] + 1
x = list(array.keys())
y = list(array.values())
df_plot = pd.DataFrame()
df_plot['x'] = x
df_plot['y'] = y
df_plot.index = x
ax = plt.gca()
ax.xaxis.set_major_locator(mdates.DayLocator(interval=13))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gcf().autofmt_xdate()
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
from sklearn.feature_extraction.text import TfidfVectorizer
vec = TfidfVectorizer(use_idf=False, norm='l1')
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
from textblob import TextBlob
import nltk
nltk.download('punkt')
def textblob_tokenizer(str_input):
blob = TextBlob(str_input.lower())
tokens = blob.words
words = [token.stem() for token in tokens]
return words
vec = CountVectorizer(tokenizer=textblob_tokenizer)
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
vec = TfidfVectorizer(tokenizer=textblob_tokenizer, stop_words='english', use_idf=True)
matrix = vec.fit_transform(titleCorpus)
df = pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names()) | code |
50215202/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
print(tags)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus) | code |
50215202/cell_25 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import nltk
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1 if today not in array else array[today] + 1
x = list(array.keys())
y = list(array.values())
df_plot = pd.DataFrame()
df_plot['x'] = x
df_plot['y'] = y
df_plot.index = x
ax = plt.gca()
ax.xaxis.set_major_locator(mdates.DayLocator(interval=13))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gcf().autofmt_xdate()
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
from sklearn.feature_extraction.text import TfidfVectorizer
vec = TfidfVectorizer(use_idf=False, norm='l1')
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
from textblob import TextBlob
import nltk
nltk.download('punkt')
def textblob_tokenizer(str_input):
blob = TextBlob(str_input.lower())
tokens = blob.words
words = [token.stem() for token in tokens]
return words
vec = CountVectorizer(tokenizer=textblob_tokenizer)
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
vec = TfidfVectorizer(tokenizer=textblob_tokenizer, stop_words='english', use_idf=True)
matrix = vec.fit_transform(titleCorpus)
df = pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
from sklearn.cluster import KMeans
number_of_clusters = 10
km = KMeans(n_clusters=number_of_clusters)
km.fit(matrix)
order_centroids = km.cluster_centers_.argsort()[:, ::-1]
terms = vec.get_feature_names()
for i in range(number_of_clusters):
top_ten_words = [terms[ind] for ind in order_centroids[i, :5]]
results = pd.DataFrame({'corpus': titleCorpus, 'category': km.labels_})
results.sort_values('category')
for k in results.sort_values('category').values:
print(k[1], ' --- ', k[0]) | code |
50215202/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
dfpub | code |
50215202/cell_23 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import nltk
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1 if today not in array else array[today] + 1
x = list(array.keys())
y = list(array.values())
df_plot = pd.DataFrame()
df_plot['x'] = x
df_plot['y'] = y
df_plot.index = x
ax = plt.gca()
ax.xaxis.set_major_locator(mdates.DayLocator(interval=13))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gcf().autofmt_xdate()
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
from sklearn.feature_extraction.text import TfidfVectorizer
vec = TfidfVectorizer(use_idf=False, norm='l1')
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
from textblob import TextBlob
import nltk
nltk.download('punkt')
def textblob_tokenizer(str_input):
blob = TextBlob(str_input.lower())
tokens = blob.words
words = [token.stem() for token in tokens]
return words
vec = CountVectorizer(tokenizer=textblob_tokenizer)
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
vec = TfidfVectorizer(tokenizer=textblob_tokenizer, stop_words='english', use_idf=True)
matrix = vec.fit_transform(titleCorpus)
df = pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
from sklearn.cluster import KMeans
number_of_clusters = 10
km = KMeans(n_clusters=number_of_clusters)
km.fit(matrix)
print('Top terms per cluster:')
order_centroids = km.cluster_centers_.argsort()[:, ::-1]
terms = vec.get_feature_names()
for i in range(number_of_clusters):
top_ten_words = [terms[ind] for ind in order_centroids[i, :5]]
print('Cluster {}: {}'.format(i, ' '.join(top_ten_words))) | code |
50215202/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import nltk
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1 if today not in array else array[today] + 1
x = list(array.keys())
y = list(array.values())
df_plot = pd.DataFrame()
df_plot['x'] = x
df_plot['y'] = y
df_plot.index = x
ax = plt.gca()
ax.xaxis.set_major_locator(mdates.DayLocator(interval=13))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gcf().autofmt_xdate()
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
from sklearn.feature_extraction.text import TfidfVectorizer
vec = TfidfVectorizer(use_idf=False, norm='l1')
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
from textblob import TextBlob
import nltk
nltk.download('punkt')
def textblob_tokenizer(str_input):
blob = TextBlob(str_input.lower())
tokens = blob.words
words = [token.stem() for token in tokens]
return words
vec = CountVectorizer(tokenizer=textblob_tokenizer)
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names()) | code |
50215202/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
dfpub['browsing_date'] | code |
50215202/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1 if today not in array else array[today] + 1
x = list(array.keys())
y = list(array.values())
df_plot = pd.DataFrame()
df_plot['x'] = x
df_plot['y'] = y
df_plot.index = x
ax = plt.gca()
ax.xaxis.set_major_locator(mdates.DayLocator(interval=13))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gcf().autofmt_xdate()
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
from sklearn.feature_extraction.text import TfidfVectorizer
vec = TfidfVectorizer(use_idf=False, norm='l1')
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names()) | code |
50215202/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 |
50215202/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
print(c)
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1 if today not in array else array[today] + 1 | code |
50215202/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(titleCorpus)
print('Count: {0}'.format(len(vectorizer.get_feature_names())))
vectorizer.get_feature_names()[1000:1030] | code |
50215202/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1 if today not in array else array[today] + 1
x = list(array.keys())
y = list(array.values())
print(len(x))
print(len(y))
df_plot = pd.DataFrame()
df_plot['x'] = x
df_plot['y'] = y
df_plot.index = x | code |
50215202/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
len(failedConvert) | code |
50215202/cell_16 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
len(titleCorpus) | code |
50215202/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
len(wordListCorpus) | code |
50215202/cell_22 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
import matplotlib.dates as mdates
import matplotlib.pylab as plt
import nltk
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1 if today not in array else array[today] + 1
x = list(array.keys())
y = list(array.values())
df_plot = pd.DataFrame()
df_plot['x'] = x
df_plot['y'] = y
df_plot.index = x
ax = plt.gca()
ax.xaxis.set_major_locator(mdates.DayLocator(interval=13))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gcf().autofmt_xdate()
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
from sklearn.feature_extraction.text import TfidfVectorizer
vec = TfidfVectorizer(use_idf=False, norm='l1')
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
from textblob import TextBlob
import nltk
nltk.download('punkt')
def textblob_tokenizer(str_input):
blob = TextBlob(str_input.lower())
tokens = blob.words
words = [token.stem() for token in tokens]
return words
vec = CountVectorizer(tokenizer=textblob_tokenizer)
matrix = vec.fit_transform(titleCorpus)
pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
vec = TfidfVectorizer(tokenizer=textblob_tokenizer, stop_words='english', use_idf=True)
matrix = vec.fit_transform(titleCorpus)
df = pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())
from sklearn.cluster import KMeans
number_of_clusters = 10
km = KMeans(n_clusters=number_of_clusters)
km.fit(matrix) | code |
50215202/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.dates as mdates
import matplotlib.pylab as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1 if today not in array else array[today] + 1
x = list(array.keys())
y = list(array.values())
df_plot = pd.DataFrame()
df_plot['x'] = x
df_plot['y'] = y
df_plot.index = x
plt.figure(figsize=(15, 6))
plt.bar(pd.to_datetime(df_plot['x']), df_plot['y'])
ax = plt.gca()
ax.xaxis.set_major_locator(mdates.DayLocator(interval=13))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gcf().autofmt_xdate()
plt.show() | code |
50215202/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import word2vec
from nltk.corpus import stopwords
import io
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfpub = pd.read_csv('/kaggle/input/academic-publications-and-journals/wiki_query_22_12_2020.csv', encoding='iso-8859-1')
array = {}
for c in dfpub['browsing_date']:
if '1601' in c:
continue
today = c.split()[0]
array[today] = 1 if today not in array else array[today] + 1
x = list(array.keys())
y = list(array.values())
df_plot = pd.DataFrame()
df_plot['x'] = x
df_plot['y'] = y
df_plot.index = x
wordListCorpus = []
titleCorpus = []
failedConvert = []
for row in dfpub['tags'].values:
try:
tags = row.split(',')
title = ' '.join(row.split(','))
except AttributeError:
failedConvert.append(row)
for k in tags:
wordListCorpus.append(k)
titleCorpus.append(title)
len(wordListCorpus)
from gensim.models import word2vec
from gensim.test.utils import common_texts, get_tmpfile
tokenized_sentences = [[j.lower() for j in st.split() if j not in stopwords.words('english')] for st in titleCorpus]
model = word2vec.Word2Vec(tokenized_sentences, min_count=1)
model.save('word2vec.model')
import io
out_v = io.open('vecs.tsv', 'w', encoding='utf-8')
out_m = io.open('meta.tsv', 'w', encoding='utf-8')
for word in model.wv.vocab:
out_m.write(word + '\n')
out_v.write('\t'.join([str(x) for x in model[word]]) + '\n')
out_v.close()
out_m.close() | code |
16154375/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
dfs = [train_df, potential_energy_df, mulliken_charges_df, scalar_coupling_contributions_df, magnetic_shielding_tensors_df, dipole_moments_df, structure_df, test_df]
names = ['train_df', 'potential_energy_df', 'mulliken_charges_df', 'scalar_coupling_contributions_df', 'magnetic_shielding_tensors_df', 'dipole_moments_df', 'structure_df', 'test_df']
def dispDF(df, name):
pass
pd.set_option('display.expand_frame_repr', False)
for df, name in zip(dfs, names):
dispDF(df, name)
dispDF(potential_energy_df, 'potential energy') | code |
16154375/cell_4 | [
"image_output_1.png"
] | import warnings
import seaborn as sns
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
print('Libraries were loaded.') | code |
16154375/cell_30 | [
"text_plain_output_1.png"
] | 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
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
colors = sns.color_palette('cubehelix', 8)
sns.set()
subsample = 100
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
scatter_colors = sns.color_palette("husl", 85003)
# 3D scatter
ax.scatter(dipole_moments_df['X'][::subsample], dipole_moments_df['Y'][::subsample],
dipole_moments_df['Z'][::subsample], s=30, alpha=0.5, c=scatter_colors[::subsample])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Dipole Moment')
distances = np.asarray([x**2 + y**2 + z**2 for x, y, z in zip(dipole_moments_df['X'],dipole_moments_df['Y'], dipole_moments_df['Z'])])
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax = ax.flatten()
# original distribution
sns.distplot(distances, color=colors[0], kde=False, norm_hist=False, ax=ax[0])
ax[0].set_xlabel('distance')
# in log
sns.distplot(np.log(distances + 0.00001), color=colors[0], kde=False, norm_hist=False, ax=ax[1])
ax[1].set_xlabel('log distance')
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
scatter_colors = sns.color_palette("husl", 29)
# 3D scatter
for i in range(29):
xx = magnetic_shielding_tensors_df.loc[magnetic_shielding_tensors_df['atom_index']==i, 'XX']
yy = magnetic_shielding_tensors_df.loc[magnetic_shielding_tensors_df['atom_index']==i, 'YY']
zz = magnetic_shielding_tensors_df.loc[magnetic_shielding_tensors_df['atom_index']==i, 'ZZ']
ax.scatter(xx[::subsample*100], yy[::subsample*100], zz[::subsample*100], s=30, alpha=0.5, c=scatter_colors[i])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Magnetic shielding tensors')
# potential energy
fig, ax = plt.subplots(2, 1, figsize=(12, 8))
ax = ax.flatten()
sns.distplot(potential_energy_df['potential_energy'],
kde=False, color=colors[0], ax=ax[0])
ax[1].plot(np.arange(0, 85003, subsample*10), potential_energy_df['potential_energy'][::subsample*10],
c=colors[0], alpha=0.5)
ax[1].set_xlabel('molecular name')
ax[1].set_ylabel('potential energy')
plt.tight_layout()
fig, ax = plt.subplots(6, 5, figsize=(12, 16))
ax = ax.flatten()
for i in range(29):
sns.distplot(mulliken_charges_df.loc[mulliken_charges_df['atom_index'] == i, 'mulliken_charge'], kde=False, color=colors[2], ax=ax[i])
ax[i].set_title('atom index ' + str(i))
ax[i].set_xlabel('')
ax[i].set_ylabel('')
plt.tight_layout() | code |
16154375/cell_33 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
dfs = [train_df, potential_energy_df, mulliken_charges_df, scalar_coupling_contributions_df, magnetic_shielding_tensors_df, dipole_moments_df, structure_df, test_df]
names = ['train_df', 'potential_energy_df', 'mulliken_charges_df', 'scalar_coupling_contributions_df', 'magnetic_shielding_tensors_df', 'dipole_moments_df', 'structure_df', 'test_df']
def dispDF(df, name):
pass
pd.set_option('display.expand_frame_repr', False)
for df, name in zip(dfs, names):
dispDF(df, name)
dispDF(scalar_coupling_contributions_df, 'scalar coupling contributions') | code |
16154375/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
dfs = [train_df, potential_energy_df, mulliken_charges_df, scalar_coupling_contributions_df, magnetic_shielding_tensors_df, dipole_moments_df, structure_df, test_df]
names = ['train_df', 'potential_energy_df', 'mulliken_charges_df', 'scalar_coupling_contributions_df', 'magnetic_shielding_tensors_df', 'dipole_moments_df', 'structure_df', 'test_df']
def dispDF(df, name):
pass
pd.set_option('display.expand_frame_repr', False)
for df, name in zip(dfs, names):
dispDF(df, name)
dispDF(magnetic_shielding_tensors_df, 'magnetic shielding tensors') | code |
16154375/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
print('All the data were loaded.') | code |
16154375/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
dfs = [train_df, potential_energy_df, mulliken_charges_df, scalar_coupling_contributions_df, magnetic_shielding_tensors_df, dipole_moments_df, structure_df, test_df]
names = ['train_df', 'potential_energy_df', 'mulliken_charges_df', 'scalar_coupling_contributions_df', 'magnetic_shielding_tensors_df', 'dipole_moments_df', 'structure_df', 'test_df']
def dispDF(df, name):
pass
pd.set_option('display.expand_frame_repr', False)
for df, name in zip(dfs, names):
dispDF(df, name)
dispDF(mulliken_charges_df, 'mulliken charges') | code |
16154375/cell_26 | [
"text_plain_output_1.png"
] | 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
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
colors = sns.color_palette('cubehelix', 8)
sns.set()
subsample = 100
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
scatter_colors = sns.color_palette("husl", 85003)
# 3D scatter
ax.scatter(dipole_moments_df['X'][::subsample], dipole_moments_df['Y'][::subsample],
dipole_moments_df['Z'][::subsample], s=30, alpha=0.5, c=scatter_colors[::subsample])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Dipole Moment')
distances = np.asarray([x**2 + y**2 + z**2 for x, y, z in zip(dipole_moments_df['X'],dipole_moments_df['Y'], dipole_moments_df['Z'])])
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax = ax.flatten()
# original distribution
sns.distplot(distances, color=colors[0], kde=False, norm_hist=False, ax=ax[0])
ax[0].set_xlabel('distance')
# in log
sns.distplot(np.log(distances + 0.00001), color=colors[0], kde=False, norm_hist=False, ax=ax[1])
ax[1].set_xlabel('log distance')
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
scatter_colors = sns.color_palette("husl", 29)
# 3D scatter
for i in range(29):
xx = magnetic_shielding_tensors_df.loc[magnetic_shielding_tensors_df['atom_index']==i, 'XX']
yy = magnetic_shielding_tensors_df.loc[magnetic_shielding_tensors_df['atom_index']==i, 'YY']
zz = magnetic_shielding_tensors_df.loc[magnetic_shielding_tensors_df['atom_index']==i, 'ZZ']
ax.scatter(xx[::subsample*100], yy[::subsample*100], zz[::subsample*100], s=30, alpha=0.5, c=scatter_colors[i])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Magnetic shielding tensors')
fig, ax = plt.subplots(2, 1, figsize=(12, 8))
ax = ax.flatten()
sns.distplot(potential_energy_df['potential_energy'], kde=False, color=colors[0], ax=ax[0])
ax[1].plot(np.arange(0, 85003, subsample * 10), potential_energy_df['potential_energy'][::subsample * 10], c=colors[0], alpha=0.5)
ax[1].set_xlabel('molecular name')
ax[1].set_ylabel('potential energy')
plt.tight_layout() | code |
16154375/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16154375/cell_18 | [
"text_plain_output_1.png"
] | 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
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
colors = sns.color_palette('cubehelix', 8)
sns.set()
subsample = 100
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
scatter_colors = sns.color_palette("husl", 85003)
# 3D scatter
ax.scatter(dipole_moments_df['X'][::subsample], dipole_moments_df['Y'][::subsample],
dipole_moments_df['Z'][::subsample], s=30, alpha=0.5, c=scatter_colors[::subsample])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Dipole Moment')
distances = np.asarray([x**2 + y**2 + z**2 for x, y, z in zip(dipole_moments_df['X'],dipole_moments_df['Y'], dipole_moments_df['Z'])])
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax = ax.flatten()
# original distribution
sns.distplot(distances, color=colors[0], kde=False, norm_hist=False, ax=ax[0])
ax[0].set_xlabel('distance')
# in log
sns.distplot(np.log(distances + 0.00001), color=colors[0], kde=False, norm_hist=False, ax=ax[1])
ax[1].set_xlabel('log distance')
outliers_dipole_moment = [m for i, m in enumerate(dipole_moments_df['molecule_name']) if distances[i] > 100]
print('outliers (dipole moments): ' + str(len(outliers_dipole_moment)) + ' molecules')
print(str(outliers_dipole_moment)) | code |
16154375/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
dfs = [train_df, potential_energy_df, mulliken_charges_df, scalar_coupling_contributions_df, magnetic_shielding_tensors_df, dipole_moments_df, structure_df, test_df]
names = ['train_df', 'potential_energy_df', 'mulliken_charges_df', 'scalar_coupling_contributions_df', 'magnetic_shielding_tensors_df', 'dipole_moments_df', 'structure_df', 'test_df']
def dispDF(df, name):
print('========== ' + name + ' ==========')
print('SHAPE:')
print(df.shape)
print('HEAD:')
print(df.head(5))
print('DATA TYPE:')
print(df.dtypes)
print('UNIQUES:')
print(df.nunique())
print('======================================')
pd.set_option('display.expand_frame_repr', False)
for df, name in zip(dfs, names):
dispDF(df, name) | code |
16154375/cell_16 | [
"text_plain_output_1.png"
] | 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
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
colors = sns.color_palette('cubehelix', 8)
sns.set()
subsample = 100
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
scatter_colors = sns.color_palette("husl", 85003)
# 3D scatter
ax.scatter(dipole_moments_df['X'][::subsample], dipole_moments_df['Y'][::subsample],
dipole_moments_df['Z'][::subsample], s=30, alpha=0.5, c=scatter_colors[::subsample])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Dipole Moment')
distances = np.asarray([x ** 2 + y ** 2 + z ** 2 for x, y, z in zip(dipole_moments_df['X'], dipole_moments_df['Y'], dipole_moments_df['Z'])])
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax = ax.flatten()
sns.distplot(distances, color=colors[0], kde=False, norm_hist=False, ax=ax[0])
ax[0].set_xlabel('distance')
sns.distplot(np.log(distances + 1e-05), color=colors[0], kde=False, norm_hist=False, ax=ax[1])
ax[1].set_xlabel('log distance') | code |
16154375/cell_14 | [
"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
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
colors = sns.color_palette('cubehelix', 8)
sns.set()
subsample = 100
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
scatter_colors = sns.color_palette('husl', 85003)
ax.scatter(dipole_moments_df['X'][::subsample], dipole_moments_df['Y'][::subsample], dipole_moments_df['Z'][::subsample], s=30, alpha=0.5, c=scatter_colors[::subsample])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Dipole Moment') | code |
16154375/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
colors = sns.color_palette('cubehelix', 8)
sns.set()
subsample = 100
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
scatter_colors = sns.color_palette("husl", 85003)
# 3D scatter
ax.scatter(dipole_moments_df['X'][::subsample], dipole_moments_df['Y'][::subsample],
dipole_moments_df['Z'][::subsample], s=30, alpha=0.5, c=scatter_colors[::subsample])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Dipole Moment')
distances = np.asarray([x**2 + y**2 + z**2 for x, y, z in zip(dipole_moments_df['X'],dipole_moments_df['Y'], dipole_moments_df['Z'])])
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax = ax.flatten()
# original distribution
sns.distplot(distances, color=colors[0], kde=False, norm_hist=False, ax=ax[0])
ax[0].set_xlabel('distance')
# in log
sns.distplot(np.log(distances + 0.00001), color=colors[0], kde=False, norm_hist=False, ax=ax[1])
ax[1].set_xlabel('log distance')
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
scatter_colors = sns.color_palette('husl', 29)
for i in range(29):
xx = magnetic_shielding_tensors_df.loc[magnetic_shielding_tensors_df['atom_index'] == i, 'XX']
yy = magnetic_shielding_tensors_df.loc[magnetic_shielding_tensors_df['atom_index'] == i, 'YY']
zz = magnetic_shielding_tensors_df.loc[magnetic_shielding_tensors_df['atom_index'] == i, 'ZZ']
ax.scatter(xx[::subsample * 100], yy[::subsample * 100], zz[::subsample * 100], s=30, alpha=0.5, c=scatter_colors[i])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Magnetic shielding tensors') | code |
16154375/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
potential_energy_df = pd.read_csv('../input/potential_energy.csv')
mulliken_charges_df = pd.read_csv('../input/mulliken_charges.csv')
scalar_coupling_contributions_df = pd.read_csv('../input/scalar_coupling_contributions.csv')
magnetic_shielding_tensors_df = pd.read_csv('../input/magnetic_shielding_tensors.csv')
dipole_moments_df = pd.read_csv('../input/dipole_moments.csv')
structure_df = pd.read_csv('../input/structures.csv')
test_df = pd.read_csv('../input/test.csv')
dfs = [train_df, potential_energy_df, mulliken_charges_df, scalar_coupling_contributions_df, magnetic_shielding_tensors_df, dipole_moments_df, structure_df, test_df]
names = ['train_df', 'potential_energy_df', 'mulliken_charges_df', 'scalar_coupling_contributions_df', 'magnetic_shielding_tensors_df', 'dipole_moments_df', 'structure_df', 'test_df']
def dispDF(df, name):
pass
pd.set_option('display.expand_frame_repr', False)
for df, name in zip(dfs, names):
dispDF(df, name)
dispDF(dipole_moments_df, 'dipole moments') | code |
50239726/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data.isnull().sum() | code |
50239726/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
map1 = sns.FacetGrid(train_data, col='Pclass', row='Sex')
map1.map_dataframe(sns.histplot, x='Survived') | code |
50239726/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
print('Survival rate of adult males:', ((train_data['Survived'] == True) & (train_data['Sex'] == 'male') & (train_data['Age'] > 14)).sum() / (train_data['Sex'] == 'male').sum(), '%') | code |
50239726/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data.head() | code |
50239726/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
print('Survival rate of females:', ((train_data['Survived'] == True) & (train_data['Sex'] == 'female')).sum() / (train_data['Sex'] == 'female').sum(), '%') | code |
50239726/cell_1 | [
"text_plain_output_1.png"
] | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
!pip install seaborn --upgrade | code |
50239726/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
print('Survival rate of males:', ((train_data['Survived'] == True) & (train_data['Sex'] == 'male')).sum() / (train_data['Sex'] == 'male').sum(), '%') | code |
50239726/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
print('Percertage survived (train_data):', (train_data['Survived'] == True).sum() * 100 / train_data.shape[0], '%') | code |
50239726/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
print('Survival rate of females:', ((train_data['Survived'] == True) & (train_data['Sex'] == 'female')).sum() / (train_data['Sex'] == 'female').sum(), '%') | code |
50239726/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
sns.countplot(x='Survived', hue='Pclass', data=train_data) | code |
50239726/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
print('Total size of train_data:', train_data.shape)
print('Total size of test_data:', test_data.shape) | code |
50239726/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum() | code |
50239726/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)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.head(20) | code |
309683/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_csv('../input/like.csv')
members = pd.read_csv('../input/member.csv')
posts = pd.read_csv('../input/post.csv')
likeResponse = pd.merge(likes.loc[likes['gid'] == 117291968282998], posts.loc[posts['gid'] == 117291968282998, ['pid', 'name']], left_on='pid', right_on='pid')
result = likeResponse.groupby(['name_y', 'name_x'])['response'].count()
finalResult = pd.DataFrame(result.index.values, columns=['NameCombo'])
finalResult['Weight'] = result.values
finalResult['From'] = finalResult['NameCombo'].map(lambda x: x[0])
finalResult['To'] = finalResult['NameCombo'].map(lambda x: x[1])
del finalResult['NameCombo']
g = nx.Graph()
g.add_edges_from([(row['From'], row['To']) for index, row in finalResult.iterrows()])
d = nx.degree(g)
spring_pos = nx.spring_layout(g)
plt.axis('off')
plt.clf()
g.number_of_nodes()
spring_pos = nx.spring_layout(g, scale=2)
nx.draw(g, spring_pos, with_labels=False, nodelist=d.keys(), node_size=[v * 5 for v in d.values()]) | code |
309683/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_csv('../input/like.csv')
members = pd.read_csv('../input/member.csv')
posts = pd.read_csv('../input/post.csv')
likeResponse = pd.merge(likes.loc[likes['gid'] == 117291968282998], posts.loc[posts['gid'] == 117291968282998, ['pid', 'name']], left_on='pid', right_on='pid')
result = likeResponse.groupby(['name_y', 'name_x'])['response'].count()
finalResult = pd.DataFrame(result.index.values, columns=['NameCombo'])
finalResult['Weight'] = result.values
finalResult['From'] = finalResult['NameCombo'].map(lambda x: x[0])
finalResult['To'] = finalResult['NameCombo'].map(lambda x: x[1])
del finalResult['NameCombo']
g = nx.Graph()
plt.figure()
g.add_edges_from([(row['From'], row['To']) for index, row in finalResult.iterrows()])
d = nx.degree(g)
spring_pos = nx.spring_layout(g)
plt.axis('off')
nx.draw_networkx(g, spring_pos, with_labels=False, nodelist=d.keys(), node_size=[v * 10 for v in d.values()])
plt.savefig('LIKE_PLOT_GROUP1.png')
plt.clf() | code |
309683/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_csv('../input/like.csv')
members = pd.read_csv('../input/member.csv')
posts = pd.read_csv('../input/post.csv')
likeResponse = pd.merge(likes.loc[likes['gid'] == 117291968282998], posts.loc[posts['gid'] == 117291968282998, ['pid', 'name']], left_on='pid', right_on='pid')
result = likeResponse.groupby(['name_y', 'name_x'])['response'].count()
finalResult = pd.DataFrame(result.index.values, columns=['NameCombo'])
finalResult['Weight'] = result.values
finalResult['From'] = finalResult['NameCombo'].map(lambda x: x[0])
finalResult['To'] = finalResult['NameCombo'].map(lambda x: x[1])
del finalResult['NameCombo']
g = nx.Graph()
g.add_edges_from([(row['From'], row['To']) for index, row in finalResult.iterrows()])
d = nx.degree(g)
spring_pos = nx.spring_layout(g)
plt.axis('off')
plt.clf()
f = open('g.json', 'w')
f.write('{"nodes":[')
str1 = ''
for i in finalResult['From'].unique():
str1 += '{"name":"' + str(i) + '","group":' + str(1) + '},'
f.write(str1[:-1])
f.write('],"links":[')
str1 = ''
for i in range(len(finalResult)):
str1 += '{"source":' + str(finalResult['From'][i]) + ',"target":' + str(finalResult['To'][i]) + ',"value":' + str(finalResult['Weight'][i]) + '},'
f.write(str1[:-1])
f.write(']}')
f.close
h1 = '\n<!DOCTYPE html>\n<meta charset="utf-8">\n<style>\n.link {stroke: #ccc;}\n.node text {pointer-events: none; font: 10px sans-serif;}\n</style>\n<body>\n<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js"></script>\n<script>\nvar width = 800, height = 800;\nvar color = d3.scale.category20();\nvar force = d3.layout.force()\n .charge(-120)\n .linkDistance(80)\n .size([width, height]);\nvar svg = d3.select("body").append("svg")\n .attr("width", width)\n .attr("height", height);\nd3.json("g.json", function(error, graph) {\n if (error) throw error;\n\tforce.nodes(graph.nodes)\n\t .links(graph.links)\n\t .start();\n\tvar link = svg.selectAll(".link")\n\t .data(graph.links)\n\t .enter().append("line")\n\t .attr("class", "link")\n\t .style("stroke-width", function (d) {return Math.sqrt(d.value);});\n\tvar node = svg.selectAll(".node")\n\t .data(graph.nodes)\n\t .enter().append("g")\n\t .attr("class", "node")\n\t .call(force.drag);\n\tnode.append("circle")\n\t .attr("r", 8)\n\t .style("fill", function (d) {return color(d.group);})\n\tnode.append("text")\n\t .attr("dx", 10)\n\t .attr("dy", ".35em")\n\t .text(function(d) { return d.name });\n\tforce.on("tick", function () {\n\t link.attr("x1", function (d) {return d.source.x;})\n\t\t.attr("y1", function (d) {return d.source.y;})\n\t\t.attr("x2", function (d) {return d.target.x;})\n\t\t.attr("y2", function (d) {return d.target.y;});\n\t d3.selectAll("circle").attr("cx", function (d) {return d.x;})\n\t\t.attr("cy", function (d) {return d.y;});\n\t d3.selectAll("text").attr("x", function (d) {return d.x;})\n\t\t.attr("y", function (d) {return d.y;});\n });\n});\n</script>\n'
f = open('output.html', 'w')
f.write(h1)
f.close | code |
32068206/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
item_categories.describe() | code |
32068206/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
print('Count of categories with count of items 100-1000 is', len(list(filter(lambda x: 100 <= x <= 1000, items['item_category_id'].value_counts(ascending=True))))) | code |
32068206/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items.info() | code |
32068206/cell_57 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
test.describe() | code |
32068206/cell_56 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
test.info() | code |
32068206/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sales_train.info() | code |
32068206/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sales_train.head(5) | code |
32068206/cell_44 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sns.set(rc={'figure.figsize':(11.7,12)})
ax = sns.countplot(y = 'item_category_id',
data = items,
order = items['item_category_id'].value_counts(ascending=True).index)
sales_train.groupby('shop_id').mean()
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sales_train.groupby('shop_id').mean().index, y=sales_train.groupby('shop_id').mean()['item_cnt_day'], color="salmon")
sales_train.groupby('shop_id').sum()
sub_sales_df = sales_train.groupby('shop_id').sum()
sub_sales_df['index_shop'] = sub_sales_df.index
sub_sales_df = sub_sales_df.sort_values(['item_cnt_day']).reset_index(drop=True)
print('Count of prices overall:', len(sales_train))
print('Count of prices < 50000:', len(sales_train[sales_train['item_price'] < 50000]))
print('Count of prices 50000 <= x <= 250000:', len(sales_train) - len(sales_train[sales_train['item_price'] > 250000]) - len(sales_train[sales_train['item_price'] < 50000]))
print('Count of prices > 250000:', len(sales_train[sales_train['item_price'] > 250000])) | code |
32068206/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
item_categories.info() | code |
32068206/cell_55 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
test.head(5) | code |
32068206/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sns.set(rc={'figure.figsize':(11.7,12)})
ax = sns.countplot(y = 'item_category_id',
data = items,
order = items['item_category_id'].value_counts(ascending=True).index)
sales_train.groupby('shop_id').mean()
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sales_train.groupby('shop_id').mean().index, y=sales_train.groupby('shop_id').mean()['item_cnt_day'], color="salmon")
sales_train.groupby('shop_id').sum() | code |
32068206/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
shops.head(5) | code |
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