path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
72120119/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
df_items = pd.merge(left=items, right=items_cat, on='item_category_id')
df_shops = pd.merge(left=train, right=shops, on='shop_id')
df = pd.merge(left=df_shops, right=df_items, on='item_id')
df.shape
df.nunique() | code |
333521/cell_13 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn import cross_validation
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import operator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
titanic['Embarked'] = titanic['Embarked'].fillna('S')
titanic.loc[titanic['Embarked'] == 'S', 'Embarked'] = 0
titanic.loc[titanic['Embarked'] == 'C', 'Embarked'] = 1
titanic.loc[titanic['Embarked'] == 'Q', 'Embarked'] = 2
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
alg = LinearRegression()
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
train_predictors = titanic[predictors].iloc[train, :]
train_target = titanic['Survived'].iloc[train]
alg.fit(train_predictors, train_target)
test_predictions = alg.predict(titanic[predictors].iloc[test, :])
predictions.append(test_predictions)
predictions = np.concatenate(predictions, axis=0)
predictions[predictions > 0.5] = 1
predictions[predictions <= 0.5] = 0
accuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)
from sklearn.linear_model import LogisticRegression
from sklearn import cross_validation
alg = LogisticRegression(random_state=1)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)
titanic_test['Age'] = titanic_test['Age'].fillna(titanic['Age'].median())
titanic_test.loc[titanic_test['Sex'] == 'male', 'Sex'] = 0
titanic_test.loc[titanic_test['Sex'] == 'female', 'Sex'] = 1
titanic_test['Embarked'] = titanic_test['Embarked'].fillna('S')
titanic_test.loc[titanic_test['Embarked'] == 'S', 'Embarked'] = 0
titanic_test.loc[titanic_test['Embarked'] == 'C', 'Embarked'] = 1
titanic_test.loc[titanic_test['Embarked'] == 'Q', 'Embarked'] = 2
titanic_test['Fare'] = titanic_test['Fare'].fillna(titanic_test['Fare'].median())
alg = LogisticRegression(random_state=1)
alg.fit(titanic[predictors], titanic['Survived'])
predictions = alg.predict(titanic_test[predictors])
submission = pd.DataFrame({'PassengerId': titanic_test['PassengerId'], 'Survived': predictions})
from sklearn import cross_validation
from sklearn.ensemble import RandomForestClassifier
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=4, min_samples_leaf=2)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)
titanic['FamilySize'] = titanic['SibSp'] + titanic['Parch']
titanic['NameLength'] = titanic['Name'].apply(lambda x: len(x))
import re
def get_title(name):
title_search = re.search(' ([A-za-z]+)\\.', name)
if title_search:
return title_search.group(1)
return ''
titles = titanic['Name'].apply(get_title)
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Dr': 5, 'Rev': 6, 'Major': 7, 'Col': 8, 'Mlle': 8, 'Mme': 8, 'Don': 9, 'Lady': 10, 'Countess': 10, 'Jonkheer': 10, 'Sir': 9, 'Capt': 7, 'Ms': 2}
for k, v in title_mapping.items():
titles[titles == k] = v
titanic['Title'] = titles
import operator
family_id_mapping = {}
def get_family_id(row):
last_name = row['Name'].split(',')[0]
family_id = '{0}{1}'.format(last_name, row['FamilySize'])
if family_id not in family_id_mapping:
if len(family_id_mapping) == 0:
current_id = 1
else:
current_id = max(family_id_mapping.items(), key=operator.itemgetter(1))[1] + 1
family_id_mapping[family_id] = current_id
return family_id_mapping[family_id]
family_ids = titanic.apply(get_family_id, axis=1)
family_ids[titanic['FamilySize'] < 3] = -1
titanic['FamilyId'] = family_ids
from sklearn.feature_selection import SelectKBest, f_classif
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'FamilySize', 'Title', 'FamilyId']
selector = SelectKBest(f_classif, k=5)
selector.fit(titanic[predictors], titanic['Survived'])
scores = -np.log10(selector.pvalues_)
import matplotlib.pyplot as plt
plt.bar(range(len(predictors)), scores)
plt.xticks(range(len(predictors)), predictors, rotation='vertical')
plt.show()
predictors = ['Pclass', 'Sex', 'Fare', 'Title']
alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)
print(scores.mean()) | code |
333521/cell_4 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
titanic['Embarked'] = titanic['Embarked'].fillna('S')
titanic.loc[titanic['Embarked'] == 'S', 'Embarked'] = 0
titanic.loc[titanic['Embarked'] == 'C', 'Embarked'] = 1
titanic.loc[titanic['Embarked'] == 'Q', 'Embarked'] = 2
print(titanic['Embarked'].unique()) | code |
333521/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
titanic['Embarked'] = titanic['Embarked'].fillna('S')
titanic.loc[titanic['Embarked'] == 'S', 'Embarked'] = 0
titanic.loc[titanic['Embarked'] == 'C', 'Embarked'] = 1
titanic.loc[titanic['Embarked'] == 'Q', 'Embarked'] = 2
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
alg = LinearRegression()
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
train_predictors = titanic[predictors].iloc[train, :]
train_target = titanic['Survived'].iloc[train]
alg.fit(train_predictors, train_target)
test_predictions = alg.predict(titanic[predictors].iloc[test, :])
predictions.append(test_predictions)
predictions = np.concatenate(predictions, axis=0)
predictions[predictions > 0.5] = 1
predictions[predictions <= 0.5] = 0
accuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)
print(accuracy) | code |
333521/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic.describe() | code |
333521/cell_11 | [
"text_html_output_1.png"
] | from sklearn import cross_validation
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
titanic['Embarked'] = titanic['Embarked'].fillna('S')
titanic.loc[titanic['Embarked'] == 'S', 'Embarked'] = 0
titanic.loc[titanic['Embarked'] == 'C', 'Embarked'] = 1
titanic.loc[titanic['Embarked'] == 'Q', 'Embarked'] = 2
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
alg = LinearRegression()
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
train_predictors = titanic[predictors].iloc[train, :]
train_target = titanic['Survived'].iloc[train]
alg.fit(train_predictors, train_target)
test_predictions = alg.predict(titanic[predictors].iloc[test, :])
predictions.append(test_predictions)
predictions = np.concatenate(predictions, axis=0)
predictions[predictions > 0.5] = 1
predictions[predictions <= 0.5] = 0
accuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)
from sklearn.linear_model import LogisticRegression
from sklearn import cross_validation
alg = LogisticRegression(random_state=1)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)
titanic_test['Age'] = titanic_test['Age'].fillna(titanic['Age'].median())
titanic_test.loc[titanic_test['Sex'] == 'male', 'Sex'] = 0
titanic_test.loc[titanic_test['Sex'] == 'female', 'Sex'] = 1
titanic_test['Embarked'] = titanic_test['Embarked'].fillna('S')
titanic_test.loc[titanic_test['Embarked'] == 'S', 'Embarked'] = 0
titanic_test.loc[titanic_test['Embarked'] == 'C', 'Embarked'] = 1
titanic_test.loc[titanic_test['Embarked'] == 'Q', 'Embarked'] = 2
titanic_test['Fare'] = titanic_test['Fare'].fillna(titanic_test['Fare'].median())
alg = LogisticRegression(random_state=1)
alg.fit(titanic[predictors], titanic['Survived'])
predictions = alg.predict(titanic_test[predictors])
submission = pd.DataFrame({'PassengerId': titanic_test['PassengerId'], 'Survived': predictions})
titanic['FamilySize'] = titanic['SibSp'] + titanic['Parch']
titanic['NameLength'] = titanic['Name'].apply(lambda x: len(x))
import re
def get_title(name):
title_search = re.search(' ([A-za-z]+)\\.', name)
if title_search:
return title_search.group(1)
return ''
titles = titanic['Name'].apply(get_title)
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Dr': 5, 'Rev': 6, 'Major': 7, 'Col': 8, 'Mlle': 8, 'Mme': 8, 'Don': 9, 'Lady': 10, 'Countess': 10, 'Jonkheer': 10, 'Sir': 9, 'Capt': 7, 'Ms': 2}
for k, v in title_mapping.items():
titles[titles == k] = v
print(pd.value_counts(titles))
titanic['Title'] = titles | code |
333521/cell_1 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic.head() | code |
333521/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
titanic['Embarked'] = titanic['Embarked'].fillna('S')
titanic.loc[titanic['Embarked'] == 'S', 'Embarked'] = 0
titanic.loc[titanic['Embarked'] == 'C', 'Embarked'] = 1
titanic.loc[titanic['Embarked'] == 'Q', 'Embarked'] = 2
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
alg = LinearRegression()
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
train_predictors = titanic[predictors].iloc[train, :]
train_target = titanic['Survived'].iloc[train]
alg.fit(train_predictors, train_target)
test_predictions = alg.predict(titanic[predictors].iloc[test, :])
predictions.append(test_predictions)
from sklearn.linear_model import LogisticRegression
from sklearn import cross_validation
alg = LogisticRegression(random_state=1)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)
print(scores.mean()) | code |
333521/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_7.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())
titanic.describe() | code |
333521/cell_10 | [
"text_html_output_1.png"
] | from sklearn import cross_validation
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
titanic['Embarked'] = titanic['Embarked'].fillna('S')
titanic.loc[titanic['Embarked'] == 'S', 'Embarked'] = 0
titanic.loc[titanic['Embarked'] == 'C', 'Embarked'] = 1
titanic.loc[titanic['Embarked'] == 'Q', 'Embarked'] = 2
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
alg = LinearRegression()
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
train_predictors = titanic[predictors].iloc[train, :]
train_target = titanic['Survived'].iloc[train]
alg.fit(train_predictors, train_target)
test_predictions = alg.predict(titanic[predictors].iloc[test, :])
predictions.append(test_predictions)
predictions = np.concatenate(predictions, axis=0)
predictions[predictions > 0.5] = 1
predictions[predictions <= 0.5] = 0
accuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)
from sklearn.linear_model import LogisticRegression
from sklearn import cross_validation
alg = LogisticRegression(random_state=1)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)
titanic_test['Age'] = titanic_test['Age'].fillna(titanic['Age'].median())
titanic_test.loc[titanic_test['Sex'] == 'male', 'Sex'] = 0
titanic_test.loc[titanic_test['Sex'] == 'female', 'Sex'] = 1
titanic_test['Embarked'] = titanic_test['Embarked'].fillna('S')
titanic_test.loc[titanic_test['Embarked'] == 'S', 'Embarked'] = 0
titanic_test.loc[titanic_test['Embarked'] == 'C', 'Embarked'] = 1
titanic_test.loc[titanic_test['Embarked'] == 'Q', 'Embarked'] = 2
titanic_test['Fare'] = titanic_test['Fare'].fillna(titanic_test['Fare'].median())
alg = LogisticRegression(random_state=1)
alg.fit(titanic[predictors], titanic['Survived'])
predictions = alg.predict(titanic_test[predictors])
submission = pd.DataFrame({'PassengerId': titanic_test['PassengerId'], 'Survived': predictions})
from sklearn import cross_validation
from sklearn.ensemble import RandomForestClassifier
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=4, min_samples_leaf=2)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)
print(scores.mean()) | code |
333521/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import cross_validation
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import numpy as np # linear algebra
import operator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic_test = pd.read_csv('../input/test.csv')
titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
titanic['Embarked'] = titanic['Embarked'].fillna('S')
titanic.loc[titanic['Embarked'] == 'S', 'Embarked'] = 0
titanic.loc[titanic['Embarked'] == 'C', 'Embarked'] = 1
titanic.loc[titanic['Embarked'] == 'Q', 'Embarked'] = 2
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
alg = LinearRegression()
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
train_predictors = titanic[predictors].iloc[train, :]
train_target = titanic['Survived'].iloc[train]
alg.fit(train_predictors, train_target)
test_predictions = alg.predict(titanic[predictors].iloc[test, :])
predictions.append(test_predictions)
predictions = np.concatenate(predictions, axis=0)
predictions[predictions > 0.5] = 1
predictions[predictions <= 0.5] = 0
accuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)
from sklearn.linear_model import LogisticRegression
from sklearn import cross_validation
alg = LogisticRegression(random_state=1)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)
titanic_test['Age'] = titanic_test['Age'].fillna(titanic['Age'].median())
titanic_test.loc[titanic_test['Sex'] == 'male', 'Sex'] = 0
titanic_test.loc[titanic_test['Sex'] == 'female', 'Sex'] = 1
titanic_test['Embarked'] = titanic_test['Embarked'].fillna('S')
titanic_test.loc[titanic_test['Embarked'] == 'S', 'Embarked'] = 0
titanic_test.loc[titanic_test['Embarked'] == 'C', 'Embarked'] = 1
titanic_test.loc[titanic_test['Embarked'] == 'Q', 'Embarked'] = 2
titanic_test['Fare'] = titanic_test['Fare'].fillna(titanic_test['Fare'].median())
alg = LogisticRegression(random_state=1)
alg.fit(titanic[predictors], titanic['Survived'])
predictions = alg.predict(titanic_test[predictors])
submission = pd.DataFrame({'PassengerId': titanic_test['PassengerId'], 'Survived': predictions})
titanic['FamilySize'] = titanic['SibSp'] + titanic['Parch']
titanic['NameLength'] = titanic['Name'].apply(lambda x: len(x))
import re
def get_title(name):
title_search = re.search(' ([A-za-z]+)\\.', name)
if title_search:
return title_search.group(1)
return ''
titles = titanic['Name'].apply(get_title)
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Dr': 5, 'Rev': 6, 'Major': 7, 'Col': 8, 'Mlle': 8, 'Mme': 8, 'Don': 9, 'Lady': 10, 'Countess': 10, 'Jonkheer': 10, 'Sir': 9, 'Capt': 7, 'Ms': 2}
for k, v in title_mapping.items():
titles[titles == k] = v
titanic['Title'] = titles
import operator
family_id_mapping = {}
def get_family_id(row):
last_name = row['Name'].split(',')[0]
family_id = '{0}{1}'.format(last_name, row['FamilySize'])
if family_id not in family_id_mapping:
if len(family_id_mapping) == 0:
current_id = 1
else:
current_id = max(family_id_mapping.items(), key=operator.itemgetter(1))[1] + 1
family_id_mapping[family_id] = current_id
return family_id_mapping[family_id]
family_ids = titanic.apply(get_family_id, axis=1)
family_ids[titanic['FamilySize'] < 3] = -1
titanic['FamilyId'] = family_ids
print(pd.value_counts(family_ids)) | code |
326551/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
crashes = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
crashes.dtypes
set(crashes['Operator'].tolist()) | code |
326551/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
crashes = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
crashes.dtypes | code |
326551/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
crashes = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
crashes.dtypes
crashes['Date'][1].split('/') | code |
326551/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
326551/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
crashes = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
crashes.dtypes
print(crashes.describe()) | code |
326551/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
crashes = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
crashes.dtypes
crashes.head() | code |
106211998/cell_13 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
n_parts_train = [len(scores_train[i].parts) for i in range(len(train_files))]
n_parts_test = [len(scores_test[i].parts) for i in range(len(test_files))]
df_train['N_PARTS'] = n_parts_train
df_test['N_PARTS'] = n_parts_test
measures_per_file_train = [len(list(scores_train[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_train))]
measures_per_file_test = [len(list(scores_test[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_test))]
df_train['N_MEASURES'] = measures_per_file_train
df_test['N_MEASURES'] = measures_per_file_test
df_train.head() | code |
106211998/cell_9 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
df_test.head() | code |
106211998/cell_34 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
n_parts_train = [len(scores_train[i].parts) for i in range(len(train_files))]
n_parts_test = [len(scores_test[i].parts) for i in range(len(test_files))]
df_train['N_PARTS'] = n_parts_train
df_test['N_PARTS'] = n_parts_test
measures_per_file_train = [len(list(scores_train[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_train))]
measures_per_file_test = [len(list(scores_test[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_test))]
df_train['N_MEASURES'] = measures_per_file_train
df_test['N_MEASURES'] = measures_per_file_test
df_train.iloc[0, 5][0:2]
df_train.head() | code |
106211998/cell_23 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
df_test.head() | code |
106211998/cell_6 | [
"text_html_output_1.png"
] | import os
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
print('Number of files in train directory: ', len(train_files))
print('Firt files in train directory: ', train_files[0:5]) | code |
106211998/cell_29 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
df_test.iloc[0, 5][0:2] | code |
106211998/cell_39 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
first_score = music21.converter.parse(os.path.join(train_directory, train_files[0]))
n_parts_train = [len(scores_train[i].parts) for i in range(len(train_files))]
n_parts_test = [len(scores_test[i].parts) for i in range(len(test_files))]
df_train['N_PARTS'] = n_parts_train
df_test['N_PARTS'] = n_parts_test
measures_per_file_train = [len(list(scores_train[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_train))]
measures_per_file_test = [len(list(scores_test[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_test))]
df_train['N_MEASURES'] = measures_per_file_train
df_test['N_MEASURES'] = measures_per_file_test
all_measures_train = []
for i in range(len(scores_train)):
part = []
measures_in_part = scores_train[i].getElementsByClass(music21.stream.Part)[0].getElementsByClass(music21.stream.Measure)
for j in range(len(measures_in_part)):
tmp_meas = []
chords = measures_in_part[j].getElementsByClass(music21.chord.Chord)
for chord in chords:
tmp_meas.append((chord, chord.duration))
part.append(tmp_meas)
all_measures_train.append(part)
all_measures_test = []
for i in range(len(scores_test)):
part = []
measures_in_part = scores_test[i].getElementsByClass(music21.stream.Part)[0].getElementsByClass(music21.stream.Measure)
for j in range(len(measures_in_part)):
tmp_meas = []
chords = measures_in_part[j].getElementsByClass(music21.chord.Chord)
for chord in chords:
tmp_meas.append((chord, chord.duration))
part.append(tmp_meas)
all_measures_test.append(part)
def remove_xml(partition):
new_partition = []
for measure in partition:
new_measure = []
for tup in measure:
ch = tup[0]
ch_list = []
for pitch in ch.pitches:
name = pitch.name
octave = pitch.octave
name_and_otacve = name + str(octave)
ch_list.append(name_and_otacve)
duration = ch.quarterLength
new_measure.append((ch_list, duration))
new_partition.append(new_measure)
return new_partition
text2_train = []
text2_test = []
for i in range(len(all_measures_train)):
text2_train.append(remove_xml(all_measures_train[i]))
for i in range(len(all_measures_test)):
text2_test.append(remove_xml(all_measures_test[i]))
df_train.iloc[0, 5][0:2]
df_test.iloc[0, 5][0:2]
all_train_pitches = set()
all_train_durations = []
for partition in text2_train:
for measure in partition:
for tup in measure:
pitches = tup[0]
duration = tup[1]
all_train_durations.append(duration)
for pitch in pitches:
all_train_pitches.add(pitch)
all_test_pitches = set()
all_test_durations = []
for partition in text2_test:
for measure in partition:
for tup in measure:
pitches = tup[0]
duration = tup[1]
all_test_durations.append(duration)
for pitch in pitches:
all_test_pitches.add(pitch)
all_pitches = all_train_pitches.union(all_test_pitches)
train_dict2 = dict()
test_dict2 = dict()
train_dict2['NOM_FICHIER'] = []
test_dict2['NOM_FICHIER'] = []
for elt in all_pitches:
train_dict2[elt] = []
test_dict2[elt] = []
for i in range(df_train.shape[0]):
partition = df_train.iloc[i, 6]
for measure in partition:
for chord_tup in measure:
for dup_list in chord_tup:
train_dict2['NOM_FICHIER'].append(df_train.iloc[i, 0])
for key in train_dict2.keys():
if key != 'NOM_FICHIER':
if key in dup_list:
train_dict2[key].append(1)
else:
train_dict2[key].append(0)
for i in range(df_test.shape[0]):
partition = df_test.iloc[i, 6]
for measure in partition:
for chord_tup in measure:
for dup_list in chord_tup:
test_dict2['NOM_FICHIER'].append(df_test.iloc[i, 0])
for key in test_dict2.keys():
if key != 'NOM_FICHIER':
if key in dup_list:
test_dict2[key].append(1)
else:
test_dict2[key].append(0)
df_train_2 = pd.DataFrame(train_dict2)
df_test_2 = pd.DataFrame(test_dict2)
df_test_2.head(16) | code |
106211998/cell_41 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
first_score = music21.converter.parse(os.path.join(train_directory, train_files[0]))
n_parts_train = [len(scores_train[i].parts) for i in range(len(train_files))]
n_parts_test = [len(scores_test[i].parts) for i in range(len(test_files))]
df_train['N_PARTS'] = n_parts_train
df_test['N_PARTS'] = n_parts_test
measures_per_file_train = [len(list(scores_train[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_train))]
measures_per_file_test = [len(list(scores_test[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_test))]
df_train['N_MEASURES'] = measures_per_file_train
df_test['N_MEASURES'] = measures_per_file_test
all_measures_train = []
for i in range(len(scores_train)):
part = []
measures_in_part = scores_train[i].getElementsByClass(music21.stream.Part)[0].getElementsByClass(music21.stream.Measure)
for j in range(len(measures_in_part)):
tmp_meas = []
chords = measures_in_part[j].getElementsByClass(music21.chord.Chord)
for chord in chords:
tmp_meas.append((chord, chord.duration))
part.append(tmp_meas)
all_measures_train.append(part)
all_measures_test = []
for i in range(len(scores_test)):
part = []
measures_in_part = scores_test[i].getElementsByClass(music21.stream.Part)[0].getElementsByClass(music21.stream.Measure)
for j in range(len(measures_in_part)):
tmp_meas = []
chords = measures_in_part[j].getElementsByClass(music21.chord.Chord)
for chord in chords:
tmp_meas.append((chord, chord.duration))
part.append(tmp_meas)
all_measures_test.append(part)
def remove_xml(partition):
new_partition = []
for measure in partition:
new_measure = []
for tup in measure:
ch = tup[0]
ch_list = []
for pitch in ch.pitches:
name = pitch.name
octave = pitch.octave
name_and_otacve = name + str(octave)
ch_list.append(name_and_otacve)
duration = ch.quarterLength
new_measure.append((ch_list, duration))
new_partition.append(new_measure)
return new_partition
text2_train = []
text2_test = []
for i in range(len(all_measures_train)):
text2_train.append(remove_xml(all_measures_train[i]))
for i in range(len(all_measures_test)):
text2_test.append(remove_xml(all_measures_test[i]))
df_train.iloc[0, 5][0:2]
df_test.iloc[0, 5][0:2]
all_train_pitches = set()
all_train_durations = []
for partition in text2_train:
for measure in partition:
for tup in measure:
pitches = tup[0]
duration = tup[1]
all_train_durations.append(duration)
for pitch in pitches:
all_train_pitches.add(pitch)
all_test_pitches = set()
all_test_durations = []
for partition in text2_test:
for measure in partition:
for tup in measure:
pitches = tup[0]
duration = tup[1]
all_test_durations.append(duration)
for pitch in pitches:
all_test_pitches.add(pitch)
all_pitches = all_train_pitches.union(all_test_pitches)
train_dict2 = dict()
test_dict2 = dict()
train_dict2['NOM_FICHIER'] = []
test_dict2['NOM_FICHIER'] = []
for elt in all_pitches:
train_dict2[elt] = []
test_dict2[elt] = []
for i in range(df_train.shape[0]):
partition = df_train.iloc[i, 6]
for measure in partition:
for chord_tup in measure:
for dup_list in chord_tup:
train_dict2['NOM_FICHIER'].append(df_train.iloc[i, 0])
for key in train_dict2.keys():
if key != 'NOM_FICHIER':
if key in dup_list:
train_dict2[key].append(1)
else:
train_dict2[key].append(0)
for i in range(df_test.shape[0]):
partition = df_test.iloc[i, 6]
for measure in partition:
for chord_tup in measure:
for dup_list in chord_tup:
test_dict2['NOM_FICHIER'].append(df_test.iloc[i, 0])
for key in test_dict2.keys():
if key != 'NOM_FICHIER':
if key in dup_list:
test_dict2[key].append(1)
else:
test_dict2[key].append(0)
df_train_2 = pd.DataFrame(train_dict2)
df_test_2 = pd.DataFrame(test_dict2)
sliding_window = 32
list_train_X = []
list_train_Y = []
df_train_2_group = df_train_2.groupby('NOM_FICHIER')
for name, group in df_train_2_group:
group_ = group.reset_index()
for i in range(group_.shape[0] - sliding_window):
temp_df_train_X = group_.iloc[i:i + sliding_window, :]
temp_df_train_Y = group_.iloc[i + sliding_window:i + sliding_window + 1, :]
list_train_X.append(temp_df_train_X)
list_train_Y.append(temp_df_train_Y)
df_train_X = pd.concat(list_train_X, ignore_index=True)
df_train_Y = pd.concat(list_train_Y, ignore_index=True)
df_train_X.head() | code |
106211998/cell_2 | [
"text_plain_output_1.png"
] | !pip install music21 | code |
106211998/cell_11 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
n_parts_train = [len(scores_train[i].parts) for i in range(len(train_files))]
n_parts_test = [len(scores_test[i].parts) for i in range(len(test_files))]
df_train['N_PARTS'] = n_parts_train
df_test['N_PARTS'] = n_parts_test
df_train.head() | code |
106211998/cell_18 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
df_test['N_MEASURES'].value_counts() | code |
106211998/cell_28 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
n_parts_train = [len(scores_train[i].parts) for i in range(len(train_files))]
n_parts_test = [len(scores_test[i].parts) for i in range(len(test_files))]
df_train['N_PARTS'] = n_parts_train
df_test['N_PARTS'] = n_parts_test
measures_per_file_train = [len(list(scores_train[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_train))]
measures_per_file_test = [len(list(scores_test[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_test))]
df_train['N_MEASURES'] = measures_per_file_train
df_test['N_MEASURES'] = measures_per_file_test
df_train.iloc[0, 5][0:2] | code |
106211998/cell_8 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
df_train.head() | code |
106211998/cell_15 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
n_parts_train = [len(scores_train[i].parts) for i in range(len(train_files))]
n_parts_test = [len(scores_test[i].parts) for i in range(len(test_files))]
df_train['N_PARTS'] = n_parts_train
df_test['N_PARTS'] = n_parts_test
measures_per_file_train = [len(list(scores_train[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_train))]
measures_per_file_test = [len(list(scores_test[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_test))]
df_train['N_MEASURES'] = measures_per_file_train
df_test['N_MEASURES'] = measures_per_file_test
df_train['N_MEASURES'].unique() | code |
106211998/cell_16 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
df_test['N_MEASURES'].unique() | code |
106211998/cell_38 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
first_score = music21.converter.parse(os.path.join(train_directory, train_files[0]))
n_parts_train = [len(scores_train[i].parts) for i in range(len(train_files))]
n_parts_test = [len(scores_test[i].parts) for i in range(len(test_files))]
df_train['N_PARTS'] = n_parts_train
df_test['N_PARTS'] = n_parts_test
measures_per_file_train = [len(list(scores_train[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_train))]
measures_per_file_test = [len(list(scores_test[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_test))]
df_train['N_MEASURES'] = measures_per_file_train
df_test['N_MEASURES'] = measures_per_file_test
all_measures_train = []
for i in range(len(scores_train)):
part = []
measures_in_part = scores_train[i].getElementsByClass(music21.stream.Part)[0].getElementsByClass(music21.stream.Measure)
for j in range(len(measures_in_part)):
tmp_meas = []
chords = measures_in_part[j].getElementsByClass(music21.chord.Chord)
for chord in chords:
tmp_meas.append((chord, chord.duration))
part.append(tmp_meas)
all_measures_train.append(part)
all_measures_test = []
for i in range(len(scores_test)):
part = []
measures_in_part = scores_test[i].getElementsByClass(music21.stream.Part)[0].getElementsByClass(music21.stream.Measure)
for j in range(len(measures_in_part)):
tmp_meas = []
chords = measures_in_part[j].getElementsByClass(music21.chord.Chord)
for chord in chords:
tmp_meas.append((chord, chord.duration))
part.append(tmp_meas)
all_measures_test.append(part)
def remove_xml(partition):
new_partition = []
for measure in partition:
new_measure = []
for tup in measure:
ch = tup[0]
ch_list = []
for pitch in ch.pitches:
name = pitch.name
octave = pitch.octave
name_and_otacve = name + str(octave)
ch_list.append(name_and_otacve)
duration = ch.quarterLength
new_measure.append((ch_list, duration))
new_partition.append(new_measure)
return new_partition
text2_train = []
text2_test = []
for i in range(len(all_measures_train)):
text2_train.append(remove_xml(all_measures_train[i]))
for i in range(len(all_measures_test)):
text2_test.append(remove_xml(all_measures_test[i]))
df_train.iloc[0, 5][0:2]
df_test.iloc[0, 5][0:2]
all_train_pitches = set()
all_train_durations = []
for partition in text2_train:
for measure in partition:
for tup in measure:
pitches = tup[0]
duration = tup[1]
all_train_durations.append(duration)
for pitch in pitches:
all_train_pitches.add(pitch)
all_test_pitches = set()
all_test_durations = []
for partition in text2_test:
for measure in partition:
for tup in measure:
pitches = tup[0]
duration = tup[1]
all_test_durations.append(duration)
for pitch in pitches:
all_test_pitches.add(pitch)
all_pitches = all_train_pitches.union(all_test_pitches)
train_dict2 = dict()
test_dict2 = dict()
train_dict2['NOM_FICHIER'] = []
test_dict2['NOM_FICHIER'] = []
for elt in all_pitches:
train_dict2[elt] = []
test_dict2[elt] = []
for i in range(df_train.shape[0]):
partition = df_train.iloc[i, 6]
for measure in partition:
for chord_tup in measure:
for dup_list in chord_tup:
train_dict2['NOM_FICHIER'].append(df_train.iloc[i, 0])
for key in train_dict2.keys():
if key != 'NOM_FICHIER':
if key in dup_list:
train_dict2[key].append(1)
else:
train_dict2[key].append(0)
for i in range(df_test.shape[0]):
partition = df_test.iloc[i, 6]
for measure in partition:
for chord_tup in measure:
for dup_list in chord_tup:
test_dict2['NOM_FICHIER'].append(df_test.iloc[i, 0])
for key in test_dict2.keys():
if key != 'NOM_FICHIER':
if key in dup_list:
test_dict2[key].append(1)
else:
test_dict2[key].append(0)
df_train_2 = pd.DataFrame(train_dict2)
df_test_2 = pd.DataFrame(test_dict2)
df_train_2.head(16) | code |
106211998/cell_17 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
n_parts_train = [len(scores_train[i].parts) for i in range(len(train_files))]
n_parts_test = [len(scores_test[i].parts) for i in range(len(test_files))]
df_train['N_PARTS'] = n_parts_train
df_test['N_PARTS'] = n_parts_test
measures_per_file_train = [len(list(scores_train[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_train))]
measures_per_file_test = [len(list(scores_test[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_test))]
df_train['N_MEASURES'] = measures_per_file_train
df_test['N_MEASURES'] = measures_per_file_test
df_train['N_MEASURES'].value_counts() | code |
106211998/cell_35 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
df_test.iloc[0, 5][0:2]
df_test.head() | code |
106211998/cell_31 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
first_score = music21.converter.parse(os.path.join(train_directory, train_files[0]))
all_measures_train = []
for i in range(len(scores_train)):
part = []
measures_in_part = scores_train[i].getElementsByClass(music21.stream.Part)[0].getElementsByClass(music21.stream.Measure)
for j in range(len(measures_in_part)):
tmp_meas = []
chords = measures_in_part[j].getElementsByClass(music21.chord.Chord)
for chord in chords:
tmp_meas.append((chord, chord.duration))
part.append(tmp_meas)
all_measures_train.append(part)
all_measures_test = []
for i in range(len(scores_test)):
part = []
measures_in_part = scores_test[i].getElementsByClass(music21.stream.Part)[0].getElementsByClass(music21.stream.Measure)
for j in range(len(measures_in_part)):
tmp_meas = []
chords = measures_in_part[j].getElementsByClass(music21.chord.Chord)
for chord in chords:
tmp_meas.append((chord, chord.duration))
part.append(tmp_meas)
all_measures_test.append(part)
def remove_xml(partition):
new_partition = []
for measure in partition:
new_measure = []
for tup in measure:
ch = tup[0]
ch_list = []
for pitch in ch.pitches:
name = pitch.name
octave = pitch.octave
name_and_otacve = name + str(octave)
ch_list.append(name_and_otacve)
duration = ch.quarterLength
new_measure.append((ch_list, duration))
new_partition.append(new_measure)
return new_partition
text2_train = []
text2_test = []
for i in range(len(all_measures_train)):
text2_train.append(remove_xml(all_measures_train[i]))
for i in range(len(all_measures_test)):
text2_test.append(remove_xml(all_measures_test[i]))
all_train_pitches = set()
all_train_durations = []
for partition in text2_train:
for measure in partition:
for tup in measure:
pitches = tup[0]
duration = tup[1]
all_train_durations.append(duration)
for pitch in pitches:
all_train_pitches.add(pitch)
all_test_pitches = set()
all_test_durations = []
for partition in text2_test:
for measure in partition:
for tup in measure:
pitches = tup[0]
duration = tup[1]
all_test_durations.append(duration)
for pitch in pitches:
all_test_pitches.add(pitch)
cardinal_pitches_train = len(all_train_pitches)
min_duration_train = min(all_train_durations)
print('total number of different train pitches: ', cardinal_pitches_train)
print('The minimum duration of a chord in train: ', min_duration_train)
cardinal_pitches_test = len(all_test_pitches)
min_duration_test = min(all_test_durations)
print('total number of different test pitches: ', cardinal_pitches_test)
print('The minimum duration of a chord in test: ', min_duration_test) | code |
106211998/cell_14 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
df_test.head() | code |
106211998/cell_22 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
n_parts_train = [len(scores_train[i].parts) for i in range(len(train_files))]
n_parts_test = [len(scores_test[i].parts) for i in range(len(test_files))]
df_train['N_PARTS'] = n_parts_train
df_test['N_PARTS'] = n_parts_test
measures_per_file_train = [len(list(scores_train[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_train))]
measures_per_file_test = [len(list(scores_test[i].recurse().getElementsByClass('Measure'))) for i in range(len(scores_test))]
df_train['N_MEASURES'] = measures_per_file_train
df_test['N_MEASURES'] = measures_per_file_test
df_train.head() | code |
106211998/cell_10 | [
"text_plain_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
first_score = music21.converter.parse(os.path.join(train_directory, train_files[0]))
print('File read: ', train_files[0])
print('Loaded score object: ', first_score) | code |
106211998/cell_12 | [
"text_html_output_1.png"
] | import music21
import os
import pandas as pd
USE_MUSESCORE = True
if USE_MUSESCORE:
env = music21.environment.Environment()
env['musescoreDirectPNGPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
env['musicxmlPath'] = '../input/data-challenge-igad-2022-data-set/3 - Challenge/musescore.exe'
train_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/train'
test_directory = '../input/data-challenge-igad-2022-data-set/3 - Challenge/X_test'
train_files = os.listdir(train_directory)
test_files = os.listdir(test_directory)
scores_train = [music21.converter.parse(os.path.join(train_directory, train_files[i])) for i in range(len(train_files))]
scores_test = [music21.converter.parse(os.path.join(test_directory, test_files[i])) for i in range(len(test_files))]
train_dict = {'NOM_FICHIER': train_files, 'SCORE': scores_train}
test_dict = {'NOM_FICHIER': test_files, 'SCORE': scores_test}
df_train = pd.DataFrame(train_dict)
df_test = pd.DataFrame(test_dict)
df_test.head() | code |
2024103/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.head() | code |
2024103/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average | code |
2024103/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
donor['contb_receipt_amt'].value_counts() | code |
2024103/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll.head() | code |
2024103/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
candidates = donor.cand_nm.unique()
candidates
party_map = {'Rubio, Marco': 'Republican', 'Santorum, Richard J.': 'Republican', 'Perry, James R. (Rick)': 'Republican', 'Carson, Benjamin S.': 'Republican', "Cruz, Rafael Edward 'Ted'": 'Republican', 'Paul, Rand': 'Republican', 'Clinton, Hillary Rodham': 'Democrat'}
donor['Party'] = donor.cand_nm.map(party_map)
donor.groupby('cand_nm')['contb_receipt_amt'].count() | code |
2024103/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor['contb_receipt_amt'].value_counts() | code |
2024103/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
candidates = donor.cand_nm.unique()
candidates
party_map = {'Rubio, Marco': 'Republican', 'Santorum, Richard J.': 'Republican', 'Perry, James R. (Rick)': 'Republican', 'Carson, Benjamin S.': 'Republican', "Cruz, Rafael Edward 'Ted'": 'Republican', 'Paul, Rand': 'Republican', 'Clinton, Hillary Rodham': 'Democrat'}
donor['Party'] = donor.cand_nm.map(party_map)
donor.head() | code |
2024103/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
poll.info() | code |
2024103/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
top_donation = donor['contb_receipt_amt'].copy()
top_donation.sort_values(ascending=False, inplace=True)
com_don = top_donation[top_donation < 2500]
com_don.hist(bins=100) | code |
2024103/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
candidates = donor.cand_nm.unique()
candidates
party_map = {'Rubio, Marco': 'Republican', 'Santorum, Richard J.': 'Republican', 'Perry, James R. (Rick)': 'Republican', 'Carson, Benjamin S.': 'Republican', "Cruz, Rafael Edward 'Ted'": 'Republican', 'Paul, Rand': 'Republican', 'Clinton, Hillary Rodham': 'Democrat'}
donor['Party'] = donor.cand_nm.map(party_map)
donor.groupby('cand_nm')['contb_receipt_amt'].count()
donor.groupby('cand_nm')['contb_receipt_amt'].sum()
cand_amount = donor.groupby('cand_nm')['contb_receipt_amt'].sum()
i = 0
for don in cand_amount:
i += 1
occupation = donor.pivot_table('contb_receipt_amt', index='contbr_occupation', columns='Party', aggfunc='sum')
occupation | code |
2024103/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
donor_mean = donor['contb_receipt_amt'].mean()
donor_std = donor['contb_receipt_amt'].std()
print('Average donation was: %0.2f with a standard deviation of: %0.2f' % (donor_mean, donor_std)) | code |
2024103/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
average.plot(yerr=std, kind='bar', legend=False, color='seagreen', fontsize=20) | code |
2024103/cell_19 | [
"text_html_output_1.png"
] | import matplotlib as plt
import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
from datetime import datetime
poll['Difference'] = (poll.Trump - poll.Clinton) / 100
poll = poll.groupby('Start Date', as_index=False).mean()
poll.plot('Start Date', 'Difference', figsize=(25, 15), marker='s', color='red', xlim=(209, 262))
plt.pyplot.axvline(x=209 + 27, linewidth=4, color='grey')
plt.pyplot.axvline(x=209 + 40, linewidth=4, color='grey')
plt.pyplot.axvline(x=209 + 50, linewidth=4, color='grey') | code |
2024103/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
sns.factorplot('Affiliation', data=poll, kind='count', legend=True, color='orange', size=6) | code |
2024103/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
from datetime import datetime
poll['Difference'] = (poll.Trump - poll.Clinton) / 100
poll = poll.groupby('Start Date', as_index=False).mean()
row_in = 0
xlimit = []
for date in poll['Start Date']:
if date[0:7] == '2016-09':
xlimit.append(row_in)
row_in += 1
else:
row_in += 1
print(min(xlimit))
row_in = 0
xlimit = []
for date in poll['Start Date']:
if date[0:7] == '2016-10':
xlimit.append(row_in)
row_in += 1
else:
row_in += 1
print(max(xlimit)) | code |
2024103/cell_28 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
top_donation = donor['contb_receipt_amt'].copy()
top_donation.sort_values(ascending=False, inplace=True)
top_donation.value_counts(sort=True).head(10) | code |
2024103/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
sns.factorplot('Affiliation', data=poll, kind='count', legend=True, hue='Population', size=6, aspect=2, palette='dark') | code |
2024103/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
from datetime import datetime
poll['Difference'] = (poll.Trump - poll.Clinton) / 100
poll.head() | code |
2024103/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
from datetime import datetime
poll['Difference'] = (poll.Trump - poll.Clinton) / 100
poll = poll.groupby('Start Date', as_index=False).mean()
poll.head() | code |
2024103/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
candidates = donor.cand_nm.unique()
candidates
party_map = {'Rubio, Marco': 'Republican', 'Santorum, Richard J.': 'Republican', 'Perry, James R. (Rick)': 'Republican', 'Carson, Benjamin S.': 'Republican', "Cruz, Rafael Edward 'Ted'": 'Republican', 'Paul, Rand': 'Republican', 'Clinton, Hillary Rodham': 'Democrat'}
donor['Party'] = donor.cand_nm.map(party_map)
donor.groupby('cand_nm')['contb_receipt_amt'].count()
donor.groupby('cand_nm')['contb_receipt_amt'].sum()
cand_amount = donor.groupby('cand_nm')['contb_receipt_amt'].sum()
i = 0
for don in cand_amount:
i += 1
donor.groupby('Party')['contb_receipt_amt'].sum().plot(kind='bar', legend=True, logy=True, color='seagreen') | code |
2024103/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
from datetime import datetime
poll['Difference'] = (poll.Trump - poll.Clinton) / 100
poll = poll.groupby('Start Date', as_index=False).mean()
poll.plot('Start Date', 'Difference', figsize=(25, 15), marker='s', color='red') | code |
2024103/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
candidates = donor.cand_nm.unique()
candidates
party_map = {'Rubio, Marco': 'Republican', 'Santorum, Richard J.': 'Republican', 'Perry, James R. (Rick)': 'Republican', 'Carson, Benjamin S.': 'Republican', "Cruz, Rafael Edward 'Ted'": 'Republican', 'Paul, Rand': 'Republican', 'Clinton, Hillary Rodham': 'Democrat'}
donor['Party'] = donor.cand_nm.map(party_map)
donor.groupby('cand_nm')['contb_receipt_amt'].count()
donor.groupby('cand_nm')['contb_receipt_amt'].sum() | code |
2024103/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
candidates = donor.cand_nm.unique()
candidates | code |
2024103/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll.plot(x='End Date', y=['Trump', 'Clinton', 'Other', 'Undecided'], linestyle='', marker='s').legend(bbox_to_anchor=(1.5, 1)) | code |
2024103/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.info() | code |
2024103/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std | code |
2024103/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
top_donation = donor['contb_receipt_amt'].copy()
top_donation.sort_values(ascending=False, inplace=True)
top_donation.head(10) | code |
2024103/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
candidates = donor.cand_nm.unique()
candidates
party_map = {'Rubio, Marco': 'Republican', 'Santorum, Richard J.': 'Republican', 'Perry, James R. (Rick)': 'Republican', 'Carson, Benjamin S.': 'Republican', "Cruz, Rafael Edward 'Ted'": 'Republican', 'Paul, Rand': 'Republican', 'Clinton, Hillary Rodham': 'Democrat'}
donor['Party'] = donor.cand_nm.map(party_map)
donor.groupby('cand_nm')['contb_receipt_amt'].count()
donor.groupby('cand_nm')['contb_receipt_amt'].sum()
cand_amount = donor.groupby('cand_nm')['contb_receipt_amt'].sum()
i = 0
for don in cand_amount:
i += 1
cand_amount.plot(kind='bar', legend=True, logy=True, color='blue') | code |
2024103/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg | code |
2024103/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
poll.head() | code |
2024103/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
poll = pd.read_csv('../input/2016-general-election-trump-vs-clinton.csv')
poll = poll.drop(['Pollster URL', 'Source URL', 'Partisan', 'Question Text', 'Question Iteration'], axis=1)
average = pd.DataFrame(poll.mean())
average.drop('Number of Observations', inplace=True)
average
std = pd.DataFrame(poll.std())
std.drop('Number of Observations', inplace=True)
std
poll_avg = pd.concat([average, std], axis=1)
poll_avg.column = ['Average', 'STD']
poll_avg
donor = pd.read_csv('../input/Donor_Data.csv')
donor.drop(donor[donor.contb_receipt_amt < 0].index, inplace=True)
candidates = donor.cand_nm.unique()
candidates
party_map = {'Rubio, Marco': 'Republican', 'Santorum, Richard J.': 'Republican', 'Perry, James R. (Rick)': 'Republican', 'Carson, Benjamin S.': 'Republican', "Cruz, Rafael Edward 'Ted'": 'Republican', 'Paul, Rand': 'Republican', 'Clinton, Hillary Rodham': 'Democrat'}
donor['Party'] = donor.cand_nm.map(party_map)
donor.groupby('cand_nm')['contb_receipt_amt'].count()
donor.groupby('cand_nm')['contb_receipt_amt'].sum()
cand_amount = donor.groupby('cand_nm')['contb_receipt_amt'].sum()
i = 0
for don in cand_amount:
print('The candidate %s raised %.0f dollars' % (cand_amount.index[i], don))
print('\n')
i += 1 | code |
122251391/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data.head() | code |
122251391/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
data.HomePlanet.unique() | code |
122251391/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.info() | code |
122251391/cell_34 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data_1 = pd.get_dummies(data, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
data_1['Transported'] = data_1['Transported'].astype(float)
data_1['VIP'] = data_1['VIP'].astype(float)
test_1 = pd.get_dummies(test, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
test_1['VIP'] = test_1['VIP'].astype(float)
test_1 = test_1.drop(columns=['PassengerId'])
from sklearn.ensemble import RandomForestRegressor
X = data_1.drop(columns=['PassengerId'])
X = X.drop(columns=['Transported'])
y = data_1[['Transported']]
model = RandomForestRegressor(n_estimators=1000, random_state=42)
model.fit(X, y.values.ravel())
y_pred = model.predict(test_1)
y_pred = y_pred >= 0.5
y_pred
y_pred = pd.DataFrame(y_pred)
y_pred.head() | code |
122251391/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data_1 = pd.get_dummies(data, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
data_1['Transported'] = data_1['Transported'].astype(float)
data_1['VIP'] = data_1['VIP'].astype(float)
from sklearn.ensemble import RandomForestRegressor
X = data_1.drop(columns=['PassengerId'])
X = X.drop(columns=['Transported'])
y = data_1[['Transported']]
y.head() | code |
122251391/cell_33 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data_1 = pd.get_dummies(data, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
data_1['Transported'] = data_1['Transported'].astype(float)
data_1['VIP'] = data_1['VIP'].astype(float)
test_1 = pd.get_dummies(test, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
test_1['VIP'] = test_1['VIP'].astype(float)
test_1 = test_1.drop(columns=['PassengerId'])
from sklearn.ensemble import RandomForestRegressor
X = data_1.drop(columns=['PassengerId'])
X = X.drop(columns=['Transported'])
y = data_1[['Transported']]
model = RandomForestRegressor(n_estimators=1000, random_state=42)
model.fit(X, y.values.ravel())
y_pred = model.predict(test_1)
y_pred = y_pred >= 0.5
y_pred | code |
122251391/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test.isnull().sum() | code |
122251391/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data_1 = pd.get_dummies(data, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
data_1['Transported'] = data_1['Transported'].astype(float)
data_1['VIP'] = data_1['VIP'].astype(float)
from sklearn.ensemble import RandomForestRegressor
X = data_1.drop(columns=['PassengerId'])
X = X.drop(columns=['Transported'])
X.head() | code |
122251391/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data_1 = pd.get_dummies(data, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
data_1['Transported'] = data_1['Transported'].astype(float)
data_1['VIP'] = data_1['VIP'].astype(float)
test_1 = pd.get_dummies(test, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
test_1['VIP'] = test_1['VIP'].astype(float)
test_1 = test_1.drop(columns=['PassengerId'])
from sklearn.ensemble import RandomForestRegressor
X = data_1.drop(columns=['PassengerId'])
X = X.drop(columns=['Transported'])
y = data_1[['Transported']]
model = RandomForestRegressor(n_estimators=1000, random_state=42)
model.fit(X, y.values.ravel())
y_pred = model.predict(test_1)
y_pred = y_pred >= 0.5
y_pred
y_pred = pd.DataFrame(y_pred)
sub = sub.drop(columns=['Transported'])
sub['Transported'] = y_pred
sub.head() | code |
122251391/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data_1 = pd.get_dummies(data, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
data_1['Transported'] = data_1['Transported'].astype(float)
data_1['VIP'] = data_1['VIP'].astype(float)
data_1.head() | code |
122251391/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 |
122251391/cell_32 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data_1 = pd.get_dummies(data, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
data_1['Transported'] = data_1['Transported'].astype(float)
data_1['VIP'] = data_1['VIP'].astype(float)
test_1 = pd.get_dummies(test, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
test_1['VIP'] = test_1['VIP'].astype(float)
test_1 = test_1.drop(columns=['PassengerId'])
from sklearn.ensemble import RandomForestRegressor
X = data_1.drop(columns=['PassengerId'])
X = X.drop(columns=['Transported'])
y = data_1[['Transported']]
model = RandomForestRegressor(n_estimators=1000, random_state=42)
model.fit(X, y.values.ravel())
y_pred = model.predict(test_1)
y_pred | code |
122251391/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum() | code |
122251391/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.head() | code |
122251391/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
test.isnull().sum() | code |
122251391/cell_35 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data_1 = pd.get_dummies(data, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
data_1['Transported'] = data_1['Transported'].astype(float)
data_1['VIP'] = data_1['VIP'].astype(float)
test_1 = pd.get_dummies(test, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
test_1['VIP'] = test_1['VIP'].astype(float)
test_1 = test_1.drop(columns=['PassengerId'])
from sklearn.ensemble import RandomForestRegressor
X = data_1.drop(columns=['PassengerId'])
X = X.drop(columns=['Transported'])
y = data_1[['Transported']]
model = RandomForestRegressor(n_estimators=1000, random_state=42)
model.fit(X, y.values.ravel())
y_pred = model.predict(test_1)
y_pred = y_pred >= 0.5
y_pred
y_pred = pd.DataFrame(y_pred)
test['Transported'] = y_pred
test.head() | code |
122251391/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.head() | code |
122251391/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data_1 = pd.get_dummies(data, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
data_1['Transported'] = data_1['Transported'].astype(float)
data_1['VIP'] = data_1['VIP'].astype(float)
test_1 = pd.get_dummies(test, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
test_1['VIP'] = test_1['VIP'].astype(float)
test_1 = test_1.drop(columns=['PassengerId'])
test_1.head() | code |
122251391/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum() | code |
122251391/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.isnull().sum()
test.isnull().sum()
data.HomePlanet.unique()
data.dropna(subset=['Cabin'], inplace=True)
test.dropna(subset=['Cabin'], inplace=True)
data.isnull().sum()
test.isnull().sum()
data.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
test.drop(columns=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Cabin', 'Name'], inplace=True)
data_1 = pd.get_dummies(data, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
data_1['Transported'] = data_1['Transported'].astype(float)
data_1['VIP'] = data_1['VIP'].astype(float)
test_1 = pd.get_dummies(test, columns=['CryoSleep', 'side', 'deck', 'HomePlanet', 'Destination'])
test_1['VIP'] = test_1['VIP'].astype(float)
test_1 = test_1.drop(columns=['PassengerId'])
from sklearn.ensemble import RandomForestRegressor
X = data_1.drop(columns=['PassengerId'])
X = X.drop(columns=['Transported'])
y = data_1[['Transported']]
model = RandomForestRegressor(n_estimators=1000, random_state=42)
model.fit(X, y.values.ravel())
y_pred = model.predict(test_1)
y_pred = y_pred >= 0.5
y_pred
y_pred = pd.DataFrame(y_pred)
test['Transported'] = y_pred
subbmission = test[['PassengerId', 'Transported']]
subbmission.head() | code |
105210810/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2, 'CompanyName', CompanyName)
cars.drop(['CarName'], axis=1, inplace=True)
cars.head() | code |
105210810/cell_9 | [
"image_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns | code |
105210810/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2, 'CompanyName', CompanyName)
cars.drop(['CarName'], axis=1, inplace=True)
cars.CompanyName.unique()
cars.CompanyName = cars.CompanyName.str.lower()
cars.CompanyName.replace('maxda', 'mazda', inplace=True)
cars.CompanyName.replace('porcshce', 'porsche', inplace=True)
cars.CompanyName.replace('toyouta', 'toyota', inplace=True)
cars.CompanyName.replace('vokswagen', 'volkswagen', inplace=True)
cars.CompanyName.replace('vw', 'volkswagen', inplace=True)
cars.CompanyName.unique()
cars.loc[cars.duplicated()]
categorical_cols = [cname for cname in cars.columns if cars[cname].dtype == 'object' or cars[cname].nunique() < 10]
print(categorical_cols) | code |
105210810/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2, 'CompanyName', CompanyName)
cars.drop(['CarName'], axis=1, inplace=True)
cars.CompanyName.unique()
cars.CompanyName = cars.CompanyName.str.lower()
cars.CompanyName.replace('maxda', 'mazda', inplace=True)
cars.CompanyName.replace('porcshce', 'porsche', inplace=True)
cars.CompanyName.replace('toyouta', 'toyota', inplace=True)
cars.CompanyName.replace('vokswagen', 'volkswagen', inplace=True)
cars.CompanyName.replace('vw', 'volkswagen', inplace=True)
cars.CompanyName.unique()
cars.loc[cars.duplicated()]
print(cars.price.describe(percentiles=[0.25, 0.5, 0.75, 0.85, 0.9, 1])) | code |
105210810/cell_6 | [
"image_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape | code |
105210810/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum() | code |
105210810/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2, 'CompanyName', CompanyName)
cars.drop(['CarName'], axis=1, inplace=True)
cars.CompanyName.unique()
cars.CompanyName = cars.CompanyName.str.lower()
cars.CompanyName.replace('maxda', 'mazda', inplace=True)
cars.CompanyName.replace('porcshce', 'porsche', inplace=True)
cars.CompanyName.replace('toyouta', 'toyota', inplace=True)
cars.CompanyName.replace('vokswagen', 'volkswagen', inplace=True)
cars.CompanyName.replace('vw', 'volkswagen', inplace=True)
cars.CompanyName.unique()
cars.loc[cars.duplicated()]
plt.figure(figsize=(20, 8))
plt.subplot(1, 2, 1)
plt.title('Car Price Distribution Plot')
sns.histplot(cars.price, kde=True)
plt.subplot(1, 2, 2)
plt.title('Car Price Spread')
sns.boxplot(y=cars.price)
plt.show() | code |
105210810/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.describe() | code |
105210810/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.info() | code |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.