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105183805/cell_8 | [
"text_plain_output_1.png"
] | from efficientnet_pytorch import EfficientNet
IMSIZE = 545
IMSIZE = EfficientNet.get_image_size('efficientnet-b5')
print(IMSIZE) | code |
105183805/cell_15 | [
"text_plain_output_1.png"
] | from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch import EfficientNet
IMSIZE = 545
IMSIZE = EfficientNet.get_image_size('efficientnet-b5')
from efficientnet_pytorch import EfficientNet
model_efficient = EfficientNet.from_pretrained('efficientnet-b7') | code |
105183805/cell_17 | [
"text_html_output_1.png"
] | from albumentations.pytorch.transforms import ToTensorV2
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch import EfficientNet
from sklearn.preprocessing import MultiLabelBinarizer
from torch.utils.data import Dataset, DataLoader
from transformers import get_cosine_schedule_with_warmup
import cv2
import cv2
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
df = pd.read_csv('../input/plant-pathology-2021-fgvc8/train.csv')
df['labels'] = df['labels'].apply(lambda string: string.split(' '))
s = list(df['labels'])
mlb = MultiLabelBinarizer()
trainx = pd.DataFrame(mlb.fit_transform(s), columns=mlb.classes_, index=df.index)
trainx.insert(0, 'image', df['image'], True)
trainx
t_df = pd.read_csv('../input/plant-pathology-2021-fgvc8/sample_submission.csv')
test_df = t_df.drop(['labels'], axis=1)
test_df
from sklearn.model_selection import train_test_split
train_df = trainx
train_df.reset_index(drop=True, inplace=True)
test_df.reset_index(drop=True, inplace=True)
class CustomDataset(Dataset):
def __init__(self, df, root_dir, transform=None, iftest=False):
self.df = df
self.root_dir = root_dir
self.transform = transform
self.iftest = iftest
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
img_name = self.root_dir + self.df.iloc[idx, 0]
image = cv2.imread(img_name, cv2.IMREAD_COLOR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if self.transform:
image = self.transform(image=image)['image']
if self.iftest:
return image
labels = torch.tensor(np.argmax(self.df.iloc[idx, 1:].values))
return (image, labels)
IMSIZE = 545
IMSIZE = EfficientNet.get_image_size('efficientnet-b5')
train_dataset = CustomDataset(df=train_df, root_dir='../input/plant-pathology-2021-fgvc8/train_images/', transform=Compose([augmentations.geometric.resize.Resize(height=IMSIZE, width=IMSIZE, always_apply=True), HorizontalFlip(p=0.5), VerticalFlip(p=0.5), ShiftScaleRotate(rotate_limit=25.0, p=0.7), OneOf([Emboss(p=1), Sharpen(p=1), Blur(p=1)], p=0.5), PiecewiseAffine(p=0.5), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), always_apply=True), ToTensorV2()]))
test_dataset = CustomDataset(df=test_df, root_dir='../input/plant-pathology-2021-fgvc8/test_images/', transform=Compose([augmentations.geometric.resize.Resize(height=IMSIZE, width=IMSIZE, always_apply=True), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), always_apply=True), ToTensorV2()]), iftest=True)
BATCH_SIZE = 1
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
use_cuda = torch.cuda.is_available()
if use_cuda:
device = 'cuda:0'
use_tpu = False
use_device = True
if use_tpu:
device = 'idk'
from efficientnet_pytorch import EfficientNet
model_efficient = EfficientNet.from_pretrained('efficientnet-b7')
ad = False
model_efficient._fc = nn.Sequential(nn.Linear(model_efficient._fc.in_features, 1000, bias=True), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(1000, 6, bias=True))
if use_device:
model_efficient = model_efficient.to(device)
NEPOCHS = 1
print(IMSIZE)
criterion_transfer = nn.CrossEntropyLoss()
learning_rate = 0.0008
optimizer_transfer = optim.AdamW(model_efficient.parameters(), learning_rate, weight_decay=0.001)
num_train_steps = int(len(train_dataset) / BATCH_SIZE * NEPOCHS)
from transformers import get_cosine_schedule_with_warmup
scheduler = get_cosine_schedule_with_warmup(optimizer_transfer, num_warmup_steps=len(train_dataset) / BATCH_SIZE * 5, num_training_steps=num_train_steps) | code |
105183805/cell_5 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
df = pd.read_csv('../input/plant-pathology-2021-fgvc8/train.csv')
df['labels'] = df['labels'].apply(lambda string: string.split(' '))
s = list(df['labels'])
mlb = MultiLabelBinarizer()
trainx = pd.DataFrame(mlb.fit_transform(s), columns=mlb.classes_, index=df.index)
trainx.insert(0, 'image', df['image'], True)
trainx
t_df = pd.read_csv('../input/plant-pathology-2021-fgvc8/sample_submission.csv')
test_df = t_df.drop(['labels'], axis=1)
test_df | code |
121154019/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
new_train = wrangle(train)
def aggregate(df, cols):
by_cols = df.groupby(cols).agg({'Survived': [('Total', 'count'), ('Survived', 'sum')]})
by_cols.columns = by_cols.columns.droplevel()
by_cols['Died'] = by_cols['Total'] - by_cols['Survived']
by_cols['Survive Rate'] = 100 * by_cols['Survived'] / by_cols['Total']
return by_cols.sort_values('Total', ascending=False)
aggregate(new_train, ['Male']) | code |
121154019/cell_25 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categories='auto', sparse=False, handle_unknown='ignore')
encoder.fit(X_train[cat_cols])
X_train_encoded = encoder.transform(X_train[cat_cols])
X_train_final = pd.concat([X_train.drop(cat_cols, axis=1), pd.DataFrame(X_train_encoded, index=X_train.index)], axis=1)
X_test_encoded = encoder.transform(X_test[cat_cols])
X_test_final = pd.concat([X_test.drop(cat_cols, axis=1), pd.DataFrame(X_test_encoded, index=X_test.index)], axis=1)
return (X_train_final, X_test_final, encoder)
xgb_classifier = xgb.XGBClassifier()
xgb_classifier.fit(X_train_final, y_train)
y_pred = xgb_classifier.predict(X_test_final)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy) | code |
121154019/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categories='auto', sparse=False, handle_unknown='ignore')
encoder.fit(X_train[cat_cols])
X_train_encoded = encoder.transform(X_train[cat_cols])
X_train_final = pd.concat([X_train.drop(cat_cols, axis=1), pd.DataFrame(X_train_encoded, index=X_train.index)], axis=1)
X_test_encoded = encoder.transform(X_test[cat_cols])
X_test_final = pd.concat([X_test.drop(cat_cols, axis=1), pd.DataFrame(X_test_encoded, index=X_test.index)], axis=1)
return (X_train_final, X_test_final, encoder)
new_train = wrangle(train)
sns.set(style='whitegrid')
sns.despine(left=True)
new_train['AgeGroup'] = pd.cut(new_train['Age'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80])
age_survival = new_train.groupby(['AgeGroup'])['Survived'].mean().reset_index()
new_train['FareGroup'] = pd.cut(new_train['Fare'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 600])
age_survival = new_train.groupby(['FareGroup'])['Survived'].mean().reset_index()
new_train = new_train.drop(['AgeGroup', 'FareGroup'], axis=1)
X_train, X_test, y_train, y_test = train_test_split(new_train.drop('Survived', axis=1), new_train['Survived'], test_size=0.2, random_state=42)
cat_cols = ['Embarked', 'Pronoun', 'Prefix', 'Letter']
X_train_final, X_test_final, encoder = encode(X_train, X_test, cat_cols)
xgb_classifier = xgb.XGBClassifier()
xgb_classifier.fit(X_train_final, y_train)
y_pred = xgb_classifier.predict(X_test_final)
accuracy = accuracy_score(y_test, y_pred)
param_grid = {'max_depth': [3, 4, 5], 'learning_rate': [0.1, 0.01], 'n_estimators': [100, 200], 'gamma': [0, 0.1], 'colsample_bytree': [0.6, 0.8]}
xgb_classifier = xgb.XGBClassifier()
grid_search = GridSearchCV(estimator=xgb_classifier, param_grid=param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train_final, y_train)
best_xgb_classifier = grid_search.best_estimator_
y_pred = best_xgb_classifier.predict(X_test_final)
accuracy = accuracy_score(y_test, y_pred)
new_test = wrangle(test)
X_train_final, X_test_final, encoder = encode(new_train.drop('Survived', axis=1), new_test, cat_cols)
y_train = new_train['Survived']
xgb_classifier = xgb.XGBClassifier()
grid_search = GridSearchCV(estimator=xgb_classifier, param_grid=param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train_final, y_train)
final_xgb_classifier = grid_search.best_estimator_
importance = final_xgb_classifier.feature_importances_
features = X_train_final.columns
encodings = encoder.get_feature_names()
translated = []
for feature in features:
if feature in range(len(encodings)):
translated.append(encodings[feature])
else:
translated.append(feature)
df_importance = pd.DataFrame({'Feature': translated, 'Importance': importance}).sort_values('Importance', ascending=False)
df_importance.head(10) | code |
121154019/cell_20 | [
"image_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categories='auto', sparse=False, handle_unknown='ignore')
encoder.fit(X_train[cat_cols])
X_train_encoded = encoder.transform(X_train[cat_cols])
X_train_final = pd.concat([X_train.drop(cat_cols, axis=1), pd.DataFrame(X_train_encoded, index=X_train.index)], axis=1)
X_test_encoded = encoder.transform(X_test[cat_cols])
X_test_final = pd.concat([X_test.drop(cat_cols, axis=1), pd.DataFrame(X_test_encoded, index=X_test.index)], axis=1)
return (X_train_final, X_test_final, encoder)
new_train = wrangle(train)
sns.set(style='whitegrid')
sns.despine(left=True)
new_train['AgeGroup'] = pd.cut(new_train['Age'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80])
age_survival = new_train.groupby(['AgeGroup'])['Survived'].mean().reset_index()
new_train['FareGroup'] = pd.cut(new_train['Fare'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 600])
age_survival = new_train.groupby(['FareGroup'])['Survived'].mean().reset_index()
sns.barplot(x='FareGroup', y='Survived', data=age_survival)
plt.title('Survival Rate by Fare Price')
plt.xlabel('Age Group')
plt.ylabel('Survival Rate')
plt.show()
new_train = new_train.drop(['AgeGroup', 'FareGroup'], axis=1) | code |
121154019/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categories='auto', sparse=False, handle_unknown='ignore')
encoder.fit(X_train[cat_cols])
X_train_encoded = encoder.transform(X_train[cat_cols])
X_train_final = pd.concat([X_train.drop(cat_cols, axis=1), pd.DataFrame(X_train_encoded, index=X_train.index)], axis=1)
X_test_encoded = encoder.transform(X_test[cat_cols])
X_test_final = pd.concat([X_test.drop(cat_cols, axis=1), pd.DataFrame(X_test_encoded, index=X_test.index)], axis=1)
return (X_train_final, X_test_final, encoder)
new_train = wrangle(train)
sns.set(style='whitegrid')
sns.despine(left=True)
new_train['AgeGroup'] = pd.cut(new_train['Age'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80])
age_survival = new_train.groupby(['AgeGroup'])['Survived'].mean().reset_index()
sns.barplot(x='AgeGroup', y='Survived', data=age_survival)
plt.title('Survival Rate by Age Group')
plt.xlabel('Age Group')
plt.ylabel('Survival Rate')
plt.show() | code |
121154019/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
new_train = wrangle(train)
sns.set(style='whitegrid')
sns.despine(left=True)
sns.histplot(new_train['Fare'], bins=50) | code |
121154019/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categories='auto', sparse=False, handle_unknown='ignore')
encoder.fit(X_train[cat_cols])
X_train_encoded = encoder.transform(X_train[cat_cols])
X_train_final = pd.concat([X_train.drop(cat_cols, axis=1), pd.DataFrame(X_train_encoded, index=X_train.index)], axis=1)
X_test_encoded = encoder.transform(X_test[cat_cols])
X_test_final = pd.concat([X_test.drop(cat_cols, axis=1), pd.DataFrame(X_test_encoded, index=X_test.index)], axis=1)
return (X_train_final, X_test_final, encoder)
new_train = wrangle(train)
sns.set(style='whitegrid')
sns.despine(left=True)
new_train['AgeGroup'] = pd.cut(new_train['Age'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80])
age_survival = new_train.groupby(['AgeGroup'])['Survived'].mean().reset_index()
new_train['FareGroup'] = pd.cut(new_train['Fare'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 600])
age_survival = new_train.groupby(['FareGroup'])['Survived'].mean().reset_index()
new_train = new_train.drop(['AgeGroup', 'FareGroup'], axis=1)
X_train, X_test, y_train, y_test = train_test_split(new_train.drop('Survived', axis=1), new_train['Survived'], test_size=0.2, random_state=42)
cat_cols = ['Embarked', 'Pronoun', 'Prefix', 'Letter']
X_train_final, X_test_final, encoder = encode(X_train, X_test, cat_cols)
xgb_classifier = xgb.XGBClassifier()
xgb_classifier.fit(X_train_final, y_train)
y_pred = xgb_classifier.predict(X_test_final)
accuracy = accuracy_score(y_test, y_pred)
param_grid = {'max_depth': [3, 4, 5], 'learning_rate': [0.1, 0.01], 'n_estimators': [100, 200], 'gamma': [0, 0.1], 'colsample_bytree': [0.6, 0.8]}
xgb_classifier = xgb.XGBClassifier()
grid_search = GridSearchCV(estimator=xgb_classifier, param_grid=param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train_final, y_train)
best_xgb_classifier = grid_search.best_estimator_
y_pred = best_xgb_classifier.predict(X_test_final)
accuracy = accuracy_score(y_test, y_pred)
new_test = wrangle(test)
X_train_final, X_test_final, encoder = encode(new_train.drop('Survived', axis=1), new_test, cat_cols)
y_train = new_train['Survived']
xgb_classifier = xgb.XGBClassifier()
grid_search = GridSearchCV(estimator=xgb_classifier, param_grid=param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train_final, y_train)
final_xgb_classifier = grid_search.best_estimator_
print('Best hyperparameters:', grid_search.best_params_) | code |
121154019/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categories='auto', sparse=False, handle_unknown='ignore')
encoder.fit(X_train[cat_cols])
X_train_encoded = encoder.transform(X_train[cat_cols])
X_train_final = pd.concat([X_train.drop(cat_cols, axis=1), pd.DataFrame(X_train_encoded, index=X_train.index)], axis=1)
X_test_encoded = encoder.transform(X_test[cat_cols])
X_test_final = pd.concat([X_test.drop(cat_cols, axis=1), pd.DataFrame(X_test_encoded, index=X_test.index)], axis=1)
return (X_train_final, X_test_final, encoder)
xgb_classifier = xgb.XGBClassifier()
xgb_classifier.fit(X_train_final, y_train)
y_pred = xgb_classifier.predict(X_test_final)
accuracy = accuracy_score(y_test, y_pred)
param_grid = {'max_depth': [3, 4, 5], 'learning_rate': [0.1, 0.01], 'n_estimators': [100, 200], 'gamma': [0, 0.1], 'colsample_bytree': [0.6, 0.8]}
xgb_classifier = xgb.XGBClassifier()
grid_search = GridSearchCV(estimator=xgb_classifier, param_grid=param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train_final, y_train)
best_xgb_classifier = grid_search.best_estimator_
y_pred = best_xgb_classifier.predict(X_test_final)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy with best hyperparameters:', accuracy) | code |
121154019/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
new_train = wrangle(train)
def aggregate(df, cols):
by_cols = df.groupby(cols).agg({'Survived': [('Total', 'count'), ('Survived', 'sum')]})
by_cols.columns = by_cols.columns.droplevel()
by_cols['Died'] = by_cols['Total'] - by_cols['Survived']
by_cols['Survive Rate'] = 100 * by_cols['Survived'] / by_cols['Total']
return by_cols.sort_values('Total', ascending=False)
aggregate(new_train, ['Pronoun']) | code |
121154019/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
new_train = wrangle(train)
def aggregate(df, cols):
by_cols = df.groupby(cols).agg({'Survived': [('Total', 'count'), ('Survived', 'sum')]})
by_cols.columns = by_cols.columns.droplevel()
by_cols['Died'] = by_cols['Total'] - by_cols['Survived']
by_cols['Survive Rate'] = 100 * by_cols['Survived'] / by_cols['Total']
return by_cols.sort_values('Total', ascending=False)
aggregate(new_train, ['Pclass', 'Male']) | code |
121154019/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
new_train = wrangle(train)
sns.set(style='whitegrid')
sns.despine(left=True)
sns.histplot(new_train['Age'], bins=20) | code |
121154019/cell_35 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categories='auto', sparse=False, handle_unknown='ignore')
encoder.fit(X_train[cat_cols])
X_train_encoded = encoder.transform(X_train[cat_cols])
X_train_final = pd.concat([X_train.drop(cat_cols, axis=1), pd.DataFrame(X_train_encoded, index=X_train.index)], axis=1)
X_test_encoded = encoder.transform(X_test[cat_cols])
X_test_final = pd.concat([X_test.drop(cat_cols, axis=1), pd.DataFrame(X_test_encoded, index=X_test.index)], axis=1)
return (X_train_final, X_test_final, encoder)
new_train = wrangle(train)
sns.set(style='whitegrid')
sns.despine(left=True)
new_train['AgeGroup'] = pd.cut(new_train['Age'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80])
age_survival = new_train.groupby(['AgeGroup'])['Survived'].mean().reset_index()
new_train['FareGroup'] = pd.cut(new_train['Fare'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 600])
age_survival = new_train.groupby(['FareGroup'])['Survived'].mean().reset_index()
new_train = new_train.drop(['AgeGroup', 'FareGroup'], axis=1)
X_train, X_test, y_train, y_test = train_test_split(new_train.drop('Survived', axis=1), new_train['Survived'], test_size=0.2, random_state=42)
cat_cols = ['Embarked', 'Pronoun', 'Prefix', 'Letter']
X_train_final, X_test_final, encoder = encode(X_train, X_test, cat_cols)
xgb_classifier = xgb.XGBClassifier()
xgb_classifier.fit(X_train_final, y_train)
y_pred = xgb_classifier.predict(X_test_final)
accuracy = accuracy_score(y_test, y_pred)
param_grid = {'max_depth': [3, 4, 5], 'learning_rate': [0.1, 0.01], 'n_estimators': [100, 200], 'gamma': [0, 0.1], 'colsample_bytree': [0.6, 0.8]}
xgb_classifier = xgb.XGBClassifier()
grid_search = GridSearchCV(estimator=xgb_classifier, param_grid=param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train_final, y_train)
best_xgb_classifier = grid_search.best_estimator_
y_pred = best_xgb_classifier.predict(X_test_final)
accuracy = accuracy_score(y_test, y_pred)
new_test = wrangle(test)
X_train_final, X_test_final, encoder = encode(new_train.drop('Survived', axis=1), new_test, cat_cols)
y_train = new_train['Survived']
xgb_classifier = xgb.XGBClassifier()
grid_search = GridSearchCV(estimator=xgb_classifier, param_grid=param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train_final, y_train)
final_xgb_classifier = grid_search.best_estimator_
importance = final_xgb_classifier.feature_importances_
features = X_train_final.columns
encodings = encoder.get_feature_names()
translated = []
for feature in features:
if feature in range(len(encodings)):
translated.append(encodings[feature])
else:
translated.append(feature)
df_importance = pd.DataFrame({'Feature': translated, 'Importance': importance}).sort_values('Importance', ascending=False)
y_pred = final_xgb_classifier.predict(X_test_final)
prediction = pd.DataFrame()
prediction['PassengerId'] = test['PassengerId']
prediction['Survived'] = y_pred
prediction.to_csv('prediction.csv', index=False)
prediction | code |
121154019/cell_24 | [
"image_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categories='auto', sparse=False, handle_unknown='ignore')
encoder.fit(X_train[cat_cols])
X_train_encoded = encoder.transform(X_train[cat_cols])
X_train_final = pd.concat([X_train.drop(cat_cols, axis=1), pd.DataFrame(X_train_encoded, index=X_train.index)], axis=1)
X_test_encoded = encoder.transform(X_test[cat_cols])
X_test_final = pd.concat([X_test.drop(cat_cols, axis=1), pd.DataFrame(X_test_encoded, index=X_test.index)], axis=1)
return (X_train_final, X_test_final, encoder)
X_train_final | code |
121154019/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
new_train = wrangle(train)
def aggregate(df, cols):
by_cols = df.groupby(cols).agg({'Survived': [('Total', 'count'), ('Survived', 'sum')]})
by_cols.columns = by_cols.columns.droplevel()
by_cols['Died'] = by_cols['Total'] - by_cols['Survived']
by_cols['Survive Rate'] = 100 * by_cols['Survived'] / by_cols['Total']
return by_cols.sort_values('Total', ascending=False)
aggregate(new_train, ['Embarked']) | code |
121154019/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
new_train = wrangle(train)
new_train.head() | code |
121154019/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categories='auto', sparse=False, handle_unknown='ignore')
encoder.fit(X_train[cat_cols])
X_train_encoded = encoder.transform(X_train[cat_cols])
X_train_final = pd.concat([X_train.drop(cat_cols, axis=1), pd.DataFrame(X_train_encoded, index=X_train.index)], axis=1)
X_test_encoded = encoder.transform(X_test[cat_cols])
X_test_final = pd.concat([X_test.drop(cat_cols, axis=1), pd.DataFrame(X_test_encoded, index=X_test.index)], axis=1)
return (X_train_final, X_test_final, encoder)
xgb_classifier = xgb.XGBClassifier()
xgb_classifier.fit(X_train_final, y_train)
y_pred = xgb_classifier.predict(X_test_final)
accuracy = accuracy_score(y_test, y_pred)
param_grid = {'max_depth': [3, 4, 5], 'learning_rate': [0.1, 0.01], 'n_estimators': [100, 200], 'gamma': [0, 0.1], 'colsample_bytree': [0.6, 0.8]}
xgb_classifier = xgb.XGBClassifier()
grid_search = GridSearchCV(estimator=xgb_classifier, param_grid=param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train_final, y_train)
print('Best hyperparameters:', grid_search.best_params_) | code |
121154019/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda x: x.split(' ')[0].split('/')[0].replace('.', '') if ' ' in x else '')
df['Letter'] = df['Cabin'].fillna('00').map(lambda x: x[0])
df['Family'] = df['SibSp'] + df['Parch']
df = df.drop(['PassengerId', 'Name', 'Ticket', 'Sex', 'Cabin'], axis=1)
return df
new_train = wrangle(train)
def aggregate(df, cols):
by_cols = df.groupby(cols).agg({'Survived': [('Total', 'count'), ('Survived', 'sum')]})
by_cols.columns = by_cols.columns.droplevel()
by_cols['Died'] = by_cols['Total'] - by_cols['Survived']
by_cols['Survive Rate'] = 100 * by_cols['Survived'] / by_cols['Total']
return by_cols.sort_values('Total', ascending=False)
aggregate(new_train, ['Pclass']) | code |
121154019/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.head() | code |
2045099/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
ted.speaker_occupation.value_counts().head(10) | code |
2045099/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.describe() | code |
2045099/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
axes.set_xlabel('views')
axes.set_ylabel('comments')
axes.plot(ted['views'], ted['comments'], ls='', marker='.') | code |
2045099/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_ylabel("comments")
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_xlim(2200000)
axes.set_ylabel("comments")
axes.set_ylim(200)
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
labels = ted.num_speaker.unique()
sizes = ted.num_speaker.value_counts()
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
ax1.set_title("Number of presenters")
plt.show()
ted.num_speaker.unique()
ted.num_speaker.value_counts()
ted.columns
ted.ratings[0] | code |
2045099/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_ylabel("comments")
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_xlim(2200000)
axes.set_ylabel("comments")
axes.set_ylim(200)
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
labels = ted.num_speaker.unique()
sizes = ted.num_speaker.value_counts()
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
ax1.axis('equal')
ax1.set_title('Number of presenters')
plt.show() | code |
2045099/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.head() | code |
2045099/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
ted.event.value_counts().tail(10) | code |
2045099/cell_1 | [
"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 |
2045099/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted[ted['speaker_occupation'].isnull()] | code |
2045099/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
ted.event.value_counts().head(10) | code |
2045099/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_ylabel("comments")
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_xlim(2200000)
axes.set_ylabel("comments")
axes.set_ylim(200)
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
labels = ted.num_speaker.unique()
sizes = ted.num_speaker.value_counts()
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
ax1.set_title("Number of presenters")
plt.show()
ted.num_speaker.unique()
ted.num_speaker.value_counts()
ted.columns | code |
2045099/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5) | code |
2045099/cell_38 | [
"text_plain_output_1.png"
] | import ast
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_ylabel("comments")
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_xlim(2200000)
axes.set_ylabel("comments")
axes.set_ylim(200)
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
labels = ted.num_speaker.unique()
sizes = ted.num_speaker.value_counts()
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
ax1.set_title("Number of presenters")
plt.show()
ted.num_speaker.unique()
ted.num_speaker.value_counts()
ted.columns
ted.ratings[0]
import ast
ted.ratings = ted.ratings.apply(lambda x: ast.literal_eval(x))
ted.ratings[0] | code |
2045099/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_ylabel("comments")
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_xlim(2200000)
axes.set_ylabel("comments")
axes.set_ylim(200)
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
labels = ted.num_speaker.unique()
sizes = ted.num_speaker.value_counts()
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
ax1.set_title("Number of presenters")
plt.show()
ted.num_speaker.unique()
ted.num_speaker.value_counts() | code |
2045099/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5) | code |
2045099/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xlabel("views")
axes.set_ylabel("comments")
axes.plot(ted["views"],ted["comments"],ls="",marker=".")
fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
axes.set_xlabel('views')
axes.set_xlim(2200000)
axes.set_ylabel('comments')
axes.set_ylim(200)
axes.plot(ted['views'], ted['comments'], ls='', marker='.') | code |
2045099/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr() | code |
2045099/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.info() | code |
17115909/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = imdb_movies[columns_to_keep]
animated_movies = imdb_movies[imdb_movies['genres'].str.contains('Animation') == True]
animated_movies_count = animated_movies.shape[0]
animated_movies = animated_movies.assign(profit=pd.Series(animated_movies.revenue - animated_movies.budget).values)
animated_movies.loc[animated_movies['popularity'].idxmax(), 'original_title']
animated_movies.loc[animated_movies['budget'].idxmax(), 'original_title']
animated_movies.loc[animated_movies['profit'].idxmax(), 'original_title']
top_10 = animated_movies.nlargest(10, 'profit')
top_10.index = top_10.original_title
plt.figure(figsize=(12, 6))
plt.title('Profit Vs Year')
plt.xlabel('Year')
plt.ylabel('Profit in Billions')
plt.scatter(animated_movies.release_year, animated_movies.profit, color='red')
plt.show() | code |
17115909/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = imdb_movies[columns_to_keep]
animated_movies = imdb_movies[imdb_movies['genres'].str.contains('Animation') == True]
animated_movies_count = animated_movies.shape[0]
animated_movies = animated_movies.assign(profit=pd.Series(animated_movies.revenue - animated_movies.budget).values)
animated_movies.loc[animated_movies['popularity'].idxmax(), 'original_title']
animated_movies.loc[animated_movies['budget'].idxmax(), 'original_title']
animated_movies.loc[animated_movies['profit'].idxmax(), 'original_title']
top_10 = animated_movies.nlargest(10, 'profit')
top_10.index = top_10.original_title
top_10[['original_title', 'profit']].plot.bar(figsize=(12, 6)) | code |
17115909/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = imdb_movies[columns_to_keep]
animated_movies = imdb_movies[imdb_movies['genres'].str.contains('Animation') == True]
animated_movies_count = animated_movies.shape[0]
animated_movies = animated_movies.assign(profit=pd.Series(animated_movies.revenue - animated_movies.budget).values)
animated_movies.head(5) | code |
17115909/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = imdb_movies[columns_to_keep]
animated_movies = imdb_movies[imdb_movies['genres'].str.contains('Animation') == True]
animated_movies_count = animated_movies.shape[0]
animated_movies = animated_movies.assign(profit=pd.Series(animated_movies.revenue - animated_movies.budget).values)
animated_movies.loc[animated_movies['popularity'].idxmax(), 'original_title'] | code |
17115909/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
17115909/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = imdb_movies[columns_to_keep]
animated_movies = imdb_movies[imdb_movies['genres'].str.contains('Animation') == True]
animated_movies_count = animated_movies.shape[0]
animated_movies = animated_movies.assign(profit=pd.Series(animated_movies.revenue - animated_movies.budget).values)
animated_movies['production_companies'].value_counts().head(5).plot.bar(figsize=(10, 5)) | code |
17115909/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = imdb_movies[columns_to_keep]
animated_movies = imdb_movies[imdb_movies['genres'].str.contains('Animation') == True]
animated_movies_count = animated_movies.shape[0]
animated_movies = animated_movies.assign(profit=pd.Series(animated_movies.revenue - animated_movies.budget).values)
print('Percentage of animated movies: {}%'.format(round(animated_movies_count / movie_count * 100, 2))) | code |
17115909/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = imdb_movies[columns_to_keep]
animated_movies = imdb_movies[imdb_movies['genres'].str.contains('Animation') == True]
animated_movies_count = animated_movies.shape[0]
animated_movies = animated_movies.assign(profit=pd.Series(animated_movies.revenue - animated_movies.budget).values)
animated_movies.loc[animated_movies['popularity'].idxmax(), 'original_title']
animated_movies.loc[animated_movies['budget'].idxmax(), 'original_title']
animated_movies.loc[animated_movies['profit'].idxmax(), 'original_title'] | code |
17115909/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = imdb_movies[columns_to_keep]
animated_movies = imdb_movies[imdb_movies['genres'].str.contains('Animation') == True]
animated_movies_count = animated_movies.shape[0]
animated_movies = animated_movies.assign(profit=pd.Series(animated_movies.revenue - animated_movies.budget).values)
animated_movies.loc[animated_movies['popularity'].idxmax(), 'original_title']
animated_movies.loc[animated_movies['budget'].idxmax(), 'original_title'] | code |
17115909/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = imdb_movies[columns_to_keep]
animated_movies = imdb_movies[imdb_movies['genres'].str.contains('Animation') == True]
animated_movies_count = animated_movies.shape[0]
animated_movies = animated_movies.assign(profit=pd.Series(animated_movies.revenue - animated_movies.budget).values)
animated_movies['release_year'].value_counts(sort=False).plot.bar(figsize=(20, 5)) | code |
1004380/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
print('Number of training samples: {}'.format(n_samples))
print('Number of features: {}'.format(n_features))
print('Number of survivors: {}'.format(n_survived))
print('Number of deaths: {}'.format(n_died)) | code |
1004380/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1004380/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
features.head(n=20) | code |
105214007/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
data.enrolled_university.unique()
data.education_level.unique()
data.major_discipline.unique()
data.experience.unique()
data.company_size.unique()
data.company_type.unique()
data.last_new_job.unique()
data.fillna({'gender': 'Other', 'enrolled_university': 'no_enrollment', 'education_level': 'Other', 'major_discipline': 'Other', 'experience': '0', 'company_size': '<10', 'company_type': 'Other', 'last_new_job': 'never'}, inplace=True)
data.isna().sum()
data.experience.unique()
data.experience.replace({'>20': '22', '<1': '0'}, inplace=True)
data.experience.unique()
data.company_size.unique()
data.company_size.replace({'<10': '1-9', '10/49': '10-49', '10000+': '10000-10500'}, inplace=True)
data.company_size.unique() | code |
105214007/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
sns.heatmap(data.isna()) | code |
105214007/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
data.enrolled_university.unique()
data.education_level.unique()
data.major_discipline.unique()
data.experience.unique()
data.company_size.unique()
data.company_type.unique()
data.last_new_job.unique()
data.fillna({'gender': 'Other', 'enrolled_university': 'no_enrollment', 'education_level': 'Other', 'major_discipline': 'Other', 'experience': '0', 'company_size': '<10', 'company_type': 'Other', 'last_new_job': 'never'}, inplace=True)
data.isna().sum()
data.experience.unique()
data.experience.replace({'>20': '22', '<1': '0'}, inplace=True)
data.experience.unique()
data.company_size.unique()
data.company_size.replace({'<10': '1-9', '10/49': '10-49', '10000+': '10000-10500'}, inplace=True)
data.company_size.unique()
data.last_new_job.replace({'>4': '5', 'never': '0'}, inplace=True)
data.last_new_job.unique()
data.dtypes
com_siz = []
for i in data.company_size:
x, y = (i.split('-')[0], i.split('-')[1])
m = (int(x) + int(y)) / 2
com_siz.append(m)
data.company_size = com_siz
data.company_size.unique()
data.dtypes | code |
105214007/cell_2 | [
"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/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.head() | code |
105214007/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
data.enrolled_university.unique()
data.education_level.unique()
data.major_discipline.unique()
data.experience.unique()
data.company_size.unique()
data.company_type.unique()
data.last_new_job.unique()
data.fillna({'gender': 'Other', 'enrolled_university': 'no_enrollment', 'education_level': 'Other', 'major_discipline': 'Other', 'experience': '0', 'company_size': '<10', 'company_type': 'Other', 'last_new_job': 'never'}, inplace=True)
data.isna().sum()
data.experience.unique()
data.experience.replace({'>20': '22', '<1': '0'}, inplace=True)
data.experience.unique()
data.company_size.unique() | code |
105214007/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 |
105214007/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
data.enrolled_university.unique()
data.education_level.unique()
data.major_discipline.unique()
data.experience.unique()
data.company_size.unique()
data.company_type.unique()
data.last_new_job.unique()
data.fillna({'gender': 'Other', 'enrolled_university': 'no_enrollment', 'education_level': 'Other', 'major_discipline': 'Other', 'experience': '0', 'company_size': '<10', 'company_type': 'Other', 'last_new_job': 'never'}, inplace=True)
data.isna().sum() | code |
105214007/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
data.enrolled_university.unique()
data.education_level.unique()
data.major_discipline.unique()
data.experience.unique()
data.company_size.unique()
data.company_type.unique()
data.last_new_job.unique()
data.fillna({'gender': 'Other', 'enrolled_university': 'no_enrollment', 'education_level': 'Other', 'major_discipline': 'Other', 'experience': '0', 'company_size': '<10', 'company_type': 'Other', 'last_new_job': 'never'}, inplace=True)
data.isna().sum()
data.experience.unique()
data.experience.replace({'>20': '22', '<1': '0'}, inplace=True)
data.experience.unique()
data.company_size.unique()
data.company_size.replace({'<10': '1-9', '10/49': '10-49', '10000+': '10000-10500'}, inplace=True)
data.company_size.unique()
data.last_new_job.replace({'>4': '5', 'never': '0'}, inplace=True)
data.last_new_job.unique()
data.dtypes
com_siz = []
for i in data.company_size:
x, y = (i.split('-')[0], i.split('-')[1])
m = (int(x) + int(y)) / 2
com_siz.append(m)
data.company_size = com_siz
data.company_size.unique() | code |
105214007/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
data.enrolled_university.unique()
data.education_level.unique()
data.major_discipline.unique()
data.experience.unique()
data.company_size.unique()
data.company_type.unique()
data.last_new_job.unique()
data.fillna({'gender': 'Other', 'enrolled_university': 'no_enrollment', 'education_level': 'Other', 'major_discipline': 'Other', 'experience': '0', 'company_size': '<10', 'company_type': 'Other', 'last_new_job': 'never'}, inplace=True)
data.isna().sum()
data.experience.unique() | code |
105214007/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
data.enrolled_university.unique()
data.education_level.unique()
data.major_discipline.unique()
data.experience.unique()
data.company_size.unique()
data.company_type.unique()
data.last_new_job.unique()
data.fillna({'gender': 'Other', 'enrolled_university': 'no_enrollment', 'education_level': 'Other', 'major_discipline': 'Other', 'experience': '0', 'company_size': '<10', 'company_type': 'Other', 'last_new_job': 'never'}, inplace=True)
data.isna().sum()
data.experience.unique()
data.experience.replace({'>20': '22', '<1': '0'}, inplace=True)
data.experience.unique()
data.company_size.unique()
data.company_size.replace({'<10': '1-9', '10/49': '10-49', '10000+': '10000-10500'}, inplace=True)
data.company_size.unique()
data.last_new_job.replace({'>4': '5', 'never': '0'}, inplace=True)
data.last_new_job.unique() | code |
105214007/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
data.enrolled_university.unique()
data.education_level.unique()
data.major_discipline.unique()
data.experience.unique()
data.company_size.unique()
data.company_type.unique()
data.last_new_job.unique()
data.fillna({'gender': 'Other', 'enrolled_university': 'no_enrollment', 'education_level': 'Other', 'major_discipline': 'Other', 'experience': '0', 'company_size': '<10', 'company_type': 'Other', 'last_new_job': 'never'}, inplace=True)
data.isna().sum()
data.experience.unique()
data.experience.replace({'>20': '22', '<1': '0'}, inplace=True)
data.experience.unique()
data.company_size.unique()
data.company_size.replace({'<10': '1-9', '10/49': '10-49', '10000+': '10000-10500'}, inplace=True)
data.company_size.unique()
data.last_new_job.replace({'>4': '5', 'never': '0'}, inplace=True)
data.last_new_job.unique()
data.dtypes | code |
105214007/cell_3 | [
"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/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum() | code |
105214007/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
data.enrolled_university.unique()
data.education_level.unique()
data.major_discipline.unique()
data.experience.unique()
data.company_size.unique()
data.company_type.unique()
data.last_new_job.unique()
data.fillna({'gender': 'Other', 'enrolled_university': 'no_enrollment', 'education_level': 'Other', 'major_discipline': 'Other', 'experience': '0', 'company_size': '<10', 'company_type': 'Other', 'last_new_job': 'never'}, inplace=True)
data.isna().sum()
data.experience.unique()
data.experience.replace({'>20': '22', '<1': '0'}, inplace=True)
data.experience.unique() | code |
105214007/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
data.enrolled_university.unique()
data.education_level.unique()
data.major_discipline.unique()
data.experience.unique()
data.company_size.unique()
data.company_type.unique()
data.last_new_job.unique() | code |
122252043/cell_42 | [
"text_plain_output_1.png"
] | x = 12
y = 8
y >> 2 | code |
122252043/cell_63 | [
"text_plain_output_1.png"
] | int('0o65416', base=8) | code |
122252043/cell_81 | [
"text_plain_output_1.png"
] | a = 12670
b = 12.344
print(f"a={{{a:5d}}},b='{b:>4.0f}'") | code |
122252043/cell_13 | [
"text_plain_output_1.png"
] | 375 | code |
122252043/cell_9 | [
"text_plain_output_1.png"
] | 74155 | code |
122252043/cell_4 | [
"text_plain_output_1.png"
] | type('2+3') | code |
122252043/cell_83 | [
"text_plain_output_1.png"
] | num = input('A number:') | code |
122252043/cell_79 | [
"text_plain_output_1.png"
] | a = 12670
b = 12.344
print(f'a={a:>+7,d},b={b:>06.1f}') | code |
122252043/cell_20 | [
"text_plain_output_1.png"
] | num = 12
num = 12 + 7.2
type(num) | code |
122252043/cell_55 | [
"text_plain_output_1.png"
] | eval("int('2020')") | code |
122252043/cell_6 | [
"text_plain_output_1.png"
] | type(18) | code |
122252043/cell_74 | [
"text_plain_output_1.png"
] | print("'''\\n represents a new line character'''") | code |
122252043/cell_40 | [
"text_plain_output_1.png"
] | x = 5
x += 2
x = 12
y = 8
x << 2 | code |
122252043/cell_29 | [
"text_plain_output_1.png"
] | 2 < 8 or (7 <= 8 and 7 > 2) | code |
122252043/cell_39 | [
"text_plain_output_1.png"
] | x = 5
x += 2
x = 12
y = 8
x >> 2 | code |
122252043/cell_26 | [
"text_plain_output_1.png"
] | 'kitty' < 'kitten' | code |
122252043/cell_48 | [
"text_plain_output_1.png"
] | (not 'piggy') + True | code |
122252043/cell_73 | [
"text_plain_output_1.png"
] | print('A back slash \\ sign.') | code |
122252043/cell_41 | [
"text_plain_output_1.png"
] | x = 12
y = 8
~y | code |
122252043/cell_54 | [
"text_plain_output_1.png"
] | eval('6**2+3*(7-1)') | code |
122252043/cell_72 | [
"text_plain_output_1.png"
] | print('"You may say I\'m a dreamer"') | code |
122252043/cell_67 | [
"text_plain_output_1.png"
] | oct(int('0b1001001', base=2)) | code |
122252043/cell_11 | [
"text_plain_output_1.png"
] | 3737 | code |
122252043/cell_60 | [
"text_plain_output_1.png"
] | hex(1024) | code |
122252043/cell_86 | [
"text_plain_output_1.png"
] | nofd = input('A num of d:') | code |
122252043/cell_64 | [
"text_plain_output_1.png"
] | hex(int('0o65416', base=8)) | code |
122252043/cell_32 | [
"text_plain_output_1.png"
] | x = 5
x += 2
x | code |
122252043/cell_68 | [
"text_plain_output_1.png"
] | hex(int('0b1001001', base=2)) | code |
122252043/cell_62 | [
"text_plain_output_1.png"
] | bin(int('0o65416', base=8)) | code |
122252043/cell_59 | [
"text_plain_output_1.png"
] | oct(1024) | code |
122252043/cell_58 | [
"text_plain_output_1.png"
] | bin(1024) | code |
122252043/cell_28 | [
"text_plain_output_1.png"
] | False and 'kitty' or True | code |
122252043/cell_78 | [
"text_plain_output_1.png"
] | a = 12670
b = 12.344
print(f'a={a:>+7d},b={b:>06.2f}') | code |
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