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stringlengths 13
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sequencelengths 1
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stringclasses 1
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|---|---|---|---|
17141241/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.isnull().sum()
test.head() | code |
17141241/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().sum()
train['Embarked'].value_counts() | code |
74058807/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
plt.figure(figsize=(24, 18))
for i in range(0, 32):
plt.subplot(8, 8, 2 * i + 1)
plt.imshow(images_gray[i], cmap='gray')
plt.title(f'Input {i + 1}')
plt.axis('off')
plt.subplot(8, 8, 2 * i + 2)
plt.imshow(images_col[i])
plt.title(f'Output {i + 1}')
plt.axis('off')
plt.show() | code |
74058807/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | img_paths = []
for r, d, f in os.walk(DIR_PATH):
for file in f:
img_paths.append(os.path.join(r, file))
np.random.shuffle(img_paths) | code |
74058807/cell_25 | [
"text_plain_output_1.png"
] | from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, MaxPooling2D, BatchNormalization, UpSampling2D
from keras.models import Sequential, Model
import tensorflow as tf
SEED = 42
INPUT_DIM = (144, 144, 1)
BATCH_SIZE = 128
EPOCHS = 100
LOSS = 'mse'
METRICS = ['accuracy']
OPTIMIZER = 'adam'
datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255, validation_split=0.2)
train_datagen = datagen.flow(images_gray, images_col, batch_size=BATCH_SIZE, shuffle=True, seed=SEED, subset='training')
test_datagen = datagen.flow(images_gray, images_col, batch_size=BATCH_SIZE, shuffle=True, seed=SEED, subset='validation')
checkpoint = tf.keras.callbacks.ModelCheckpoint(monitor='loss', mode='min', save_best_only=True, save_weights_only=True, filepath='./modelcheck')
model_callbacks = [checkpoint]
def Colorize():
encoder_input = Input(shape=INPUT_DIM)
encoder_output = Conv2D(64, (3, 3), activation='relu', padding='same', strides=2)(encoder_input)
encoder_output = Conv2D(128, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(128, (3, 3), activation='relu', padding='same', strides=2)(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='valid', strides=3)(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same')(encoder_output)
decoder_output = Conv2D(128, (3, 3), activation='relu', padding='same')(encoder_output)
decoder_output = UpSampling2D((3, 3))(decoder_output)
decoder_output = Conv2D(64, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
decoder_output = Conv2D(32, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(16, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(3, (3, 3), activation='tanh', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
model = Model(inputs=encoder_input, outputs=decoder_output)
return model
model = Colorize()
model.compile(optimizer=OPTIMIZER, loss=LOSS, metrics=METRICS)
model.summary()
history = model.fit(train_datagen, batch_size=BATCH_SIZE, validation_data=test_datagen, epochs=EPOCHS, callbacks=model_callbacks) | code |
74058807/cell_11 | [
"text_plain_output_1.png"
] | images_col = []
images_gray = []
for i, img_path in tqdm(enumerate(img_paths)):
img = np.asarray(Image.open(img_path))
if img.shape == (150, 150, 3):
resized_image = tf.image.resize(img, [144, 144])
images_col.append(resized_image)
images_gray.append(tf.image.rgb_to_grayscale(resized_image))
images_col = np.asarray(images_col, dtype='int32')
images_gray = np.asarray(images_gray, dtype='int32') | code |
74058807/cell_28 | [
"text_plain_output_1.png"
] | from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, MaxPooling2D, BatchNormalization, UpSampling2D
from keras.models import Sequential, Model
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
SEED = 42
for i in range(0, 32):
plt.axis('off')
plt.axis('off')
INPUT_DIM = (144, 144, 1)
BATCH_SIZE = 128
EPOCHS = 100
LOSS = 'mse'
METRICS = ['accuracy']
OPTIMIZER = 'adam'
datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255, validation_split=0.2)
train_datagen = datagen.flow(images_gray, images_col, batch_size=BATCH_SIZE, shuffle=True, seed=SEED, subset='training')
test_datagen = datagen.flow(images_gray, images_col, batch_size=BATCH_SIZE, shuffle=True, seed=SEED, subset='validation')
checkpoint = tf.keras.callbacks.ModelCheckpoint(monitor='loss', mode='min', save_best_only=True, save_weights_only=True, filepath='./modelcheck')
model_callbacks = [checkpoint]
def Colorize():
encoder_input = Input(shape=INPUT_DIM)
encoder_output = Conv2D(64, (3, 3), activation='relu', padding='same', strides=2)(encoder_input)
encoder_output = Conv2D(128, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(128, (3, 3), activation='relu', padding='same', strides=2)(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='valid', strides=3)(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same')(encoder_output)
decoder_output = Conv2D(128, (3, 3), activation='relu', padding='same')(encoder_output)
decoder_output = UpSampling2D((3, 3))(decoder_output)
decoder_output = Conv2D(64, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
decoder_output = Conv2D(32, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(16, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(3, (3, 3), activation='tanh', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
model = Model(inputs=encoder_input, outputs=decoder_output)
return model
model = Colorize()
model.compile(optimizer=OPTIMIZER, loss=LOSS, metrics=METRICS)
model.summary()
history = model.fit(train_datagen, batch_size=BATCH_SIZE, validation_data=test_datagen, epochs=EPOCHS, callbacks=model_callbacks)
preds = model.predict(test_datagen)
preds.shape
plt.imshow(preds[0]) | code |
74058807/cell_24 | [
"image_output_1.png"
] | from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, MaxPooling2D, BatchNormalization, UpSampling2D
from keras.models import Sequential, Model
INPUT_DIM = (144, 144, 1)
BATCH_SIZE = 128
EPOCHS = 100
LOSS = 'mse'
METRICS = ['accuracy']
OPTIMIZER = 'adam'
def Colorize():
encoder_input = Input(shape=INPUT_DIM)
encoder_output = Conv2D(64, (3, 3), activation='relu', padding='same', strides=2)(encoder_input)
encoder_output = Conv2D(128, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(128, (3, 3), activation='relu', padding='same', strides=2)(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='valid', strides=3)(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same')(encoder_output)
decoder_output = Conv2D(128, (3, 3), activation='relu', padding='same')(encoder_output)
decoder_output = UpSampling2D((3, 3))(decoder_output)
decoder_output = Conv2D(64, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
decoder_output = Conv2D(32, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(16, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(3, (3, 3), activation='tanh', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
model = Model(inputs=encoder_input, outputs=decoder_output)
return model
model = Colorize()
model.compile(optimizer=OPTIMIZER, loss=LOSS, metrics=METRICS)
model.summary() | code |
74058807/cell_10 | [
"text_plain_output_1.png"
] | len(img_paths) | code |
74058807/cell_27 | [
"text_plain_output_1.png"
] | from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, MaxPooling2D, BatchNormalization, UpSampling2D
from keras.models import Sequential, Model
import tensorflow as tf
SEED = 42
INPUT_DIM = (144, 144, 1)
BATCH_SIZE = 128
EPOCHS = 100
LOSS = 'mse'
METRICS = ['accuracy']
OPTIMIZER = 'adam'
datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255, validation_split=0.2)
train_datagen = datagen.flow(images_gray, images_col, batch_size=BATCH_SIZE, shuffle=True, seed=SEED, subset='training')
test_datagen = datagen.flow(images_gray, images_col, batch_size=BATCH_SIZE, shuffle=True, seed=SEED, subset='validation')
checkpoint = tf.keras.callbacks.ModelCheckpoint(monitor='loss', mode='min', save_best_only=True, save_weights_only=True, filepath='./modelcheck')
model_callbacks = [checkpoint]
def Colorize():
encoder_input = Input(shape=INPUT_DIM)
encoder_output = Conv2D(64, (3, 3), activation='relu', padding='same', strides=2)(encoder_input)
encoder_output = Conv2D(128, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(128, (3, 3), activation='relu', padding='same', strides=2)(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='valid', strides=3)(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same')(encoder_output)
decoder_output = Conv2D(128, (3, 3), activation='relu', padding='same')(encoder_output)
decoder_output = UpSampling2D((3, 3))(decoder_output)
decoder_output = Conv2D(64, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
decoder_output = Conv2D(32, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(16, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(3, (3, 3), activation='tanh', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
model = Model(inputs=encoder_input, outputs=decoder_output)
return model
model = Colorize()
model.compile(optimizer=OPTIMIZER, loss=LOSS, metrics=METRICS)
model.summary()
history = model.fit(train_datagen, batch_size=BATCH_SIZE, validation_data=test_datagen, epochs=EPOCHS, callbacks=model_callbacks)
preds = model.predict(test_datagen)
preds.shape | code |
74058807/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | print(images_gray.shape)
print(images_col.shape) | code |
2038627/cell_42 | [
"text_html_output_1.png"
] | test_mean.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
test.isnull().sum()
test[test.Item_Weight.isnull()] | code |
2038627/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
test.isnull().sum()
test.describe(include='all') | code |
2038627/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum() | code |
2038627/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
test.isnull().sum()
test[test.Item_Weight.isnull()]
test[test.Outlet_Size.isnull()]
test['Outlet_Size'].value_counts(dropna=False) | code |
2038627/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
print(train['Outlet_Establishment_Year'].max())
print('\n')
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts() | code |
2038627/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
train.isnull().sum()
test.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train.groupby('Outlet_Type').Outlet_Size.value_counts(dropna=False)
test[test.Item_Weight.isnull()]
test[test.Outlet_Size.isnull()]
train.groupby('Item_Type').Item_Fat_Content.value_counts(dropna=False)
train_1 = train.dropna(subset=['Item_Weight'])
train_mean = train.fillna(value=train_1['Item_Weight'].mean())
test_1 = test.dropna(subset=['Item_Weight'])
test_mean = test.fillna(value=test_1['Item_Weight'].mean())
train_2 = train.dropna(subset=['Item_Weight'])
train_median = train.fillna(value=train_2['Item_Weight'].median())
test_2 = test.dropna(subset=['Item_Weight'])
test_median = train.fillna(value=train_2['Item_Weight'].median())
train_median.head() | code |
2038627/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | train_median.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train.groupby('Outlet_Type').Outlet_Size.value_counts(dropna=False) | code |
2038627/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.head() | code |
2038627/cell_40 | [
"text_html_output_1.png"
] | train_mean.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train.groupby('Outlet_Type').Outlet_Size.value_counts(dropna=False)
train.groupby('Item_Type').Item_Fat_Content.value_counts(dropna=False) | code |
2038627/cell_39 | [
"text_html_output_1.png"
] | train.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_41 | [
"text_html_output_1.png"
] | test.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
print(train.shape) | code |
2038627/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
test.isnull().head() | code |
2038627/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train['Outlet_Size'].value_counts(dropna=False) | code |
2038627/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
print(test['Outlet_Establishment_Year'].max())
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts() | code |
2038627/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | test_mode.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train['Item_Weight'].value_counts(dropna=False) | code |
2038627/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
train.isnull().sum()
test.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train.groupby('Outlet_Type').Outlet_Size.value_counts(dropna=False)
test[test.Item_Weight.isnull()]
test[test.Outlet_Size.isnull()]
train.groupby('Item_Type').Item_Fat_Content.value_counts(dropna=False)
train_1 = train.dropna(subset=['Item_Weight'])
train_mean = train.fillna(value=train_1['Item_Weight'].mean())
test_1 = test.dropna(subset=['Item_Weight'])
test_mean = test.fillna(value=test_1['Item_Weight'].mean())
test_mean.head() | code |
2038627/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
test.isnull().sum()
test[test.Item_Weight.isnull()]
test[test.Outlet_Size.isnull()]
test.head() | code |
2038627/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().head() | code |
2038627/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()] | code |
2038627/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
train.isnull().sum()
test.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train.groupby('Outlet_Type').Outlet_Size.value_counts(dropna=False)
test[test.Item_Weight.isnull()]
test[test.Outlet_Size.isnull()]
train.groupby('Item_Type').Item_Fat_Content.value_counts(dropna=False)
train_1 = train.dropna(subset=['Item_Weight'])
train_mean = train.fillna(value=train_1['Item_Weight'].mean())
test_1 = test.dropna(subset=['Item_Weight'])
test_mean = test.fillna(value=test_1['Item_Weight'].mean())
train_2 = train.dropna(subset=['Item_Weight'])
train_median = train.fillna(value=train_2['Item_Weight'].median())
test_2 = test.dropna(subset=['Item_Weight'])
test_median = train.fillna(value=train_2['Item_Weight'].median())
train_3 = train.dropna(subset=['Item_Weight'])
train_mode = train.fillna(value=train_3['Item_Weight'].mode())
test_3 = test.dropna(subset=['Item_Weight'])
test_mode = test.fillna(value=test_3['Item_Weight'].mode())
test_mode.head() | code |
2038627/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.head() | code |
2038627/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
train.isnull().sum()
test.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train.groupby('Outlet_Type').Outlet_Size.value_counts(dropna=False)
test[test.Item_Weight.isnull()]
test[test.Outlet_Size.isnull()]
train.groupby('Item_Type').Item_Fat_Content.value_counts(dropna=False)
train_1 = train.dropna(subset=['Item_Weight'])
train_mean = train.fillna(value=train_1['Item_Weight'].mean())
test_1 = test.dropna(subset=['Item_Weight'])
test_mean = test.fillna(value=test_1['Item_Weight'].mean())
train_2 = train.dropna(subset=['Item_Weight'])
train_median = train.fillna(value=train_2['Item_Weight'].median())
test_2 = test.dropna(subset=['Item_Weight'])
test_median = train.fillna(value=train_2['Item_Weight'].median())
test_median.head() | code |
2038627/cell_43 | [
"text_html_output_1.png"
] | train_mode.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
train.isnull().sum()
test.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train.groupby('Outlet_Type').Outlet_Size.value_counts(dropna=False)
test[test.Item_Weight.isnull()]
test[test.Outlet_Size.isnull()]
train.groupby('Item_Type').Item_Fat_Content.value_counts(dropna=False)
train_1 = train.dropna(subset=['Item_Weight'])
train_mean = train.fillna(value=train_1['Item_Weight'].mean())
test_1 = test.dropna(subset=['Item_Weight'])
test_mean = test.fillna(value=test_1['Item_Weight'].mean())
train_mean.head() | code |
2038627/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | test_median.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
test.isnull().sum()
test[test.Item_Weight.isnull()]
test[test.Outlet_Size.isnull()]
test['Item_Weight'].value_counts(dropna=False) | code |
2038627/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()] | code |
2038627/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
test.isnull().sum()
test[test.Item_Weight.isnull()]
test[test.Outlet_Size.isnull()] | code |
2038627/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train.describe(include='all') | code |
2038627/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train.groupby('Outlet_Type').Outlet_Size.value_counts(dropna=False)
train.head() | code |
2038627/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
train.isnull().sum()
test.isnull().sum()
train[train.Item_Weight.isnull()]
train[train.Outlet_Size.isnull()]
train.groupby('Outlet_Type').Outlet_Size.value_counts(dropna=False)
test[test.Item_Weight.isnull()]
test[test.Outlet_Size.isnull()]
train.groupby('Item_Type').Item_Fat_Content.value_counts(dropna=False)
train_1 = train.dropna(subset=['Item_Weight'])
train_mean = train.fillna(value=train_1['Item_Weight'].mean())
test_1 = test.dropna(subset=['Item_Weight'])
test_mean = test.fillna(value=test_1['Item_Weight'].mean())
train_2 = train.dropna(subset=['Item_Weight'])
train_median = train.fillna(value=train_2['Item_Weight'].median())
test_2 = test.dropna(subset=['Item_Weight'])
test_median = train.fillna(value=train_2['Item_Weight'].median())
train_3 = train.dropna(subset=['Item_Weight'])
train_mode = train.fillna(value=train_3['Item_Weight'].mode())
test_3 = test.dropna(subset=['Item_Weight'])
test_mode = test.fillna(value=test_3['Item_Weight'].mode())
train_mode.head() | code |
2038627/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts()
test.isnull().sum() | code |
2038627/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape | code |
73094936/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
numImages = 16
fig = plt.figure(figsize=(7, 7))
imgData = np.zeros(shape=(numImages, 36963))
for i in range(1, numImages + 1):
filename = '../input/foodpics/pics/Picture' + str(i) + '.jpg'
img = mpimg.imread(filename)
ax = fig.add_subplot(4, 4, i)
plt.imshow(img)
plt.axis('off')
ax.set_title(str(i))
imgData[i - 1] = np.array(img.flatten()).reshape(1, img.shape[0] * img.shape[1] * img.shape[2]) | code |
73094936/cell_21 | [
"text_html_output_1.png"
] | import io
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import requests
import io
url = 'https://cgi.luddy.indiana.edu/~yye/b565/data/DTW_prec.csv'
s = requests.get(url).content
daily = pd.read_csv(io.StringIO(s.decode('utf-8')), header='infer')
daily.index = pd.to_datetime(daily['DATE'])
daily = daily['PRCP']
ax = daily.plot(kind='line', figsize=(15, 3))
ax.set_title('Daily Precipitation (variance = %.4f)' % daily.var()) | code |
73094936/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25] | code |
73094936/cell_9 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
print('Number of rows in original data = %d' % data.shape[0])
data2 = data.dropna()
print('Number of rows after discarding missing values = %d' % data2.shape[0]) | code |
73094936/cell_25 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import io
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
#modified to load from remote URL, Ye 2021
import requests
import io
url = "https://cgi.luddy.indiana.edu/~yye/b565/data/DTW_prec.csv"
s=requests.get(url).content
daily = pd.read_csv(io.StringIO(s.decode('utf-8')), header='infer')
daily.index = pd.to_datetime(daily['DATE'])
daily = daily['PRCP']
ax = daily.plot(kind='line',figsize=(15,3))
ax.set_title('Daily Precipitation (variance = %.4f)' % (daily.var()))
monthly = daily.groupby(pd.Grouper(freq='M')).sum()
ax = monthly.plot(kind='line',figsize=(15,3))
ax.set_title('Monthly Precipitation (variance = %.4f)' % (monthly.var()))
annual = daily.groupby(pd.Grouper(freq='Y')).sum()
ax = annual.plot(kind='line', figsize=(15, 3))
ax.set_title('Annual Precipitation (variance = %.4f)' % annual.var()) | code |
73094936/cell_23 | [
"text_plain_output_1.png"
] | import io
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
#modified to load from remote URL, Ye 2021
import requests
import io
url = "https://cgi.luddy.indiana.edu/~yye/b565/data/DTW_prec.csv"
s=requests.get(url).content
daily = pd.read_csv(io.StringIO(s.decode('utf-8')), header='infer')
daily.index = pd.to_datetime(daily['DATE'])
daily = daily['PRCP']
ax = daily.plot(kind='line',figsize=(15,3))
ax.set_title('Daily Precipitation (variance = %.4f)' % (daily.var()))
monthly = daily.groupby(pd.Grouper(freq='M')).sum()
ax = monthly.plot(kind='line', figsize=(15, 3))
ax.set_title('Monthly Precipitation (variance = %.4f)' % monthly.var()) | code |
73094936/cell_33 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
dups = data.duplicated()
data.loc[[11, 28]]
data2 = data.drop_duplicates()
sample = data.sample(n=3)
sample
sample = data.sample(frac=0.01, random_state=1)
sample
sample = data.sample(frac=0.01, replace=True, random_state=1)
sample | code |
73094936/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import io
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
dups = data.duplicated()
data.loc[[11, 28]]
data2 = data.drop_duplicates()
#modified to load from remote URL, Ye 2021
import requests
import io
url = "https://cgi.luddy.indiana.edu/~yye/b565/data/DTW_prec.csv"
s=requests.get(url).content
daily = pd.read_csv(io.StringIO(s.decode('utf-8')), header='infer')
daily.index = pd.to_datetime(daily['DATE'])
daily = daily['PRCP']
ax = daily.plot(kind='line',figsize=(15,3))
ax.set_title('Daily Precipitation (variance = %.4f)' % (daily.var()))
monthly = daily.groupby(pd.Grouper(freq='M')).sum()
ax = monthly.plot(kind='line',figsize=(15,3))
ax.set_title('Monthly Precipitation (variance = %.4f)' % (monthly.var()))
annual = daily.groupby(pd.Grouper(freq='Y')).sum()
ax = annual.plot(kind='line',figsize=(15,3))
ax.set_title('Annual Precipitation (variance = %.4f)' % (annual.var()))
sample = data.sample(n=3)
sample
sample = data.sample(frac=0.01, random_state=1)
sample
sample = data.sample(frac=0.01, replace=True, random_state=1)
sample
bins = pd.cut(data['Clump Thickness'], 4)
bins.value_counts(sort=False)
bins = pd.qcut(data['Clump Thickness'], 4)
bins.value_counts(sort=False)
import pandas as pd
from sklearn.decomposition import PCA
numComponents = 2
pca = PCA(n_components=numComponents)
pca.fit(imgData)
projected = pca.transform(imgData)
projected = pd.DataFrame(projected, columns=['pc1', 'pc2'], index=range(1, numImages + 1))
projected['food'] = ['burger', 'burger', 'burger', 'burger', 'drink', 'drink', 'drink', 'drink', 'pasta', 'pasta', 'pasta', 'pasta', 'chicken', 'chicken', 'chicken', 'chicken']
projected | code |
73094936/cell_40 | [
"text_html_output_1.png"
] | import io
import numpy as np
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
dups = data.duplicated()
data.loc[[11, 28]]
data2 = data.drop_duplicates()
#modified to load from remote URL, Ye 2021
import requests
import io
url = "https://cgi.luddy.indiana.edu/~yye/b565/data/DTW_prec.csv"
s=requests.get(url).content
daily = pd.read_csv(io.StringIO(s.decode('utf-8')), header='infer')
daily.index = pd.to_datetime(daily['DATE'])
daily = daily['PRCP']
ax = daily.plot(kind='line',figsize=(15,3))
ax.set_title('Daily Precipitation (variance = %.4f)' % (daily.var()))
monthly = daily.groupby(pd.Grouper(freq='M')).sum()
ax = monthly.plot(kind='line',figsize=(15,3))
ax.set_title('Monthly Precipitation (variance = %.4f)' % (monthly.var()))
annual = daily.groupby(pd.Grouper(freq='Y')).sum()
ax = annual.plot(kind='line',figsize=(15,3))
ax.set_title('Annual Precipitation (variance = %.4f)' % (annual.var()))
sample = data.sample(n=3)
sample
sample = data.sample(frac=0.01, random_state=1)
sample
sample = data.sample(frac=0.01, replace=True, random_state=1)
sample
bins = pd.cut(data['Clump Thickness'], 4)
bins.value_counts(sort=False)
bins = pd.qcut(data['Clump Thickness'], 4)
bins.value_counts(sort=False) | code |
73094936/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
dups = data.duplicated()
data.loc[[11, 28]]
data2 = data.drop_duplicates()
sample = data.sample(n=3)
sample | code |
73094936/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | data2 = data.drop(['Class'], axis=1)
data2['Bare Nuclei'] = pd.to_numeric(data2['Bare Nuclei'])
data2.boxplot(figsize=(20, 3)) | code |
73094936/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
dups = data.duplicated()
data.loc[[11, 28]]
print('Number of rows before discarding duplicates = %d' % data.shape[0])
data2 = data.drop_duplicates()
print('Number of rows after discarding duplicates = %d' % data2.shape[0]) | code |
73094936/cell_7 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
print('Before replacing missing values:')
print(data2[20:25])
data2 = data2.fillna(data2.median())
print('\nAfter replacing missing values:')
print(data2[20:25]) | code |
73094936/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
print('Number of rows before discarding outliers = %d' % Z.shape[0])
Z2 = Z.loc[((Z > -3).sum(axis=1) == 9) & ((Z <= 3).sum(axis=1) == 9), :]
print('Number of rows after discarding missing values = %d' % Z2.shape[0]) | code |
73094936/cell_38 | [
"text_html_output_1.png"
] | import io
import numpy as np
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
dups = data.duplicated()
data.loc[[11, 28]]
data2 = data.drop_duplicates()
#modified to load from remote URL, Ye 2021
import requests
import io
url = "https://cgi.luddy.indiana.edu/~yye/b565/data/DTW_prec.csv"
s=requests.get(url).content
daily = pd.read_csv(io.StringIO(s.decode('utf-8')), header='infer')
daily.index = pd.to_datetime(daily['DATE'])
daily = daily['PRCP']
ax = daily.plot(kind='line',figsize=(15,3))
ax.set_title('Daily Precipitation (variance = %.4f)' % (daily.var()))
monthly = daily.groupby(pd.Grouper(freq='M')).sum()
ax = monthly.plot(kind='line',figsize=(15,3))
ax.set_title('Monthly Precipitation (variance = %.4f)' % (monthly.var()))
annual = daily.groupby(pd.Grouper(freq='Y')).sum()
ax = annual.plot(kind='line',figsize=(15,3))
ax.set_title('Annual Precipitation (variance = %.4f)' % (annual.var()))
sample = data.sample(n=3)
sample
sample = data.sample(frac=0.01, random_state=1)
sample
sample = data.sample(frac=0.01, replace=True, random_state=1)
sample
bins = pd.cut(data['Clump Thickness'], 4)
bins.value_counts(sort=False) | code |
73094936/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
print('Number of instances = %d' % data.shape[0])
print('Number of attributes = %d' % data.shape[1])
data.head() | code |
73094936/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
dups = data.duplicated()
print('Number of duplicate rows = %d' % dups.sum())
data.loc[[11, 28]] | code |
73094936/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
dups = data.duplicated()
data.loc[[11, 28]]
data2 = data.drop_duplicates()
sample = data.sample(n=3)
sample
sample = data.sample(frac=0.01, random_state=1)
sample | code |
73094936/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import io
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
dups = data.duplicated()
data.loc[[11, 28]]
data2 = data.drop_duplicates()
#modified to load from remote URL, Ye 2021
import requests
import io
url = "https://cgi.luddy.indiana.edu/~yye/b565/data/DTW_prec.csv"
s=requests.get(url).content
daily = pd.read_csv(io.StringIO(s.decode('utf-8')), header='infer')
daily.index = pd.to_datetime(daily['DATE'])
daily = daily['PRCP']
ax = daily.plot(kind='line',figsize=(15,3))
ax.set_title('Daily Precipitation (variance = %.4f)' % (daily.var()))
monthly = daily.groupby(pd.Grouper(freq='M')).sum()
ax = monthly.plot(kind='line',figsize=(15,3))
ax.set_title('Monthly Precipitation (variance = %.4f)' % (monthly.var()))
annual = daily.groupby(pd.Grouper(freq='Y')).sum()
ax = annual.plot(kind='line',figsize=(15,3))
ax.set_title('Annual Precipitation (variance = %.4f)' % (annual.var()))
sample = data.sample(n=3)
sample
sample = data.sample(frac=0.01, random_state=1)
sample
sample = data.sample(frac=0.01, replace=True, random_state=1)
sample
bins = pd.cut(data['Clump Thickness'], 4)
bins.value_counts(sort=False)
bins = pd.qcut(data['Clump Thickness'], 4)
bins.value_counts(sort=False)
import pandas as pd
from sklearn.decomposition import PCA
numComponents = 2
pca = PCA(n_components=numComponents)
pca.fit(imgData)
projected = pca.transform(imgData)
projected = pd.DataFrame(projected, columns=['pc1', 'pc2'], index=range(1, numImages + 1))
projected['food'] = ['burger', 'burger', 'burger', 'burger', 'drink', 'drink', 'drink', 'drink', 'pasta', 'pasta', 'pasta', 'pasta', 'chicken', 'chicken', 'chicken', 'chicken']
projected
import matplotlib.pyplot as plt
colors = {'burger': 'b', 'drink': 'r', 'pasta': 'g', 'chicken': 'k'}
markerTypes = {'burger': '+', 'drink': 'x', 'pasta': 'o', 'chicken': 's'}
for foodType in markerTypes:
d = projected[projected['food'] == foodType]
plt.scatter(d['pc1'], d['pc2'], c=colors[foodType], s=60, marker=markerTypes[foodType]) | code |
73094936/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
dups = data.duplicated()
data.loc[[11, 28]]
data2 = data.drop_duplicates()
data.head() | code |
73094936/cell_5 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
print('Number of instances = %d' % data.shape[0])
print('Number of attributes = %d' % data.shape[1])
print('Number of missing values:')
for col in data.columns:
print('\t%s: %d' % (col, data[col].isna().sum())) | code |
73094936/cell_36 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']
data = data.drop(['Sample code'], axis=1)
import numpy as np
data = data.replace('?', np.NaN)
data2 = data['Bare Nuclei']
data2 = data2.fillna(data2.median())
data2 = data.dropna()
Z = (data2 - data2.mean()) / data2.std()
Z[20:25]
dups = data.duplicated()
data.loc[[11, 28]]
data2 = data.drop_duplicates()
sample = data.sample(n=3)
sample
sample = data.sample(frac=0.01, random_state=1)
sample
sample = data.sample(frac=0.01, replace=True, random_state=1)
sample
data['Clump Thickness'].hist(bins=10)
data['Clump Thickness'].value_counts(sort=False) | code |
89132155/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 |
2022597/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#boxplot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
var3 = 'sqft_above'
data = pd.concat([df['price'], df[var3]], axis=1)
data.plot.scatter(x=var3, y='price', ylim=(0, 8000000)) | code |
2022597/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.describe() | code |
2022597/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y='price', data=data)
fig.axis(ymin=0, ymax=8000000) | code |
2022597/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.info() | code |
2022597/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
f, ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax) | code |
2022597/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#boxplot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
var3 = 'sqft_above'
data = pd.concat([df['price'], df[var3]], axis=1)
var4 = 'bathrooms'
data = pd.concat([df['price'], df[var4]], axis=1)
X = df[[var, var1, var2, var3, var4]]
y = df['price']
LinReg = LinearRegression(normalize=True)
LinReg.fit(X, y)
print(LinReg.score(X, y)) | code |
2022597/cell_3 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import scale
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
scale = StandardScaler() | code |
2022597/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
import statsmodels.api as sm
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#boxplot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
var3 = 'sqft_above'
data = pd.concat([df['price'], df[var3]], axis=1)
var4 = 'bathrooms'
data = pd.concat([df['price'], df[var4]], axis=1)
X = df[[var, var1, var2, var3, var4]]
y = df['price']
est = sm.OLS(y, X).fit()
est.summary() | code |
2022597/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#boxplot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
var3 = 'sqft_above'
data = pd.concat([df['price'], df[var3]], axis=1)
var4 = 'bathrooms'
data = pd.concat([df['price'], df[var4]], axis=1)
data.plot.scatter(x=var4, y='price', ylim=(0, 8000000)) | code |
2022597/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
data.plot.scatter(x=var, y='price', ylim=(0, 8000000)) | code |
2022597/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#boxplot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
data.plot.scatter(x=var2, y='price', ylim=(0, 8000000)) | code |
2022597/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.head() | code |
1009893/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from glob import glob
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_2')) + 1:-4] for s in type_2_files])
type_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_3')) + 1:-4] for s in type_3_files])
def get_filename(image_id, image_type):
"""
Method to get image file path from its id and type
"""
try:
['Type_1', 'Type_2', 'Type_3'].index(image_type)
except:
raise Exception('Image type {} is not recognized'.format(image_type))
ext = 'jpg'
data_path = os.path.join(TRAIN_DATA, image_type)
return os.path.join(data_path, '{}.{}'.format(image_id, ext))
import cv2
def get_image_data(image_id, image_type):
"""
Method to get image data as np.array specifying image id and type
"""
fname = get_filename(image_id, image_type)
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img
l = len(type_1_ids)
np.floor(25.6)
tile_size
tile_size = (256, 256)
n = 10
m = int(np.floor(l / n))
test_zeros = np.zeros((2, 4, 3), dtype=np.uint8)
test_zeros
tile_size = (256, 256)
n = 15
complete_images = []
for k, type_ids in enumerate([type_1_ids, type_2_ids, type_3_ids]):
m = int(np.floor(len(type_ids) / n))
complete_image = np.zeros((m * (tile_size[0] + 2), n * (tile_size[1] + 2), 3), dtype=np.uint8)
train_ids = sorted(type_ids)
counter = 0
for i in range(m):
ys = i * (tile_size[1] + 2)
ye = ys + tile_size[1]
for j in range(n):
xs = j * (tile_size[0] + 2)
xe = xs + tile_size[0]
image_id = train_ids[counter]
counter += 1
img = get_image_data(image_id, 'Type_%i' % (k + 1))
img = cv2.resize(img, dsize=tile_size)
complete_image[ys:ye, xs:xe] = img[:, :, :]
complete_images.append(complete_image)
plt_st()
plt.title('Training dataset of type 1')
plt.imshow(complete_image) | code |
1009893/cell_6 | [
"text_plain_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_2')) + 1:-4] for s in type_2_files])
type_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_3')) + 1:-4] for s in type_3_files])
l = len(type_1_ids)
np.floor(25.6)
tile_size | code |
1009893/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_2')) + 1:-4] for s in type_2_files])
type_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_3')) + 1:-4] for s in type_3_files])
print(len(type_1_files), len(type_2_files), len(type_3_files))
print('Type 1', type_1_ids[:10])
print('Type 2', type_2_ids[:10])
print('Type 3', type_3_ids[:10]) | code |
1009893/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 |
1009893/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_2')) + 1:-4] for s in type_2_files])
type_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_3')) + 1:-4] for s in type_3_files])
l = len(type_1_ids)
np.floor(25.6)
tile_size
tile_size = (256, 256)
n = 10
m = int(np.floor(l / n))
test_zeros = np.zeros((2, 4, 3), dtype=np.uint8)
test_zeros | code |
1009893/cell_17 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_2')) + 1:-4] for s in type_2_files])
type_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_3')) + 1:-4] for s in type_3_files])
def get_filename(image_id, image_type):
"""
Method to get image file path from its id and type
"""
try:
['Type_1', 'Type_2', 'Type_3'].index(image_type)
except:
raise Exception('Image type {} is not recognized'.format(image_type))
ext = 'jpg'
data_path = os.path.join(TRAIN_DATA, image_type)
return os.path.join(data_path, '{}.{}'.format(image_id, ext))
import cv2
def get_image_data(image_id, image_type):
"""
Method to get image data as np.array specifying image id and type
"""
fname = get_filename(image_id, image_type)
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img
l = len(type_1_ids)
np.floor(25.6)
tile_size
tile_size = (256, 256)
n = 10
m = int(np.floor(l / n))
test_zeros = np.zeros((2, 4, 3), dtype=np.uint8)
test_zeros
tile_size = (256, 256)
n = 15
complete_images = []
for k, type_ids in enumerate([type_1_ids, type_2_ids, type_3_ids]):
m = int(np.floor(len(type_ids) / n))
complete_image = np.zeros((m * (tile_size[0] + 2), n * (tile_size[1] + 2), 3), dtype=np.uint8)
train_ids = sorted(type_ids)
counter = 0
for i in range(m):
ys = i * (tile_size[1] + 2)
ye = ys + tile_size[1]
for j in range(n):
xs = j * (tile_size[0] + 2)
xe = xs + tile_size[0]
image_id = train_ids[counter]
counter += 1
img = get_image_data(image_id, 'Type_%i' % (k + 1))
img = cv2.resize(img, dsize=tile_size)
complete_image[ys:ye, xs:xe] = img[:, :, :]
complete_images.append(complete_image)
plt_st()
plt_st(20, 20)
plt.imshow(complete_images[0])
plt.title('Training dataset of type %i' % 1) | code |
90154229/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
import numpy as np
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-30]
diabetes_y_test = diabetes.target[-20:]
model = linear_model.LinearRegression()
model.fit(diabetes_X_train, diabetes_y_train)
diabetes_y_predicted = model.predict(diabetes_X_test)
plt.plot(diabetes_X_test, diabetes_y_predicted) | code |
90154229/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
import numpy as np
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-30]
diabetes_y_test = diabetes.target[-20:]
model = linear_model.LinearRegression()
model.fit(diabetes_X_train, diabetes_y_train)
diabetes_y_predicted = model.predict(diabetes_X_test)
print('weights:', model.coef_)
print('Intercept:', model.intercept_) | code |
90154229/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets, linear_model
import numpy as np
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-30]
diabetes_y_test = diabetes.target[-20:]
model = linear_model.LinearRegression()
model.fit(diabetes_X_train, diabetes_y_train) | code |
90154229/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets, linear_model
diabetes = datasets.load_diabetes()
print(diabetes.keys()) | code |
90154229/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import numpy as np
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-30]
diabetes_y_test = diabetes.target[-20:]
model = linear_model.LinearRegression()
model.fit(diabetes_X_train, diabetes_y_train)
diabetes_y_predicted = model.predict(diabetes_X_test)
print('Mean squared error is:', mean_squared_error(diabetes_y_test, diabetes_y_predicted)) | code |
90154229/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
import numpy as np
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-30]
diabetes_y_test = diabetes.target[-20:]
plt.scatter(diabetes_X_test, diabetes_y_test) | code |
106191525/cell_21 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import LocalOutlierFactor
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
C = data['diagnosis'].value_counts()
corr = data.corr()
top_feature = corr.index[abs(corr['diagnosis']) > 0.5]
Important_Data = data[top_feature.values]
top_corr = data[top_feature].corr()
radius = data[['radius_mean', 'radius_se', 'radius_worst', 'diagnosis']]
texture = data[['texture_mean', 'texture_se', 'texture_worst', 'diagnosis']]
perimeter = data[['perimeter_mean', 'perimeter_se', 'perimeter_worst', 'diagnosis']]
area = data[['area_mean', 'area_se', 'area_worst', 'diagnosis']]
smoothness = data[['smoothness_mean', 'smoothness_se', 'smoothness_worst', 'diagnosis']]
compactness = data[['compactness_mean', 'compactness_se', 'compactness_worst', 'diagnosis']]
concavity = data[['concavity_mean', 'concavity_se', 'concavity_worst', 'diagnosis']]
concave_points = data[['concave points_mean', 'concave points_se', 'concave points_worst', 'diagnosis']]
symmetry = data[['symmetry_mean', 'symmetry_se', 'symmetry_worst', 'diagnosis']]
fractal_dimension = data[['fractal_dimension_mean', 'fractal_dimension_se', 'fractal_dimension_worst', 'diagnosis']]
X = Important_Data.drop(['diagnosis'], axis=1)
Y = Important_Data.diagnosis
columns = Important_Data.columns.tolist()
lof = LocalOutlierFactor()
y_pred = lof.fit_predict(X)
y_pred[0:30]
x_score = lof.negative_outlier_factor_
outlier_score = pd.DataFrame()
outlier_score['score'] = x_score
lofthreshold = -2.5
loffilter = outlier_score['score'] < lofthreshold
outlier_index = outlier_score[loffilter].index.tolist()
radius = (x_score.max() - x_score) / (x_score.max() - x_score.min())
outlier_score['radius'] = radius
X = X.drop(outlier_index)
Y = Y.drop(outlier_index).values
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=10)
print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
x_train.head() | code |
106191525/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
C = data['diagnosis'].value_counts()
corr = data.corr()
top_feature = corr.index[abs(corr['diagnosis']) > 0.5]
print(top_feature.values)
Important_Data = data[top_feature.values]
plt.subplots(figsize=(20, 10))
top_corr = data[top_feature].corr()
sns.heatmap(top_corr, annot=True)
plt.show() | code |
106191525/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
plt.title('Diagnostic Distribution')
C = data['diagnosis'].value_counts()
C.plot(kind='pie')
print(C) | code |
106191525/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.neighbors import LocalOutlierFactor
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
C = data['diagnosis'].value_counts()
corr = data.corr()
top_feature = corr.index[abs(corr['diagnosis']) > 0.5]
Important_Data = data[top_feature.values]
top_corr = data[top_feature].corr()
radius = data[['radius_mean', 'radius_se', 'radius_worst', 'diagnosis']]
texture = data[['texture_mean', 'texture_se', 'texture_worst', 'diagnosis']]
perimeter = data[['perimeter_mean', 'perimeter_se', 'perimeter_worst', 'diagnosis']]
area = data[['area_mean', 'area_se', 'area_worst', 'diagnosis']]
smoothness = data[['smoothness_mean', 'smoothness_se', 'smoothness_worst', 'diagnosis']]
compactness = data[['compactness_mean', 'compactness_se', 'compactness_worst', 'diagnosis']]
concavity = data[['concavity_mean', 'concavity_se', 'concavity_worst', 'diagnosis']]
concave_points = data[['concave points_mean', 'concave points_se', 'concave points_worst', 'diagnosis']]
symmetry = data[['symmetry_mean', 'symmetry_se', 'symmetry_worst', 'diagnosis']]
fractal_dimension = data[['fractal_dimension_mean', 'fractal_dimension_se', 'fractal_dimension_worst', 'diagnosis']]
X = Important_Data.drop(['diagnosis'], axis=1)
Y = Important_Data.diagnosis
columns = Important_Data.columns.tolist()
lof = LocalOutlierFactor()
y_pred = lof.fit_predict(X)
y_pred[0:30]
x_score = lof.negative_outlier_factor_
outlier_score = pd.DataFrame()
outlier_score['score'] = x_score
lofthreshold = -2.5
loffilter = outlier_score['score'] < lofthreshold
outlier_index = outlier_score[loffilter].index.tolist()
plt.figure(figsize=(12, 8.0))
plt.scatter(X.iloc[outlier_index, 0], X.iloc[outlier_index, 4], color='blue', s=50, label='outliers')
plt.scatter(X.iloc[:, 0], X.iloc[:, 4], color='k', s=3, label='Data Points')
radius = (x_score.max() - x_score) / (x_score.max() - x_score.min())
outlier_score['radius'] = radius
plt.scatter(X.iloc[:, 0], X.iloc[:, 4], s=1000 * radius, edgecolors='r', facecolors='none', label='outlier scores')
plt.legend()
X = X.drop(outlier_index)
Y = Y.drop(outlier_index).values | code |
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