path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
17105701/cell_6 | [
"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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
#plt.show()
happy.head() | code |
17105701/cell_26 | [
"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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
#plt.show()
happy.columns
happy.columns = [each.split()[0] + '_' + each.split()[1] if len(each.split()) > 1 else each for each in happy.columns]
happy.columns
plt.clf()
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
x = happy['Corruption'] > 140
happy[x]
x = 2
def f():
x = 3
return x
x = 5
def f():
y = 2 * x
return y
def square():
""" return square of value """
def add():
""" add two local variable """
x = 2
y = 3
z = x + y
return z
return add() ** 2
print(square()) | code |
17105701/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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
#plt.show()
happy.columns
happy.columns = [each.split()[0] + '_' + each.split()[1] if len(each.split()) > 1 else each for each in happy.columns]
happy.columns
happy.plot(kind='scatter', x='Ladder', y='Log_of', alpha=0.5, color='red')
plt.xlabel('Ladder')
plt.ylabel('Log_of_GDP\nper_capita')
plt.title('Ladder & Log_of_GDP\nper_capita Scatter Plot')
plt.show() | code |
17105701/cell_19 | [
"image_output_1.png"
] | print(3 > 2)
print(3 != 2)
print(True and False)
print(True or False) | code |
17105701/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
17105701/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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
#plt.show()
happy.tail() | code |
17105701/cell_18 | [
"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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
#plt.show()
happy.columns
happy.columns = [each.split()[0] + '_' + each.split()[1] if len(each.split()) > 1 else each for each in happy.columns]
happy.columns
plt.clf()
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
series = happy['Corruption']
print(type(series))
dataFrame = happy[['Corruption']]
print(type(dataFrame)) | code |
17105701/cell_8 | [
"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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
#plt.show()
happy.columns | code |
17105701/cell_15 | [
"text_plain_output_1.png"
] | dictionary = {'spain': 'madrid', 'usa': 'vegas'}
dictionary['spain'] = 'barcelona'
print(dictionary)
dictionary['france'] = 'paris'
print(dictionary)
del dictionary['spain']
print(dictionary)
print('france' in dictionary, 'paris' in dictionary)
dictionary.clear()
print(dictionary) | code |
17105701/cell_16 | [
"text_plain_output_1.png"
] | dictionary = {'spain': 'madrid', 'usa': 'vegas'}
dictionary['spain'] = 'barcelona'
dictionary['france'] = 'paris'
del dictionary['spain']
dictionary.clear()
del dictionary
print(dictionary) | code |
17105701/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.info() | code |
17105701/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | def tuble_ex():
""" return defined t tuble"""
t = (1, 2, 3)
return t
a, b, c = tuble_ex()
print(a, b, c) | code |
17105701/cell_14 | [
"text_html_output_1.png"
] | dictionary = {'spain': 'madrid', 'usa': 'vegas'}
print(dictionary.keys())
print(dictionary.values()) | code |
17105701/cell_22 | [
"text_plain_output_1.png"
] | i = 0
while i != 5:
print('i is: ', i)
i += 1
print(i, ' is equal to 5') | code |
17105701/cell_10 | [
"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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
#plt.show()
happy.columns
happy.columns = [each.split()[0] + '_' + each.split()[1] if len(each.split()) > 1 else each for each in happy.columns]
happy.columns
happy.Freedom.plot(kind='line', color='g', label='Speed', linewidth=1, alpha=0.5, grid=True, linestyle=':')
happy.Generosity.plot(color='r', label='Defense', linewidth=1, alpha=0.5, grid=True, linestyle='-.')
plt.legend(loc='upper right')
plt.xlabel('x axis')
plt.ylabel('y axis')
plt.title('Line Plot')
plt.show() | code |
17105701/cell_12 | [
"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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
#plt.show()
happy.columns
happy.columns = [each.split()[0] + '_' + each.split()[1] if len(each.split()) > 1 else each for each in happy.columns]
happy.columns
happy.Corruption.plot(kind='hist', bins=50, figsize=(12, 12))
plt.show() | code |
17105701/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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
f, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax) | code |
2015709/cell_13 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
from keras.models import Sequential
from keras.utils import np_utils
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
df_train = pd.read_csv('../input/train.csv', encoding='big5')
df_train[:5]
from sklearn.model_selection import train_test_split
from keras.utils import np_utils
X_train = df_train[df_train.columns[1:]].values
y_train = df_train['label'].values
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1) / 255
y_train_oneHot = np_utils.to_categorical(y_train, num_classes=10)
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=(28, 28, 1), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.25))
model.add(Dense(1024, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train_oneHot, validation_split=0.2, epochs=20, batch_size=250, verbose=1)
model.evaluate(X_train, y_train_oneHot)
df_test = pd.read_csv('../input/test.csv', encoding='big5')
X_test = df_test.values
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1) / 255
test_img = np.reshape(X_test[:1, :], (28, 28))
prediction = model.predict_classes(X_test)
prediction[:1] | code |
2015709/cell_9 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
from keras.models import Sequential
from keras.utils import np_utils
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
df_train = pd.read_csv('../input/train.csv', encoding='big5')
df_train[:5]
from sklearn.model_selection import train_test_split
from keras.utils import np_utils
X_train = df_train[df_train.columns[1:]].values
y_train = df_train['label'].values
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1) / 255
y_train_oneHot = np_utils.to_categorical(y_train, num_classes=10)
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=(28, 28, 1), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.25))
model.add(Dense(1024, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train_oneHot, validation_split=0.2, epochs=20, batch_size=250, verbose=1)
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plot_train_history(history, 'loss', 'val_loss')
plt.subplot(1, 2, 2)
plot_train_history(history, 'acc', 'val_acc') | code |
2015709/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
plt.figure(figsize=(12, 3))
plt.plot(X_train[:1].reshape(-1))
plt.figure(figsize=(6, 6))
plt.matshow(X_train[:1].reshape(28, 28), cmap=plt.get_cmap('binary'))
y_train[:1] | code |
2015709/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
from keras.models import Sequential
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=(28, 28, 1), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.25))
model.add(Dense(1024, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary() | code |
2015709/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
df_train = pd.read_csv('../input/train.csv', encoding='big5')
df_train[:5] | code |
2015709/cell_7 | [
"image_output_1.png"
] | from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
from keras.models import Sequential
from keras.utils import np_utils
import pandas as pd
import pandas as pd
import numpy as np
df_train = pd.read_csv('../input/train.csv', encoding='big5')
df_train[:5]
from sklearn.model_selection import train_test_split
from keras.utils import np_utils
X_train = df_train[df_train.columns[1:]].values
y_train = df_train['label'].values
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1) / 255
y_train_oneHot = np_utils.to_categorical(y_train, num_classes=10)
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=(28, 28, 1), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.25))
model.add(Dense(1024, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train_oneHot, validation_split=0.2, epochs=20, batch_size=250, verbose=1) | code |
2015709/cell_3 | [
"text_plain_output_1.png"
] | from keras.utils import np_utils
import pandas as pd
import pandas as pd
import numpy as np
df_train = pd.read_csv('../input/train.csv', encoding='big5')
df_train[:5]
from sklearn.model_selection import train_test_split
from keras.utils import np_utils
X_train = df_train[df_train.columns[1:]].values
y_train = df_train['label'].values
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1) / 255
y_train_oneHot = np_utils.to_categorical(y_train, num_classes=10) | code |
2015709/cell_10 | [
"text_html_output_1.png"
] | from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
from keras.models import Sequential
from keras.utils import np_utils
import pandas as pd
import pandas as pd
import numpy as np
df_train = pd.read_csv('../input/train.csv', encoding='big5')
df_train[:5]
from sklearn.model_selection import train_test_split
from keras.utils import np_utils
X_train = df_train[df_train.columns[1:]].values
y_train = df_train['label'].values
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1) / 255
y_train_oneHot = np_utils.to_categorical(y_train, num_classes=10)
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=(28, 28, 1), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.25))
model.add(Dense(1024, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train_oneHot, validation_split=0.2, epochs=20, batch_size=250, verbose=1)
model.evaluate(X_train, y_train_oneHot) | code |
2015709/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
from keras.models import Sequential
from keras.utils import np_utils
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
df_train = pd.read_csv('../input/train.csv', encoding='big5')
df_train[:5]
from sklearn.model_selection import train_test_split
from keras.utils import np_utils
X_train = df_train[df_train.columns[1:]].values
y_train = df_train['label'].values
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1) / 255
y_train_oneHot = np_utils.to_categorical(y_train, num_classes=10)
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=(28, 28, 1), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.25))
model.add(Dense(1024, activation='relu', kernel_initializer='normal'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train_oneHot, validation_split=0.2, epochs=20, batch_size=250, verbose=1)
df_test = pd.read_csv('../input/test.csv', encoding='big5')
X_test = df_test.values
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1) / 255
test_img = np.reshape(X_test[:1, :], (28, 28))
plt.matshow(test_img, cmap=plt.get_cmap('binary'))
plt.show() | code |
1008052/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic_train = pd.read_csv('../input/train.csv')
titanic_train['Age'].fillna(titanic_train['Age'].median(), inplace=True)
titanic_train['Embarked'].fillna('S', inplace=True)
titanic_train.describe() | code |
1008052/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic_train = pd.read_csv('../input/train.csv')
titanic_train['Age'].fillna(titanic_train['Age'].median(), inplace=True)
titanic_train['Embarked'].fillna('S', inplace=True)
titanic_train['Sex'].replace({'male': 0, 'female': 1}, inplace=True)
titanic_train['Embarked'].replace({'S': 0, 'C': 1, 'Q': 2}, inplace=True)
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
algo = LinearRegression()
kf = KFold(titanic_train.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
train_predictors = titanic_train[predictors].iloc[train, :]
train_target = titanic_train['Survived'].iloc[train]
algo.fit(train_predictors, train_target)
test_prediction = algo.predict(titanic_train[predictors].iloc[test, :])
predictions.append(test_prediction)
predictions | code |
1008052/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 |
1008052/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic_train = pd.read_csv('../input/train.csv')
titanic_train['Age'].fillna(titanic_train['Age'].median(), inplace=True)
titanic_train['Embarked'].fillna('S', inplace=True)
titanic_train['Sex'].replace({'male': 0, 'female': 1}, inplace=True)
titanic_train['Embarked'].replace({'S': 0, 'C': 1, 'Q': 2}, inplace=True)
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
algo = LinearRegression()
kf = KFold(titanic_train.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
train_predictors = titanic_train[predictors].iloc[train, :]
train_target = titanic_train['Survived'].iloc[train]
algo.fit(train_predictors, train_target)
test_prediction = algo.predict(titanic_train[predictors].iloc[test, :])
predictions.append(test_prediction)
predictions
accuracy = sum([1 if x == True else 0 for x in titanic_train['Survived'] == [1 if p > 0.5 else 0 for p in np.concatenate(predictions, axis=0)]]) / len(titanic_train)
accuracy | code |
1008052/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic_train = pd.read_csv('../input/train.csv')
titanic_train.describe() | code |
1008052/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic_train = pd.read_csv('../input/train.csv')
titanic_train['Age'].fillna(titanic_train['Age'].median(), inplace=True)
titanic_train['Embarked'].fillna('S', inplace=True)
titanic_train['Sex'].replace({'male': 0, 'female': 1}, inplace=True)
titanic_train['Embarked'].replace({'S': 0, 'C': 1, 'Q': 2}, inplace=True)
titanic_train.describe() | code |
90102951/cell_11 | [
"text_plain_output_1.png"
] | from colorama import Fore, Style, Back
from dataclasses import dataclass
from functools import reduce
from typing import Any
import math
import pandas as pd
import re
import numpy as np
import pandas as pd
import re
import math
from functools import reduce
from dataclasses import dataclass
data = pd.read_csv('../input/dogsofcambridge2/Dogs_of_Cambridge_2021.csv')
clean_data = pd.read_csv('../input/dogsofcambridge2/clean_dogs_reference.csv')
clean_data.drop('Unnamed: 0', axis=1, inplace=True)
def fix_lat_long(data):
lat, long = zip(*[map(lambda a: float(a), re.findall('([-\\d\\.]+)', loc)) for loc in data.Location_masked])
data.Latitude_masked = lat
data.Longitude_masked = long
data.drop('Location_masked', axis=1, inplace=True)
def impute_name(data):
data.Dog_Name = ['Poopsy' if pd.isna(name) else name for name in data.Dog_Name]
impute_name(data)
@dataclass
class Location:
name: str
lat: float
long: float
def get_distance(self, lat, long):
return math.sqrt((self.lat - lat) ** 2 + (self.long - long) ** 2)
nhs = [Location('East Cambridge', 42.369204, -71.079015), Location('Area 2/MIT', 42.359145, -71.094415), Location('Wellington-Harrington', 42.371264, -71.092608), Location('The Port', 42.365604, -71.09691), Location('Cambridgeport', 42.3586, -71.109293), Location('Mid-Cambridge', 42.372655, -71.108721), Location('Riverside', 42.36757, -71.1136), Location('Agassiz', 42.380667, -71.116386), Location('Neighborhood Nine', 42.386545, -71.127079), Location('West Cambridge', 42.376936, -71.136375), Location('North Cambridge', 42.394835, -71.132134), Location('Cambridge Highlands', 42.390774, -71.149859), Location('Strawberry Hill', 42.37938, -71.152475)]
def closest_neighborhood(lat, long):
close = nhs[0]
for nh in nhs:
if nh.get_distance(lat, long) < close.get_distance(lat, long):
close = nh
return close.name
def impute_neighborhood(data):
data.Neighborhood = [closest_neighborhood(data.loc[idx, 'Latitude_masked'], data.loc[idx, 'Longitude_masked']) if pd.isna(item) else item for idx, item in data.Neighborhood.items()]
def impute_breed(data):
bn, i = ({}, {})
for nh in data.Neighborhood.unique():
i[nh] = []
vc = data[data.Neighborhood == nh].Dog_Breed.value_counts()
res = [[] for x in range(vc[0] + 1)]
for idx, item in vc.items():
res[item].append(idx)
[a.sort() for a in res]
res = reduce(lambda a, b: b + (a if a else []), res)
bn[nh] = res
data.Dog_Breed = [bn[data.loc[idx, 'Neighborhood']][len(i[data.loc[idx, 'Neighborhood']]) + (0 if i[data.loc[idx, 'Neighborhood']].append('') else 0)] if pd.isna(item) else item for idx, item in data.Dog_Breed.items()]
@dataclass
class Pair:
one: Any
two: Any
def ascii_histogram(data, width=50):
res = []
for k, v in data.items():
res.append(Pair(k, v))
res.sort(key=lambda a: a.two, reverse=True)
colors = [None, 'BLACK', 'RED', 'GREEN', 'YELLOW', 'BLUE', 'MAGENTA', 'CYAN']
def ascii_scatterplot(data, height=50):
global colors
if len(data[0]) == 2:
data = list(map(lambda a: [a[0], a[1], 1], data))
xs, ys, code = map(list, zip(*data))
xmax, xmin, ymax, ymin = (max(xs), min(xs), max(ys), min(ys))
height = height * 2
scale = (height - 1) / (ymax - ymin)
width = round((xmax - xmin) * scale + 0.5)
scale_point = lambda x, y: ((x - xmin) * scale, (y - ymin) * scale)
chars = [[0 for i in range(width)] for i in range(height)]
for x, y, c in data:
x, y = scale_point(x, y)
chars[round(y - 0.5)][round(x - 0.5)] = c
for i in reversed(range(0, height, 2)):
res = ''
bottom, top = chars[i:i + 2]
for i in range(width):
if top[i] and bottom[i]:
if top[i] == bottom[i]:
res += f'{getattr(Fore, colors[top[i]])}█{Style.RESET_ALL}'
else:
res += f'{getattr(Fore, colors[bottom[i]])}{getattr(Back, colors[top[i]])}▄{Style.RESET_ALL}'
elif top[i]:
res += f'{getattr(Fore, colors[top[i]])}▀{Style.RESET_ALL}'
elif bottom[i]:
res += f'{getattr(Fore, colors[bottom[i]])}▄{Style.RESET_ALL}'
else:
res += ' '
print(res)
coords = list(zip(*[data.Longitude_masked, data.Latitude_masked]))
ascii_scatterplot(coords, 35) | code |
90102951/cell_10 | [
"text_plain_output_1.png"
] | from colorama import Fore, Style, Back
from dataclasses import dataclass
from functools import reduce
from typing import Any
import math
import pandas as pd
import re
import numpy as np
import pandas as pd
import re
import math
from functools import reduce
from dataclasses import dataclass
data = pd.read_csv('../input/dogsofcambridge2/Dogs_of_Cambridge_2021.csv')
clean_data = pd.read_csv('../input/dogsofcambridge2/clean_dogs_reference.csv')
clean_data.drop('Unnamed: 0', axis=1, inplace=True)
def fix_lat_long(data):
lat, long = zip(*[map(lambda a: float(a), re.findall('([-\\d\\.]+)', loc)) for loc in data.Location_masked])
data.Latitude_masked = lat
data.Longitude_masked = long
data.drop('Location_masked', axis=1, inplace=True)
def impute_name(data):
data.Dog_Name = ['Poopsy' if pd.isna(name) else name for name in data.Dog_Name]
impute_name(data)
@dataclass
class Location:
name: str
lat: float
long: float
def get_distance(self, lat, long):
return math.sqrt((self.lat - lat) ** 2 + (self.long - long) ** 2)
nhs = [Location('East Cambridge', 42.369204, -71.079015), Location('Area 2/MIT', 42.359145, -71.094415), Location('Wellington-Harrington', 42.371264, -71.092608), Location('The Port', 42.365604, -71.09691), Location('Cambridgeport', 42.3586, -71.109293), Location('Mid-Cambridge', 42.372655, -71.108721), Location('Riverside', 42.36757, -71.1136), Location('Agassiz', 42.380667, -71.116386), Location('Neighborhood Nine', 42.386545, -71.127079), Location('West Cambridge', 42.376936, -71.136375), Location('North Cambridge', 42.394835, -71.132134), Location('Cambridge Highlands', 42.390774, -71.149859), Location('Strawberry Hill', 42.37938, -71.152475)]
def closest_neighborhood(lat, long):
close = nhs[0]
for nh in nhs:
if nh.get_distance(lat, long) < close.get_distance(lat, long):
close = nh
return close.name
def impute_neighborhood(data):
data.Neighborhood = [closest_neighborhood(data.loc[idx, 'Latitude_masked'], data.loc[idx, 'Longitude_masked']) if pd.isna(item) else item for idx, item in data.Neighborhood.items()]
def impute_breed(data):
bn, i = ({}, {})
for nh in data.Neighborhood.unique():
i[nh] = []
vc = data[data.Neighborhood == nh].Dog_Breed.value_counts()
res = [[] for x in range(vc[0] + 1)]
for idx, item in vc.items():
res[item].append(idx)
[a.sort() for a in res]
res = reduce(lambda a, b: b + (a if a else []), res)
bn[nh] = res
data.Dog_Breed = [bn[data.loc[idx, 'Neighborhood']][len(i[data.loc[idx, 'Neighborhood']]) + (0 if i[data.loc[idx, 'Neighborhood']].append('') else 0)] if pd.isna(item) else item for idx, item in data.Dog_Breed.items()]
@dataclass
class Pair:
one: Any
two: Any
def ascii_histogram(data, width=50):
res = []
for k, v in data.items():
res.append(Pair(k, v))
res.sort(key=lambda a: a.two, reverse=True)
for i, item in enumerate(res):
print(f'{(Back.BLACK + Fore.WHITE if i % 2 else Back.CYAN + Fore.BLACK)}' + f"{i + 1}{' ' * (int(item.two * width / res[0].two) - int(math.log(item.two, 10)) - int(math.log(i + 1, 10)))}" + f'{item.two}{Style.RESET_ALL} {item.one}')
ascii_histogram(data.Dog_Name.value_counts()[:10].to_dict()) | code |
322536/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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_test = pd.merge(test, ppl, on='people_id')
del train, test, ppl
date_x = pd.DataFrame()
date_x['Class probability'] = df_train.groupby('date_x')['outcome'].mean()
date_x['Frequency'] = df_train.groupby('date_x')['outcome'].size()
date_y = pd.DataFrame()
date_y['Class probability'] = df_train.groupby('date_y')['outcome'].mean()
date_y['Frequency'] = df_train.groupby('date_y')['outcome'].size()
i = int(len(date_y) / 3)
date_y[:i].plot(secondary_y='Frequency', figsize=(20, 5), title='date_y Year 1')
date_y[i:2 * i].plot(secondary_y='Frequency', figsize=(20, 5), title='date_y Year 2')
date_y[2 * i:].plot(secondary_y='Frequency', figsize=(20, 5), title='date_y Year 3') | code |
322536/cell_11 | [
"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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_test = pd.merge(test, ppl, on='people_id')
del train, test, ppl
date_x = pd.DataFrame()
date_x['Class probability'] = df_train.groupby('date_x')['outcome'].mean()
date_x['Frequency'] = df_train.groupby('date_x')['outcome'].size()
date_y = pd.DataFrame()
date_y['Class probability'] = df_train.groupby('date_y')['outcome'].mean()
date_y['Frequency'] = df_train.groupby('date_y')['outcome'].size()
i = int(len(date_y) / 3)
date_x_freq = pd.DataFrame()
date_x_freq['Training set'] = df_train.groupby('date_x')['activity_id'].count()
date_x_freq['Testing set'] = df_test.groupby('date_x')['activity_id'].count()
date_x_freq.plot(secondary_y='Testing set', figsize=(20, 8), title='Comparison of date_x distribution between training/testing set')
date_y_freq = pd.DataFrame()
date_y_freq['Training set'] = df_train.groupby('date_y')['activity_id'].count()
date_y_freq['Testing set'] = df_test.groupby('date_y')['activity_id'].count()
date_y_freq[:i].plot(secondary_y='Testing set', figsize=(20, 8), title='Comparison of date_y distribution between training/testing set (first year)')
date_y_freq[2 * i:].plot(secondary_y='Testing set', figsize=(20, 8), title='Comparison of date_y distribution between training/testing set (last year)') | code |
322536/cell_7 | [
"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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_test = pd.merge(test, ppl, on='people_id')
del train, test, ppl
date_x = pd.DataFrame()
date_x['Class probability'] = df_train.groupby('date_x')['outcome'].mean()
date_x['Frequency'] = df_train.groupby('date_x')['outcome'].size()
date_x.plot(secondary_y='Frequency', figsize=(20, 10)) | code |
322536/cell_16 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import roc_auc_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_test = pd.merge(test, ppl, on='people_id')
del train, test, ppl
date_x = pd.DataFrame()
date_x['Class probability'] = df_train.groupby('date_x')['outcome'].mean()
date_x['Frequency'] = df_train.groupby('date_x')['outcome'].size()
date_y = pd.DataFrame()
date_y['Class probability'] = df_train.groupby('date_y')['outcome'].mean()
date_y['Frequency'] = df_train.groupby('date_y')['outcome'].size()
i = int(len(date_y) / 3)
date_x_freq = pd.DataFrame()
date_x_freq['Training set'] = df_train.groupby('date_x')['activity_id'].count()
date_x_freq['Testing set'] = df_test.groupby('date_x')['activity_id'].count()
date_y_freq = pd.DataFrame()
date_y_freq['Training set'] = df_train.groupby('date_y')['activity_id'].count()
date_y_freq['Testing set'] = df_test.groupby('date_y')['activity_id'].count()
from sklearn.metrics import roc_auc_score
features = pd.DataFrame()
features['date_x_prob'] = df_train.groupby('date_x')['outcome'].transform('mean')
features['date_y_prob'] = df_train.groupby('date_y')['outcome'].transform('mean')
features['date_x_count'] = df_train.groupby('date_x')['outcome'].transform('count')
features['date_y_count'] = df_train.groupby('date_y')['outcome'].transform('count')
_ = [print(f.ljust(12) + ' AUC: ' + str(round(roc_auc_score(df_train['outcome'], features[f]), 6))) for f in features.columns] | code |
322536/cell_14 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_test = pd.merge(test, ppl, on='people_id')
del train, test, ppl
date_x = pd.DataFrame()
date_x['Class probability'] = df_train.groupby('date_x')['outcome'].mean()
date_x['Frequency'] = df_train.groupby('date_x')['outcome'].size()
date_y = pd.DataFrame()
date_y['Class probability'] = df_train.groupby('date_y')['outcome'].mean()
date_y['Frequency'] = df_train.groupby('date_y')['outcome'].size()
i = int(len(date_y) / 3)
date_x_freq = pd.DataFrame()
date_x_freq['Training set'] = df_train.groupby('date_x')['activity_id'].count()
date_x_freq['Testing set'] = df_test.groupby('date_x')['activity_id'].count()
date_y_freq = pd.DataFrame()
date_y_freq['Training set'] = df_train.groupby('date_y')['activity_id'].count()
date_y_freq['Testing set'] = df_test.groupby('date_y')['activity_id'].count()
print('date_y correlation in year 1: ' + str(np.corrcoef(date_y_freq[:i].fillna(0).T)[0, 1]))
print('date_y correlation in year 2: ' + str(np.corrcoef(date_y_freq[i:2 * i].fillna(0).T)[0, 1]))
print('date_y correlation in year 3: ' + str(np.corrcoef(date_y_freq[2 * i:].fillna(0).T)[0, 1])) | code |
322536/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_test = pd.merge(test, ppl, on='people_id')
del train, test, ppl
date_x = pd.DataFrame()
date_x['Class probability'] = df_train.groupby('date_x')['outcome'].mean()
date_x['Frequency'] = df_train.groupby('date_x')['outcome'].size()
date_y = pd.DataFrame()
date_y['Class probability'] = df_train.groupby('date_y')['outcome'].mean()
date_y['Frequency'] = df_train.groupby('date_y')['outcome'].size()
i = int(len(date_y) / 3)
date_x_freq = pd.DataFrame()
date_x_freq['Training set'] = df_train.groupby('date_x')['activity_id'].count()
date_x_freq['Testing set'] = df_test.groupby('date_x')['activity_id'].count()
date_y_freq = pd.DataFrame()
date_y_freq['Training set'] = df_train.groupby('date_y')['activity_id'].count()
date_y_freq['Testing set'] = df_test.groupby('date_y')['activity_id'].count()
print('Correlation of date_x distribution in training/testing sets: ' + str(np.corrcoef(date_x_freq.T)[0, 1]))
print('Correlation of date_y distribution in training/testing sets: ' + str(np.corrcoef(date_y_freq.fillna(0).T)[0, 1])) | code |
322536/cell_5 | [
"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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_test = pd.merge(test, ppl, on='people_id')
del train, test, ppl
for d in ['date_x', 'date_y']:
print('Start of ' + d + ': ' + str(df_train[d].min().date()))
print(' End of ' + d + ': ' + str(df_train[d].max().date()))
print('Range of ' + d + ': ' + str(df_train[d].max() - df_train[d].min()) + '\n') | code |
1009501/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
t16 = data.loc[(data.id == 288) & (data.technical_16 != 0.0) & (~data.technical_16.isnull()) ,['timestamp', 'technical_16']]
ax = t16.plot(use_index=False)
ax=t16.technical_16.plot(use_index=False)
t16 = data.loc[(data.id == 1201) & (data.technical_16 != 0.0) & (~data.technical_16.isnull()) ,['timestamp', 'technical_16']]
ax=t16.technical_16.plot(use_index=False)
ax = t16.plot(use_index=False) | code |
1009501/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
t16 = data.loc[(data.id == 288) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['timestamp', 'technical_16']]
ax = t16.plot(use_index=False) | code |
1009501/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
t16 = data.loc[(data.id == 288) & (data.technical_16 != 0.0) & (~data.technical_16.isnull()) ,['timestamp', 'technical_16']]
ax = t16.plot(use_index=False)
ax = t16.technical_16.plot(use_index=False) | code |
1009501/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5') | code |
1009501/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
t16 = data.loc[(data.id == 288) & (data.technical_16 != 0.0) & (~data.technical_16.isnull()) ,['timestamp', 'technical_16']]
ax = t16.plot(use_index=False)
ax=t16.technical_16.plot(use_index=False)
t16 = data.loc[(data.id == 1201) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['timestamp', 'technical_16']]
ax = t16.technical_16.plot(use_index=False) | code |
1009501/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
data.technical_16.describe() | code |
1005437/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
uber_data = pd.read_csv('../input/uber-raw-data-janjune-15.csv')
uber_data.shape
Month = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
Index = [0, 1, 2, 3, 4, 5]
Monthly_pickup = uber_data.groupby(['Month']).size()
plt.xticks(Index, Month)
month = ['01', '02', '03', '04', '05', '06']
idx = [0, 7, 14, 21, 27]
def daily_pickup_plot(month):
plot_data = uber_data[uber_data['Month'] == month]
plot_data = plot_data.groupby(['Date']).size()
plt.xticks(idx, plot_data.index[idx])
for i in range(0, 6):
plt.ylim(0, 140000)
Hourly_pickup = uber_data.groupby(['Hour']).size()
mean = Hourly_pickup.mean()
hour = [i for i in range(0, 24)]
plt.xticks(hour, hour)
def hourly_pickup_plot(month):
plot_data = uber_data[uber_data['Month'] == month]
plot_data = plot_data.groupby(['Hour']).size()
plot_data.plot(kind='bar')
plt.xlabel('')
plt.xticks(hour, plot_data.index[hour])
plt.figure(1, figsize=(12, 24))
for i in range(0, 6):
plt.subplot(3, 2, i + 1)
hourly_pickup_plot(month[i])
plt.title('Hourly Pickup of ' + Month[i] + ' 2015')
plt.xlabel('')
plt.ylim(0, 200000) | code |
1005437/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
uber_data = pd.read_csv('../input/uber-raw-data-janjune-15.csv')
uber_data.shape | code |
1005437/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
uber_data = pd.read_csv('../input/uber-raw-data-janjune-15.csv')
uber_data.shape
Month = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
Index = [0, 1, 2, 3, 4, 5]
Monthly_pickup = uber_data.groupby(['Month']).size()
plt.figure(1, figsize=(12, 6))
plt.bar(Index, Monthly_pickup)
plt.xticks(Index, Month)
plt.title('UBER Monthly Pickup Summary in NYC') | code |
1005437/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
uber_data = pd.read_csv('../input/uber-raw-data-janjune-15.csv')
uber_data.shape
Month = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
Index = [0, 1, 2, 3, 4, 5]
Monthly_pickup = uber_data.groupby(['Month']).size()
plt.xticks(Index, Month)
month = ['01', '02', '03', '04', '05', '06']
idx = [0, 7, 14, 21, 27]
def daily_pickup_plot(month):
plot_data = uber_data[uber_data['Month'] == month]
plot_data = plot_data.groupby(['Date']).size()
plot_data.plot(kind='bar', rot=45)
plt.xlabel('')
plt.xticks(idx, plot_data.index[idx])
plt.figure(1, figsize=(12, 24))
for i in range(0, 6):
plt.subplot(3, 2, i + 1)
daily_pickup_plot(month[i])
plt.ylim(0, 140000)
plt.title('Daily Pickup of ' + Month[i]) | code |
1005437/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
uber_data = pd.read_csv('../input/uber-raw-data-janjune-15.csv')
uber_data.shape
Month = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
Index = [0, 1, 2, 3, 4, 5]
Monthly_pickup = uber_data.groupby(['Month']).size()
plt.xticks(Index, Month)
month = ['01', '02', '03', '04', '05', '06']
idx = [0, 7, 14, 21, 27]
def daily_pickup_plot(month):
plot_data = uber_data[uber_data['Month'] == month]
plot_data = plot_data.groupby(['Date']).size()
plt.xticks(idx, plot_data.index[idx])
for i in range(0, 6):
plt.ylim(0, 140000)
Hourly_pickup = uber_data.groupby(['Hour']).size()
mean = Hourly_pickup.mean()
hour = [i for i in range(0, 24)]
plt.figure(1, figsize=(12, 6))
plt.bar(hour, Hourly_pickup)
plt.title('UBER Hourly Pickup Summary of NYC, Jan 2015 - Jun 2015')
plt.xlabel('')
plt.xticks(hour, hour)
plt.show() | code |
330906/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame()
new_df['Decade'] = df_pivot.index.get_level_values('Decade')
new_df['Name'] = df_pivot.index.get_level_values('Name')
new_df['Gender'] = df_pivot.index.get_level_values('Gender')
new_df['Count'] = df_pivot.values
decadeList = list(new_df['Decade'].unique())
boys_percentileList = []
girls_percentileList = []
boys_df = new_df[new_df['Gender'] == 'M'].copy()
girls_df = new_df[new_df['Gender'] == 'F'].copy()
for i in decadeList:
scaler = MinMaxScaler()
boys_percentileList.extend(scaler.fit_transform(boys_df[boys_df['Decade'] == i][['Count']]))
girls_percentileList.extend(scaler.fit_transform(girls_df[girls_df['Decade'] == i][['Count']]))
boys_df['decade_percentile'] = boys_percentileList
girls_df['decade_percentile'] = girls_percentileList
new_df = boys_df.append(girls_df)
new_df['decade_percentile'] = new_df['decade_percentile'].apply(lambda x: float(x) * 100)
new_df.sort_index(inplace=True)
del boys_df
del girls_df
plt.plot(new_df[(new_df['Name'] == 'John') & (new_df['Gender'] == 'M')]['Decade'], new_df[(new_df['Name'] == 'John') & (new_df['Gender'] == 'M')]['decade_percentile']) | code |
330906/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame()
new_df['Decade'] = df_pivot.index.get_level_values('Decade')
new_df['Name'] = df_pivot.index.get_level_values('Name')
new_df['Gender'] = df_pivot.index.get_level_values('Gender')
new_df['Count'] = df_pivot.values
new_df.head() | code |
330906/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df.head() | code |
330906/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame()
new_df['Decade'] = df_pivot.index.get_level_values('Decade')
new_df['Name'] = df_pivot.index.get_level_values('Name')
new_df['Gender'] = df_pivot.index.get_level_values('Gender')
new_df['Count'] = df_pivot.values
decadeList = list(new_df['Decade'].unique())
boys_percentileList = []
girls_percentileList = []
boys_df = new_df[new_df['Gender'] == 'M'].copy()
girls_df = new_df[new_df['Gender'] == 'F'].copy()
for i in decadeList:
scaler = MinMaxScaler()
boys_percentileList.extend(scaler.fit_transform(boys_df[boys_df['Decade'] == i][['Count']]))
girls_percentileList.extend(scaler.fit_transform(girls_df[girls_df['Decade'] == i][['Count']]))
boys_df['decade_percentile'] = boys_percentileList
girls_df['decade_percentile'] = girls_percentileList
new_df = boys_df.append(girls_df)
new_df['decade_percentile'] = new_df['decade_percentile'].apply(lambda x: float(x) * 100)
new_df.sort_index(inplace=True)
del boys_df
del girls_df
def nameFilter(decade, gender, lowerBound, upperBound, startsWith=None):
"""
This function helps you find rare/common baby names!
Inputs:
decade : integer = Decade as a 4 digit number, e.g. 1980.
gender : string = Gender as a single letter string, e.g. 'M' for Male
lowerBound: float = Lower percentage of the names you want to query, e.g. 25 for 25%, NOT 0.25
upperBound: float = Upper percentage of the names you want to query
startsWith: str = (Optional) Single letter representing the starting letter of a name
Returns:
A dataframe slice fitting your parameters.
"""
if upperBound < lowerBound:
raise ValueError('lowerBound needs to be less than upperBound')
if startsWith != None:
result_df = new_df[(new_df['Decade'] == decade) & (new_df['Gender'] == gender) & (new_df['decade_percentile'] >= lowerBound) & (new_df['decade_percentile'] <= upperBound) & (new_df['Name'].str[0] == startsWith.upper())]
else:
result_df = new_df[(new_df['Decade'] == decade) & (new_df['Gender'] == gender) & (new_df['decade_percentile'] >= lowerBound) & (new_df['decade_percentile'] <= upperBound)]
return result_df
nameFilter(decade=1980, gender='M', lowerBound=50, upperBound=100, startsWith='C') | code |
330906/cell_19 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame()
new_df['Decade'] = df_pivot.index.get_level_values('Decade')
new_df['Name'] = df_pivot.index.get_level_values('Name')
new_df['Gender'] = df_pivot.index.get_level_values('Gender')
new_df['Count'] = df_pivot.values
decadeList = list(new_df['Decade'].unique())
boys_percentileList = []
girls_percentileList = []
boys_df = new_df[new_df['Gender'] == 'M'].copy()
girls_df = new_df[new_df['Gender'] == 'F'].copy()
for i in decadeList:
scaler = MinMaxScaler()
boys_percentileList.extend(scaler.fit_transform(boys_df[boys_df['Decade'] == i][['Count']]))
girls_percentileList.extend(scaler.fit_transform(girls_df[girls_df['Decade'] == i][['Count']]))
boys_df['decade_percentile'] = boys_percentileList
girls_df['decade_percentile'] = girls_percentileList
new_df = boys_df.append(girls_df)
new_df['decade_percentile'] = new_df['decade_percentile'].apply(lambda x: float(x) * 100)
new_df.sort_index(inplace=True)
del boys_df
del girls_df
plt.figure()
sns.distplot(new_df[new_df['Gender'] == 'M']['decade_percentile'], bins=100)
plt.xlim(xmin=0, xmax=100)
plt.title('Boys Name Popularity Distribution')
plt.figure()
sns.distplot(new_df[new_df['Gender'] == 'F']['decade_percentile'], bins=100)
plt.xlim(xmin=0, xmax=100)
plt.title('Girls Name Popularity Distribution')
plt.show() | code |
330906/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df.tail() | code |
330906/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame()
new_df['Decade'] = df_pivot.index.get_level_values('Decade')
new_df['Name'] = df_pivot.index.get_level_values('Name')
new_df['Gender'] = df_pivot.index.get_level_values('Gender')
new_df['Count'] = df_pivot.values
decadeList = list(new_df['Decade'].unique())
boys_percentileList = []
girls_percentileList = []
boys_df = new_df[new_df['Gender'] == 'M'].copy()
girls_df = new_df[new_df['Gender'] == 'F'].copy()
for i in decadeList:
scaler = MinMaxScaler()
boys_percentileList.extend(scaler.fit_transform(boys_df[boys_df['Decade'] == i][['Count']]))
girls_percentileList.extend(scaler.fit_transform(girls_df[girls_df['Decade'] == i][['Count']]))
boys_df['decade_percentile'] = boys_percentileList
girls_df['decade_percentile'] = girls_percentileList
new_df = boys_df.append(girls_df)
new_df['decade_percentile'] = new_df['decade_percentile'].apply(lambda x: float(x) * 100)
new_df.sort_index(inplace=True)
del boys_df
del girls_df
new_df[new_df['decade_percentile'] >= 99.0] | code |
330906/cell_17 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame()
new_df['Decade'] = df_pivot.index.get_level_values('Decade')
new_df['Name'] = df_pivot.index.get_level_values('Name')
new_df['Gender'] = df_pivot.index.get_level_values('Gender')
new_df['Count'] = df_pivot.values
decadeList = list(new_df['Decade'].unique())
boys_percentileList = []
girls_percentileList = []
boys_df = new_df[new_df['Gender'] == 'M'].copy()
girls_df = new_df[new_df['Gender'] == 'F'].copy()
for i in decadeList:
scaler = MinMaxScaler()
boys_percentileList.extend(scaler.fit_transform(boys_df[boys_df['Decade'] == i][['Count']]))
girls_percentileList.extend(scaler.fit_transform(girls_df[girls_df['Decade'] == i][['Count']]))
boys_df['decade_percentile'] = boys_percentileList
girls_df['decade_percentile'] = girls_percentileList
new_df = boys_df.append(girls_df)
new_df['decade_percentile'] = new_df['decade_percentile'].apply(lambda x: float(x) * 100)
new_df.sort_index(inplace=True)
del boys_df
del girls_df
new_df[new_df['decade_percentile'] < 1] | code |
330906/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
print('Data year ranges from {} to {}'.format(min(df['Year']), max(df['Year']))) | code |
2004143/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
full_set = pd.concat([train_full_set, test_full_set])
full_set = full_set.reset_index(drop=True)
""" We have to drop three columns from our study,namely: Cabin,Ticket, PassengerId
We dropped Cabin because of two main reasons:
a. Cabin has 687 Null Values out of 891 i.e almost 77% values are null.
b. Cabin number is directly related to the Class as cabin was alotted based on the level
of class.
So, cabin can easily be dropped from our analysis.
We dropped Ticket and PassengerId from our analysis because these two could not have affected the survival of the passengers.
It is just a demographic information."""
full_set.drop(['Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)
plt.figure(figsize=(15, 8))
sns.set_style('whitegrid')
ax = sns.countplot(x='Title', data=full_set)
ax.set_ylabel('COUNT', size=20, color='black', alpha=0.5)
ax.set_xlabel('TITLE', size=20, color='black', alpha=0.5)
ax.set_title('COUNT OF TITLES IN EACH CATEGORY BEFORE COMBINATION', size=20, color='black', alpha=0.5) | code |
2004143/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
full_set = pd.concat([train_full_set, test_full_set])
full_set = full_set.reset_index(drop=True)
""" We have to drop three columns from our study,namely: Cabin,Ticket, PassengerId
We dropped Cabin because of two main reasons:
a. Cabin has 687 Null Values out of 891 i.e almost 77% values are null.
b. Cabin number is directly related to the Class as cabin was alotted based on the level
of class.
So, cabin can easily be dropped from our analysis.
We dropped Ticket and PassengerId from our analysis because these two could not have affected the survival of the passengers.
It is just a demographic information."""
full_set.drop(['Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)
print(full_set.describe()) | code |
2004143/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
print('\n\nInformation about Null/ empty data points in each Column of Test set\n\n')
print(test_full_set.info()) | code |
2004143/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
full_set = pd.concat([train_full_set, test_full_set])
full_set = full_set.reset_index(drop=True)
""" We have to drop three columns from our study,namely: Cabin,Ticket, PassengerId
We dropped Cabin because of two main reasons:
a. Cabin has 687 Null Values out of 891 i.e almost 77% values are null.
b. Cabin number is directly related to the Class as cabin was alotted based on the level
of class.
So, cabin can easily be dropped from our analysis.
We dropped Ticket and PassengerId from our analysis because these two could not have affected the survival of the passengers.
It is just a demographic information."""
full_set.drop(['Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)
full_set.loc[full_set['Title'].isin(['Dona.', 'Lady.', 'Countess.', 'Capt.', 'Col.', 'Don.', 'Dr.', 'Major.', 'Rev.', 'Sir.', 'Jonkheer.']), 'Title'] = 'Rare'
full_set.loc[full_set['Title'].isin(['Mlle.', 'Ms.', 'Mme.']), 'Title'] = 'Miss.'
""" 1 ---Family Size =1
2 ---Family Size between 2 and 4(included)
3 ---Family Size more than 4"""
family_size = []
for row in full_set.FamilyMembers:
if row in [1]:
family_size.append(1)
elif row in [2, 3, 4]:
family_size.append(2)
else:
family_size.append(3)
full_set['FamilySize'] = family_size
print('\n\n Number of null in each column before imputing:\n')
print(full_set.isnull().sum()) | code |
2004143/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
full_set = pd.concat([train_full_set, test_full_set])
full_set = full_set.reset_index(drop=True)
""" We have to drop three columns from our study,namely: Cabin,Ticket, PassengerId
We dropped Cabin because of two main reasons:
a. Cabin has 687 Null Values out of 891 i.e almost 77% values are null.
b. Cabin number is directly related to the Class as cabin was alotted based on the level
of class.
So, cabin can easily be dropped from our analysis.
We dropped Ticket and PassengerId from our analysis because these two could not have affected the survival of the passengers.
It is just a demographic information."""
full_set.drop(['Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)
plt.figure(figsize=(15,8))
sns.set_style("whitegrid")
ax=sns.countplot(x="Title", data=full_set)
ax.set_ylabel("COUNT",size = 20,color="black",alpha=0.5)
ax.set_xlabel("TITLE",size = 20,color="black",alpha=0.5)
ax.set_title("COUNT OF TITLES IN EACH CATEGORY BEFORE COMBINATION",size = 20,color="black",alpha=0.5)
full_set.loc[full_set['Title'].isin(['Dona.', 'Lady.', 'Countess.', 'Capt.', 'Col.', 'Don.', 'Dr.', 'Major.', 'Rev.', 'Sir.', 'Jonkheer.']), 'Title'] = 'Rare'
full_set.loc[full_set['Title'].isin(['Mlle.', 'Ms.', 'Mme.']), 'Title'] = 'Miss.'
plt.figure(figsize=(15,8))
sns.set_style("whitegrid")
ax=sns.countplot(x="Title", data=full_set)
ax.set_ylabel("COUNT",size = 20,color="black",alpha=0.5)
ax.set_xlabel("TITLE",size = 20,color="black",alpha=0.5)
ax.set_title("COUNT OF TITLES IN EACH CATEGORY AFTER COMBINATION",size = 20,color="black",alpha=0.5)
family_size_survival = full_set[['FamilyMembers', 'Survived']].groupby(['FamilyMembers'], as_index=False).count().sort_values(by='Survived', ascending=False)
plt.figure(figsize=(15, 8))
sns.set_style('whitegrid')
ax = sns.barplot(x='FamilyMembers', y='Survived', data=family_size_survival)
ax.set_title('SURVIVED PASSENGER COUNT BASED ON FAMILY SIZE', size=20, color='black', alpha=0.5)
ax.set_ylabel('NUMBER SURVIVED', size=20, color='black', alpha=0.5)
ax.set_xlabel('FAMILY SIZE', size=20, color='black', alpha=0.5) | code |
2004143/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
full_set = pd.concat([train_full_set, test_full_set])
full_set = full_set.reset_index(drop=True)
print(full_set.head(5)) | code |
2004143/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
print('/n/nInformation about Null/ empty data points in each Column of Training set\n\n')
print(train_full_set.info()) | code |
2004143/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
from sklearn.cross_validation import cross_val_score | code |
2004143/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
full_set = pd.concat([train_full_set, test_full_set])
full_set = full_set.reset_index(drop=True)
print('Information about Null/ empty data points in each Column\n\n')
print(full_set.info()) | code |
2004143/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
full_set = pd.concat([train_full_set, test_full_set])
full_set = full_set.reset_index(drop=True)
""" We have to drop three columns from our study,namely: Cabin,Ticket, PassengerId
We dropped Cabin because of two main reasons:
a. Cabin has 687 Null Values out of 891 i.e almost 77% values are null.
b. Cabin number is directly related to the Class as cabin was alotted based on the level
of class.
So, cabin can easily be dropped from our analysis.
We dropped Ticket and PassengerId from our analysis because these two could not have affected the survival of the passengers.
It is just a demographic information."""
full_set.drop(['Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)
full_set.loc[full_set['Title'].isin(['Dona.', 'Lady.', 'Countess.', 'Capt.', 'Col.', 'Don.', 'Dr.', 'Major.', 'Rev.', 'Sir.', 'Jonkheer.']), 'Title'] = 'Rare'
full_set.loc[full_set['Title'].isin(['Mlle.', 'Ms.', 'Mme.']), 'Title'] = 'Miss.'
print(full_set.Title.value_counts()) | code |
2004143/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
print(corr)
plt.figure()
plt.imshow(corr, cmap='GnBu')
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
plt.suptitle('Correlation Matrix', fontsize=15, fontweight='bold')
plt.show() | code |
2004143/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
full_set = pd.concat([train_full_set, test_full_set])
full_set = full_set.reset_index(drop=True)
""" We have to drop three columns from our study,namely: Cabin,Ticket, PassengerId
We dropped Cabin because of two main reasons:
a. Cabin has 687 Null Values out of 891 i.e almost 77% values are null.
b. Cabin number is directly related to the Class as cabin was alotted based on the level
of class.
So, cabin can easily be dropped from our analysis.
We dropped Ticket and PassengerId from our analysis because these two could not have affected the survival of the passengers.
It is just a demographic information."""
full_set.drop(['Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)
plt.figure(figsize=(15,8))
sns.set_style("whitegrid")
ax=sns.countplot(x="Title", data=full_set)
ax.set_ylabel("COUNT",size = 20,color="black",alpha=0.5)
ax.set_xlabel("TITLE",size = 20,color="black",alpha=0.5)
ax.set_title("COUNT OF TITLES IN EACH CATEGORY BEFORE COMBINATION",size = 20,color="black",alpha=0.5)
full_set.loc[full_set['Title'].isin(['Dona.', 'Lady.', 'Countess.', 'Capt.', 'Col.', 'Don.', 'Dr.', 'Major.', 'Rev.', 'Sir.', 'Jonkheer.']), 'Title'] = 'Rare'
full_set.loc[full_set['Title'].isin(['Mlle.', 'Ms.', 'Mme.']), 'Title'] = 'Miss.'
plt.figure(figsize=(15, 8))
sns.set_style('whitegrid')
ax = sns.countplot(x='Title', data=full_set)
ax.set_ylabel('COUNT', size=20, color='black', alpha=0.5)
ax.set_xlabel('TITLE', size=20, color='black', alpha=0.5)
ax.set_title('COUNT OF TITLES IN EACH CATEGORY AFTER COMBINATION', size=20, color='black', alpha=0.5) | code |
2004143/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
full_set = pd.concat([train_full_set, test_full_set])
full_set = full_set.reset_index(drop=True)
""" We have to drop three columns from our study,namely: Cabin,Ticket, PassengerId
We dropped Cabin because of two main reasons:
a. Cabin has 687 Null Values out of 891 i.e almost 77% values are null.
b. Cabin number is directly related to the Class as cabin was alotted based on the level
of class.
So, cabin can easily be dropped from our analysis.
We dropped Ticket and PassengerId from our analysis because these two could not have affected the survival of the passengers.
It is just a demographic information."""
full_set.drop(['Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)
full_set.loc[full_set['Title'].isin(['Dona.', 'Lady.', 'Countess.', 'Capt.', 'Col.', 'Don.', 'Dr.', 'Major.', 'Rev.', 'Sir.', 'Jonkheer.']), 'Title'] = 'Rare'
full_set.loc[full_set['Title'].isin(['Mlle.', 'Ms.', 'Mme.']), 'Title'] = 'Miss.'
""" 1 ---Family Size =1
2 ---Family Size between 2 and 4(included)
3 ---Family Size more than 4"""
family_size = []
for row in full_set.FamilyMembers:
if row in [1]:
family_size.append(1)
elif row in [2, 3, 4]:
family_size.append(2)
else:
family_size.append(3)
full_set['FamilySize'] = family_size
full_set[full_set['Embarked'].isnull()] | code |
2004143/cell_22 | [
"text_plain_output_1.png"
] | """IMPUTING MISSING VALUES""" | code |
2004143/cell_10 | [
"text_plain_output_1.png"
] | """Feature Creation"""
'Creating Title' | code |
2004143/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = full_set_initial.corr()
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns)
test_full_set = pd.read_csv('../input/test.csv')
full_set = pd.concat([train_full_set, test_full_set])
full_set = full_set.reset_index(drop=True)
""" We have to drop three columns from our study,namely: Cabin,Ticket, PassengerId
We dropped Cabin because of two main reasons:
a. Cabin has 687 Null Values out of 891 i.e almost 77% values are null.
b. Cabin number is directly related to the Class as cabin was alotted based on the level
of class.
So, cabin can easily be dropped from our analysis.
We dropped Ticket and PassengerId from our analysis because these two could not have affected the survival of the passengers.
It is just a demographic information."""
full_set.drop(['Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)
print(full_set.Title.value_counts()) | code |
50244377/cell_13 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 128
IMAGE_SIZE = (IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS = 3
TRAIN_DIRECTORY = '/kaggle/working/train/'
TEST_DIRECTORY = '/kaggle/working/test1'
def get_filenames(directory):
filenames = os.listdir(directory)
return filenames
def load_data(filenames, directory):
i = 50
i = len(filenames)
X = []
y = []
for name in filenames:
img = mpimg.imread(os.path.join(directory, name))
X.append(cv2.resize(img, IMAGE_SIZE))
cat = name.split('.')[0]
if cat == 'dog':
y.append(0)
else:
y.append(1)
i -= 1
if i <= 0:
break
return (X, y)
def refine_data(X, y):
X = np.array(X)
X = X.reshape(X.shape[0], -1)
X = X.T
y = np.array(y)
y = y.reshape((1, y.shape[0]))
return (X, y)
X, y = refine_data(X, y)
print(X.shape)
print(y.shape) | code |
50244377/cell_20 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 128
IMAGE_SIZE = (IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS = 3
TRAIN_DIRECTORY = '/kaggle/working/train/'
TEST_DIRECTORY = '/kaggle/working/test1'
def get_filenames(directory):
filenames = os.listdir(directory)
return filenames
def load_data(filenames, directory):
i = 50
i = len(filenames)
X = []
y = []
for name in filenames:
img = mpimg.imread(os.path.join(directory, name))
X.append(cv2.resize(img, IMAGE_SIZE))
cat = name.split('.')[0]
if cat == 'dog':
y.append(0)
else:
y.append(1)
i -= 1
if i <= 0:
break
return (X, y)
def refine_data(X, y):
X = np.array(X)
X = X.reshape(X.shape[0], -1)
X = X.T
y = np.array(y)
y = y.reshape((1, y.shape[0]))
return (X, y)
X, y = refine_data(X, y)
layer_dims = [X.shape[0], 20, 7, 5, 1]
def initialize_parameters(layer_dims):
np.random.seed(3)
parameters = {}
L = len(layer_dims)
for l in range(1, L):
parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l - 1]) * 0.01
parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
return parameters
parameters = initialize_parameters(layer_dims)
parameters
def linear_fwd(A, W, b):
Z = np.dot(W, A) + b
cache = (A, W, b)
return (Z, cache)
Z, cache = linear_fwd(X, parameters['W1'], parameters['b1'])
Z.shape
def sigmoid(Z):
A = 1 / (1 + np.exp(-Z))
cache = Z
return (A, Z)
def relu(Z):
A = np.maximum(Z, 0)
cache = Z
return (A, Z)
sigmoid(np.array([0, 2])) | code |
50244377/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import random
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 128
IMAGE_SIZE = (IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS = 3
TRAIN_DIRECTORY = '/kaggle/working/train/'
TEST_DIRECTORY = '/kaggle/working/test1'
def get_filenames(directory):
filenames = os.listdir(directory)
return filenames
def load_data(filenames, directory):
i = 50
i = len(filenames)
X = []
y = []
for name in filenames:
img = mpimg.imread(os.path.join(directory, name))
X.append(cv2.resize(img, IMAGE_SIZE))
cat = name.split('.')[0]
if cat == 'dog':
y.append(0)
else:
y.append(1)
i -= 1
if i <= 0:
break
return (X, y)
filenames = get_filenames(TRAIN_DIRECTORY)
X, y = load_data(filenames, TRAIN_DIRECTORY)
def show_image(filenames, directory):
sample = random.choice(filenames)
show_image(filenames, TRAIN_DIRECTORY) | code |
50244377/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
print(os.listdir('../input/dogs-vs-cats')) | code |
50244377/cell_18 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 128
IMAGE_SIZE = (IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS = 3
TRAIN_DIRECTORY = '/kaggle/working/train/'
TEST_DIRECTORY = '/kaggle/working/test1'
def get_filenames(directory):
filenames = os.listdir(directory)
return filenames
def load_data(filenames, directory):
i = 50
i = len(filenames)
X = []
y = []
for name in filenames:
img = mpimg.imread(os.path.join(directory, name))
X.append(cv2.resize(img, IMAGE_SIZE))
cat = name.split('.')[0]
if cat == 'dog':
y.append(0)
else:
y.append(1)
i -= 1
if i <= 0:
break
return (X, y)
def refine_data(X, y):
X = np.array(X)
X = X.reshape(X.shape[0], -1)
X = X.T
y = np.array(y)
y = y.reshape((1, y.shape[0]))
return (X, y)
X, y = refine_data(X, y)
layer_dims = [X.shape[0], 20, 7, 5, 1]
def initialize_parameters(layer_dims):
np.random.seed(3)
parameters = {}
L = len(layer_dims)
for l in range(1, L):
parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l - 1]) * 0.01
parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
return parameters
parameters = initialize_parameters(layer_dims)
parameters
def linear_fwd(A, W, b):
Z = np.dot(W, A) + b
cache = (A, W, b)
return (Z, cache)
Z, cache = linear_fwd(X, parameters['W1'], parameters['b1'])
Z.shape | code |
50244377/cell_16 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 128
IMAGE_SIZE = (IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS = 3
TRAIN_DIRECTORY = '/kaggle/working/train/'
TEST_DIRECTORY = '/kaggle/working/test1'
def get_filenames(directory):
filenames = os.listdir(directory)
return filenames
def load_data(filenames, directory):
i = 50
i = len(filenames)
X = []
y = []
for name in filenames:
img = mpimg.imread(os.path.join(directory, name))
X.append(cv2.resize(img, IMAGE_SIZE))
cat = name.split('.')[0]
if cat == 'dog':
y.append(0)
else:
y.append(1)
i -= 1
if i <= 0:
break
return (X, y)
def refine_data(X, y):
X = np.array(X)
X = X.reshape(X.shape[0], -1)
X = X.T
y = np.array(y)
y = y.reshape((1, y.shape[0]))
return (X, y)
X, y = refine_data(X, y)
layer_dims = [X.shape[0], 20, 7, 5, 1]
def initialize_parameters(layer_dims):
np.random.seed(3)
parameters = {}
L = len(layer_dims)
for l in range(1, L):
parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l - 1]) * 0.01
parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
return parameters
parameters = initialize_parameters(layer_dims)
parameters | code |
17123393/cell_25 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.isnull().sum()
df_numeric = df_numeric.dropna()
df_numeric = df_numeric[df_numeric['vote_count'] > 30]
df_numeric.shape
from sklearn import preprocessing
minmax_processed = preprocessing.MinMaxScaler().fit_transform(df_numeric.drop('title', axis=1))
df_numeric_scaled = pd.DataFrame(minmax_processed, index=df_numeric.index, columns=df_numeric.columns[:-1])
plt.figure(figsize=(12,7))
axis = sns.barplot(x=np.arange(0,5,1),y=df_numeric.groupby(['cluster']).count()['budget'].values)
x=axis.set_xlabel("Cluster Number")
x=axis.set_ylabel("Number of movies")
size_array = list(df_numeric.groupby(['cluster']).count()['budget'].values)
size_array
df_numeric[df_numeric['cluster'] == 3].tail(5) | code |
17123393/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/movies_metadata.csv')
df.head(2) | code |
17123393/cell_20 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.isnull().sum()
df_numeric = df_numeric.dropna()
df_numeric = df_numeric[df_numeric['vote_count'] > 30]
df_numeric.shape
from sklearn import preprocessing
minmax_processed = preprocessing.MinMaxScaler().fit_transform(df_numeric.drop('title', axis=1))
df_numeric_scaled = pd.DataFrame(minmax_processed, index=df_numeric.index, columns=df_numeric.columns[:-1])
plt.figure(figsize=(12, 7))
axis = sns.barplot(x=np.arange(0, 5, 1), y=df_numeric.groupby(['cluster']).count()['budget'].values)
x = axis.set_xlabel('Cluster Number')
x = axis.set_ylabel('Number of movies') | code |
17123393/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.head() | code |
17123393/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17123393/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.isnull().sum() | code |
17123393/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.isnull().sum()
df_numeric = df_numeric.dropna()
df_numeric = df_numeric[df_numeric['vote_count'] > 30]
df_numeric.shape
from sklearn import preprocessing
minmax_processed = preprocessing.MinMaxScaler().fit_transform(df_numeric.drop('title', axis=1))
df_numeric_scaled = pd.DataFrame(minmax_processed, index=df_numeric.index, columns=df_numeric.columns[:-1])
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=5)
kmeans.fit(df_numeric_scaled)
len(kmeans.labels_) | code |
17123393/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/movies_metadata.csv') | code |
17123393/cell_17 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.isnull().sum()
df_numeric = df_numeric.dropna()
df_numeric = df_numeric[df_numeric['vote_count'] > 30]
df_numeric.shape
from sklearn import preprocessing
minmax_processed = preprocessing.MinMaxScaler().fit_transform(df_numeric.drop('title', axis=1))
df_numeric_scaled = pd.DataFrame(minmax_processed, index=df_numeric.index, columns=df_numeric.columns[:-1])
df_numeric.head() | code |
17123393/cell_24 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.isnull().sum()
df_numeric = df_numeric.dropna()
df_numeric = df_numeric[df_numeric['vote_count'] > 30]
df_numeric.shape
from sklearn import preprocessing
minmax_processed = preprocessing.MinMaxScaler().fit_transform(df_numeric.drop('title', axis=1))
df_numeric_scaled = pd.DataFrame(minmax_processed, index=df_numeric.index, columns=df_numeric.columns[:-1])
plt.figure(figsize=(12,7))
axis = sns.barplot(x=np.arange(0,5,1),y=df_numeric.groupby(['cluster']).count()['budget'].values)
x=axis.set_xlabel("Cluster Number")
x=axis.set_ylabel("Number of movies")
size_array = list(df_numeric.groupby(['cluster']).count()['budget'].values)
size_array
df_numeric[df_numeric['cluster'] == 2].head(5) | code |
17123393/cell_14 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.isnull().sum()
df_numeric = df_numeric.dropna()
df_numeric = df_numeric[df_numeric['vote_count'] > 30]
df_numeric.shape
from sklearn import preprocessing
minmax_processed = preprocessing.MinMaxScaler().fit_transform(df_numeric.drop('title', axis=1))
df_numeric_scaled = pd.DataFrame(minmax_processed, index=df_numeric.index, columns=df_numeric.columns[:-1])
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=5)
kmeans.fit(df_numeric_scaled) | code |
17123393/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.isnull().sum()
df_numeric = df_numeric.dropna()
df_numeric = df_numeric[df_numeric['vote_count'] > 30]
df_numeric.shape
from sklearn import preprocessing
minmax_processed = preprocessing.MinMaxScaler().fit_transform(df_numeric.drop('title', axis=1))
df_numeric_scaled = pd.DataFrame(minmax_processed, index=df_numeric.index, columns=df_numeric.columns[:-1])
plt.figure(figsize=(12,7))
axis = sns.barplot(x=np.arange(0,5,1),y=df_numeric.groupby(['cluster']).count()['budget'].values)
x=axis.set_xlabel("Cluster Number")
x=axis.set_ylabel("Number of movies")
size_array = list(df_numeric.groupby(['cluster']).count()['budget'].values)
size_array | code |
17123393/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.isnull().sum()
df_numeric = df_numeric.dropna()
df_numeric = df_numeric[df_numeric['vote_count'] > 30]
df_numeric.shape | code |
17123393/cell_12 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/movies_metadata.csv')
df_numeric = df[['budget', 'popularity', 'revenue', 'runtime', 'vote_average', 'vote_count', 'title']]
df_numeric.isnull().sum()
df_numeric = df_numeric.dropna()
df_numeric = df_numeric[df_numeric['vote_count'] > 30]
df_numeric.shape
from sklearn import preprocessing
minmax_processed = preprocessing.MinMaxScaler().fit_transform(df_numeric.drop('title', axis=1))
df_numeric_scaled = pd.DataFrame(minmax_processed, index=df_numeric.index, columns=df_numeric.columns[:-1])
df_numeric_scaled.head() | code |
17123567/cell_6 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('hacker_news', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
for table in tables:
print(table.table_id) | code |
17123567/cell_8 | [
"text_html_output_1.png"
] | from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('hacker_news', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref = dataset_ref.table('full')
table = client.get_table(table_ref)
table.schema | code |
17123567/cell_3 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
client = bigquery.Client() | code |
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