path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
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, ... | 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, ... | 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, ... | 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, ... | 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, ... | 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, ... | 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, ... | 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, ... | 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, ... | 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... | 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_... | 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(fi... | 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 skle... | 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_tra... | 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 skle... | 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... | 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'].fil... | 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(), inplac... | 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)
ti... | 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('..... | 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('..... | 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_... | 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_... | 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_... | 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... | 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.... | 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.... | 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_... | 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 = ... | 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 = ... | 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',... | 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, Monthl... | 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',... | 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',... | 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(... | 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'] = d... | 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.ind... | 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')... | 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.ind... | 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.ind... | 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', 'Ca... | 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 = f... | 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 = f... | 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 = f... | 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', 'Ca... | 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 = f... | 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 = f... | 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 = f... | 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 = f... | 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', 'Ca... | 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 = f... | 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 = f... | 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 = 12... | 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 = 12... | 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_W... | 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 = 12... | 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 = 12... | 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', 'p... | 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', 'p... | 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... | 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 ... | 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', 'p... | 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... | 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', 'p... | 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 =... | 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 ... | 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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