path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73067465/cell_28 | [
"image_output_1.png"
] | 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
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preproce... | code |
73067465/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,VotingClassifier
from sklearn.impute import KNNImputer,IterativeImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_r... | code |
73067465/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,VotingClassifier
from sklearn.impute import KNNImputer,IterativeImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_r... | code |
73067465/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preproce... | code |
73067465/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preproce... | code |
73067465/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,VotingClassifier
from sklearn.impute import KNNImputer,IterativeImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_r... | code |
122251830/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test_pass_id = test.PassengerId.copy()
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train_pass_id = train.PassengerId.copy()
X = train.drop(columns='Transported')
y = train[['Transported']]
df = pd.concat([X, test... | code |
122251830/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test_pass_id = test.PassengerId.copy()
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train_pass_id = train.PassengerId.copy()
X = train.drop(columns='Transported')
y = train[['Transported']]
df = pd.concat([X, test... | code |
122251830/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test_pass_id = test.PassengerId.copy()
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train_pass_id = train.PassengerId.copy()
X = train.drop(columns='Transported')
y = train[['Transported']]
df = pd.concat([X, test... | code |
122251830/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test_pass_id = test.PassengerId.copy()
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train_pass_id = train.PassengerId.copy()
X = train.drop(columns='Transported')
y = train[['Transported']]
df = pd.concat([X, test... | code |
122251830/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test_pass_id = test.PassengerId.copy()
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train_pass_id = train.PassengerId.copy()
X = train.drop(columns='Transported')
y = train[['Transported']]
df = pd.concat([X, test... | code |
122251830/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test_pass_id = test.PassengerId.copy()
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train_pass_id = train.PassengerId.copy()
X = train.drop(columns='Transported')
y = train[['Transported']]
df = pd.concat([X, test... | code |
122251830/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test_pass_id = test.PassengerId.copy()
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train_pass_id = train.PassengerId.copy()
X = train.drop(columns='Transported')
y = train[['Transported']]
df = pd.concat([X, test... | code |
122251830/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test_pass_id = test.PassengerId.copy()
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train_pass_id = train.PassengerId.copy()
X = train.drop(columns='Transported')
y = train[['Transported']]
df = pd.concat([X, test... | code |
130026158/cell_42 | [
"text_plain_output_1.png"
] | number = input('Enter an integer: ')
number = int(input('Enter an integer:'))
print('The number is', number)
print(type(number)) | code |
130026158/cell_9 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis | code |
130026158/cell_25 | [
"text_plain_output_1.png"
] | message = 'Python is a programming language.'
message.split() | code |
130026158/cell_4 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis | code |
130026158/cell_57 | [
"text_plain_output_1.png"
] | x = 3.14
x += 5
x = 3.14
x -= 5
x = 3.14
x *= 5
x = 3.14
x /= 5
print(x) | code |
130026158/cell_56 | [
"text_plain_output_1.png"
] | x = 3.14
x += 5
x = 3.14
x -= 5
x = 3.14
x *= 5
print(x) | code |
130026158/cell_34 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', ... | code |
130026158/cell_23 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', ... | code |
130026158/cell_20 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', ... | code |
130026158/cell_55 | [
"text_plain_output_1.png"
] | x = 3.14
x += 5
x = 3.14
x -= 5
print(x) | code |
130026158/cell_40 | [
"text_plain_output_1.png"
] | text = 'p,y,t,h,o,n'
text.split(',')
text = input('Enter a string:')
print('The text is', text)
print(type(text)) | code |
130026158/cell_29 | [
"text_plain_output_1.png"
] | nlis_1 = ['a', 'b', 'hello', 'Python']
nlis_2 = [1, 2, 3, 4, 5, 6]
print(len(nlis_1))
print(len(nlis_2))
print(nlis_1 + nlis_2)
print(nlis_1 * 3)
print(nlis_2 * 3)
for i in nlis_1:
print(i)
for i in nlis_2:
print(i)
print(4 in nlis_1)
print(4 in nlis_2) | code |
130026158/cell_48 | [
"text_plain_output_1.png"
] | expression = '8+7'
total = eval(expression)
a = float(input('Enter the pi number:'))
b = float(input('Enter the golden ratio:'))
total = a + b
a = input('Enter your favorite fruit:')
b = input('Enter your favorite food:')
print('I like {} and {}.'.format(a, b))
print('I like {0} and {1}.'.format(a, b))
print('I like ... | code |
130026158/cell_41 | [
"text_plain_output_1.png"
] | number = input('Enter an integer: ')
print('The number is', number)
print(type(number)) | code |
130026158/cell_54 | [
"text_plain_output_1.png"
] | x = 3.14
x += 5
print(x) | code |
130026158/cell_60 | [
"text_plain_output_1.png"
] | x = 3.14
x += 5
x = 3.14
x -= 5
x = 3.14
x *= 5
x = 3.14
x /= 5
x = 3.14
x %= 5
x = 3.14
x //= 5
x = 3.14
x **= 5
print(x) | code |
130026158/cell_50 | [
"text_plain_output_1.png"
] | expression = '8+7'
total = eval(expression)
a = float(input('Enter the pi number:'))
b = float(input('Enter the golden ratio:'))
total = a + b
a = input('Enter your favorite fruit:')
b = input('Enter your favorite food:')
a = 3.14
b = 1.618
print('a>b is:', a > b)
print('a<b is:', a < b)
print('a<=b is:', a <= b)
pr... | code |
130026158/cell_52 | [
"text_plain_output_1.png"
] | expression = '8+7'
total = eval(expression)
a = float(input('Enter the pi number:'))
b = float(input('Enter the golden ratio:'))
total = a + b
a = input('Enter your favorite fruit:')
b = input('Enter your favorite food:')
a = 3.14
b = 1.618
a = 3.14
b = 1.618
c = 12
d = 3.14
print(a > b and c > a)
print(b > c and d... | code |
130026158/cell_7 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis | code |
130026158/cell_45 | [
"text_plain_output_1.png"
] | expression = '8+7'
total = eval(expression)
print('Sum of the expression is', total)
print(type(expression))
print(type(total)) | code |
130026158/cell_18 | [
"text_plain_output_1.png"
] | lis = [1, 2, 3, 4, 5, 6, 7]
print(len(lis))
lis.append(4)
print(lis)
print(lis.count(4))
print(lis.index(2))
lis.insert(8, 9)
print(lis)
print(max(lis))
print(min(lis))
print(sum(lis)) | code |
130026158/cell_32 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', ... | code |
130026158/cell_62 | [
"text_plain_output_1.png"
] | expression = '8+7'
total = eval(expression)
a = float(input('Enter the pi number:'))
b = float(input('Enter the golden ratio:'))
total = a + b
a = input('Enter your favorite fruit:')
b = input('Enter your favorite food:')
a = 3.14
b = 1.618
a = 3.14
b = 1.618
c = 12
d = 3.14
a = 3.14
b = 1.618
print(a is b)
print(... | code |
130026158/cell_59 | [
"text_plain_output_1.png"
] | x = 3.14
x += 5
x = 3.14
x -= 5
x = 3.14
x *= 5
x = 3.14
x /= 5
x = 3.14
x %= 5
x = 3.14
x //= 5
print(x) | code |
130026158/cell_58 | [
"text_plain_output_1.png"
] | x = 3.14
x += 5
x = 3.14
x -= 5
x = 3.14
x *= 5
x = 3.14
x /= 5
x = 3.14
x %= 5
print(x) | code |
130026158/cell_16 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', ... | code |
130026158/cell_47 | [
"text_plain_output_1.png"
] | expression = '8+7'
total = eval(expression)
a = float(input('Enter the pi number:'))
b = float(input('Enter the golden ratio:'))
total = a + b
print('Sum of {} and {} is {}.'.format(a, b, total)) | code |
130026158/cell_35 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', ... | code |
130026158/cell_43 | [
"text_plain_output_1.png"
] | number = input('Enter an integer: ')
number = int(input('Enter an integer:'))
number = float(input('Enter an integer:'))
print('The number is', number)
print(type(number)) | code |
130026158/cell_31 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', ... | code |
130026158/cell_14 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', ... | code |
130026158/cell_22 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', ... | code |
130026158/cell_10 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
len(nlis) | code |
130026158/cell_27 | [
"text_plain_output_1.png"
] | text = 'p,y,t,h,o,n'
text.split(',') | code |
130026158/cell_37 | [
"text_plain_output_1.png"
] | a_list = ['a', 'b', ['c', 'd'], 'e']
b_list = [1, 2, 3, 4, 5, (6, 7), True, False]
new_list = a_list + b_list
print(new_list) | code |
130026158/cell_12 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
nlis = ['python', 3.14, 2022, [1, 1, 2, 3, 5, 8, 13, 21, 34], ('hello', 'python', 3, 14, 2022)]
nlis
print(nlis[0:2])
print(nlis[2:4])
print(nlis[4:6]) | code |
130026158/cell_5 | [
"text_plain_output_1.png"
] | nlis = ['python', 25, 2022]
nlis
print('Positive and negative indexing of the first element: \n - Positive index:', nlis[0], '\n - Negative index:', nlis[-3])
print('Positive and negative indexing of the second element: \n - Positive index:', nlis[1], '\n - Negative index:', nlis[-2])
print('Positive and negative inde... | code |
34129362/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
train_set = pd.read_csv('/kaggle/input/preprocessed-sales-data/train_set.csv')
validation_set = pd.read_csv('/kaggle/input/preprocessed-sales-data/validation_set.csv')
test_set = pd.read_csv('/kaggle/input/competitive-data-science-pred... | code |
34129362/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('/kaggle/input/preprocessed-sales-data/train_set.csv')
validation_set = pd.read_csv('/kaggle/input/preprocessed-sales-data/validation_set.csv')
test_set = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sa... | code |
34129362/cell_23 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
train_set = pd.read_csv('/kaggle/in... | code |
34129362/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34129362/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('/kaggle/input/preprocessed-sales-data/train_set.csv')
validation_set = pd.read_csv('/kaggle/input/preprocessed-sales-data/validation_set.c... | code |
34129362/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
train_set = pd.read_csv('/kaggle/input/preprocessed-sales-data/train_set.csv')
validation_set = pd.read_csv('/kaggle/input/preprocessed-sales-data/validation_set.csv')
test_set = pd.read_csv('/kaggle/input/competitive-data-science-pred... | code |
34129362/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('/kaggle/input/preprocessed-sales-data/train_set.csv')
validation_set = pd.read_csv('/kaggle/input/preprocessed-sales-data/validation_set.csv')
test_set = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sa... | code |
17101817/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df = df[df['Category'] != '1.9']
c3 = df['Genres'].value_counts(dropna=False, sort=False)
c3 | code |
17101817/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.head() | code |
17101817/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df = df[df['Category'] != '1.9']
c5 = df['Content Rating'].value_counts(dropna=False, sort=True, normalize=True)
c5 | code |
17101817/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='ticks')
sns.set_context('paper')
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import os
df = pd.... | code |
17101817/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df = df[df['Category'] != '1.9']
c5 = df['Content Rating'].value_counts(dropna=False, sort=True, normalize=True)
c5.plot.bar() | code |
17101817/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
c1 = df['Category'].value_counts(dropna=False, sort=False)
c1 | code |
17101817/cell_1 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='ticks')
sns.set_context('paper')
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import os
print(os.listdir('../input')) | code |
17101817/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
c1 = df['Category'].value_counts(dropna=False, sort=False)
c1
len(c1) | code |
17101817/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df = df[df['Category'] != '1.9']
c2 = df['Category'].value_counts(dropna=True, sort=True, normalize=True).plot.bar()
plt.title('Frequency of App Categories')
df['Genres_Sele'].value_counts(normalize=True).plot.bar(... | code |
17101817/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='ticks')
sns.set_context('paper')
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import os
df = pd.... | code |
17101817/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df = df[df['Category'] != '1.9']
df['Price'] = df[df.columns[7]].replace('[\\$,]', '', regex=True).astype(float)
print('Max:{} Min:{}'.format(df['Price'].max(), df['Price'].min())) | code |
17101817/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='ticks')
sns.set_context('paper')
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import os
df = pd.... | code |
17101817/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='ticks')
sns.set_context('paper')
import plotly
import plotly.plotly as py
import plotly.graph_objs as go... | code |
17101817/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='ticks')
sns.set_context('paper')
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import os
df = pd.... | code |
17101817/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df = df[df['Category'] != '1.9']
df['Genres'].describe() | code |
17101817/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df = df[df['Category'] != '1.9']
c2 = df['Category'].value_counts(dropna=True, sort=True, normalize=True).plot.bar()
plt.title('Frequency of App Categories') | code |
88082030/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd
mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Sing... | code |
88082030/cell_21 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict | code |
88082030/cell_13 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys() | code |
88082030/cell_9 | [
"text_plain_output_1.png"
] | mydict = {}
mydict | code |
88082030/cell_25 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Singapore', 'Japan'}
val ... | code |
88082030/cell_34 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Singapore', 'Japan'}
val ... | code |
88082030/cell_30 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Singapore', 'Japan'}
val ... | code |
88082030/cell_33 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Singapore', 'Japan'}
val ... | code |
88082030/cell_20 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem() | code |
88082030/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd
mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Sing... | code |
88082030/cell_29 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Singapore', 'Japan'}
val ... | code |
88082030/cell_26 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Singapore', 'Japan'}
val ... | code |
88082030/cell_11 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values() | code |
88082030/cell_19 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys() | code |
88082030/cell_7 | [
"text_plain_output_1.png"
] | mydict = {}
mydict | code |
88082030/cell_18 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e') | code |
88082030/cell_32 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Singapore', 'Japan'}
val ... | code |
88082030/cell_15 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items() | code |
88082030/cell_16 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a') | code |
88082030/cell_35 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Singapore', 'Japan'}
val ... | code |
88082030/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd
mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Sing... | code |
88082030/cell_14 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values() | code |
88082030/cell_10 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys() | code |
88082030/cell_37 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Singapore', 'Japan'}
val ... | code |
88082030/cell_5 | [
"text_html_output_1.png"
] | mydict = {}
type(mydict) | code |
88082030/cell_36 | [
"text_plain_output_1.png"
] | mydict = {}
mydict.keys()
mydict.values()
mydict.update({'c': 'cat', 'd': 'dog', 'e': 'elephant'})
mydict.keys()
mydict.values()
mydict.items()
mydict.get('a')
mydict.get('g')
mydict.pop('e')
mydict.keys()
mydict.popitem()
mydict.clear()
keys = {'India', 'Srilanka', 'Bangladesh', 'Singapore', 'Japan'}
val ... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.