path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
74045375/cell_9 | [
"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
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr() | code |
74045375/cell_23 | [
"text_html_output_1.png"
] | import builtins
import builtins
dir(builtins) | code |
74045375/cell_20 | [
"text_plain_output_1.png"
] | def tuple_ex():
""" return defined t tuple """
t = (1, 2, 3)
return t
a, b, c = tuple_ex()
print(a, b, c)
d, e, _ = tuple_ex()
print(d, e) | code |
74045375/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
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.head(10) | code |
74045375/cell_11 | [
"text_plain_output_1.png"
] | dictionary = {'Barcelona': 'Messi', 'Real Madrid': 'Modric'}
print(dictionary.keys())
print(dictionary.values()) | code |
74045375/cell_19 | [
"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
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74045375/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
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_18 | [
"text_plain_output_1.png"
] | i = 0
while i != 6:
print('i i: ', i)
i += 2
print(i, ' is equal to 6') | code |
74045375/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
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_15 | [
"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
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkar... | code |
74045375/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.info() | code |
74045375/cell_17 | [
"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
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_14 | [
"text_plain_output_1.png"
] | print(5 < 9)
print(8 != 8)
print(True & False)
print(True or False) | code |
74045375/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_12 | [
"text_html_output_1.png"
] | dictionary = {'Barcelona': 'Messi', 'Real Madrid': 'Modric'}
dictionary['Barcelona'] = 'Messi'
print(dictionary)
dictionary['PSG'] = 'Neymar'
print(dictionary)
del dictionary['Real Madrid']
print(dictionary)
print('Bayern Münih' in dictionary)
print('Barcelona' in dictionary)
dictionary.clear()
print(dictionary) | code |
74045375/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
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
f, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(data.corr(), annot=True, linewi... | code |
72085764/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits | code |
72085764/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1 | code |
72085764/cell_49 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
72085764/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
fruits = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]})
fruits
fruits_1 = pd.DataFrame({'Apples': [30, 50, 40], 'Bananas': [21, 10, 15]}, index=['January', 'February', 'March'])
fruits_1
fruits_2 = pd.DataFrame([[60, 35], [5, 15], [21, 36]], columns=['Apples', 'Bananas'], index=[... | code |
1008453/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 seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
X = data['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']
y = data['Species'] | code |
1008453/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
sns.pairplot(data.drop('Id', axis=1), hue='Species', size=2) | code |
1008453/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
sns.violinplot(x='Species', y='PetalLengthCm', data=data) | code |
1008453/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
, # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas... | code |
1008453/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
sns.violinplot(x='Species', y='PetalWidthCm', data=data) | code |
1008453/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.head() | code |
1008453/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
X = data['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']
y = data['Species']
X | code |
1008184/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
np.random.seed(0)
X = np.random.random(size=(20, 1))
y = 3 * X.squeeze() + 2 + np.random.randn(20)
plt.plot(X, y, 'o') | code |
1008184/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 |
1008184/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.random.seed(0)
X = np.random.random(size=(20, 1))
y = 3 * X.squeeze() + 2 + np.random.randn(20)
print(y) | code |
1008184/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
np.random.seed(0)
X = np.random.random(size=(20, 1))
y = 3 * X.squeeze() + 2 + np.random.randn(20)
model = LinearRegression()
model.fit(X, y)
X_fit = np.linspace(0, 1, 100)[:, np... | code |
73066276/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '../input/learnplatform-covid19-impact-on-digital-learning/'
engagement_path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
district_path = '../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv'
products_path = '../in... | code |
73066276/cell_6 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '../input/learnplatform-covid19-impact-on-digital-learning/'
engagement_path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
district_path = '../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv'
products_path = '../in... | code |
73066276/cell_11 | [
"text_html_output_1.png"
] | import os
import pandas as pd
dataset_path = '../input/learnplatform-covid19-impact-on-digital-learning/'
engagement_path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
district_path = '../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv'
products_path = '../in... | code |
73066276/cell_7 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '../input/learnplatform-covid19-impact-on-digital-learning/'
engagement_path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
district_path = '../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv'
products_path = '../in... | code |
73066276/cell_10 | [
"text_html_output_1.png"
] | import os
import pandas as pd
dataset_path = '../input/learnplatform-covid19-impact-on-digital-learning/'
engagement_path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
district_path = '../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv'
products_path = '../in... | code |
1005917/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['Age'].fillna(train.Age.mean(), i... | code |
1005917/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['Age'].fillna(train.Age.mean(), i... | code |
1005917/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['Age'].fillna(train.Age.mean(), i... | code |
1005917/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
tra... | code |
1005917/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import roc_auc_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from subprocess import check_output
train = pd.read_csv('../inp... | code |
1005917/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input... | code |
1005917/cell_12 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import roc_auc_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from subprocess import check_output
train = pd.read_csv('../inp... | code |
17130660/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fastai.vision import ImageDataBunch,Learner
from torch import optim,nn
from torchvision import transforms,models
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
from fastai.vision import ImageDataBunch, Learner
from PIL import Image
import torch
from torch import optim, nn
... | code |
17130660/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import fastai as fa
import os
print(os.listdir('../input')) | code |
17130660/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from fastai.vision import ImageDataBunch,Learner
from torch import optim,nn
from torchvision import transforms,models
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import numpy as np
import pandas as pd
import fastai as fa
import os
from fast... | code |
17130660/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from fastai.vision import ImageDataBunch,Learner
from torch import optim,nn
from torchvision import transforms,models
import os
import pandas
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import numpy as np
import pandas as pd
import fastai as fa
impor... | code |
17130660/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fastai.vision import ImageDataBunch,Learner
from torch import optim,nn
from torchvision import transforms,models
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
from fastai.vision import ImageDataBunch, Learner
from PIL import Image
import torch
from torch import optim, nn
... | code |
17130660/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fastai.vision import ImageDataBunch,Learner
from torch import optim,nn
from torchvision import transforms,models
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
from fastai.vision import ImageDataBunch, Learner
from PIL import Image
import torch
from torch import optim, nn
... | code |
129028469/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
path = '/kaggle/input/goodreadsbooks/books.csv'
df = pd.read_csv(path, error_bad_lines=False)
df.shape
df.head(10) | code |
129028469/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
path = '/kaggle/input/goodreadsbooks/books.csv'
df = pd.read_csv(path, error_bad_lines=False)
df.shape
def book_search():
user_choice = int(input('Enter 1 for Author, 2 for Publisher:'))
match user_choice:
case 1:
key = str(input('Enter Author name:'))
auth... | code |
129028469/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
path = '/kaggle/input/goodreadsbooks/books.csv'
df = pd.read_csv(path, error_bad_lines=False) | code |
129028469/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '/kaggle/input/goodreadsbooks/books.csv'
df = pd.read_csv(path, error_bad_lines=False)
df.shape | code |
34133452/cell_21 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
BASE_DIR = '/kaggle/input/global-wheat-detection/'
train_data = pd.read_csv(BASE_DIR + 'train.csv')
submission_file = pd.read_csv(BASE_DIR + 'sample_submission.csv')
trai... | code |
34133452/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection/'
train_data = pd.read_csv(BASE_DIR + 'train.csv')
submission_file = pd.read_csv(BASE_DIR + 'sample_submission.csv')
train_images_dir = BASE_DIR + 'train/'
train_data.isna().sum() | code |
34133452/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection/'
train_data = pd.read_csv(BASE_DIR + 'train.csv')
submission_file = pd.read_csv(BASE_DIR + 'sample_submission.csv')
train_images_dir = BASE_DIR + 'train/'
submission_file.head() | code |
34133452/cell_7 | [
"image_output_1.png"
] | import os
import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34133452/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection/'
train_data = pd.read_csv(BASE_DIR + 'train.csv')
submission_file = pd.read_csv(BASE_DIR + 'sample_submission.csv')
train_images_dir = BASE_DIR + 'train/'
train_data.isna().sum()
train_data.nuniqu... | code |
34133452/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection/'
train_data = pd.read_csv(BASE_DIR + 'train.csv')
submission_file = pd.read_csv(BASE_DIR + 'sample_submission.csv')
train_images_dir = BASE_DIR + 'train/'
train_data.isna().sum()
train_data.nuniqu... | code |
34133452/cell_22 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
BASE_DIR = '/kaggle/input/global-wheat-detection/'
train_data = pd.read_csv(BASE_DIR + 'train.csv')
submission_file = pd.read_csv(BASE_DIR + 'sample_submission.csv')
trai... | code |
34133452/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection/'
train_data = pd.read_csv(BASE_DIR + 'train.csv')
submission_file = pd.read_csv(BASE_DIR + 'sample_submission.csv')
train_images_dir = BASE_DIR + 'train/'
train_data.head() | code |
34133452/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection/'
train_data = pd.read_csv(BASE_DIR + 'train.csv')
submission_file = pd.read_csv(BASE_DIR + 'sample_submission.csv')
train_images_dir = BASE_DIR + 'train/'
print('The training data has {} rows and {... | code |
34135429/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as p... | code |
34135429/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.tail(5) | code |
34135429/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as p... | code |
34135429/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as py
from plotly import tools
py.init_notebook_mode(conne... | code |
34135429/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as p... | code |
34135429/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.isnull().sum() | code |
34135429/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as p... | code |
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