path
stringlengths 13
17
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sequencelengths 1
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stringlengths 0
40.4k
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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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
data.Turbidity.plot(kind='hist', bins=50, figsize=(12, 12))
plt.show() | 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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
plt.clf()
x = data['Sulfate'] > 470
data[x]
x = 2
def f():
x = 3
return x
x = 5
def f():
y = 2 * x
return y
def square():
""" return square of value """
def add():
""" add two local variable """
x = 2
y = 3
z = x + y
return z
return add() ** 2
def f(a, b=1, c=2):
y = a + b + c
return y
print(f(5))
print(f(5, 6, 7)) | 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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns | 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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
plt.clf()
dictionary = {'Barcelona': 'Messi', 'Real Madrid': 'Modric'}
dictionary['Barcelona'] = 'Messi'
dictionary['PSG'] = 'Neymar'
del dictionary['Real Madrid']
dictionary.clear()
i = 0
while i != 6:
i += 2
lis = [1, 3, 5, 7, 9, 11]
for i in lis:
print('i is: ', i)
print('')
for index, value in enumerate(lis):
print(index, ' : ', value)
print('')
dictionary = {'Barcelona': 'Messi', 'PSG': 'Neymar'}
for key, value in dictionary.items():
print(key, ' : ', value)
print('')
for index, value in data[['Sulfate']][0:5].iterrows():
print(index, ' : ', value) | 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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
data.Hardness.plot(kind='line', color='green', label='Hardness', linewidth=1, alpha=0.6, grid=True, linestyle=':', figsize=(18, 18), marker='o', markerfacecolor='magenta', markersize=7)
data.Sulfate.plot(color='blue', label='Sulfate', linewidth=1, alpha=0.6, grid=True, linestyle='-.', figsize=(18, 18), marker='o', markerfacecolor='yellow', markersize=7)
plt.legend(loc='upper right')
plt.xlabel('x axis')
plt.ylabel('y axis')
plt.title('Line Plot')
plt.show() | 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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
data.plot(kind='scatter', x='Hardness', y='Sulfate', alpha=0.7, color='red', figsize=(13, 13), marker='+')
plt.xlabel('Hardness')
plt.ylabel('Sulfate')
plt.title('Hardness Sulfate Scatter Plot')
plt.show() | 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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
plt.clf()
x = data['Sulfate'] > 470
data[x] | 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 çıkardık
f,ax = plt.subplots(figsize = (15, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
plt.clf()
data[np.logical_and(data['Sulfate'] > 470, data['Hardness'] > 200)] | 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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
plt.clf()
data[(data['Sulfate'] > 470) & (data['Hardness'] > 200)] | 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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
plt.clf()
x = data['Sulfate'] > 470
data[x]
x = 2
def f():
x = 3
return x
x = 5
def f():
y = 2 * x
return y
def square():
""" return square of value """
def add():
""" add two local variable """
x = 2
y = 3
z = x + y
return z
return add() ** 2
print(square()) | code |
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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
plt.clf()
x = data['Sulfate'] > 470
data[x]
x = 2
def f():
x = 3
return x
x = 5
def f():
y = 2 * x
return y
print(f()) | 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, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
data.Turbidity.plot(kind='hist', bins=50)
plt.clf() | 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, linewidths=0.5, fmt='.2f', ax=ax)
plt.show() | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details') | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.columns | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.describe() | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.columns
wine_reviews.columns[0]
wine_reviews.country.unique()
wine_reviews.country.value_counts()
wine_reviews['country'].head() | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000]) | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.columns
wine_reviews.columns[0]
wine_reviews.country.unique()
wine_reviews.country.value_counts() | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape | 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=['April', 'May', 'June'])
fruits_2
fruits = fruits_1.append(fruits_2)
fruits | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.columns
wine_reviews.columns[0]
wine_reviews.country.unique()
wine_reviews.country.value_counts()
wine_reviews.iloc[5]
wine_reviews.iloc[:, 4] | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.columns
wine_reviews.columns[0]
wine_reviews.country.unique()
wine_reviews.country.value_counts()
wine_reviews.iloc[5]
wine_reviews.iloc[:, 4]
wine_reviews.iloc[:3, [4, 5]] | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.columns
wine_reviews.columns[0]
wine_reviews.country.unique()
wine_reviews.country.value_counts()
wine_reviews.iloc[5] | 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=['April', 'May', 'June'])
fruits_2
fruits = fruits_1.append(fruits_2)
fruits
fruits.insert(0, 'Pineapple', [1, 5, 0, 2, 8, 'NaN'])
fruits | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews['price'].mean() | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.head(3) | 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=['April', 'May', 'June'])
fruits_2 | 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=['April', 'May', 'June'])
fruits_2
fruits = fruits_1.append(fruits_2)
fruits
fruits | 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=['April', 'May', 'June'])
fruits_2
fruits = fruits_1.append(fruits_2)
fruits
fruits['Orange'] = [10, 56, 78, 23, 78, 36]
fruits | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.columns
wine_reviews.columns[0] | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.columns
wine_reviews.columns[0]
wine_reviews.country.unique()
wine_reviews.country.value_counts()
wine_reviews.country.head() | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.columns
wine_reviews.columns[0]
wine_reviews.country.unique()
wine_reviews.country.value_counts()
wine_reviews['country'][1] | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
fruits = fruits_1.append(fruits_2)
fruits
grocery = fruits_1.append(items_AB)
grocery | 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=['April', 'May', 'June'])
fruits_2
items_AB = pd.DataFrame([[30, 21], [50, 10], [40, 15]], columns=list('AB'), index=['January', 'February', 'March'])
items_AB
pd.Series([1981, 5.2, 'Ann', 20000])
pd.Series([1981, 5.2, 'Ann', 2000], index=['Birth Year', 'Height', 'First Name', 'Paid'], name='Customer Details')
file_path = '../input/wine-reviews/winemag-data-130k-v2.csv'
wine_reviews = pd.read_csv(file_path)
wine_reviews
wine_reviews = pd.read_csv(file_path, index_col=0)
wine_reviews
wine_reviews.shape
wine_reviews.columns
wine_reviews.columns[0]
wine_reviews.country.unique() | 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=['April', 'May', 'June'])
fruits_2
fruits = fruits_1.append(fruits_2)
fruits
fruits_1 | 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 as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
# Any results you write to the current directory are saved as output.
# ^^^ DEFAULT SETUP ABOVE HERE. EVERYTHING BELOW MUST BE ADDED
import seaborn as sns # plotting
from sklearn import tree # classification tree, see http://scikit-learn.org/stable/modules/tree.html | 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.newaxis]
y_fit = model.predict(X_fit)
y_model = 3 * X_fit.ravel() + 2 + np.random.randn(100)
plt.plot(X, y, 'o')
plt.plot(X_fit, y_fit)
plt.plot(X_fit, y_model) | 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 = '../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv'
def create_dataset_from_engagement(engagement_path):
dataset = pd.DataFrame()
for path, dirc, files in os.walk(engagement_path):
for file in files:
if dataset.empty:
dataset = pd.read_csv(os.path.join(path, file))
dataset['dis_id'] = file.split('.')[0]
else:
next_frame = pd.read_csv(os.path.join(path, file))
next_frame['dis_id'] = file.split('.')[0]
dataset = pd.concat([dataset[:], next_frame[:]])
return dataset
district_df = pd.read_csv(district_path)
products_df = pd.read_csv(products_path)
str(district_df[district_df['district_id'] == 1624]['state'].values[0]) != 'nan' | 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 = '../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv'
def create_dataset_from_engagement(engagement_path):
dataset = pd.DataFrame()
for path, dirc, files in os.walk(engagement_path):
for file in files:
if dataset.empty:
dataset = pd.read_csv(os.path.join(path, file))
dataset['dis_id'] = file.split('.')[0]
else:
next_frame = pd.read_csv(os.path.join(path, file))
next_frame['dis_id'] = file.split('.')[0]
dataset = pd.concat([dataset[:], next_frame[:]])
return dataset
district_df = pd.read_csv(district_path)
products_df = pd.read_csv(products_path)
district_df.head() | 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 = '../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv'
def create_dataset_from_engagement(engagement_path):
dataset = pd.DataFrame()
for path, dirc, files in os.walk(engagement_path):
for file in files:
if dataset.empty:
dataset = pd.read_csv(os.path.join(path, file))
dataset['dis_id'] = file.split('.')[0]
else:
next_frame = pd.read_csv(os.path.join(path, file))
next_frame['dis_id'] = file.split('.')[0]
dataset = pd.concat([dataset[:], next_frame[:]])
return dataset
district_df = pd.read_csv(district_path)
products_df = pd.read_csv(products_path)
print('Before removing null values')
district_df.isnull().sum() * 100 / district_df.shape[0]
print('After removing null values')
district_df.dropna(subset=['state'], inplace=True)
district_df.isnull().sum() * 100 / district_df.shape[0] | 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 = '../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv'
def create_dataset_from_engagement(engagement_path):
dataset = pd.DataFrame()
for path, dirc, files in os.walk(engagement_path):
for file in files:
if dataset.empty:
dataset = pd.read_csv(os.path.join(path, file))
dataset['dis_id'] = file.split('.')[0]
else:
next_frame = pd.read_csv(os.path.join(path, file))
next_frame['dis_id'] = file.split('.')[0]
dataset = pd.concat([dataset[:], next_frame[:]])
return dataset
district_df = pd.read_csv(district_path)
products_df = pd.read_csv(products_path)
products_df.head() | 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 = '../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv'
def create_dataset_from_engagement(engagement_path):
dataset = pd.DataFrame()
for path, dirc, files in os.walk(engagement_path):
for file in files:
if dataset.empty:
dataset = pd.read_csv(os.path.join(path, file))
dataset['dis_id'] = file.split('.')[0]
else:
next_frame = pd.read_csv(os.path.join(path, file))
next_frame['dis_id'] = file.split('.')[0]
dataset = pd.concat([dataset[:], next_frame[:]])
return dataset
district_df = pd.read_csv(district_path)
products_df = pd.read_csv(products_path)
engagement_district = []
for path, directory, files in os.walk(engagement_path):
for file in files:
state_name = str(district_df[district_df['district_id'] == int(file.split('.')[0])]['state'].values[0])
if state_name != 'nan':
engagement_district.append({'state': state_name, 'file': os.path.join(path, file)})
for item in engagement_district:
if item['state'] == 'Ohio':
print(item['file']) | 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(), inplace=True)
df = pd.DataFrame(train, index=['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'])
y_train, X_train = (train['Survived'], train.drop(['Survived', 'Name', 'Ticket', 'Cabin', 'PassengerId'], axis=1))
X_train
tonumeric = ['Sex', 'Embarked']
for variable in tonumeric:
X_train[variable].fillna('Missing', inplace=True)
dummies = pd.get_dummies(X_train[variable], prefix=variable)
X_train = pd.concat([X_train, dummies], axis=1)
X_train.drop([variable], axis=1, inplace=True)
X_train | 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(), inplace=True)
train['Age'].describe() | 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(), inplace=True)
df = pd.DataFrame(train, index=['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'])
y_train, X_train = (train['Survived'], train.drop(['Survived', 'Name', 'Ticket', 'Cabin', 'PassengerId'], axis=1))
X_train | 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')
train.describe() | 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('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['Age'].fillna(train.Age.mean(), inplace=True)
df = pd.DataFrame(train, index=['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'])
y_train, X_train = (train['Survived'], train.drop(['Survived', 'Name', 'Ticket', 'Cabin', 'PassengerId'], axis=1))
X_train
tonumeric = ['Sex', 'Embarked']
for variable in tonumeric:
X_train[variable].fillna('Missing', inplace=True)
dummies = pd.get_dummies(X_train[variable], prefix=variable)
X_train = pd.concat([X_train, dummies], axis=1)
X_train.drop([variable], axis=1, inplace=True)
model = RandomForestRegressor(n_estimators=100, oob_score=True, random_state=50)
model.fit(X_train, y_train)
from sklearn.metrics import roc_auc_score
roc_auc_score(y_train, model.oob_prediction_) | 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/test.csv')
train['Age'].fillna(train.Age.mean(), inplace=True)
df = pd.DataFrame(train, index=['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'])
y_train, X_train = (train['Survived'], train.drop(['Survived', 'Name', 'Ticket', 'Cabin', 'PassengerId'], axis=1))
X_train
tonumeric = ['Sex', 'Embarked']
for variable in tonumeric:
X_train[variable].fillna('Missing', inplace=True)
dummies = pd.get_dummies(X_train[variable], prefix=variable)
X_train = pd.concat([X_train, dummies], axis=1)
X_train.drop([variable], axis=1, inplace=True)
model = RandomForestRegressor(n_estimators=100, oob_score=True, random_state=50)
model.fit(X_train, y_train) | 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('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['Age'].fillna(train.Age.mean(), inplace=True)
df = pd.DataFrame(train, index=['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'])
y_train, X_train = (train['Survived'], train.drop(['Survived', 'Name', 'Ticket', 'Cabin', 'PassengerId'], axis=1))
X_train
tonumeric = ['Sex', 'Embarked']
for variable in tonumeric:
X_train[variable].fillna('Missing', inplace=True)
dummies = pd.get_dummies(X_train[variable], prefix=variable)
X_train = pd.concat([X_train, dummies], axis=1)
X_train.drop([variable], axis=1, inplace=True)
model = RandomForestRegressor(n_estimators=100, oob_score=True, random_state=50)
model.fit(X_train, y_train)
from sklearn.metrics import roc_auc_score
roc_auc_score(y_train, model.oob_prediction_)
importance = pd.Series(model.feature_importances_, index=X_train.columns)
importance.sort()
importance.plot(kind='barh', figsize=(7, 6)) | 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
import cv2 as cv
from torchvision import transforms, models
from torch.utils.data.sampler import SubsetRandomSampler
import numpy as np
PATH = '../input'
df_train = pd.read_csv(PATH + '/train.csv', dtype={'id_code': str, 'diagnosis': int})
norm_values = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
dataBunch = ImageDataBunch.from_df(path=PATH, df=df_train, folder='train_images', suffix='.png', size=224, ds_tfms=get_transforms(do_flip=True, max_warp=0), test='test_images', bs=32, device=device).normalize(norm_values)
model = models.resnext101_32x8d(pretrained=True)
for param in model.parameters():
param.requires_grad = False
fc = nn.Sequential(nn.Dropout(p=0.6), nn.Linear(2048, 500), nn.BatchNorm1d(500), nn.ReLU(), nn.Linear(500, 5), nn.LogSoftmax(dim=1))
model.fc = fc
model.to(device)
learn = Learner(data=dataBunch, model=model, model_dir='/tmp/model', metrics=[accuracy])
learn.fit_one_cycle(7, 0.008) | 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 fastai.vision import ImageDataBunch, Learner
from PIL import Image
import torch
from torch import optim, nn
import cv2 as cv
from torchvision import transforms, models
from torch.utils.data.sampler import SubsetRandomSampler
import numpy as np
PATH = '../input'
df_train = pd.read_csv(PATH + '/train.csv', dtype={'id_code': str, 'diagnosis': int})
norm_values = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
dataBunch = ImageDataBunch.from_df(path=PATH, df=df_train, folder='train_images', suffix='.png', size=224, ds_tfms=get_transforms(do_flip=True, max_warp=0), test='test_images', bs=32, device=device).normalize(norm_values)
model = models.resnext101_32x8d(pretrained=True)
for param in model.parameters():
param.requires_grad = False
fc = nn.Sequential(nn.Dropout(p=0.6), nn.Linear(2048, 500), nn.BatchNorm1d(500), nn.ReLU(), nn.Linear(500, 5), nn.LogSoftmax(dim=1))
model.fc = fc
model.to(device)
learn = Learner(data=dataBunch, model=model, model_dir='/tmp/model', metrics=[accuracy])
learn.fit_one_cycle(7, 0.008)
learn.save('bl')
model = learn.load('bl').model
class CreateTrainDataset(Dataset):
def __init__(self, csv_file, root_dir, transform=None):
self.data_frame = pd.read_csv(csv_file)
self.root_dir = root_dir
self.transform = transform
def __len__(self):
return len(self.data_frame)
def __getitem__(self, idx):
img_name = os.path.join(self.root_dir, self.data_frame.iloc[idx, 0] + '.png')
image = Image.open(img_name)
sample = {'image': transforms.ToTensor()(image)}
return sample
testset = CreateTrainDataset(csv_file='../input/test.csv', root_dir='../input/test_images')
testloader = torch.utils.data.DataLoader(testset, batch_size=1)
model.eval()
diag = []
idc = []
dd = pd.read_csv('../input/test.csv')
idc = dd['id_code']
idc = list(idc)
for id, d in enumerate(testloader):
images = d['image']
images = images.to(device)
output = model(images)
pred = torch.exp(output)
top_ps, top_class = pred.topk(1, dim=1)
diag.append(int(top_class.cpu()))
print(type(list(diag))) | 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
import os
from fastai.vision import ImageDataBunch, Learner
from PIL import Image
import torch
from torch import optim, nn
import cv2 as cv
from torchvision import transforms, models
from torch.utils.data.sampler import SubsetRandomSampler
import numpy as np
PATH = '../input'
df_train = pd.read_csv(PATH + '/train.csv', dtype={'id_code': str, 'diagnosis': int})
norm_values = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
dataBunch = ImageDataBunch.from_df(path=PATH, df=df_train, folder='train_images', suffix='.png', size=224, ds_tfms=get_transforms(do_flip=True, max_warp=0), test='test_images', bs=32, device=device).normalize(norm_values)
model = models.resnext101_32x8d(pretrained=True)
for param in model.parameters():
param.requires_grad = False
fc = nn.Sequential(nn.Dropout(p=0.6), nn.Linear(2048, 500), nn.BatchNorm1d(500), nn.ReLU(), nn.Linear(500, 5), nn.LogSoftmax(dim=1))
model.fc = fc
model.to(device)
learn = Learner(data=dataBunch, model=model, model_dir='/tmp/model', metrics=[accuracy])
learn.fit_one_cycle(7, 0.008)
learn.save('bl')
model = learn.load('bl').model
class CreateTrainDataset(Dataset):
def __init__(self, csv_file, root_dir, transform=None):
self.data_frame = pd.read_csv(csv_file)
self.root_dir = root_dir
self.transform = transform
def __len__(self):
return len(self.data_frame)
def __getitem__(self, idx):
img_name = os.path.join(self.root_dir, self.data_frame.iloc[idx, 0] + '.png')
image = Image.open(img_name)
sample = {'image': transforms.ToTensor()(image)}
return sample
testset = CreateTrainDataset(csv_file='../input/test.csv', root_dir='../input/test_images')
testloader = torch.utils.data.DataLoader(testset, batch_size=1)
model.eval()
diag = []
idc = []
dd = pd.read_csv('../input/test.csv')
idc = dd['id_code']
idc = list(idc)
for id, d in enumerate(testloader):
images = d['image']
images = images.to(device)
output = model(images)
pred = torch.exp(output)
top_ps, top_class = pred.topk(1, dim=1)
diag.append(int(top_class.cpu()))
import pandas
ddf = pandas.DataFrame(data={'id_code': idc, 'diagnosis': diag})
ddf.to_csv('./submission.csv', sep=',', index=False) | 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
import cv2 as cv
from torchvision import transforms, models
from torch.utils.data.sampler import SubsetRandomSampler
import numpy as np
PATH = '../input'
df_train = pd.read_csv(PATH + '/train.csv', dtype={'id_code': str, 'diagnosis': int})
norm_values = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
dataBunch = ImageDataBunch.from_df(path=PATH, df=df_train, folder='train_images', suffix='.png', size=224, ds_tfms=get_transforms(do_flip=True, max_warp=0), test='test_images', bs=32, device=device).normalize(norm_values)
model = models.resnext101_32x8d(pretrained=True)
for param in model.parameters():
param.requires_grad = False
fc = nn.Sequential(nn.Dropout(p=0.6), nn.Linear(2048, 500), nn.BatchNorm1d(500), nn.ReLU(), nn.Linear(500, 5), nn.LogSoftmax(dim=1))
model.fc = fc
model.to(device)
learn = Learner(data=dataBunch, model=model, model_dir='/tmp/model', metrics=[accuracy])
print(learn) | 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
import cv2 as cv
from torchvision import transforms, models
from torch.utils.data.sampler import SubsetRandomSampler
import numpy as np
PATH = '../input'
df_train = pd.read_csv(PATH + '/train.csv', dtype={'id_code': str, 'diagnosis': int})
norm_values = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
dataBunch = ImageDataBunch.from_df(path=PATH, df=df_train, folder='train_images', suffix='.png', size=224, ds_tfms=get_transforms(do_flip=True, max_warp=0), test='test_images', bs=32, device=device).normalize(norm_values)
model = models.resnext101_32x8d(pretrained=True)
for param in model.parameters():
param.requires_grad = False
fc = nn.Sequential(nn.Dropout(p=0.6), nn.Linear(2048, 500), nn.BatchNorm1d(500), nn.ReLU(), nn.Linear(500, 5), nn.LogSoftmax(dim=1))
model.fc = fc
model.to(device)
learn = Learner(data=dataBunch, model=model, model_dir='/tmp/model', metrics=[accuracy])
learn.fit_one_cycle(7, 0.008)
learn.save('bl')
model = learn.load('bl').model | 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:'))
author_list = pd.DataFrame(df[df['authors'] == key]['title'].unique())
return author_list
case 2:
key = str(input('Enter Publisher name:'))
publisher_list = pd.DataFrame(df[df['publisher'] == key]['title'].unique())
return publisher_list
case other:
search_data = pd.DataFrame(book_search())
search_data | 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')
train_images_dir = BASE_DIR + 'train/'
train_data.isna().sum()
train_data.nunique()
train_data.source.unique()
all_images = set((x.split('.')[0] for x in os.listdir(train_images_dir)))
images_with_bb = set(train_data.image_id.unique())
images_without_bb = all_images ^ images_with_bb
def plot_images(image_list,rows,cols,title):
fig,ax = plt.subplots(rows,cols,figsize = (16,5))
ax = ax.flatten()
for i, image_id in enumerate(image_list):
image = cv2.imread(train_images_dir+'{}.jpg'.format(image_id))
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
ax[i].imshow(image)
ax[i].set_axis_off()
ax[i].set_title(image_id)
plt.suptitle(title)
# def plot_images_with_bb(train_data,rows,cols,title):
# fig,ax = plt.subplots(rows,cols,figsize = (16,5))
# ax = ax.flatten()
# for row in train_data.iterrows():
# image = cv2.imread(train_images_dir+'{}.jpg'.format(row[1]["image_id"]))
# image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
# image = cv2.rectangle(image,(row[1]["bbox"][0],row[1]["bbox"][1]),(row[1]["bbox"][0]+row[1]["bbox"][2],row[1]["bbox"][1]+row[1]["bbox"][3]),255,2)
# ax[i].imshow(image)
# ax[i].set_axis_off()
# ax[i].set_title(image_id)
# plt.suptitle(title)
plot_images(train_data.sample(10)['image_id'].values, 2, 5, 'Images with wheat') | 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.nunique()
train_data.source.unique() | 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.nunique() | 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')
train_images_dir = BASE_DIR + 'train/'
train_data.isna().sum()
train_data.nunique()
train_data.source.unique()
all_images = set((x.split('.')[0] for x in os.listdir(train_images_dir)))
images_with_bb = set(train_data.image_id.unique())
images_without_bb = all_images ^ images_with_bb
df_images_without_bb = pd.DataFrame(images_without_bb, columns=['image_id'])
def plot_images(image_list,rows,cols,title):
fig,ax = plt.subplots(rows,cols,figsize = (16,5))
ax = ax.flatten()
for i, image_id in enumerate(image_list):
image = cv2.imread(train_images_dir+'{}.jpg'.format(image_id))
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
ax[i].imshow(image)
ax[i].set_axis_off()
ax[i].set_title(image_id)
plt.suptitle(title)
# def plot_images_with_bb(train_data,rows,cols,title):
# fig,ax = plt.subplots(rows,cols,figsize = (16,5))
# ax = ax.flatten()
# for row in train_data.iterrows():
# image = cv2.imread(train_images_dir+'{}.jpg'.format(row[1]["image_id"]))
# image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
# image = cv2.rectangle(image,(row[1]["bbox"][0],row[1]["bbox"][1]),(row[1]["bbox"][0]+row[1]["bbox"][2],row[1]["bbox"][1]+row[1]["bbox"][3]),255,2)
# ax[i].imshow(image)
# ax[i].set_axis_off()
# ax[i].set_title(image_id)
# plt.suptitle(title)
plot_images(df_images_without_bb.sample(10)['image_id'].values, 2, 5, 'Images without wheat') | 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 {} columns'.format(train_data.shape[0], train_data.shape[1])) | 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 py
from plotly import tools
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.isnull().sum()
nifty_50_df = nifty_50_df.fillna(method='ffill')
def plot_attribute(df, attritube ,start='2000', end='2020',color ='blue'):
fig, ax = plt.subplots(1, figsize=(20,5))
ax.plot(df[start:end].index, df[start:end][attritube],'tab:{}'.format(color))
ax.set_title("Nifty 50 stock {} from 2000 to 2020".format(attritube))
ax.axhline(y=df[start:end].describe()[attritube]["max"],linewidth=2, color='m')
ax.axhline(y=df[start:end].describe()[attritube]["min"],linewidth=2, color='c')
ax.axvline(x=df[attritube].idxmax(),linewidth=2, color='b')
ax.axvline(x=df[attritube].idxmin() ,linewidth=2, color='y')
ax.text(x=df[attritube].idxmax(),
y=df[start:end].describe()[attritube]["max"],
s='MAX',
horizontalalignment='right',
verticalalignment='bottom',
color='blue',
fontsize=20)
ax.text(x=df[attritube].idxmin(),
y=df[start:end].describe()[attritube]["min"],
s='MIN',
horizontalalignment='left',
verticalalignment='top',
color='red',
fontsize=20)
plt.show()
print("Max Value : ",df[start:end].describe()[attritube]["max"])
print("Min Value : ",df[start:end].describe()[attritube]["min"])
plot_attribute(nifty_50_df, 'Close', color='red') | 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 py
from plotly import tools
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.isnull().sum()
nifty_50_df = nifty_50_df.fillna(method='ffill')
def plot_attribute(df, attritube ,start='2000', end='2020',color ='blue'):
fig, ax = plt.subplots(1, figsize=(20,5))
ax.plot(df[start:end].index, df[start:end][attritube],'tab:{}'.format(color))
ax.set_title("Nifty 50 stock {} from 2000 to 2020".format(attritube))
ax.axhline(y=df[start:end].describe()[attritube]["max"],linewidth=2, color='m')
ax.axhline(y=df[start:end].describe()[attritube]["min"],linewidth=2, color='c')
ax.axvline(x=df[attritube].idxmax(),linewidth=2, color='b')
ax.axvline(x=df[attritube].idxmin() ,linewidth=2, color='y')
ax.text(x=df[attritube].idxmax(),
y=df[start:end].describe()[attritube]["max"],
s='MAX',
horizontalalignment='right',
verticalalignment='bottom',
color='blue',
fontsize=20)
ax.text(x=df[attritube].idxmin(),
y=df[start:end].describe()[attritube]["min"],
s='MIN',
horizontalalignment='left',
verticalalignment='top',
color='red',
fontsize=20)
plt.show()
print("Max Value : ",df[start:end].describe()[attritube]["max"])
print("Min Value : ",df[start:end].describe()[attritube]["min"])
plot_attribute(nifty_50_df, 'Div Yield', color='blue') | 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(connected=True)
import plotly.graph_objs as go
import plotly.express as px | 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 py
from plotly import tools
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.isnull().sum()
nifty_50_df = nifty_50_df.fillna(method='ffill')
def plot_attribute(df, attritube ,start='2000', end='2020',color ='blue'):
fig, ax = plt.subplots(1, figsize=(20,5))
ax.plot(df[start:end].index, df[start:end][attritube],'tab:{}'.format(color))
ax.set_title("Nifty 50 stock {} from 2000 to 2020".format(attritube))
ax.axhline(y=df[start:end].describe()[attritube]["max"],linewidth=2, color='m')
ax.axhline(y=df[start:end].describe()[attritube]["min"],linewidth=2, color='c')
ax.axvline(x=df[attritube].idxmax(),linewidth=2, color='b')
ax.axvline(x=df[attritube].idxmin() ,linewidth=2, color='y')
ax.text(x=df[attritube].idxmax(),
y=df[start:end].describe()[attritube]["max"],
s='MAX',
horizontalalignment='right',
verticalalignment='bottom',
color='blue',
fontsize=20)
ax.text(x=df[attritube].idxmin(),
y=df[start:end].describe()[attritube]["min"],
s='MIN',
horizontalalignment='left',
verticalalignment='top',
color='red',
fontsize=20)
plt.show()
print("Max Value : ",df[start:end].describe()[attritube]["max"])
print("Min Value : ",df[start:end].describe()[attritube]["min"])
plot_attribute(nifty_50_df, 'P/B', color='orange') | 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 py
from plotly import tools
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.isnull().sum()
nifty_50_df = nifty_50_df.fillna(method='ffill')
def plot_attribute(df, attritube ,start='2000', end='2020',color ='blue'):
fig, ax = plt.subplots(1, figsize=(20,5))
ax.plot(df[start:end].index, df[start:end][attritube],'tab:{}'.format(color))
ax.set_title("Nifty 50 stock {} from 2000 to 2020".format(attritube))
ax.axhline(y=df[start:end].describe()[attritube]["max"],linewidth=2, color='m')
ax.axhline(y=df[start:end].describe()[attritube]["min"],linewidth=2, color='c')
ax.axvline(x=df[attritube].idxmax(),linewidth=2, color='b')
ax.axvline(x=df[attritube].idxmin() ,linewidth=2, color='y')
ax.text(x=df[attritube].idxmax(),
y=df[start:end].describe()[attritube]["max"],
s='MAX',
horizontalalignment='right',
verticalalignment='bottom',
color='blue',
fontsize=20)
ax.text(x=df[attritube].idxmin(),
y=df[start:end].describe()[attritube]["min"],
s='MIN',
horizontalalignment='left',
verticalalignment='top',
color='red',
fontsize=20)
plt.show()
print("Max Value : ",df[start:end].describe()[attritube]["max"])
print("Min Value : ",df[start:end].describe()[attritube]["min"])
plot_attribute(nifty_50_df, 'P/E', color='green') | code |
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